WO2012010584A1 - Complex mirna sets as novel biomarkers for gastric cancer - Google Patents

Complex mirna sets as novel biomarkers for gastric cancer Download PDF

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WO2012010584A1
WO2012010584A1 PCT/EP2011/062326 EP2011062326W WO2012010584A1 WO 2012010584 A1 WO2012010584 A1 WO 2012010584A1 EP 2011062326 W EP2011062326 W EP 2011062326W WO 2012010584 A1 WO2012010584 A1 WO 2012010584A1
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seq
mirnas
gastric cancer
mirna
nucleotide sequences
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PCT/EP2011/062326
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French (fr)
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Andreas Keller
Markus Beier
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Febit Holding Gmbh
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • the present invention relates to single polynucleotides or sets of polynucleotides for detecting single miRNAs or sets of miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample such as a blood cell sample from a human patient. Further, the present invention relates to means for diagnosing and/or prognosing of gastric cancer comprising said polynucleotides or sets of polynucleotides. Furthermore, the present invention relates to a method for diagnosing and/or prognosing of gastric cancer based on the determination of expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer.
  • the present invention relates to a kit for diagnosing and/or prognosing of gastric cancer comprising means for determining expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer and optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
  • biomarkers play a key role in early diagnosis, risk stratification, and therapeutic management of various diseases.
  • many biomarkers were mainly discovered by candidate approach.
  • the recent development of high-throughput molecular technologies that allow with a reasonable effort the analysis of whole transcriptomes, proteomes, and metabolomes of individuals at risk, may lead to the discovery of novel biomarkers in an unbiased approach.
  • MicroRNAs are a new class of biomarkers. They represent a group of regulatory elements that enable cells to fine-tune complex gene expression cascades in a wide range of biological processes, such as proliferation, differentiation, apoptosis, stress-response, and oncogenesis. Since recently it is known that miRNAs are not only present in tissues but also in human blood both as free circulating nucleic acids and in mononuclear cells. This may be due to the fact that miRNAs expressed in diverse tissues or cells may be able to be released into circulating blood. Although the mechanism why miRNAs are found in human blood is not fully understood yet, this finding renders miRNAs to biological markers for diagnostics for various types of diseases based on blood analysis including gastric cancer.
  • Gastric cancer is a highly aggressive and lethal malignancy. On a global basis, this tumor represents about 9% of the entire cancer burden and the second leading cancer cause of death.
  • miRNA markers have been proposed to indicate gastric cancer. However, many of these markers have shortcomings such as low sensitivity, no sufficient specificity or do not allow timely diagnosis. Accordingly, there is still a need to provide novel and efficient miRNAs or sets of miRNAs as biomarkers, effective methods and kits for the diagnosis and/or prognosis of said disease. Particularly, the potential role of miRNAs present in human blood as biomarkers for the diagnosis and/or prognosis of gastric cancer has not been systematically evaluated yet.
  • the inventors of the present invention assessed for the first time the expression of miRNAs on a whole-genome level in subjects suffering from gastric cancer. They identified novel miRNAs which are significantly dysregulated in blood of subjects suffering from gastric cancer in comparison to healthy controls. The inventors of the present invention revealed that said single miRNAs can predict or determine gastric cancer with high specificity, sensitivity and accuracy. The inventors of the present invention also pursued a multiple biomarker strategy to circumvent the above mentioned limitations by adding accuracy and predictive power. In detail, by using a machine learning approach, they identified unique miRNA signatures that can predict or determine gastric cancer with even higher power, indicating that both, single miRNAs and especially complex miRNA signatures or sets derived from a blood sample, e.g. blood cell sample, can be used as novel biomarkers for the diagnosis and/or prognosis of gastric cancer in a human patient.
  • a blood sample e.g. blood cell sample
  • the invention provides a polynucleotide for detecting a miRNA or a set comprising at least two polynucleotides for detecting a set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
  • the invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • the invention provides means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect.
  • the invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
  • i means for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto; and
  • test compounds As used in this specification and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents, unless the content clearly dictates otherwise. For example, the term “a test compound” also includes “test compounds”.
  • microRNA or “miRNA” refer to single-stranded RNA molecules of at least
  • the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally labels and/or elongated sequences (e.g. biotin stretches).
  • the miRNAs regulate gene expression and are encoded by genes from whose DNA they are transcribed but miRNAs are not translated into protein (i.e.
  • miRNAs are non- coding RNAs).
  • the genes encoding miRNAs are longer than the processed mature miRNA molecules.
  • the miRNAs are first transcribed as primary transcripts or pri-miRNAs with a cap and poly- A tail and processed to short, 70 nucleotide stem-loop structures known as pre- miRNAs in the cell nucleus. This processing is performed in animals by a protein complex known as the Microprocessor complex consisting of the nuclease Drosha and the double- stranded RNA binding protein Pasha.
  • These pre-miRNAs are then processed to mature miRNAs in the cytoplasm by interaction with the endonuclease Dicer, which also initiates the formation of the RNA-induced silencing complex (RISC).
  • RISC RNA-induced silencing complex
  • the miRNA* is derived from the same hairpin structure like the "normal” miRNAs. So if the "normal” miRNA is then later called the “mature miRNA” or "guide strand”, the miRNA* is the "anti-guide strand” or "passenger strand”.
  • microRNA* refers to single-stranded RNA molecules of at least 10 nucleotides and of not more than 35 nucleotides covalently linked together.
  • the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally labels and/or elongated sequences (e.g. biotin stretches).
  • the “miRNA*s”, also known as the "anti-guide strands” or “passenger strands”, are mostly complementary to the “mature miRNAs” or “guide strands”, but have usually single-stranded overhangs on each end. There are usually one or more mispairs and there are sometimes extra or missing bases causing single-stranded “bubbles”.
  • the miRNA* s are likely to act in a regulatory fashion as the miRNAs (see also above).
  • the terms “miRNA” and “miRNA*” are interchangeable used.
  • the present invention encompasses (target) miRNAs which are dysregulated in blood of human patients with gastric cancer in comparison to healthy controls.
  • Said (target) miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 and, preferably, additionally consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
  • miRBase refers to a well established repository of validated miRNAs.
  • the miRBase (ww .mirbase. org) is a searchable database of published miRNA sequences and annotation. Each entry in the miRBase Sequence database represents a predicted hairpin portion of a miRNA transcript (termed mir in the database), with information on the location and sequence of the mature miRNA sequence (termed miR). Both hairpin and mature sequences are available for searching and browsing, and entries can also be retrieved by name, keyword, references and annotation. All sequence and annotation data are also available for download.
  • the sequences of the miRNAs for diagnosis and/or prognosis of gastric cancer listed in Figure 2 are based on miRBase version 12.0, 13.0 and 14.0.
  • nucleotides refers to structural components, or building blocks, of DNA and RNA. Nucleotides consist of a base (one of four chemicals: adenine, thymine, guanine, and cytosine) plus a molecule of sugar and one of phosphoric acid.
  • nucleosides refers to glycosylamine consisting of a nucleobase (often referred to simply base) bound to a ribose or deoxyribose sugar. Examples of nucleosides include cytidine, uridine, adenosine, guanosine, thymidine and inosine. Nucleosides can be phosphorylated by specific kinases in the cell on the sugar's primary alcohol group (-CH2-OH), producing nucleotides, which are the molecular building blocks of DNA and RNA.
  • -CH2-OH primary alcohol group
  • polynucleotide means a molecule of at least 10 nucleotides and of not more than 35 nucleotides covalently linked together.
  • the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally spacer elements and/or elongation elements described below.
  • polynucleotide means a polymer of deoxyribonucleotide or ribonucleotide bases and includes DNA and RNA molecules, both sense and anti-sense strands.
  • the polynucleotide may be DNA, both cDNA and genomic DNA, RNA, cRNA or a hybrid, where the polynucleotide sequence may contain combinations of deoxyribonucleotide or ribonucleotide bases, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine, hypoxanthine, isocytosine and isoguanine.
  • Polynucleotides may be obtained by chemical synthesis methods or by recombinant methods.
  • a polynucleotide as a single polynucleotide strand provides a probe (e.g. miRNA capture probe) that is capable of binding to, hybridizing with, or detecting a target of complementary sequence, such as a nucleotide sequence of a miRNA or miRNA*, through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation.
  • a target of complementary sequence such as a nucleotide sequence of a miRNA or miRNA*
  • Polynucleotides in their function as probes may bind target sequences, such as nucleotide sequences of miRNAs or miRNAs*, lacking complete complementarity with the polynucleotide sequences depending upon the stringency of the hybridization condition.
  • the present invention encompasses polynucleotides in form of single polynucleotide strands as probes for binding to, hybridizing with or detecting complementary sequences of (target) miRNAs for diagnosing and/or prognosing of gastric caner.
  • Said (target) miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 and, preferably, additionally consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
  • the polynucleotide e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*, may be unlabeled, directly labeled, or indirectly labeled, such as with biotin to which a streptavidin complex may later bind.
  • the polynucleotide e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*
  • EL elongation
  • a polynucleotide with an elongation element may be used as a probe.
  • the elongation element comprises a nucleotide sequence with 1 to 30 nucleotides chosen on the basis of showing low complementarity to potential target sequences, such as nucleotide sequences of miRNAs or miRNAs*, therefore resulting in no or a low degree of cross-hybridization to a target mixture.
  • the polynucleotide e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*, may be present in form of a tandem, i.e. in form of a polynucleotide hybrid of two different or identical polynucleotides, both in the same orientation, i.e. 5' to 3' or 3' to 5', or in different orientation, i.e. 5' to 3' and 3' to 5'.
  • Said polynucleotide hybrid/tandem may comprise a spacer element.
  • the polynucleotide hybrid/tandem as a probe may comprise a spacer (SP) element.
  • a or C, or T or G is preferred.
  • Particularly preferred is a non-homomeric sequence stretch that can be used as a priming site for a polymerase for a downstream amplification reaction.
  • the miRNA(s) or miRNA*(s) may be employed unlabeled, directly labeled, or indirectly labeled, such as with biotin to which a streptavidin complex may later bind.
  • label means a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, chemical, or other physical means.
  • useful labels include 32P, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin, digoxigenin, or haptens and other entities which are or can be made detectable.
  • a label may be incorporated into nucleic acids at any position, e.g. at the 3' or 5' end or internally.
  • the polynucleotide for detecting a miRNA (polynucleotide probe) and/or the miRNA itself may be labeled.
  • stringent hybridization conditions means conditions under which a first nucleotide sequence (e.g. polynucleotide in its function as a probe for detecting a miRNA or miRNA*) will hybridize to a second nucleotide sequence (e.g. target sequence such as nucleotide sequence of a miRNA or miRNA*), such as in a complex mixture of nucleotide sequences.
  • Stringent conditions are sequence-dependent and will be different in different circumstances. Stringent conditions may be selected to be about 5 to 10°C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength, pH.
  • the Tm may be the temperature (under defined ionic strength, pH, and nucleic acid concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium).
  • Stringent conditions may be those in which the salt concentration is less than about 1.0 M sodium ion, such as about 0.01 tol .O M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 20°C for short probes (e.g. about 10-35 nucleotides) and up to 60°C for long probes (e.g. greater than about 50 nucleotides).
  • Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide.
  • a positive signal may be at least 2 to 10 times background hybridization.
  • Exemplary stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %, Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
  • antisense refers to nucleotide sequences which are complementary to a specific DNA or RNA sequence.
  • antisense strand is used in reference to a nucleic acid strand that is complementary to the "sense” strand.
  • sensitivity means a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies gastric cancer into the correct type out of two or more possible types (e.g. gastric cancer type and healthy type).
  • the sensitivity for class A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A".
  • a theoretical, optimal prediction can achieve 100% sensitivity (i.e. predict all patients from the sick group as sick).
  • the term "specificity”, as used herein, means a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies gastric cancer into the correct type out of two or more possible types (e.g. gastric cancer type and healthy type).
  • the specificity for class A is the proportion of cases that are determined to belong to class "not A” by the test out of the cases that are in class "not A”.
  • a theoretical, optimal prediction can achieve 100% specificity (i.e. not predict anyone from the healthy group as sick).
  • accuracy means a statistical measure for the correctness of classification or identification of sample types.
  • the accuracy is the proportion of true results (both true positives and true negatives).
  • gastric cancer also known as stomach cancer
  • stomach cancer encompasses any group of clinical symptoms compatible with gastric cancer (also known as stomach cancer).
  • gastric cancer and “stomach cancer” are interchangeable used herein.
  • Gastric cancer can develop in any part of the stomach.
  • the stomach is part of the digestive system. It is located in the upper abdomen, between the esophagus and the small intestine. Most (85%) cases of gastric cancer are adenocarcinomas that occur in the lining of the stomach (mucosa). Approximately 40% of cases develop in the lower part of the stomach (pylorus); 40% develop in the middle part (body); and 15% develop in the upper part (cardia).
  • Stomach cancer can spread (metastasize) to the esophagus or the small intestine, and can extend through the stomach wall to nearby lymph nodes and organs (e.g. liver, pancreas, colon). It also can metastasize to other parts of the body (e.g. lungs, ovaries, bones).
  • patient may mean a human subject suspected to be affected by gastric cancer.
  • the patient may be diagnosed to be affected by gastric cancer, i.e. diseased, or may be diagnosed to be not affected by gastric cancer, i.e. healthy.
  • the patient may further be prognosed to develop gastric cancer, as the inventors of the present invention surprisingly found that miRNAs representative for gastric cancer are already present in a blood sample, e.g. blood cell sample, before gastric cancer occurs or during the early stage of gastric cancer.
  • patient as used in the context of the present invention, may also mean a human subject which is affected by gastric cancer, i.e. diseased.
  • the patient may be retested for gastric cancer and may be diagnosed to be still affected by gastric cancer, i.e. diseased, or not affected by gastric cancer anymore, i.e. healthy, for example after therapeutic intervention (e.g. to evaluate the success of surgery and/or chemotherapy). It should be noted that a patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer, or as staying healthy, i.e. not developing gastric cancer, may possibly suffer from another disease not tested/known.
  • control subject may refer to a human subject known to be affected by gastric cancer (positive control), i.e. diseased.
  • control subject as used in the context of the present invention, may also refer to a human subject known to be not affected by gastric cancer (negative control), i.e. healthy. It may also refer to a human subject known to be affected by another disease/condition (see definition "(clinical) condition”). It should be noted that a control subject that is known to be healthy, i.e. not suffering from gastric cancer, may possibly suffer from another disease not tested/known.
  • the inventors of the present invention analysed the expression level of miRNAs in blood samples of a cohort of controls (healthy subjects) and in blood samples of subjects suffering from gastric cancer. They succeeded in determining the miRNAs that are differentially regulated in blood samples from subjects having gastric cancer compared to a cohort of controls (healthy subjects) (see experimental section for experimental details). Additionally, the inventors of the present invention performed hypothesis tests (e.g. t-test, limma-test) or other measurements (e.g. AUC, mutual information) on the expression level of the found miRNAs in all controls (healthy subjects) and subjects having gastric cancer. These tests resulted in a significance value (p-value) for each miRNA.
  • This p-value is a measure for the diagnostic power of each miRNA to discriminate, for example, between two clinical conditions, e.g. healthy, i.e. absence of gastric cancer, and diseased, i.e. presence of gastric cancer. Since a manifold of tests are carried out, one for each miRNA, the p-values may be too optimistic and, thus, over-estimate the actual discriminatory power. Hence, the p-values are corrected for multiple testing by the Benjamini Hochberg approach.
  • FIG. 1 An overview of the miRNAs that are found to be significantly dysregulated in blood samples of human subjects having gastric cancer and that performed best according to t-test, limma-test or AUC is provided in Figure 1 (Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median gl : median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with gastric cancer, qmedian: ratio of median gl/median g2, logqmedian: log of qmedian, ttest rawp: p-value obtained when applying t-test, ttest adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, limma rawp: p-value obtained when applying limma- test, limma adjp: adjusted p-value in order to reduce false discovery rate by Benjamini- Hochberg adjustment.
  • the first group comprises miRNAs according to SEQ ID NO: 1 to SEQ ID NO: 103 and the second group comprises miRNAs according to SEQ ID NO: 104 to 119.
  • intensity p ⁇ 0.05, median gl and/or g2 preferably > 100
  • the most predictive miRNAs in group I are listed first. It should be noted that the lower the ttest adjp value of a single miRNA, the higher is the diagnostic power of said miRNA.
  • the median intensity obtained from the microarray represents a measure for the abundance of a miRNA in a sample and represents the expression level of this miRNA. The higher the intensity of a single miRNA, the higher the abundance/expression of said miRNA.
  • the diagnostic power of a single miRNA biomarker is not sufficient to reach high accuracy, specificity and sensitivity, for example, for the discrimination between two clinical conditions, e.g. healthy, i.e. absence of gastric cancer, and diseased, i.e. presence of gastric cancer.
  • the inventors of the present invention surprisingly found that also a single miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, can predict or determine with high diagnostic accuracy, specificity and sensitivity gastric cancer in a human patient.
  • the inventors of the present invention also employed more than one miRNA biomarker, i.e. sets (signatures) of miRNA biomarkers, to further increase and/or improve the performance for diagnosing and/or prognosing of patients having gastric cancer.
  • miRNA biomarker i.e. sets (signatures) of miRNA biomarkers
  • the inventors of the present invention applied a machine learning approach (e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means) which leads to an algorithm or a mathematical function that is trained by reference data (i.e. data of reference miRNA expression profiles from the two or more clinical conditions, e.g. presence of gastric cancer and absence of gastric cancer, for the defined set (signature) of miRNA markers) to discriminate between the two or more statistical classes (i.e. two or more clinical conditions), e.g.
  • a machine learning approach e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means
  • reference data i.e. data of reference miRNA expression profiles from the two or more clinical conditions, e.g. presence of gastric cancer and absence of gastric cancer, for the defined set (signature) of miRNA markers
  • miRNA sets comprise at least two miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (see for example Figures 3 to 11).
  • the inventors of the present invention further found that the sets of at least two miRNAs, wherein the nucleotide sequence of said miRNA or nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, can be completed by at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, preferably in order to improve the diagnostic accuracy, specificity and sensitivity in the determination of gastric cancer.
  • Step 1 Total RNA (or subfractions thereof) is extracted from a blood (including plasma, serum, PBMC or other blood fractions) sample of a human subject or human subjects with gastric cancer using suitable kits and/or purification methods.
  • a blood including plasma, serum, PBMC or other blood fractions
  • Step 2 From the respective sample, the quantity (expression level) of one miRNA or sets of at least two miRNAs selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 is measured using experimental techniques. These techniques include, but are not restricted to, array based approaches, amplification methods
  • PCR RT-PCR, or qPCR
  • sequencing next generation sequencing
  • mass spectroscopy PCR, RT-PCR, or qPCR
  • Step 3 In order to gather information on the diagnostic/prognostic value and the redundancy of each of the single miRNA biomarkers, mathematical methods are applied. These methods include, but are not restricted to, basic mathematic approaches (e.g. Fold Quotients, Signal-to-Noise ratios, Correlation), statistical methods as hypothesis tests (e.g. t-test, Wilcoxon-Mann- Whitney test), the Area under the Receiver operator Characteristics Curve, information theory approaches, (e.g. the Mutual Information, Cross-entropy), probability theory (e.g. joint and conditional probabilities) or combinations and modifications of the previously mentioned methods.
  • basic mathematic approaches e.g. Fold Quotients, Signal-to-Noise ratios, Correlation
  • statistical methods as hypothesis tests e.g. t-test, Wilcoxon-Mann- Whitney test
  • the Area under the Receiver operator Characteristics Curve e.g. the Mutual Information, Cross-entropy
  • probability theory e.g.
  • Step 4 The information gathered in step 3) is used to estimate for each miRNA biomarker the diagnostic content or value. Usually, however, this diagnostic value is too small to get a highly accurate diagnosis with accuracy rates, specificities and sensitivities beyond the 80% barrier.
  • the diagnostic content of the miRNAs suitable for diagnosing/prognosing gastric cancer is listed in Figure 1 (Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median gl : median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with gastric cancer, qmedian: ratio of median gl/median g2, logqmedian: log of qmedian, ttest rawp: p-value obtained when applying t-test, ttest adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, limma rawp: p-value obtained
  • Step 5 In order to increase the performance for diagnosing/prognosing of individuals suffering from gastric cancer, more than one miRNA biomarker needs to be employed.
  • statistical learning / machine learning / bioinformatics / computational approaches are applied for set selection in order to select/define sets of miRNA biomarkers (comprising miRNAs SEQ ID NO: 1 to SEQ ID NO: 103) that are tailored for the detection of gastric cancer.
  • These techniques include, but are not restricted to, Wrapper subset selection techniques (e.g. forward step- wise, backward step-wise, combinatorial approaches, optimization approaches), filter subset selection methods (e.g. the methods mentioned in Step 3), principal component analysis, or combinations and modifications of such methods (e.g. hybrid approaches).
  • Step 6 The subsets, selected/defined in Step 5, which may range from only a small number (at least two for the set) to all measured biomarkers, are then used to carry out a diagnosis/prognosis of gastric cancer.
  • statistical learning / machine learning / bioinformatics / computational approaches are applied that include but are not restricted to any type of supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support
  • Regression techniques e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal Probit-Regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression
  • Clustering techniques e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
  • Step 7 By combination of subset selection (Step 5) and machine learning approaches
  • Step 6 an algorithm or a mathematical function for diagnosing/prognosing gastric cancer is obtained. This algorithm or mathematical function is applied to a miRNA expression profile (miRNA expression profile data) of an individual (patient) to be diagnosed for gastric cancer.
  • the present invention relates to (the use of) a (single) polynucleotide for detecting a miRNA or a set (signature) comprising, essentially consisting of, or consisting of at least two polynucleotides for detecting a set comprising, essentially consisting of, or consisting of at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
  • the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
  • hemopoietic cells refers to mature cell types and their immature precursors that are identifiable either by morphology or, mostly, by a distinct pattern of cell surface markers. The term is used to distinguish these cells from other cell types found in the body and also includes T-cells and distinctive subsets, which are the only hematopoietic cells that are not generated in the bone marrow.
  • the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
  • Erythrocytes are also known as red blood cells (RBCs), red cells, red blood corpuscles (an archaic term), haematids, or erythroid cells.
  • RBCs red blood cells
  • red cells red cells
  • red blood corpuscles an archaic term
  • haematids or erythroid cells.
  • the term “erythrocytes” comes from the Greek erythros for "red” and kytos for "hollow”, with cyte translated as "cell” in modern usage.
  • mammals such as humans, erythrocytes are devoid of a nucleus and have the shape of a biconcave lens.
  • vertebrates e.g. fishes, amphibians, reptilians and birds
  • they have a nucleus.
  • the red cells are rich in haemoglobin, a protein able to bind in a faint manner to oxygen.
  • these cells are responsible for providing oxygen to tissues and partly for recovering C0 2 produced as waste.
  • C0 2 is carried by plasma, in the form of soluble carbonates.
  • the lack of nucleus allows more room for haemoglobin and the biconcave shape of these cells raises the surface and cytoplasmic volume ratio. These characteristics make more efficient the diffusion of oxygen by these cells.
  • thrombocytes are also known as platelets.
  • the term "thrombocytes” comes from Greek ⁇ , "clot” and ⁇ , "cell”.
  • the main function of thrombocytes is to stop the loss of blood from wounds (hematostasis). To this purpose, they aggregate and release factors which promote the blood coagulation.
  • serotonin which reduces the diameter of lesioned vessels and slows down the hematic flux
  • fibrin which trap cells and forms the clotting. Their diameter is about 2-3 ⁇ , hence they are much smaller than erythrocytes.
  • Platelets release a multitude of growth factors including Platelet-derived growth factor (PDGF), a potent chemotactic agent, and TGF beta, which stimulates the deposition of extracellular matrix. Both of these growth factors have been shown to play a significant role in the repair and regeneration of connective tissues. Other healing-associated growth factors produced by platelets include basic fibroblast growth factor, insulin-like growth factor 1, platelet-derived epidermal growth factor, and vascular endothelial growth factor. Local application of these factors in increased concentrations through Platelet-rich plasma (PRP) has been used as an adjunct to wound healing for several decades.
  • PDGF Platelet-derived growth factor
  • TGF beta TGF beta
  • Leukocytes are also known as white cells. Particularly, as the term "leuco” means white in Greek. Leukocytes are cells of the immune system which are involved in defending the body against both infectious disease and foreign materials. The number of leukocytes in the blood is often an indicator of disease. There are normally between 4x 109 and 1.1 x 1010 white blood cells in a litre of blood, making up approximately 1% of blood in a healthy adult. An increase in the number of leukocytes over the upper limits is called leukocytosis, and a decrease below the lower limit is called leukopenia. The physical properties of leukocytes, such as volume, conductivity, and granularity, may change due to activation, the presence of immature cells, or the presence of malignant leukocytes in leukemia.
  • the leukocytes are granulocytes and/or lymphoid cells.
  • the term granulocyte is due to the presence of granules in the cytoplasm of these cells. In the different types of granulocytes, the granules are different which helps to distinguish them. In fact, these granules have a different affinity towards neutral, acid or basic stains and give the cytoplasm different colours.
  • the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells.
  • the lymphoid cells are lymphocytes and/or monocytes.
  • the lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs). Accordingly, the following leukocytes may be present in the blood sample: neutrophils, eosinophils, basophils, lymphocytes and/or monocytes. Particularly, the following leukocytes may be present in the blood sample in different proportions: 50 - 70 % neutrophils, 2 - 4 % eosinophils, 0.5 - 1 % basophils, 20 - 40 % lymphocytes and/or 3 - 8 % monocytes.
  • PBMCs peripheral blood mononuclear cells
  • PBMCs Peripheral blood mononuclear cells
  • lymphocytes lymphocytes, monocytes or macrophages
  • macrophages may be used in the method of the present invention.
  • the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
  • blood cells can be isolated from whole blood via centrifugation such as density gradient centrifugation. Because of their relative density, lymphocytes and monocytes (encompassed by the term PBMCs) are comprised in the interphase, plasma and thrombocytes are comprised in the supernatant and erythrocytes and granulocytes are comprised in the cell sediment after centrifugation.
  • centrifugation such as density gradient centrifugation.
  • lymphocytes and monocytes are comprised in the interphase
  • plasma and thrombocytes are comprised in the supernatant
  • erythrocytes and granulocytes are comprised in the cell sediment after centrifugation.
  • the blood sample is collected by a blood collection tube.
  • the blood collection tube includes means for stabilizing the RNA-fraction, especially the small RNA fraction within the blood sample.
  • RNA-stabilization agent are PAXgene tubes (www.Preanalytix.com), or Tempus Blood RNA Tubes (Ambion, Applied Biosystems).
  • Conventionally blood collection tubes, to which optionally a RNA-stabilizing agent like RNAlater (Ambion) can be added, are EDTA-, Heparin-, or Serum-tubes.
  • the blood sample from a human patient has a volume of between 0.1 and 20 ml, more preferably of between 0.5 and 10 ml, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml. It is preferred that the blood sample is from a human patient that has not been therapeutically treated or has been therapeutically treated. In one embodiment, the therapeutical treatment is monitored on the basis of the detection of the miRNA or set of miRNAs by the nucleotide or set of polynucleotides. It is also preferred that total RNA or subfractions thereof, isolated (e.g. extracted) from a blood sample of a human patient is (are) used for detecting the miRNA or set of miRNAs by the polynucleotide or set of polynucleotides.
  • the blood samples may further be diluted with saline, buffer or a physiologically acceptable diluent. Alternatively, such samples may be concentrated by conventional means.
  • the present invention relates to (the use of) a (single) polynucleotide for detecting a miRNA or a set (signature) comprising, essentially consisting of, or consisting of at least two polynucleotides for detecting a set comprising, essentially consisting of, or consisting of at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
  • the (miRNA) set comprises, essentially consists of, or consists of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprises/consists of 103 miRNAs, wherein the nucleotide sequences of
  • the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 1 and SEQ ID NO: 2
  • the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 3
  • the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 4
  • the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5
  • the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 6
  • the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 7
  • the nucleotide sequences of the at least 8 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 2 and SEQ ID NO: 3, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16, the nucleotide sequences of the at least 4 miRNAs
  • the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a blood cell sample from a human patient.
  • the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in an erythrocyte sample/erythrocyte containing sample from a human patient,
  • the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a leukocyte sample/leukocyte containing sample from a human patient, and/or
  • the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a thrombocyte sample/thrombocyte containing sample from a human patient.
  • the blood cells may also be PBMCs such as lymphocytes and/or monocytes.
  • the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
  • the nucleotide sequences of said miRNAs are selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets (signatures) listed in Figure 13.
  • the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in an erythrocyte sample/erythrocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13,
  • the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a leukocyte sample/leukocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13, and/or
  • the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a thrombocyte sample/thrombocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
  • the blood cells may also be PBMCs such as lymphocytes and/or monocytes.
  • the (miRNA) set as defined above comprises at least one further miRNA, e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
  • the (miRNA) set comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or comprises 103 miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group
  • nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to SEQ ID NO.
  • nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 1 1, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO: 7 to SEQ ID NO: 16, (viii) SEQ ID NO: 8 to SEQ ID NO: 17, (ix) SEQ ID NO: 9 to SEQ ID NO: 18, (x) SEQ ID NO: 10 to SEQ ID NO: 19, or (xi) SEQ ID NO: 11 to SEQ ID NO: 20.
  • nucleotide sequences of the miRNAs comprised in the set have (i) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, (ii) SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16, (iii) SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, and SEQ ID NO: 4, (iv) SEQ ID NO: 4, (iv) SEQ
  • the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20. It is particularly preferred that the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, and SEQ ID NO: 5.
  • the polynucleotide of the present invention is complementary to the miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO:
  • the polynucleotides comprised in the set of the present invention are complementary to the miRNAs comprised in the set, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103, and wherein preferably the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO : 104 to SEQ ID NO : 119,
  • the polynucleotide is a fragment of the polynucleotide according to (i), preferably the polynucleotide is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the polynucleotide according to (i), or the polynucleotides comprised in the set are fragments of the polynucleotides comprised in the set according to (i), preferably the polynucleotides comprised in the set are fragments which are between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the polynucleotides comprised in the set according to (i), or
  • the polynucleotide has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or the polynucleotides comprised in the set have at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81,
  • the polynucleotide as defined in (iii) has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or that the polynucleotides comprised in the set as defined in (iii) have at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e.
  • sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
  • polynucleotide or polynucleotides as defined in (ii) i.e. polynucleotide fragment(s)) or (iii) (i.e. polynucleotide variant(s) or polynucleotide fragment variant(s)) is (are) only regarded as a polynucleotide or polynucleotides as defined in (ii) (i.e. polynucleotide fragment(s)) or (iii) (i.e.
  • polynucleotide variant(s) or polynucleotide fragment variant(s)) within the context of the present invention if it is or they are still capable of binding to, hybridizing with, or detecting a target miRNA of complementary sequence or target miRNAs of complementary sequences, e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119, through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation under stringent hybridization conditions.
  • the skilled person can readily assess whether a polynucleotide or polynucleotides as defined in (ii) (i.e. polynucleotide fragment(s)) or (iii) (i.e.
  • polynucleotide variant(s) or polynucleotide fragment variant(s)) is (are) still capable of binding to, hybridizing with, recognizing or detecting a target miRNA of complementary sequence or target miRNAs of complementary sequences, e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119.
  • Suitable assays to determine whether hybridization under stringent conditions still occurs are well known in the art.
  • a suitable assay to determine whether hybridization still occurs comprises the steps of: (a) incubating the polynucleotide or polynucleotides as defined in (ii) or (iii) attached onto a biochip with the miRNA(s) of complementary sequence(s), e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119, labeled with biotin under stringent hybridization conditions, (b) washing the biochip to remove unspecific bindings, (c) subjecting the biochip to a detection system, and (c) analyzing whether the polynucleotide(s) can still hybridize with the target miRNA(s) of complementary sequence(s), e.g.
  • the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119.
  • the respective non-mutated and not fragmented polynucleotide as defined in (i) may be used.
  • stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %,Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
  • the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5.
  • the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
  • the present invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
  • miRNA expression profile represents the expression level of a single miRNA or a collection of expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or of 103 miRNAs comprised in a
  • Said set of miRNAs may be completed by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the expression profile of a single miRNA or the expression profile of at least two miRNAs comprised in a set is determined in a blood sample from a human patient.
  • the miRNAs disclosed herein are expressed within and/or among cells or tissues and are subsequently released/transferred into circulating blood and/or (ii) as the miRNAs described herein are directly expressed in hemopoietic cells, also known as blood cells, e.g. erythrocytes, leukocytes and/or thrombocytes.
  • hemopoietic cells also known as blood cells, e.g. erythrocytes, leukocytes and/or thrombocytes.
  • Said miRNAs may be expressed within and/or among surrounding cells or tissues and may be subsequently released/transferred into circulating blood and/or said miRNAs may be directly expressed in blood, namely in blood cells, e.g. peripheral blood mononuclear cells (PBMCs).
  • PBMCs peripheral blood mononuclear cells
  • the expression levels of miRNAs determined in a blood sample indirectly represent the expression levels of said miRNAs in the cells or tissues from which they originate and/or directly represent the expression levels of miRNAs in blood cells.
  • the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
  • the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
  • the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
  • PBMCs peripheral blood mononuclear cells
  • the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
  • the present invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity thereto, and
  • the miRNA expression profiles may be generated by any convenient means for determining miRNA expression levels (see below) and allow the analysis of differential miRNA expression levels between samples, for example, between a sample of a human patient and between (a) sample(s) of (a) control subject(s), e.g. between a sample of a human patient and a sample of a subject known not to suffer from gastric cancer (i.e. being healthy), or between a sample of a human patient and a sample of a subject known to suffer from gastric cancer (i.e. being diseased).
  • each miRNA is represented by a numerical value. The higher the value of an individual miRNA, the higher is the expression level of said miRNA.
  • the miRNA expression profile may include expression data for 1, 2, 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100, or 103 miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • differential expression of miRNA means qualitative and/or quantitative differences in the temporal and/or local miRNA expression patterns within and/or among cells, tissues, or within blood.
  • a differentially expressed miRNA may qualitatively have its expression altered, including an activation or inactivation in, for example, normal tissue versus diseased tissue.
  • the difference in miRNA expression may also be quantitative, e.g. in that expression is modulated, i.e. either up-regulated, resulting in an increased amount of miRNA, or down-regulated, resulting in a decreased amount of miRNA.
  • the degree to which miRNA expression differs need only be large enough to be quantified via standard characterization techniques, e.g. quantitative hybridization of miRNA, labeled miRNA, or amplified miRNA, quantitative PCR (qPCR) such as real time quantitative PCR (RT qPCR), ELISA for quantitation, next generation sequencing and the like.
  • a single miRNA or a set comprising at least two miRNAs representative for gastric cancer refers to a fixed defined single miRNA which is known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and is, thus, representative for gastric cancer, or it refers to at least two fixed defined miRNAs comprised in a set which are known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and are, thus, representative for gastric cancer.
  • the nucleotide sequence of said fixed defined single miRNA or the nucleotide sequences of said at least two fixed defined miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned set may be supplemented by at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%) sequence identity thereto.
  • At least one supplementary miRNA which is known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and is, thus, also representative for gastric cancer, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto, may be added to the fixed defined set of at least two miRNAs as mentioned above and an expression profile may be determined from this supplemented set, for example, to improve the diagnostic accuracy, specificity and sensitivity in the determination of gastric cancer.
  • a polynucleotide (probe) capable of detecting this fixed defined miRNA or polynucleotides (probes) capable of detecting this fixed defined miRNA set is (are) attached to a solid support, substrate, surface, platform, or matrix, e.g. biochip.
  • a polynucleotide (probe) capable of detecting this fixed defined miRNA or polynucleotides (probes) capable of detecting this fixed defined miRNA set is (are) attached to a solid support, substrate, surface, platform, or matrix, e.g. biochip.
  • the fixed defined set of miRNAs for diagnosing gastric cancer comprises or consists of 30 miRNAs
  • polynucleotides capable of detecting these 30 miRNAs are attached to a solid support, substrate, surface, platform or matrix, e.g. biochip, in order to perform the diagnostic/prognostic sample analysis.
  • an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient is determined in the step (i) of the method of the present invention, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the set comprises, essentially consists of, or consists of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprises/consists of 103 miRNAs, wherein the nucleotide
  • the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 1 and SEQ ID NO: 2
  • the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 3
  • the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 4
  • the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5
  • the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 6
  • the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 7
  • the nucleotide sequences of the at least 8 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 2 and SEQ ID NO: 3, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16, the nucleotide sequences of the at least 4 miRNAs
  • the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a blood cell sample from a human patient.
  • the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in an erythrocyte sample/erythrocyte containing sample from a human patient
  • the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a leukocyte sample/leukocyte containing sample from a human patient
  • the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a thrombocyte sample/thrombocyte containing sample from a human patient.
  • the blood cells may also be PBMCs such as lymphocytes and/or monocytes.
  • the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
  • the nucleotide sequences of said miRNAs are selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets (signatures) listed in Figure 13.
  • the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in an erythrocyte sample/erythrocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13,
  • the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a leukocyte sample/leukocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13, and/or
  • the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a thrombocyte sample/thrombocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
  • the blood cells may also be PBMCs such as lymphocytes and/or monocytes.
  • the (miRNA) set as defined above comprises at least one further miRNA, e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e.
  • the (miRNA) set comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88,
  • nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to SEQ ID NO.
  • nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 11, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO: 7 to SEQ ID NO: 16, (viii) SEQ ID NO: 8 to SEQ ID NO: 17, (ix) SEQ ID NO: 9 to SEQ ID NO: 18, (x) SEQ ID NO: 10 to SEQ ID NO: 19, or (xi) SEQ ID NO: 11 to SEQ ID NO: 20.
  • nucleotide sequences of the miRNAs comprised in the set have (i) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, (ii) SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16, (iii) SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, and SEQ ID NO: 4, (iv) SEQ ID NO: 4, (iv) SEQ
  • the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20. It is particularly preferred that the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, and SEQ ID NO: 5.
  • an expression profile of a (single) miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient or an expression profile of a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of
  • nucleotide sequence that is a fragment of the nucleotide sequence according to (i), preferably, a nucleotide sequence that is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the nucleotide sequence according to (i), and
  • nucleotide sequence that has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the nucleotide sequence according to (i) or nucleotide sequence fragment according to (ii), and wherein the nucleotide sequence of the at least one further miRNA comprised in the set is selected from the group consisting of
  • nucleotide sequence that is a fragment of the nucleotide sequence according to (i), preferably, a nucleotide sequence that is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the nucleotide sequence according to (i), and
  • nucleotide sequence that has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the nucleotide sequence according to (i) or nucleotide sequence fragment according to (ii).
  • the nucleotide sequence as defined in (iii) has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the nucleotide sequence of the nucleotide according to (i) or nucleotide fragment according to (ii).
  • nucleotide sequence as defined in (ii) i.e. nucleotide sequence fragment
  • nucleotide sequence variant or nucleotide sequence fragment variant is only regarded as a nucleotide sequence as defined in (ii) (i.e. nucleotide sequence fragment) or (iii) (i.e. nucleotide sequence variant or nucleotide sequence fragment variant) within the context of the present invention, if it can still be bound, hybridized, recognized, or detected by a polynucleotide (probe) of complementary sequence, e.g.
  • a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i), through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation under stringent hybridization conditions.
  • the skilled person can readily assess whether a nucleotide sequence as defined in (ii) (i.e. nucleotide sequence fragment) or (iii) (i.e. nucleotide sequence variant or nucleotide sequence fragment variant) can still be bound, hybridized, recognized, or detected by a polynucleotide (probe) of complementary sequence, e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i).
  • a suitable assay to determine whether hybridization under stringent conditions still occurs comprises the steps of: (a) incubating a nucleotide sequence as defined in (ii) or (iii) labelled with biotin with a polynucleotide (probe) of complementary sequence, e.g.
  • a polynucleotide (probe) of complementary sequence e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i).
  • the respective miRNA as defined in (i) may be used.
  • Preferably stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %,Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
  • a polynucleotide according to the first aspect of the present invention is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient.
  • a polynucleotide is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% identity thereto.
  • a polynucleotide according to the first aspect of the present invention is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20.
  • the polynucleotide is in single stranded form and attached to a solid support, substrate, surface, platform or matrix, e.g. biochip, and is incubated with a miRNA of complementary sequence, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20 for determining an expression profile of said miRNA.
  • a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention is used for determining an expression profile of a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient.
  • a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consi
  • the above mentioned set of polynucleotides used in step (i) of the method of the present invention comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for determining an expression profile of at least on further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)) comprised in the above mentioned set of miRNAs, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 1 19, a fragment thereof, and a sequence having at least 80% identity thereto.
  • the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 1 19, a fragment thereof, and a sequence having at least 80% identity thereto.
  • the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
  • the blood sample is a whole blood sample or a blood fraction sample.
  • the blood fraction sample is a blood cell sample, a blood plasma sample or a blood serum sample. More preferably, the blood fraction sample is a blood cell sample.
  • Blood cells also known as hemopoietic cells may also be used, e.g. erythrocytes, leukocytes and/or thrombocytes (see above).
  • the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
  • Blood samples may be collected by any convenient method, as known in the art. It is preferred that 0.1 to 20 ml blood, preferably 0.5 to 10 ml blood, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml blood, is collected.
  • the blood sample is obtained from a human patient prior to initiation of therapeutic treatment, during therapeutic treatment and/or after therapeutic treatment. It is particularly preferred that total RNA or subfractions thereof including the miRNA is (are) isolated, e.g. extracted, from the blood sample in order to determine the expression profile of a miRNA or miRNAs comprised in the blood sample.
  • the inventors of the present invention surprisingly found that miRNAs are not only present in a blood sample but also that miRNAs remain stable and that, thus, blood miRNAs can be used as biomarkers for detecting and/or prognosis of gastric cancer in human patients. Further, the inventors found that the miRNAs present in blood are different from the ones found in tissue of individuals suffering from gastric cancer. Furthermore, the use of blood samples in the method of the present invention for detection and/or prognosis of gastric cancer has a number of advantage, for example, blood miRNAs have a high sensitivity, blood is relatively easy to obtain and even can be collected via routine physical examination, the costs for detection are low, and the samples can easily be preserved (e.g. at - 20°C).
  • blood circulates to all tissues in the body and, therefore, blood is able to reflect the physiological pathology of the whole organism and the detection of blood miRNAs results in an indicator of human health, and according to the present invention, as an indication whether a patient suffers from gastric cancer. Furthermore, this method can widely be used in general screening for gastric cancer. Moreover, the inventors of the present invention surprisingly found that blood is an efficient mean for early diagnosis of gastric cancer. As novel disease markers, blood miRNAs improve the low- specificity and low-sensitivity caused by individual differences which are difficult to overcome with other markers, and notably increase the clinical detection rate of gastric cancer so as to achieve early diagnosis of gastric cancer.
  • a first diagnosis of gastric cancer can be performed employing, as disclosed, miRNA-detection in a blood sample such as blood cell sample, followed by a second diagnosis that is based on other methods (e.g. other biomarkers and/or imaging methods).
  • an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient is determined.
  • the determination may be carried out by any convenient means for determining a RNA expression level or RNA expression levels.
  • qualitative, semi-quantitative and/or quantitative detection methods may be used. A variety of techniques are well known to the person skilled in the art. It is preferred that the expression profile of the miRNA(s) representative for gastric cancer is determined by nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy or any combination thereof.
  • Nucleic acid amplification may be performed using real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR).
  • RT-PCR real time polymerase chain reaction
  • the real time polymerase chain reaction (RT-PCR) is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs). It is particularly suitable for detecting low abundance miRNAs.
  • the real time quantitative polymerase chain reaction (RT qPCR) allows the analysis of a single miRNA as well as a complex set of miRNAs (e.g.
  • a set of at least 2 to 103 miRNAs such as a set of 50, 60, 70, 80, 90, or 100 miRNAs
  • a blood sample such as blood cell sample from a human patient, e.g. a single miRNA or a set comprising at least two miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (e.g. a set of 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103).
  • RT-PCR real time polymerase chain reaction
  • a blood e.g. whole blood, particularly blood cell, serum, or plasma
  • RT RNA reverse transcription
  • miRNA-specific primers e.g. whole blood, particularly blood cell, serum, or plasma
  • whole blood, particularly blood cell, serum, or plasma sample being a buffer so as to prepare cDNA samples, (ii) designing miRNA-specific cDNA forward primers and providing universal reverse primers to amplify the cDNA via polymerase chain reaction (PCR), (iii) adding a fluorescent probe to conduct PCR, and (iv) detecting the miRNA(s) level in the blood (e.g. whole blood, serum, or plasma) sample.
  • PCR polymerase chain reaction
  • RT-PCR real time polymerase chain reaction
  • RT qPCR real time quantitative polymerase chain reaction
  • reverse transcription of miRNAs may be performed using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems) according to manufacturer's recommendations. Briefly, miRNA may be combined with dNTPs, MultiScribe reverse transcriptase and the primer specific for the target miRNA. The resulting cDNA may be diluted and may be used for PCR reaction. The PCR may be performed according to the manufacturer's recommendation (Applied Biosystems). Briefly, cDNA may be combined with the TaqMan assay specific for the target miRNA and PCR reaction may be performed using ABI7300.
  • Nucleic acid hybridization may be performed using a microarray/biochip or in situ hybridization. In situ hybridization is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs).
  • the microarray/biochip allows the analysis of a single miRNA as well as a complex set of miRNAs (e.g. a set of at least 2 to 103 miRNAs such as a set of at least 50, 60, 70, 80, 90, or 100 miRNAs) comprised in a blood sample such as blood cell sample from a human patient, e.g.
  • nucleotide sequence of said miRNA or said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (e.g. a set of 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103).
  • the polynucleotides (probes) according to the first aspect of the present invention with complementarity to the corresponding miRNAs to be detected are attached to a solid phase to generate a microarray/biochip (e.g. 103 polynucleotides (probes) which are complementary to the 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103 comprised in a set).
  • a microarray/biochip e.g. 103 polynucleotides (probes) which are complementary to the 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103 comprised in a set.
  • Said microarray/biochip is then incubated with miRNAs, isolated (e.g. extracted) from a blood sample such as blood cell sample of a human patient, which may be labelled, e.g. fluorescently labelled, or unlabelled.
  • the success of hybridisation may be controlled and the intensity of hybridization may be determined via the hybridisation signal of the label in order to determine the expression level of each tested miRNA in said blood sample such as blood cell sample.
  • the nucleic acid hybridization is performed using a microarray/biochip, or using in situ hybridization, and/or (ii) the nucleic acid amplification is performed using real-time PCR.
  • an expression profile of a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/cons
  • an expression profile of a set comprising at least 5 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 5 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to
  • an expression profile of a set comprising at least 10 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 10 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 1 1, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO:
  • an expression profile of a set comprising at least 50 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 50 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 50.
  • an expression profile of a set comprising at least 80 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 80 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 80.
  • an expression profile of a set comprising at least 100 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 100 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 100.
  • the miRNA or the set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample such as blood cell sample from a human patient may be established on one experimental platform (e.g. microarray), while for routine diagnosis/prognosis another experimental platform (e.g. qPCR) may be chosen.
  • one experimental platform e.g. microarray
  • another experimental platform e.g. qPCR
  • an expression profile (data) of a (single) miRNA representative for gastric cancer as defined above, or of a set comprising at least two miRNAs representative for gastric cancer as defined above in a blood sample such as blood cell sample from a human patient in step (i) of the method according to the present invention, said expression profile (data) is compared to a reference in step (ii) of the method according to the present invention, wherein the comparison of said expression profile (data) to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or an algorithm or a mathematical function is applied to said expression profile (data) in step (ii) of the method of the present invention, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
  • the reference may be any reference which allows for the diagnosis and/or prognosis of gastric cancer, e.g. an indicated value or values, and/or the algorithm or mathematical function may be any algorithm or mathematical function which allows for the diagnosis and/or prognosis of gastric cancer.
  • (clinical) condition means a status of a subject that can be described by physical, mental or social criteria. It includes so- called “healthy” and “diseased” conditions. For the definition of “healthy” and “diseased” conditions it is referred to the international classification of diseases (ICD) of the WHO (http://wwwint/classifications/icd/en/index.html).
  • ICD international classification of diseases
  • the expression profile (data) determined in a human patient is compared to a reference of one known (clinical) condition or when an algorithm or a mathematical function obtained from a reference of one known (clinical) condition is applied to the expression profile (data) determined in a human patient according to preferred embodiments of the method of the present invention, it is understood that said condition is gastric cancer (i.e. diseased condition), or that said condition is no gastric cancer (i.e. healthy/healthiness).
  • gastric cancer i.e. diseased condition
  • no gastric cancer i.e. healthy/healthiness
  • the expression profile (data) determined in a human patient may be compared to a reference of two known (clinical) conditions, which are gastric cancer and no gastric cancer, or an algorithm or a mathematical function obtained from a reference of two known (clinical) conditions, which are gastric cancer and no gastric cancer, may be applied to the expression profile (data) determined in a human patient.
  • reference expression profile (data) means an expression profile (data) of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i), or an expression profile (data) of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i), but means an expression profile which is obtained from a (control) subject(s) with a known clinical condition, e.g. gastric cancer, or no gastric cancer.
  • a known clinical condition e.g. gastric cancer, or no gastric cancer.
  • the nucleotide sequence(s) of the miRNA(s) of step (i) and the nucleotide sequence(s) of the miRNA(s) of the reference expression profile differ in 1 to 5, more preferably in 1 to 3, and most preferably in 1 to 2 nucleotides, i.e. in 1, 2, 3, 4, or 5 nucleotides.
  • said difference resides in 1 to 5, more preferably 1 to 3, and most preferably 1 to 2 nucleotide mutations, i.e. 1, 2, 3, 4, or 5 nucleotide mutations (e.g. substitutions, additions, insertions, and/or deletions).
  • the miRNAs within the human species may differ.
  • the miRNA(s) of step (i) and the miRNA(s) of the reference expression profile do not differ in their nucleotide sequence, i.e. are identical.
  • the miRNA(s) of step (i) and the miRNA(s) of the reference expression profile are derived from subject/patients of the same gender and/or similar age/phase of life.
  • both the reference expression profile and the expression profile of step (i) are determined in the same type of blood sample, for example, blood serum sample, blood plasma sample, or blood cell (e.g. erythrocytes, leukocytes and/or thrombocytes) sample.
  • the reference expression profile is not necessarily obtained from a single (control) subject, e.g. a subject known to be affected by gastric cancer, or a subject known to be not affected by gastric cancer, but may be an average reference expression profile of a plurality of (control) subjects, e.g. subjects known to be affected by gastric cancer, or subjects known to be not affected by gastric cancer, e.g. at least 2 to 40 subjects, more preferably at least 10 to 25 subjects, and most preferably at least 15 to 20 subjects, i.e. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
  • both the reference expression profile and the expression profile of step (i) are obtained from a subject/patient of the same gender (e.g. female or male) and/or of a similar age/phase of life (e.g. infant, young child, juvenile, adult).
  • the reference is a reference expression profile (data) of at least one subject, preferably the reference is an average expression profile (data) of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
  • the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • said reference may be a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy) or known to be affected by gastric cancer (i.e. diseased), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • the comparison of the expression profile of the human patient to be diagnosed and/or prognosed to the (average) reference expression profile (data), may then allow for diagnosing and/or prognosing of gastric cancer (step (ii)).
  • diagnosing preferably means comparing the expression profile
  • Prognosing preferably means comparing the expression profile (data) of a human patient determined in step (i) to the (average) reference expression profile (data) as mentioned above to decide, if the at least one known clinical condition, which is gastric cancer, or which is no gastric cancer (i.e. healthy), will likely be present in said patient.
  • the human patient may be diagnosed as not suffering from gastric cancer (i.e. being healthy), or as suffering from gastric cancer (i.e. being diseased). Further, for example, the human patient may be prognosed as not developing gastric cancer (i.e. staying healthy), or as developing gastric cancer (i.e. getting diseased).
  • Diagnosing/prognosing of gastric cancer based on (a) reference expression profile (data) as reference may take place as follows: For instance, (i) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of at least 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, at least 2 fold higher (up-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy), the human patient tested is diagnosed as suffering from gastric cancer (i.e.
  • step (i) e.g. a single miRNA or a set of 2 miRNAs
  • the miRNA(s) of step (i) e.g. a single miRNA or a set of 2 miRNAs
  • the miRNA(s) of step (i) e.g. a single miRNA or a set of 2 miRNAs
  • the miRNA(s) of step (i) is (are), for example, at least 2 fold lower (down-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy)
  • the human patient tested is diagnosed as suffering from gastric cancer (i.e. being diseased) or prognosed as likely developing gastric cancer (i.e. getting diseased).
  • step (i) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of at least 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, not at least 2 fold higher (up-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy), the human patient tested is diagnosed as not suffering from gastric cancer (i.e. being healthy) or prognosed as likely not developing gastric cancer (i.e.
  • step (i) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, not at least 2 fold lower (down-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of human subject known not to suffer from gastric cancer (i.e. being healthy), the patient tested (e.g. human or animal) is diagnosed as not suffering from gastric cancer (i.e. being healthy) or prognosed as likely not developing gastric cancer (i.e. staying healthy).
  • the miRNA(s) of step (i) e.g. a single miRNA or a set of 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, not at least 2 fold lower (down-regulated) compared to said miRNA(s) (e.g
  • a human patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer may possibly suffer from another disease not tested/known, or a human patient that is prognosed as staying healthy, i.e. likely not developing gastric cancer, may possibly developing another disease not tested/known.
  • the algorithm or mathematical function is obtained from a reference expression profile (data) of at least one subject, preferably of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e.
  • the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i), or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • said algorithm or mathematical function may be obtained from a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy), or known to be affected by gastric cancer (i.e. diseased), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • the algorithm or mathematical function is obtained from reference expression profiles (data) of at least two subjects, preferably of at least 3 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with at least two known clinical conditions, preferably at least 2 to 5, more preferably at least 2 to 4 (i.e.
  • the reference expression profiles are the profiles of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • the two known clinical conditions are gastric cancer and no gastric cancer.
  • said algorithm or mathematical function may be obtained from reference expression profiles of at least two subjects, at least one subject known to suffer from gastric cancer (i.e. diseased) and at least one subject known not to suffer from gastric cancer (i.e. healthy), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that corresponds (are identical), to the nucleotide sequences of the miRNAs of step (i).
  • the above mentioned algorithm or mathematical function is obtained from reference expression profiles of the same number of (control) subjects (e.g. subjects known to be healthy or diseased).
  • the algorithm or mathematical function may be obtained from reference expression profiles of 10 subjects known to suffer from gastric cancer (positive control) and 10 subjects known not to suffer from gastric cancer (negative control).
  • the algorithm or mathematical function may also be obtained from reference expression profiles of 20 subjects known to suffer from gastric cancer (positive control) and 20 subjects known not to suffer from gastric cancer (negative control).
  • Machine learning approaches may include but are not limited to supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
  • classification techniques e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches
  • Regression techniques e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression
  • the inventors of the present invention surprisingly found that the application of a machine learning approach (e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means) leads to the obtainment of an algorithm or a mathematical function that is trained by the reference expression profile(s) (data) mentioned above and that this allows (i) a better discrimination between the at least two (e.g. 2 or 3) known clinical conditions (the at least two statistical classes) or (ii) a better decision, whether the at least one known clinical condition (the at least one statistical class) is present. In this way, the performance for diagnosing/prognosing of individuals suffering from gastric cancer can be increased (see also experimental section for details).
  • a machine learning approach e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means
  • the machine learning approach involves the following steps:
  • item (ii) encompasses both that the computed algorithm or mathematical function is suitable to distinguish between the clinical condition of gastric cancer and any other clinical condition(s), preferably the clinical condition of no gastric cancer, and that the computed algorithm or mathematical function is suitable to distinguish between the likely clinical condition of gastric cancer and any other likely clinical condition(s), preferably the clinical condition of no gastric cancer.
  • “likely” means that it is to be expected that the patient will develop said clinical condition(s).
  • the machine learning approach involves the following steps:
  • the machine learning approach involves the following steps:
  • the application of the algorithm or mathematical function as mentioned above to the expression profile of the human patient to be diagnosed and/or prognosed may then allow for diagnosing and/or prognosing of gastric cancer.
  • diagnosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide, if the at least one known clinical condition, which is gastric cancer (i.e. diseased condition), or which is no gastric cancer (i.e. healthy condition), is present in said patient.
  • Prognosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide, if the at least one known clinical condition, which is gastric cancer (i.e. diseased condition), or which is no gastric cancer (i.e. healthy condition), will likely be present in said patient.
  • the human patient may be diagnosed as not suffering from gastric cancer (i.e. being healthy), or as suffering from gastric cancer (i.e. being diseased). Further, for example, the human patient may be prognosed as not developing gastric cancer (i.e. staying healthy), or as developing gastric cancer (i.e. getting diseased).
  • diagnosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide which of the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer, is (are) present in said patient, or to distinguish between the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer.
  • Prognosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide which of the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer, will likely be present in said patient, or to distinguish between the at least two likely clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer.
  • the human patient may be diagnosed as suffering from gastric cancer (i.e. being diseased), or as not suffering from gastric cancer (i.e. being healthy). If the at least two known clinical conditions are gastric cancer (i.e. diseased condition) and no gastric cancer (i.e. healthy condition), the human patient may be prognosed as developing gastric cancer (i.e. getting diseased), or as not developing gastric cancer (i.e. staying healthy).
  • Diagnosing/prognosing of gastric cancer based on an algorithm or a mathematical function may take place as follows: For instance, if the algorithm or mathematical function, which is obtained from a reference expression profile of at least one subject with the known clinical condition of gastric cancer, is applied to the expression profile (data) of a human patient, the human patient is classified as suffering from gastric cancer (i.e. being diseased), if the resulting score is below a specified threshold, or if the algorithm or mathematical function, which is obtained from a reference expression profile of at least one subject with the known clinical condition of no gastric cancer, is applied to the expression profile (data) of the human patient, the human patient is classified as not suffering from gastric cancer (i.e. being healthy), if the resulting score is above a specified threshold.
  • the algorithm or mathematical function which is obtained from a reference expression profile of at least one subject with the known clinical condition of gastric cancer and from a reference expression profile of at least one subject with the known clinical condition of no gastric cancer, is applied to the expression profile (data) of a human patient, the human patient is classified as suffering from gastric cancer (i.e. being diseased), if the resulting score is below a specified threshold, and the human patient is classified as not suffering from gastric cancer i.e. being healthy), if the resulting score is above a specified threshold.
  • SVMs Support vector machines
  • training examples e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer
  • each marked as belonging to one of one category e.g. condition of gastric cancer or no gastric cancer
  • one of two categories e.g.
  • an SVM algorithm builds a model that predicts whether a new example (e.g. sample from a human patient to be tested) falls into one category or the other (e.g. condition of gastric cancer or no gastric cancer).
  • a SVM model is a representation of the training examples (e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer) as points in space, mapped so that the training examples of the separate categories (e.g. condition of gastric cancer or no gastric cancer) are divided by a clear gap that is as wide as possible.
  • New examples e.g.
  • a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks.
  • a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
  • Classifying data is a preferred task in machine learning. For example, considering some given data points each belong to one (e.g. gastric cancer or no gastric cancer) of two classes (e.g. gastric cancer and no gastric cancer), the goal is to decide which class a new data point (e.g. achieved from a human patient) will be in.
  • a data point is viewed as a p-dimensional vector (a list of p numbers), and the question is, whether it is possible to separate such points with a p-1 -dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data.
  • hyperplane One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes (e.g. condition of gastric cancer or no gastric cancer).
  • the hyperplane should be chosen so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier.
  • training data D e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer
  • n subject samples e.g. subjects known to suffer from gastric cancer and/or any other known clinical condition, for example, no gastric cancer
  • v ((3 ⁇ 4 Q) I 3 ⁇ 4 ir , a ⁇ —l, ijj i
  • c is either 1 or -1, indicating the class labels (e.g. gastric cancer and/or any other known clinical condition(s), for example, no gastric cancer) to which the miRNA biomarker intensities x belongs.
  • Each x i is a / ⁇ -dimensional real vector (with p being the number of miRNA biomarkers).
  • the vector w is a normal vector: it is perpendicular to the
  • the parameter I determines the offset of the hyperplane from the origin along the normal vector W.
  • W and b should be chosen to maximize the margin, or distance between the parallel hyperplanes that are as far apart as possible while still separating the data.
  • These hyperplanes can be described by the equations w ⁇ x— b— 1
  • the two hyperplanes of the margin can be selected in a way that there are no points between them and then by trying to maximize their distance. For example, by using geometry, the distance between these two hyperplanes is li w li , so ll ⁇ ' 11 should be minimized. To also prevent data points falling into the margin, the following constraint should be added: for each i either
  • the a terms constitute a dual representation for the weight vector in terms of the training
  • hyperplane passes through the origin of the coordinate system.
  • Such hyperplanes are called unbiased, whereas general hyperplanes not necessarily passing through the origin are called biased.
  • Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning.
  • the learner is also given a set
  • SVMs belong to a family of generalized linear classifiers. They can also be considered a special case of Tikhonov regularization. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers.
  • Corinna Cortes and Vladimir Vapnik suggested a modified maximum margin idea that allows for mislabeled examples. If there exists no hyperplane that can split the "yes" and "no" examples, the Soft Margin method will choose a hyperplane that splits the examples as cleanly as possible, while still maximizing the distance to the nearest cleanly split examples.
  • the method introduces slack variables, 3 ⁇ 4, which measure the degree of misclassification of the datum x,
  • the objective function is then increased by a function which penalizes non-zero ⁇ ,, and the optimization becomes a trade off between a large margin, and a small error penalty. If the penalty function is linear, the optimization problem becomes:
  • the original optimal hyperplane algorithm proposed by Vladimir Vapnik in 1963 was a linear classifier.
  • Bernhard Boser, Isabelle Guyon and Vapnik suggested a way to create non-linear classifiers by applying the kernel trick (originally proposed by Aizerman et al. [4] ) to maximum-margin hyperplanes.
  • the resulting algorithm is formally similar, except that every dot product is replaced by a non-linear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space.
  • the transformation may be non- linear and the transformed space high dimensional; thus though the classifier is a hyperplane in the high-dimensional feature space, it may be non-linear in the original input space.
  • the kernel used is a Gaussian radial basis function
  • the corresponding feature space is a Hilbert space of infinite dimension.
  • Maximum margin classifiers are well regularized, so the infinite dimension does not spoil the results.
  • Some common kernels include, ⁇ Polynomial (homogeneous): ⁇ ⁇ ; x j) ( x i ' x j)
  • the kernel is related to the transform ! r j ( x i) by the equation ( x i? x j) ⁇ *iH U *
  • the value w is also in the transformed space, with 3 ⁇ 4 ⁇ j °* i ' ⁇ ( x *) 'Dot products with w for classification can again be computed by the kernel trick, i.e. w " ⁇ ( x ) ⁇ ri i-l3 ⁇ 4t. j ⁇ (x ⁇ , x)
  • w' such that w ' vK x / — " , X; .
  • the method of the present invention is for diagnosing gastric cancer in a human patient.
  • the diagnosis comprises (i) determining the occurrence/presence of gastric cancer, (ii) monitoring the course of gastric cancer, (iii) staging of gastric cancer, (iv) measuring the response of a patient with gastric cancer to therapeutic intervention, and/or (v) segmentation of a patient suffering from gastric cancer.
  • the method of the present invention is for prognosis of gastric cancer in a human patient.
  • the prognosis comprises (i) identifying of a patient who has a risk to develop gastric cancer, (ii) predicting/estimating the occurrence, preferably the severity of occurrence, of gastric cancer, and/or (iii) predicting the response of a patient with gastric cancer to therapeutic intervention.
  • the reference miRNA expression profiles may be classified using machine learning approaches in order to compute accuracy, specificity, and sensitivity for the diagnosis and/or prognosis of gastric cancer (see experimental section for more details).
  • miRNA sets (signatures) that performed best for the diagnosis of gastric cancer according to their accuracy, specificity, and sensitivity are sets of miRNAs having SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11 (see Figure 3), having SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16 (see Figure 4), having SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO
  • the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) stored in a database, e.g. an internet database, a centralized, or a decentralized database. It is also preferred that the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) stored on a data carrier, e.g. electronically data carrier. It is further preferred that the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) comprised in a computer program stored on an electronically data carrier.
  • the present invention provides means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention.
  • the means for diagnosing and/or prognosing of gastric cancer comprise a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,
  • said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a solid support, substrate, surface, platform or matrix comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention.
  • a polynucleotide probe
  • a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention.
  • the means for diagnosing and/or prognosing of gastric cancer comprise a solid support, substrate, surface, platform or matrix comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
  • said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned polynucleotide(s) is (are) attached to or immobilized on the solid support, substrate, surface, platform or matrix. It is possible to include appropriate controls for non-specific hybridization on the solid support, substrate, surface, platform or matrix.
  • said means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a microarray/biochip comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention.
  • the means for diagnosing and/or prognosing of gastric cancer comprise a microarray/biochip comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82
  • said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned polynucleotide(s) is (are) attached to or immobilized on the microarray/biochip. It is possible to include appropriate controls for non-specific hybridization on the microarray/biochip.
  • polynucleotide(s) may also be comprised as polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants in the means for diagnosing and/or prognosing of gastric cancer.
  • polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants may be comprised in the solid support, substrate, surface, platform or matrix, preferably microarray/biochip.
  • polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants may be attached or linked to the solid support, substrate, surface, platform or matrix, preferably microarray/biochip.
  • the definition of said polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants and as to the preferred polynucleotide (probe) or sets of polynucleotides (probes) it is referred to the first aspect of the present invention.
  • biochip or “microarray”, as used herein, refer to a solid phase comprising an attached or immobilized polynucleotide described herein as probe or a set (plurality) of polynucleotides described herein attached or immobilized as probes.
  • the polynucleotide probes may be capable of hybridizing to a target sequence, such as a complementary miRNA or miRNA* sequence, under stringent hybridization conditions.
  • the polynucleotide probes may be attached or immobilized at spatially defined locations on the solid phase.
  • One or more than one nucleotide (probe) per target sequence may be used.
  • the polynucleotide probes may either be synthesized first, with subsequent attachment to the biochip, or may be directly synthesized on the biochip.
  • the solid phase may be a material that may be modified to contain discrete individual sites appropriate for the attachment or association of the polynucleotide probes and is amenable to at least one detection method.
  • solid phase materials include glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, Teflon, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses and plastics.
  • the solid phase may allow optical detection without appreciably fluorescing.
  • the solid phase may be planar, although other configurations of solid phase may be used as well.
  • polynucleotide probes may be placed on the inside surface of a tube, for flow- through sample analysis to minimize sample volume.
  • the solid phase may be flexible, such as flexible foam, including closed cell foams made of particular plastics.
  • the solid phase of the biochip and the probe may be modified with chemical functional groups for subsequent attachment of the two.
  • the biochip may be modified with a chemical functional group including, but not limited to, amino groups, carboxyl groups, oxo groups or thiol groups.
  • the probes may be attached using functional groups on the probes either directly or indirectly using a linker.
  • the polynucleotide probes may be attached to the solid support by either the 5' terminus, 3' terminus, or via an internal nucleotide.
  • the polynucleotide probe may also be attached to the solid support non-covalently.
  • biotinylated polynucleotides can be made, which may bind to surfaces covalently coated with streptavidin, resulting in attachment.
  • polynucleotide probes may be synthesized on the surface using techniques such as photopolymerization and photolithography.
  • biochip and “microarray” are interchangeable used.
  • the terms “attached” or “immobilized”, as used herein, refer to the binding between the polynucleotide and the solid support/phase and may mean that the binding between the polynucleotide probe and the solid support is sufficient to be stable under conditions of binding, washing, analysis and removal.
  • the binding may be covalent or non-covalent. Covalent bonds may be formed directly between the polynucleotide and the solid support or may be formed by a cross linker or by inclusion of specific reactive groups on either the solid support or the polynucleotide, or both.
  • Non-covalent binding may be electrostatic, hydrophilic and hydrophobic interactions or combinations thereof. Immobilization or attachment may also involve a combination of covalent and non-covalent interactions.
  • said means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a set of beads or microspheres comprising a polynucleotide (probe) or a set comprising at least two polynucleotides (probes) according to the first aspect of the present invention.
  • the means for diagnosing and/or prognosing of gastric cancer comprise a set of beads or microspheres comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,
  • polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%) sequence identity thereto.
  • the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned polynucleotide(s) is (are) attached to or immobilized on the beads or microspheres, e.g. via a covalent or non-covalent linkage (see above).
  • said beads or microspheres are made of a synthetic material, e.g. polystyrene, polyethylene or polypropylene. It is preferred that said beads or microspheres have a mean diameter of between 2 to 20 microns, preferably 4 to 10 microns, most preferably 5 to 7 microns, i.e. 2, 3, 4, 5, 6, 7, 8,
  • said beads or microspheres are internally dyed, preferably with red and infrared fluorophores.
  • the bead or microsphere setting from Luminex may be used.
  • the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
  • the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
  • a blood cell also known as hemopoietic cell
  • the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
  • the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
  • PBMCs peripheral blood mononuclear cells
  • the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
  • the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
  • the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising (i) means for determining an expression profile of a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample, preferably blood cell sample, such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets listed in Figure 13, and
  • nucleotide sequences of said miRNAs may be selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets listed in Figure 13.
  • miRNA sets and blood samples such as blood cell samples
  • it is referred to the first and second aspect of the present invention.
  • the above mentioned (miRNA) set comprises at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • miRNA e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)
  • the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
  • Said means may be a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention; means according to the third aspect of the present invention; primers suitable to perform reverse transcriptase reaction and/or real time polymerase chain reaction such as quantitative polymerase chain reaction; and/or means for conducting next generation sequencing.
  • said kit comprises means according to the third aspect of the present invention.
  • said kit preferably comprises means for diagnosing and/or prognosis of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect of the present invention, or more preferably comprises means for diagnosing and/or prognosis of gastric cancer, wherein said means comprise a microarray/biochip, or a set of beads or microspheres comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect of the present invention.
  • said kit comprises (ia) a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention, and
  • RNA sample e.g. blood serum, blood plasma, or blood cell sample
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • said kit comprises
  • a solid support, substrate, surface, platform or matrix comprising a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according of the first aspect of the present invention
  • RNA or fractions thereof, e.g. miRNA
  • means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample means for input/injection of a blood sample, means for holding the solid support, substrate, platform or matrix comprising the polynucleotide(s) (probe(s)), means for labelling the isolated miRNA (e.g. NTP/biotin-NTP), means to carry out hybridization, means to carry out enzymatic reactions (e.g. exonuclease I and/or Klenow enzyme), means for washing steps, means for detecting the hybridization signal, and means for analysing the detected hybridization signal,
  • enzymatic reactions e.g. exonuclease I and/or Klenow enzyme
  • a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient
  • the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned polynucleotide or the polynucleotides comprised in a set is (are) attached to or immobilized on the solid support
  • said kit comprises
  • a microarray/biochip comprising a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according of the first aspect of the present invention
  • RNA or fractions thereof, e.g. miRNA
  • means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample means for input/injection of a blood sample, means for holding the microarray/biochip comprising the polynucleotide(s) (probe(s)), means for labelling the isolated miRNA (e.g. NTP/biotin-NTP), means to carry out hybridization, means to carry out enzymatic reactions (e.g. exonuclease I and/or Klenow enzyme), means for washing steps, means for detecting the hybridization signal, and means for analysing the detected hybridization signal,
  • enzymatic reactions e.g. exonuclease I and/or Klenow enzyme
  • a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient
  • the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the above mentioned polynucleotide or the polynucleotides comprised in a set is (are) attached to or immobilized on a micro
  • said kit comprises
  • a miRNA-specific primer for reverse transcription of miRNA in miRNA-specific cDNA for a single miRNA or at least two miRNA-specific primers for reverse transcription of miRNAs in miRNA-specific cDNAs for at least 2 miRNAs preferably for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95
  • a primer set comprising a forward primer which is specific for the cDNA obtained from the miRNA and an universal reverse primer for amplifying the cDNA obtained from the miRNA via real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR) for the single cDNA obtained from the miRNA or at least two primer sets comprising a forward primer which is specific for the single cDNA obtained from the miRNA and an universal reverse primer for amplifying the cDNA obtained from the miRNA via real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR) for at least 2, preferably for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
  • RT-PCR
  • RNA in cDNA e.g. reverse transcriptase (RT) enzyme, puffers, dNTPs, RNAse inhibitor
  • RT-PCR real time polymerase chain reaction
  • RT qPCR real time quantitative PCR
  • RNA or a set comprising, essentially consisting of, or consisting of at least 2, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87
  • nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • the primer as defined in (ia) above may also be an oligo-dT primer, e.g. if the miRNA comprises a polyA tail (e.g. as a result of a miRNA elongation, for example, subsequent to RNA extraction) or a miRNA specific looped RT primer.
  • said kit comprises means (e.g. a setting) for conducting next generation sequencing in order to determine an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • Said kit optionally comprises means selected from the group consisting of: means to collect a blood sample from a human patient, means for conserving the RNA-fraction within the blood probe, and means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample.
  • said kit comprises means (e.g. a setting or settings) for conducting a real time polymerase chain reaction (RT-PCR), preferably a real time quantitative polymerase chain reaction (RT-qPCR), in order to determine an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • RT-PCR real time polymerase chain reaction
  • RT-qPCR real time quantitative polymerase chain reaction
  • a RT-PCR reaction preferably a RT-qPCR reaction
  • a setting for conducting a reverse transcription reaction may be combined with a setting for conducting a reverse transcription reaction, or may be two separate settings, meaning that both reactions, namely the reverse transcription reaction and RT-PCR reaction, preferably the RT-qPCR reaction, can be run together in one setting or separately in two settings.
  • kits may optionally comprise at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
  • a comparison to the at least one reference may allow for the diagnosis and/or prognosis of gastric cancer and/or an application of the at least one algorithm or mathematical function may allow for the diagnosis and/or prognosis of gastric cancer.
  • the at least one reference may be any reference which allows for the diagnosis and/or prognosis of gastric cancer, e.g. an indicated value or values, and/or the at least one algorithm or mathematical function may be any algorithm or mathematical function which allows for the diagnosis and/or prognosis of gastric cancer.
  • said reference is a reference expression profile (data) of at least one subject, preferably the reference is an average expression profile (data) of at least 2 to 40 subjects, more preferably at least 10 to 25 subjects, and most preferably at least 15 to 20 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e.
  • the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
  • Said reference may be a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy), or known to be affected by gastric cancer (i.e. diseased).
  • the term "essentially corresponds (essentially identical)" means that the miRNA(s) which expression profile is determinable by the means of (i) and the miRNA(s) of the reference expression profile may slightly differ in their nucleotide sequence, whereas said difference is so marginal that it may still allow for the diagnosis and/or prognosis of gastric cancer.
  • the nucleotide sequence(s) of the miRNA(s) which expression profile is determinable by the means of (i) and the nucleotide sequence(s) of the miRNA(s) of the reference expression profile differ in 1 to 5, more preferably in 1 to 3, and most preferably in 1 to 2 nucleotides, i.e.
  • nucleotide difference resides in 1 to 5, more preferably 1 to 3, and most preferably 1 to 2 nucleotide mutations, i.e. 1, 2, 3, 4, or 5 nucleotide mutations (e.g. substitutions, additions, insertions, and/or deletions).
  • the expression profile (data) of the human patient will be determined in the same type of blood sample such as blood cell sample and/or will be obtained from a human patient of the same gender and/or similar age or stage of life.
  • the algorithm or mathematical function is obtained from a reference expression profile (data) of at least one subject, preferably of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
  • the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i), or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
  • the algorithm or mathematical function is obtained from reference expression profiles (data) of at least two subjects, preferably of at least 3 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
  • the reference expression profiles are the profiles of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
  • the two known clinical conditions are gastric cancer and no gastric cancer.
  • the algorithm or mathematical function is obtained using a machine learning approach.
  • a machine learning approach e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means
  • the application of a machine learning approach leads to the obtainment of an algorithm or a mathematical function that is trained by the reference expression profile(s) (data) mentioned above (see also second aspect of the present invention).
  • the kit may optionally comprise at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
  • Said data carrier may be a graphically data carrier such as an information leaflet or an information sheet (e.g. for comparing tested single reference miRNA biomarkers with the expression profile data of a human patient to be diagnosed and/or prognosed) or an electronically data carrier such as a floppy disk, a compact disk (CD), or a digital versatile disk (DVD) (e.g. for comparing tested sets of miRNA biomarkers with the expression profile data of a human patient to be diagnosed and/or prognosed).
  • Said reference, preferably said (average) reference expression profile (data), and/or said algorithm or mathematical function may further be comprised in a computer program which is saved on an electronically data carrier.
  • the kit may alternatively comprise an access code comprised on a data carrier which allows the access to a database, e.g. an internet database, a centralized, or a decentralized database, where (i) the reference, preferably the (average) reference expression profile (data), and/or the algorithm or mathematical function is (are) comprised, or (ii) where a computer program comprising the reference, preferably the (average) reference expression profile (data), and/or the algorithm or mathematical function can be downloaded.
  • a database e.g. an internet database, a centralized, or a decentralized database
  • a computer program comprising the reference, preferably the (average) reference expression profile (data), and/or the algorithm or mathematical function can be downloaded.
  • kits may comprise (i) references (data), preferably (average) reference expression profile(s) (data), which may be comprised on an information leaflet and/or on a compact disk (CD), e.g. two expression profiles (data), for example, one from a subject(s) known to be healthy and one from a subject(s) known to have gastric cancer, and/or (ii) algorithms or mathematical functions, which may be comprised on a compact disc (CD) and/or on a digital versatile disk (DVD), e.g. two algorithms or mathematical functions, for example, one obtained from a reference expression profile of a subject(s) known to be healthy and one obtained from a reference expression profile of a subject(s) known to have gastric cancer.
  • references e.g. reference expression profiles
  • algorithms or mathematical functions may be accessible by on or more access codes as mentioned above which may alternatively be comprised in the kit (see above).
  • reference or preferred embodiments of the reference e.g. reference expression profile(s) (data)
  • algorithm or mathematical function it is also referred to the second aspect of the present invention (see above).
  • the present invention relates to a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) preferably selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto,
  • the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
  • a blood cell also known as hemopoietic cell
  • a blood plasma sample or a blood serum sample.
  • the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
  • the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
  • PBMCs peripheral blood mononuclear cells
  • the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
  • the present invention relates to a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • a polynucleotide according to the first aspect of the present invention for detecting a miRNA representative for gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient or a set comprising at least two polynucleotides according to the first aspect of the present invention for detecting a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient,
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) preferably selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto,
  • human patient with an unknown clinical condition refers to a human patient, which may suffer from gastric cancer (i.e. diseased patient), or which may not suffer from gastric cancer (i.e. healthy patient). It is also possible to determine, whether the human patient to be prognosed will develop the above mentioned disease as the inventors of the present invention surprisingly found that miRNAs representative for gastric cancer are already present in the blood sample such as blood cell sample before gastric cancer occurs or during the early stage of gastric cancer. It should be noted that a human patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer, may possibly suffer from another disease not tested/known.
  • miRNA fragments As to the definition of said miRNA fragments, miRNA variants, or miRNA fragment variants, it is referred to the second aspect of the present invention.
  • the algorithm or mathematical function, the preferred embodiments of the blood sample such as blood cell sample and the definition of an expression profile it is referred to the second aspect of the present invention.
  • the present invention relates to a miRNA or a set comprising at least two miRNAs as biomarker(s) for the diagnosis and/or prognosis of gastric cancer, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103.
  • the present invention relates to a set comprising at least two miRNAs as biomarkers for the diagnosis and/or prognosis of gastric cancer, wherein the nucleotide sequences of said miRNAs are selected from one or more sets listed in Figure 13.
  • the above mentioned miRNA set comprises at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to 119.
  • a blood sample such as a whole blood sample or a blood fraction sample (e.g. a blood cell, plasma or serum sample) from a human patient is preferably used.
  • a blood fraction sample e.g. a blood cell, plasma or serum sample
  • miRNA sets and blood samples such as blood cells samples, it is referred to the first and second aspect of the present invention.
  • the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116.
  • the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5.
  • the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
  • the blood sample such s blood cell sample as mentioned in the aspects above does not comprise isolated exosomes.
  • the present invention is composed of the following:
  • a polynucleotide for detecting a miRNA or a set comprising at least two polynucleotides for detecting a set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample from a human patient wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
  • polynucleotide or set comprising the polynucleotides of item 1, wherein (i) the polynucleotide is complementary to the miRNA according to item 1, or the polynucleotides comprised in the set are complementary to the miRNAs comprised in the set according to item 1,
  • the polynucleotide is a fragment of the polynucleotide according to (i), or the polynucleotides comprised in the set are fragments of the polynucleotides comprised in the set according to (i), or
  • the polynucleotide has at least 80% sequence identity to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or the polynucleotides comprised in the set have at least 80% sequence identity to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1
  • nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 30,
  • nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24, or
  • nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO:
  • SEQ ID NO: 25 SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30.
  • polynucleotides of items 1 to 5 wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
  • a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
  • nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and
  • the set comprises at least 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 miRNAs, or comprises 103 miRNAs, and wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5
  • nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4,
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO:
  • nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO:
  • SEQ ID NO: 25 SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30.
  • nucleotide sequence of the miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10.
  • step (i) a polynucleotide or a set comprising at least two polynucleotides according to items 1 to 7 is used for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient.
  • the nucleic acid hybridization is performed using a microarray/biochip, or using in situ hybridization, and/or (ii) the nucleic acid amplification is performed using real-time PCR.
  • the algorithm or mathematical function is obtained from a reference expression profile of at least one subject with one known clinical condition which is gastric cancer, or which is no gastric cancer, wherein the reference expression profile is the profile of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
  • the algorithm or mathematical function is obtained from reference expression profiles of at least two subjects with at least two known clinical conditions (only one known clinical condition per subject) which are gastric cancer and any other known clinical condition(s), wherein the reference expression profiles are the profiles of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
  • the reference is a reference expression profile of at least one subject with one known clinical condition which is gastric cancer, or which is no gastric cancer, wherein the reference expression profile is the profile of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
  • said blood sample is a whole blood sample or a blood fraction sample.
  • the blood fraction sample is a blood cell sample, a blood plasma sample or a blood serum sample.
  • Means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to items 1 to 7.
  • a kit for diagnosing and/or prognosing of gastric cancer comprising
  • FIG. 1 MiRNAs for diagnosis or prognosis of gastric cancer.
  • Experimental data obtained for analysis of miRNAs according to SEQ ID NO: 1 to SEQ ID NO: 119.
  • Figure 2 List of miRNAs including sequences (SEQ ID NO: 1 to SEQ ID NO: 119) found to be relevant for diagnosis and/or prognosis of gastric cancer.
  • Figure 4 Diagnostic miRNA-signature employing SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8
  • Figure 9 Diagnostic miRNA-signature employing SEQ ID NO: 23, SEQ ID NO: 5, SEQ ID NO:
  • Figure 12 Diagram describing the general approach for determining miRNA signatures for use as biomarkers in the diagnosis and/or prognosis of gastric cancer.
  • RNA Blood of patients has been extracted as previously described [1]. In brief, 2.5 to 5 ml blood was extracted in PAXgene Blood RNA tubes (BD, Franklin Lakes, New Jersey USA) and centrifuged at 5000 x g for 10 min at room temperature. The miRNeasy kit (Qiagen GmbH, Hilden) was used to isolate total RNA including miRNA from the resuspended pellet according to manufacturer's instructions. The eluted RNA was stored at -70°C.
  • the median signal intensity was calculated. Following a background correction step, the median of the 7 replicates of each miRNA was computed. To normalize the data across different arrays, quantile normalization [3] was applied and all further analyses were carried out using the normalized and background subtracted intensity values. Since the miRBase has been upgraded twice in the past year from version 12.0 to 14, we used for the final data analysis the 863 miRNAs that were consistently present in all three versions. Statistical analysis
  • Vapnik V The nature of statistical learning theory., 2nd edition edn. New York: Spinger; 2000.

Abstract

The present invention relates to single polynucleotides or sets of polynucleotides for detecting single miRNAs or sets of miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample such as blood cell sample from a human patient. Further, the present invention relates to means for diagnosing and/or prognosing of gastric cancer comprising said polynucleotides or sets of polynucleotides. Furthermore, the present invention relates to a method for diagnosing and/or prognosing of gastric cancer based on the determination of expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer. In addition, the present invention relates to a kit for diagnosing and/or prognosing of gastric cancer comprising means for determining expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer and optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.

Description

COMPLEX MIRNA SETS AS NOVEL BIOMARKERS FOR GASTRIC CANCER TECHNICAL FIELD OF THE INVENTION
The present invention relates to single polynucleotides or sets of polynucleotides for detecting single miRNAs or sets of miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample such as a blood cell sample from a human patient. Further, the present invention relates to means for diagnosing and/or prognosing of gastric cancer comprising said polynucleotides or sets of polynucleotides. Furthermore, the present invention relates to a method for diagnosing and/or prognosing of gastric cancer based on the determination of expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer. In addition, the present invention relates to a kit for diagnosing and/or prognosing of gastric cancer comprising means for determining expression profiles of single miRNAs or sets of miRNAs representative for gastric cancer and optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
BACKGROUND OF THE INVENTION Today, biomarkers play a key role in early diagnosis, risk stratification, and therapeutic management of various diseases. However, many biomarkers were mainly discovered by candidate approach. By contrast, the recent development of high-throughput molecular technologies that allow with a reasonable effort the analysis of whole transcriptomes, proteomes, and metabolomes of individuals at risk, may lead to the discovery of novel biomarkers in an unbiased approach.
MicroRNAs (miRNAs) are a new class of biomarkers. They represent a group of regulatory elements that enable cells to fine-tune complex gene expression cascades in a wide range of biological processes, such as proliferation, differentiation, apoptosis, stress-response, and oncogenesis. Since recently it is known that miRNAs are not only present in tissues but also in human blood both as free circulating nucleic acids and in mononuclear cells. This may be due to the fact that miRNAs expressed in diverse tissues or cells may be able to be released into circulating blood. Although the mechanism why miRNAs are found in human blood is not fully understood yet, this finding renders miRNAs to biological markers for diagnostics for various types of diseases based on blood analysis including gastric cancer. Gastric cancer is a highly aggressive and lethal malignancy. On a global basis, this tumor represents about 9% of the entire cancer burden and the second leading cancer cause of death. Several miRNA markers have been proposed to indicate gastric cancer. However, many of these markers have shortcomings such as low sensitivity, no sufficient specificity or do not allow timely diagnosis. Accordingly, there is still a need to provide novel and efficient miRNAs or sets of miRNAs as biomarkers, effective methods and kits for the diagnosis and/or prognosis of said disease. Particularly, the potential role of miRNAs present in human blood as biomarkers for the diagnosis and/or prognosis of gastric cancer has not been systematically evaluated yet.
The inventors of the present invention assessed for the first time the expression of miRNAs on a whole-genome level in subjects suffering from gastric cancer. They identified novel miRNAs which are significantly dysregulated in blood of subjects suffering from gastric cancer in comparison to healthy controls. The inventors of the present invention revealed that said single miRNAs can predict or determine gastric cancer with high specificity, sensitivity and accuracy. The inventors of the present invention also pursued a multiple biomarker strategy to circumvent the above mentioned limitations by adding accuracy and predictive power. In detail, by using a machine learning approach, they identified unique miRNA signatures that can predict or determine gastric cancer with even higher power, indicating that both, single miRNAs and especially complex miRNA signatures or sets derived from a blood sample, e.g. blood cell sample, can be used as novel biomarkers for the diagnosis and/or prognosis of gastric cancer in a human patient.
SUMMARY OF THE INVENTION
In a first aspect, the invention provides a polynucleotide for detecting a miRNA or a set comprising at least two polynucleotides for detecting a set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
In a second aspect, the invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and
(ii) comparing said expression profile to a reference, wherein the comparison of said expression profile to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or applying an algorithm or a mathematical function to said expression profile, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
In a third aspect, the invention provides means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect.
In a fourth aspect, the invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
(i) means for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto; and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
This summary of the invention does not necessarily describe all features of the invention. DETAILED DESCRIPTION OF THE INVENTION
Before the present invention is described in detail below, it is to be understood that this invention is not limited to the particular methodology, protocols and reagents described herein as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art.
In the following, the elements of the present invention will be described. These elements are listed with specific embodiments, however, it should be understood that they may be combined in any manner and in any number to create additional embodiments. The variously described examples and preferred embodiments should not be construed to limit the present invention to only the explicitly described embodiments. This description should be understood to support and encompass embodiments which combine the explicitly described embodiments with any number of the disclosed and/or preferred elements. Furthermore, any permutations and combinations of all described elements in this application should be considered disclosed by the description of the present application unless the context indicates otherwise.
Preferably, the terms used herein are defined as described in "A multilingual glossary of biotechnological terms: (IUPAC Recommendations)", H.G.W. Leuenberger, B. Nagel, and H. Kolbl, Eds., Helvetica Chimica Acta, CH-4010 Basel, Switzerland, (1995).
To practice the present invention, unless otherwise indicated, conventional methods of chemistry, biochemistry, and recombinant DNA techniques are employed which are explained in the literature in the field (cf, e.g., Molecular Cloning: A Laboratory Manual, 2nd Edition, J. Sambrook et al. eds., Cold Spring Harbor Laboratory Press, Cold Spring Harbor 1989).
Several documents are cited throughout the text of this specification. Each of the documents cited herein (including all patents, patent applications, scientific publications, manufacturer's specifications, instructions, etc.), whether supra or infra, are hereby incorporated by reference in their entirety. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention.
Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.
As used in this specification and in the appended claims, the singular forms "a", "an", and "the" include plural referents, unless the content clearly dictates otherwise. For example, the term "a test compound" also includes "test compounds".
The terms "microRNA" or "miRNA" refer to single-stranded RNA molecules of at least
10 nucleotides and of not more than 35 nucleotides covalently linked together. Preferably, the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally labels and/or elongated sequences (e.g. biotin stretches). The miRNAs regulate gene expression and are encoded by genes from whose DNA they are transcribed but miRNAs are not translated into protein (i.e. miRNAs are non- coding RNAs). The genes encoding miRNAs are longer than the processed mature miRNA molecules. The miRNAs are first transcribed as primary transcripts or pri-miRNAs with a cap and poly- A tail and processed to short, 70 nucleotide stem-loop structures known as pre- miRNAs in the cell nucleus. This processing is performed in animals by a protein complex known as the Microprocessor complex consisting of the nuclease Drosha and the double- stranded RNA binding protein Pasha. These pre-miRNAs are then processed to mature miRNAs in the cytoplasm by interaction with the endonuclease Dicer, which also initiates the formation of the RNA-induced silencing complex (RISC). When Dicer cleaves the pre-miRNA stem-loop, two complementary short RNA molecules are formed, but only one is integrated into the RISC. This strand is known as the guide strand and is selected by the argonaute protein, the catalytically active RNase in the RISC, on the basis of the stability of the 5' end. The remaining strand, known as the miRNA*, anti-guide (anti-strand), or passenger strand, is degraded as a RISC substrate. Therefore, the miRNA*s are derived from the same hairpin structure like the "normal" miRNAs. So if the "normal" miRNA is then later called the "mature miRNA" or "guide strand", the miRNA* is the "anti-guide strand" or "passenger strand".
The terms "microRNA*" or "miRNA*" refer to single-stranded RNA molecules of at least 10 nucleotides and of not more than 35 nucleotides covalently linked together. Preferably, the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally labels and/or elongated sequences (e.g. biotin stretches). The "miRNA*s", also known as the "anti-guide strands" or "passenger strands", are mostly complementary to the "mature miRNAs" or "guide strands", but have usually single-stranded overhangs on each end. There are usually one or more mispairs and there are sometimes extra or missing bases causing single-stranded "bubbles". The miRNA* s are likely to act in a regulatory fashion as the miRNAs (see also above). In the context of the present invention, the terms "miRNA" and "miRNA*" are interchangeable used. The present invention encompasses (target) miRNAs which are dysregulated in blood of human patients with gastric cancer in comparison to healthy controls. Said (target) miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 and, preferably, additionally consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
The term "miRBase" refers to a well established repository of validated miRNAs. The miRBase (ww .mirbase. org) is a searchable database of published miRNA sequences and annotation. Each entry in the miRBase Sequence database represents a predicted hairpin portion of a miRNA transcript (termed mir in the database), with information on the location and sequence of the mature miRNA sequence (termed miR). Both hairpin and mature sequences are available for searching and browsing, and entries can also be retrieved by name, keyword, references and annotation. All sequence and annotation data are also available for download. The sequences of the miRNAs for diagnosis and/or prognosis of gastric cancer listed in Figure 2 are based on miRBase version 12.0, 13.0 and 14.0.
As used herein, the term "nucleotides" refers to structural components, or building blocks, of DNA and RNA. Nucleotides consist of a base (one of four chemicals: adenine, thymine, guanine, and cytosine) plus a molecule of sugar and one of phosphoric acid. The term "nucleosides" refers to glycosylamine consisting of a nucleobase (often referred to simply base) bound to a ribose or deoxyribose sugar. Examples of nucleosides include cytidine, uridine, adenosine, guanosine, thymidine and inosine. Nucleosides can be phosphorylated by specific kinases in the cell on the sugar's primary alcohol group (-CH2-OH), producing nucleotides, which are the molecular building blocks of DNA and RNA.
The term "polynucleotide", as used herein, means a molecule of at least 10 nucleotides and of not more than 35 nucleotides covalently linked together. Preferably, the polynucleotides of the present invention are molecules of 10 to 33 nucleotides or 15 to 30 nucleotides in length, more preferably of 17 to 27 nucleotides or 18 to 26 nucleotides in length, i.e. 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 nucleotides in length, not including optionally spacer elements and/or elongation elements described below. The depiction of a single strand of a polynucleotide also defines the sequence of the complementary strand. Polynucleotides may be single stranded or double stranded, or may contain portions of both double stranded and single stranded sequences. The term "polynucleotide" means a polymer of deoxyribonucleotide or ribonucleotide bases and includes DNA and RNA molecules, both sense and anti-sense strands. In detail, the polynucleotide may be DNA, both cDNA and genomic DNA, RNA, cRNA or a hybrid, where the polynucleotide sequence may contain combinations of deoxyribonucleotide or ribonucleotide bases, and combinations of bases including uracil, adenine, thymine, cytosine, guanine, inosine, xanthine, hypoxanthine, isocytosine and isoguanine. Polynucleotides may be obtained by chemical synthesis methods or by recombinant methods.
In the context of the present invention, a polynucleotide as a single polynucleotide strand provides a probe (e.g. miRNA capture probe) that is capable of binding to, hybridizing with, or detecting a target of complementary sequence, such as a nucleotide sequence of a miRNA or miRNA*, through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. Polynucleotides in their function as probes may bind target sequences, such as nucleotide sequences of miRNAs or miRNAs*, lacking complete complementarity with the polynucleotide sequences depending upon the stringency of the hybridization condition. There may be any number of base pair mismatches which will interfere with hybridization between the target sequence, such as a nucleotide sequence of a miRNA or miRNA*, and the single stranded polynucleotide described herein. However, if the number of mutations is so great that no hybridization can occur under even the least stringent hybridization conditions, the sequences are no complementary sequences. The present invention encompasses polynucleotides in form of single polynucleotide strands as probes for binding to, hybridizing with or detecting complementary sequences of (target) miRNAs for diagnosing and/or prognosing of gastric caner. Said (target) miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 and, preferably, additionally consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
The polynucleotide, e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*, may be unlabeled, directly labeled, or indirectly labeled, such as with biotin to which a streptavidin complex may later bind. The polynucleotide, e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*, may also be modified, e.g. may comprise an elongation (EL) element. For use in a RAKE or MPEA assay, a polynucleotide with an elongation element may be used as a probe. The elongation element comprises a nucleotide sequence with 1 to 30 nucleotides chosen on the basis of showing low complementarity to potential target sequences, such as nucleotide sequences of miRNAs or miRNAs*, therefore resulting in no or a low degree of cross-hybridization to a target mixture. In one embodiment of the invention, a homomeric sequence stretch N„ with n = 1 to 30, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30, and N = A or C, or T or G is preferred. Particularly preferred is a homomeric sequence stretch Nn with n = 1 to 12, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, and N = A or C, or T or G. The polynucleotide, e.g. the polynucleotide used as a probe for detecting a miRNA or miRNA*, may be present in form of a tandem, i.e. in form of a polynucleotide hybrid of two different or identical polynucleotides, both in the same orientation, i.e. 5' to 3' or 3' to 5', or in different orientation, i.e. 5' to 3' and 3' to 5'. Said polynucleotide hybrid/tandem may comprise a spacer element. For use in a tandem hybridization assay, the polynucleotide hybrid/tandem as a probe may comprise a spacer (SP) element. The spacer element represents a nucleotide sequence with n = 0 to 12, i.e. 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, or 12, nucleotides chosen on the basis of showing low complementarity to potential target sequences, such as nucleotide sequences of miRNAs or anti-miRNAs, therefore resulting in no or a low degree of cross-hybridization to a target mixture. It is preferred that n is 0, i.e. that there is no spacer between the two miRNA sequence stretches. In another embodiment of the invention, a non-homomeric sequence stretch Nn with n = 1 to 30, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30, and N = A or C, or T or G is preferred. Particularly preferred is a non-homomeric sequence stretch that can be used as a priming site for a polymerase for a downstream amplification reaction. For detection purposes, the miRNA(s) or miRNA*(s) may be employed unlabeled, directly labeled, or indirectly labeled, such as with biotin to which a streptavidin complex may later bind.
The term "label", as used herein, means a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, chemical, or other physical means. For example, useful labels include 32P, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin, digoxigenin, or haptens and other entities which are or can be made detectable. A label may be incorporated into nucleic acids at any position, e.g. at the 3' or 5' end or internally. The polynucleotide for detecting a miRNA (polynucleotide probe) and/or the miRNA itself may be labeled.
The term "stringent hybridization conditions", as used herein, means conditions under which a first nucleotide sequence (e.g. polynucleotide in its function as a probe for detecting a miRNA or miRNA*) will hybridize to a second nucleotide sequence (e.g. target sequence such as nucleotide sequence of a miRNA or miRNA*), such as in a complex mixture of nucleotide sequences. Stringent conditions are sequence-dependent and will be different in different circumstances. Stringent conditions may be selected to be about 5 to 10°C lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength, pH. The Tm may be the temperature (under defined ionic strength, pH, and nucleic acid concentration) at which 50% of the probes complementary to the target hybridize to the target sequence at equilibrium (as the target sequences are present in excess, at Tm, 50% of the probes are occupied at equilibrium). Stringent conditions may be those in which the salt concentration is less than about 1.0 M sodium ion, such as about 0.01 tol .O M sodium ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 20°C for short probes (e.g. about 10-35 nucleotides) and up to 60°C for long probes (e.g. greater than about 50 nucleotides). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. For selective or specific hybridization, a positive signal may be at least 2 to 10 times background hybridization. Exemplary stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %, Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
The term "antisense", as used herein, refers to nucleotide sequences which are complementary to a specific DNA or RNA sequence. The term "antisense strand" is used in reference to a nucleic acid strand that is complementary to the "sense" strand. The term "sensitivity", as used herein, means a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies gastric cancer into the correct type out of two or more possible types (e.g. gastric cancer type and healthy type). The sensitivity for class A is the proportion of cases that are determined to belong to class "A" by the test out of the cases that are in class "A". A theoretical, optimal prediction can achieve 100% sensitivity (i.e. predict all patients from the sick group as sick).
The term "specificity", as used herein, means a statistical measure of how well a binary classification test correctly identifies a condition, for example how frequently it correctly classifies gastric cancer into the correct type out of two or more possible types (e.g. gastric cancer type and healthy type). The specificity for class A is the proportion of cases that are determined to belong to class "not A" by the test out of the cases that are in class "not A". A theoretical, optimal prediction can achieve 100% specificity (i.e. not predict anyone from the healthy group as sick).
The term "accuracy", as used herein, means a statistical measure for the correctness of classification or identification of sample types. The accuracy is the proportion of true results (both true positives and true negatives).
The term "gastric cancer" (also known as stomach cancer), as used herein, encompasses any group of clinical symptoms compatible with gastric cancer (also known as stomach cancer). The terms "gastric cancer" and "stomach cancer" are interchangeable used herein. Gastric cancer can develop in any part of the stomach. The stomach is part of the digestive system. It is located in the upper abdomen, between the esophagus and the small intestine. Most (85%) cases of gastric cancer are adenocarcinomas that occur in the lining of the stomach (mucosa). Approximately 40% of cases develop in the lower part of the stomach (pylorus); 40% develop in the middle part (body); and 15% develop in the upper part (cardia). In about 10% of cases, cancer develops in more than one part of the organ. Stomach cancer can spread (metastasize) to the esophagus or the small intestine, and can extend through the stomach wall to nearby lymph nodes and organs (e.g. liver, pancreas, colon). It also can metastasize to other parts of the body (e.g. lungs, ovaries, bones).
The term "patient", as used in the context of the present invention, may mean a human subject suspected to be affected by gastric cancer. The patient may be diagnosed to be affected by gastric cancer, i.e. diseased, or may be diagnosed to be not affected by gastric cancer, i.e. healthy. The patient may further be prognosed to develop gastric cancer, as the inventors of the present invention surprisingly found that miRNAs representative for gastric cancer are already present in a blood sample, e.g. blood cell sample, before gastric cancer occurs or during the early stage of gastric cancer. The term "patient", as used in the context of the present invention, may also mean a human subject which is affected by gastric cancer, i.e. diseased. The patient may be retested for gastric cancer and may be diagnosed to be still affected by gastric cancer, i.e. diseased, or not affected by gastric cancer anymore, i.e. healthy, for example after therapeutic intervention (e.g. to evaluate the success of surgery and/or chemotherapy). It should be noted that a patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer, or as staying healthy, i.e. not developing gastric cancer, may possibly suffer from another disease not tested/known.
The term "(control) subject", as used in the context of the present invention, may refer to a human subject known to be affected by gastric cancer (positive control), i.e. diseased. The term "control subject", as used in the context of the present invention, may also refer to a human subject known to be not affected by gastric cancer (negative control), i.e. healthy. It may also refer to a human subject known to be affected by another disease/condition (see definition "(clinical) condition"). It should be noted that a control subject that is known to be healthy, i.e. not suffering from gastric cancer, may possibly suffer from another disease not tested/known.
The inventors of the present invention analysed the expression level of miRNAs in blood samples of a cohort of controls (healthy subjects) and in blood samples of subjects suffering from gastric cancer. They succeeded in determining the miRNAs that are differentially regulated in blood samples from subjects having gastric cancer compared to a cohort of controls (healthy subjects) (see experimental section for experimental details). Additionally, the inventors of the present invention performed hypothesis tests (e.g. t-test, limma-test) or other measurements (e.g. AUC, mutual information) on the expression level of the found miRNAs in all controls (healthy subjects) and subjects having gastric cancer. These tests resulted in a significance value (p-value) for each miRNA. This p-value is a measure for the diagnostic power of each miRNA to discriminate, for example, between two clinical conditions, e.g. healthy, i.e. absence of gastric cancer, and diseased, i.e. presence of gastric cancer. Since a manifold of tests are carried out, one for each miRNA, the p-values may be too optimistic and, thus, over-estimate the actual discriminatory power. Hence, the p-values are corrected for multiple testing by the Benjamini Hochberg approach.
An overview of the miRNAs that are found to be significantly dysregulated in blood samples of human subjects having gastric cancer and that performed best according to t-test, limma-test or AUC is provided in Figure 1 (Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median gl : median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with gastric cancer, qmedian: ratio of median gl/median g2, logqmedian: log of qmedian, ttest rawp: p-value obtained when applying t-test, ttest adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, limma rawp: p-value obtained when applying limma- test, limma adjp: adjusted p-value in order to reduce false discovery rate by Benjamini- Hochberg adjustment.). Two miRNA groups are formed. The first group comprises miRNAs according to SEQ ID NO: 1 to SEQ ID NO: 103 and the second group comprises miRNAs according to SEQ ID NO: 104 to 119. The miRNAs comprised in both groups are sorted in order of their t-test significance (see ttest_adjp= adjusted p-value calculated according to ttest) and intensity (p < 0.05, median gl and/or g2 preferably > 100) as described in more detail in the experimental section. In addition, the most predictive miRNAs in group I are listed first. It should be noted that the lower the ttest adjp value of a single miRNA, the higher is the diagnostic power of said miRNA. The median intensity obtained from the microarray represents a measure for the abundance of a miRNA in a sample and represents the expression level of this miRNA. The higher the intensity of a single miRNA, the higher the abundance/expression of said miRNA.
Usually the diagnostic power of a single miRNA biomarker is not sufficient to reach high accuracy, specificity and sensitivity, for example, for the discrimination between two clinical conditions, e.g. healthy, i.e. absence of gastric cancer, and diseased, i.e. presence of gastric cancer. However, the inventors of the present invention surprisingly found that also a single miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, can predict or determine with high diagnostic accuracy, specificity and sensitivity gastric cancer in a human patient.
The inventors of the present invention also employed more than one miRNA biomarker, i.e. sets (signatures) of miRNA biomarkers, to further increase and/or improve the performance for diagnosing and/or prognosing of patients having gastric cancer.
In order to be able to better discriminate, for example, between two or more clinical conditions, e.g. presence of gastric cancer and absence of gastric cancer, for a defined set (signature) of miRNA biomarkers, the inventors of the present invention applied a machine learning approach (e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means) which leads to an algorithm or a mathematical function that is trained by reference data (i.e. data of reference miRNA expression profiles from the two or more clinical conditions, e.g. presence of gastric cancer and absence of gastric cancer, for the defined set (signature) of miRNA markers) to discriminate between the two or more statistical classes (i.e. two or more clinical conditions), e.g. presence of gastric cancer and absence of gastric cancer. The inventors of the present invention surprisingly found that this approach yields miRNA sets (signatures) that can predict or determine with very high diagnostic accuracy, specificity and sensitivity gastric cancer in a human patient. Said miRNA sets (signatures) comprise at least two miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (see for example Figures 3 to 11). The inventors of the present invention further found that the sets of at least two miRNAs, wherein the nucleotide sequence of said miRNA or nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, can be completed by at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, preferably in order to improve the diagnostic accuracy, specificity and sensitivity in the determination of gastric cancer.
An exemplarily approach to arrive at miRNA sets/signatures that correlate with gastric cancer is summarized below. In addition, the general work flow is shown in Figure 12.
Step 1 : Total RNA (or subfractions thereof) is extracted from a blood (including plasma, serum, PBMC or other blood fractions) sample of a human subject or human subjects with gastric cancer using suitable kits and/or purification methods.
Step 2: From the respective sample, the quantity (expression level) of one miRNA or sets of at least two miRNAs selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 is measured using experimental techniques. These techniques include, but are not restricted to, array based approaches, amplification methods
(PCR, RT-PCR, or qPCR), sequencing, next generation sequencing, and/or mass spectroscopy.
Step 3 : In order to gather information on the diagnostic/prognostic value and the redundancy of each of the single miRNA biomarkers, mathematical methods are applied. These methods include, but are not restricted to, basic mathematic approaches (e.g. Fold Quotients, Signal-to-Noise ratios, Correlation), statistical methods as hypothesis tests (e.g. t-test, Wilcoxon-Mann- Whitney test), the Area under the Receiver operator Characteristics Curve, information theory approaches, (e.g. the Mutual Information, Cross-entropy), probability theory (e.g. joint and conditional probabilities) or combinations and modifications of the previously mentioned methods.
Step 4: The information gathered in step 3) is used to estimate for each miRNA biomarker the diagnostic content or value. Usually, however, this diagnostic value is too small to get a highly accurate diagnosis with accuracy rates, specificities and sensitivities beyond the 80% barrier. The diagnostic content of the miRNAs suitable for diagnosing/prognosing gastric cancer is listed in Figure 1 (Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median gl : median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with gastric cancer, qmedian: ratio of median gl/median g2, logqmedian: log of qmedian, ttest rawp: p-value obtained when applying t-test, ttest adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, limma rawp: p-value obtained when applying limma-test, limma adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment.). This Figure includes the miRNAs according to SEQ ID NO: 1 to SEQ ID NO: 103.
Step 5: In order to increase the performance for diagnosing/prognosing of individuals suffering from gastric cancer, more than one miRNA biomarker needs to be employed. Thus statistical learning / machine learning / bioinformatics / computational approaches are applied for set selection in order to select/define sets of miRNA biomarkers (comprising miRNAs SEQ ID NO: 1 to SEQ ID NO: 103) that are tailored for the detection of gastric cancer. These techniques include, but are not restricted to, Wrapper subset selection techniques (e.g. forward step- wise, backward step-wise, combinatorial approaches, optimization approaches), filter subset selection methods (e.g. the methods mentioned in Step 3), principal component analysis, or combinations and modifications of such methods (e.g. hybrid approaches).
Step 6: The subsets, selected/defined in Step 5, which may range from only a small number (at least two for the set) to all measured biomarkers, are then used to carry out a diagnosis/prognosis of gastric cancer. To this end, statistical learning / machine learning / bioinformatics / computational approaches are applied that include but are not restricted to any type of supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support
Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal Probit-Regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
Step 7: By combination of subset selection (Step 5) and machine learning approaches
(Step 6) an algorithm or a mathematical function for diagnosing/prognosing gastric cancer is obtained. This algorithm or mathematical function is applied to a miRNA expression profile (miRNA expression profile data) of an individual (patient) to be diagnosed for gastric cancer.
Thus, in a first aspect, the present invention relates to (the use of) a (single) polynucleotide for detecting a miRNA or a set (signature) comprising, essentially consisting of, or consisting of at least two polynucleotides for detecting a set comprising, essentially consisting of, or consisting of at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
Preferably, the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample. The term "hemopoietic cells" refers to mature cell types and their immature precursors that are identifiable either by morphology or, mostly, by a distinct pattern of cell surface markers. The term is used to distinguish these cells from other cell types found in the body and also includes T-cells and distinctive subsets, which are the only hematopoietic cells that are not generated in the bone marrow.
It is preferred that the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
Erythrocytes are also known as red blood cells (RBCs), red cells, red blood corpuscles (an archaic term), haematids, or erythroid cells. The term "erythrocytes" comes from the Greek erythros for "red" and kytos for "hollow", with cyte translated as "cell" in modern usage. In mammals such as humans, erythrocytes are devoid of a nucleus and have the shape of a biconcave lens. In the other vertebrates (e.g. fishes, amphibians, reptilians and birds), they have a nucleus. The red cells are rich in haemoglobin, a protein able to bind in a faint manner to oxygen. Hence, these cells are responsible for providing oxygen to tissues and partly for recovering C02 produced as waste. However, most C02 is carried by plasma, in the form of soluble carbonates. In the red cells of the mammalians such as humans, the lack of nucleus allows more room for haemoglobin and the biconcave shape of these cells raises the surface and cytoplasmic volume ratio. These characteristics make more efficient the diffusion of oxygen by these cells.
Thrombocytes are also known as platelets. The term "thrombocytes" comes from Greek θρόμβος, "clot" and κύτος, "cell". The main function of thrombocytes is to stop the loss of blood from wounds (hematostasis). To this purpose, they aggregate and release factors which promote the blood coagulation. Among them, there are the serotonin which reduces the diameter of lesioned vessels and slows down the hematic flux, the fibrin which trap cells and forms the clotting. Their diameter is about 2-3 μπι, hence they are much smaller than erythrocytes. Platelets release a multitude of growth factors including Platelet-derived growth factor (PDGF), a potent chemotactic agent, and TGF beta, which stimulates the deposition of extracellular matrix. Both of these growth factors have been shown to play a significant role in the repair and regeneration of connective tissues. Other healing-associated growth factors produced by platelets include basic fibroblast growth factor, insulin-like growth factor 1, platelet-derived epidermal growth factor, and vascular endothelial growth factor. Local application of these factors in increased concentrations through Platelet-rich plasma (PRP) has been used as an adjunct to wound healing for several decades.
Leukocytes are also known as white cells. Particularly, as the term "leuco" means white in Greek. Leukocytes are cells of the immune system which are involved in defending the body against both infectious disease and foreign materials. The number of leukocytes in the blood is often an indicator of disease. There are normally between 4x 109 and 1.1 x 1010 white blood cells in a litre of blood, making up approximately 1% of blood in a healthy adult. An increase in the number of leukocytes over the upper limits is called leukocytosis, and a decrease below the lower limit is called leukopenia. The physical properties of leukocytes, such as volume, conductivity, and granularity, may change due to activation, the presence of immature cells, or the presence of malignant leukocytes in leukemia.
It is more preferred that the leukocytes are granulocytes and/or lymphoid cells. The term granulocyte is due to the presence of granules in the cytoplasm of these cells. In the different types of granulocytes, the granules are different which helps to distinguish them. In fact, these granules have a different affinity towards neutral, acid or basic stains and give the cytoplasm different colours.
It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells.
It is also most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs). Accordingly, the following leukocytes may be present in the blood sample: neutrophils, eosinophils, basophils, lymphocytes and/or monocytes. Particularly, the following leukocytes may be present in the blood sample in different proportions: 50 - 70 % neutrophils, 2 - 4 % eosinophils, 0.5 - 1 % basophils, 20 - 40 % lymphocytes and/or 3 - 8 % monocytes.
Peripheral blood mononuclear cells (PBMCs) such as lymphocytes, monocytes or macrophages may be used in the method of the present invention.
In preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
The person skilled in the art is aware of techniques in order to isolate blood cells. For example, blood cells can be isolated from whole blood via centrifugation such as density gradient centrifugation. Because of their relative density, lymphocytes and monocytes (encompassed by the term PBMCs) are comprised in the interphase, plasma and thrombocytes are comprised in the supernatant and erythrocytes and granulocytes are comprised in the cell sediment after centrifugation.
Preferably the blood sample is collected by a blood collection tube. It is preferred that the blood collection tube includes means for stabilizing the RNA-fraction, especially the small RNA fraction within the blood sample. Not limiting examples for blood collection tubes already including a RNA-stabilization agent are PAXgene tubes (www.Preanalytix.com), or Tempus Blood RNA Tubes (Ambion, Applied Biosystems). Conventionally blood collection tubes, to which optionally a RNA-stabilizing agent like RNAlater (Ambion) can be added, are EDTA-, Heparin-, or Serum-tubes. As to the blood sample collection such as blood cell sample collection with PAXgene tubes, it is referred to the following references: Rainen L, Oelmueller U, Jurgensen S, Wyrich R, Ballas C, Schram J, Herdman C, Bankaitis-Davis D, Nicholls N, Trollinger D, Tryon V. "Stabilization of mRNA expression in whole blood samples" Clin Chem. 2002 Nov;48(l l): 1883-90; Hammerle-Fickinger A, Riedmaier I, Becker C, Meyer HH, Pfaffl MW, Ulbrich SE. "Validation of extraction methods for total RNA and miRNA from bovine blood prior to quantitative gene expression analyses". Biotechnol Lett. 2010 Jan;32(l):35-44; and QuantiGene Technote07_07_30 Factors affecting blood gene expression (http://www.panomics.com/downloads/QuantiGeneTechNote_07_07_30.pdf).
Preferably, the blood sample from a human patient has a volume of between 0.1 and 20 ml, more preferably of between 0.5 and 10 ml, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml. It is preferred that the blood sample is from a human patient that has not been therapeutically treated or has been therapeutically treated. In one embodiment, the therapeutical treatment is monitored on the basis of the detection of the miRNA or set of miRNAs by the nucleotide or set of polynucleotides. It is also preferred that total RNA or subfractions thereof, isolated (e.g. extracted) from a blood sample of a human patient is (are) used for detecting the miRNA or set of miRNAs by the polynucleotide or set of polynucleotides.
The blood samples may further be diluted with saline, buffer or a physiologically acceptable diluent. Alternatively, such samples may be concentrated by conventional means.
Thus, in a preferred embodiment, the present invention relates to (the use of) a (single) polynucleotide for detecting a miRNA or a set (signature) comprising, essentially consisting of, or consisting of at least two polynucleotides for detecting a set comprising, essentially consisting of, or consisting of at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
It is preferred that the (miRNA) set comprises, essentially consists of, or consists of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprises/consists of 103 miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103.
Preferably, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 1 and SEQ ID NO: 2, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 3, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 4, the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 6, the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 7, the nucleotide sequences of the at least 8 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 8, the nucleotide sequences of the at least 9 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 9, the nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 10, the nucleotide sequences of the at least 11 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 11, the nucleotide sequences of the at least 12 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 12, the nucleotide sequences of the at least 13 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 13, the nucleotide sequences of the at least 14 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 14, the nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 15, the nucleotide sequences of the at least 16 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 16, the nucleotide sequences of the at least 17 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 17, the nucleotide sequences of the at least 18 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 18, the nucleotide sequences of the at least 19 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 19, the nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 20, the nucleotide sequences of the at least 21 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 21, the nucleotide sequences of the at least 22 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 22, the nucleotide sequences of the at least 23 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 23, the nucleotide sequences of the at least 24 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 24, the nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 25, the nucleotide sequences of the at least 26 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 26, the nucleotide sequences of the at least 27 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 27, the nucleotide sequences of the at least 28 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 28, the nucleotide sequences of the at least 29 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 29, the nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 30, the nucleotide sequences of the at least 31 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 31, the nucleotide sequences of the at least 32 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 32, the nucleotide sequences of the at least 33 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 33, the nucleotide sequences of the at least 34 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 34, the nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 35, the nucleotide sequences of the at least 36 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 36, the nucleotide sequences of the at least 37 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 37, the nucleotide sequences of the at least 38 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 38, the nucleotide sequences of the at least 39 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 39, the nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 40, the nucleotide sequences of the at least 41 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 41, the nucleotide sequences of the at least 42 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 42, the nucleotide sequences of the at least 43 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 43, the nucleotide sequences of the at least 44 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 44, the nucleotide sequences of the at least 45 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 45, the nucleotide sequences of the at least 46 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 46, the nucleotide sequences of the at least 47 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 47, the nucleotide sequences of the at least 48 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 48, the nucleotide sequences of the at least 49 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 49, the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 50, the nucleotide sequences of the at least 51 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 51, the nucleotide sequences of the at least 52 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 52, the nucleotide sequences of the at least 53 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 53, the nucleotide sequences of the at least 54 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 54, the nucleotide sequences of the at least 55 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 55, the nucleotide sequences of the at least 56 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 56, the nucleotide sequences of the at least 57 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 57, the nucleotide sequences of the at least 58 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 58, the nucleotide sequences of the at least 59 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 59, the nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 60, the nucleotide sequences of the at least 61 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 61, the nucleotide sequences of the at least 62 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 62, the nucleotide sequences of the at least 63 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 63, the nucleotide sequences of the at least 64 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 64, the nucleotide sequences of the at least 65 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 65, the nucleotide sequences of the at least 66 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 66, the nucleotide sequences of the at least 67 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 67, the nucleotide sequences of the at least 68 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 68, the nucleotide sequences of the at least 69 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 69, the nucleotide sequences of the at least 70 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 70, the nucleotide sequences of the at least 71 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 71, the nucleotide sequences of the at least 72 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 72, the nucleotide sequences of the at least 73 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 73, the nucleotide sequences of the at least 74 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 74, the nucleotide sequences of the at least 75 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 75, the nucleotide sequences of the at least 76 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 76, the nucleotide sequences of the at least 77 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 77, the nucleotide sequences of the at least 78 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 78, the nucleotide sequences of the at least 79 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 79, the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 80, the nucleotide sequences of the at least 81 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 81, the nucleotide sequences of the at least 82 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 82, the nucleotide sequences of the at least 83 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 83, the nucleotide sequences of the at least 84 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 84, the nucleotide sequences of the at least 85 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 85, the nucleotide sequences of the at least 86 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 86, the nucleotide sequences of the at least 87 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 87, the nucleotide sequences of the at least 88 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 88, the nucleotide sequences of the at least 89 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 89, the nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 90, the nucleotide sequences of the at least 91 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 91, the nucleotide sequences of the at least 92 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 92, the nucleotide sequences of the at least 93 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 93, the nucleotide sequences of the at least 94 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 94, the nucleotide sequences of the at least 95 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 95, the nucleotide sequences of the at least 96 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 96, the nucleotide sequences of the at least 97 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 97, the nucleotide sequences of the at least 98 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 98, the nucleotide sequences of the at least 99 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 99, the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 100, the nucleotide sequences of the at least 101 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 101, or the nucleotide sequences of the at least 102 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 102, and more preferably, the nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
It is also preferred that the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 2 and SEQ ID NO: 3, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 31, SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, and SEQ ID NO: 36, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, and SEQ ID NO: 42, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, and SEQ ID NO: 48, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, and SEQ ID NO: 54, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, and SEQ ID NO: 60, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, and SEQ ID NO: 66, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, and SEQ ID NO: 72, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, and SEQ ID NO: 78, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, and SEQ ID NO: 84, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, and SEQ ID NO: 90, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, and SEQ ID NO: 96, or the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102 and SEQ ID NO: 103.
In preferred embodiments, the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a blood cell sample from a human patient.
In more preferred embodiments,
(i) the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in an erythrocyte sample/erythrocyte containing sample from a human patient,
(ii) the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a leukocyte sample/leukocyte containing sample from a human patient, and/or
(iii) the polynucleotides are for detecting a set comprising the above mentioned miRNAs for diagnosing and/or prognosing of gastric cancer in a thrombocyte sample/thrombocyte containing sample from a human patient.
The blood cells may also be PBMCs such as lymphocytes and/or monocytes.
In other preferred embodiments, the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13. For example, the nucleotide sequences of said miRNAs are selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets (signatures) listed in Figure 13.
In more preferred embodiments,
(i) the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in an erythrocyte sample/erythrocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13,
(ii) the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a leukocyte sample/leukocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13, and/or
(iii) the polynucleotides are for detecting a set (signature) comprising miRNAs for diagnosing and/or prognosing of gastric cancer in a thrombocyte sample/thrombocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
The blood cells may also be PBMCs such as lymphocytes and/or monocytes.
Preferably, the (miRNA) set as defined above comprises at least one further miRNA, e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
Thus, in a preferred embodiment of the present invention, the (miRNA) set comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or comprises 103 miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, and wherein the (miRNA) set comprises
(i) at least one further miRNA having the nucleotide sequence according to SEQ ID NO:
104,
(ii) at least 2 further miRNAs having the nucleotide sequence according to SEQ ID NO: 104 and SEQ ID NO: 105,
(iii) at least 3 further miRNAs having the nucleotide sequence according to SEQ ID NO: 104 to SEQ ID NO: 106,
(iv) at least 4 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 107,
(v) at least 5 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 108,
(vi) at least 6 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 109,
(vii) at least 7 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 110,
(viii) at least 8 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 111, (ix) at least 9 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 112,
(x) at least 10 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 113,
(xi) at least 11 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 114,
(xii) at least 12 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 115,
(xiii) at least 13 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 116,
(xiv) at least 14 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 117,
(xv) at least 15 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 118, or
(xvi) at least 16 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 119.
It is particularly preferred that the nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to SEQ ID NO. 13, (x) SEQ ID NO: 10 to SEQ ID NO: 14, or (xi) SEQ ID NO: 1 1 to SEQ ID NO: 15. It is also particularly preferred that the nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 1 1, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO: 7 to SEQ ID NO: 16, (viii) SEQ ID NO: 8 to SEQ ID NO: 17, (ix) SEQ ID NO: 9 to SEQ ID NO: 18, (x) SEQ ID NO: 10 to SEQ ID NO: 19, or (xi) SEQ ID NO: 11 to SEQ ID NO: 20. The above mentioned sets may also be combined with each other, e.g. (i) with (ii), (ii) with (iii), (iii) with (iv), (iv) with (v), (v) with (vi), (vi) with (vii), or any other possible combination.
It is further particularly preferred that the nucleotide sequences of the miRNAs comprised in the set have (i) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, (ii) SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16, (iii) SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, and SEQ ID NO: 4, (iv) SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, and SEQ ID NO: 6, (v) SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 17, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 7, and SEQ ID NO: 8, (vi) SEQ ID NO: 23, SEQ ID NO: 5, SEQ ID NO: 107, SEQ ID NO: 35, SEQ ID NO: 38, SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, and SEQ ID NO: 36, (vii) SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, and SEQ ID NO: 15, or (viii) SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, SEQ ID NO: 15, SEQ ID NO: 117, SEQ ID NO: 77, SEQ ID NO: 85, SEQ ID NO: 39, and SEQ ID NO: 8. The above mentioned sets may also be combined with each other, e.g. (i) with (ii), (ii) with (iii), (iii) with (iv), (iv) with (v), (v) with (vi), (vi) with (vii), or any other possible combination.
In a preferred embodiment, the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20. It is particularly preferred that the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, and SEQ ID NO: 5.
It is preferred that
(i) the polynucleotide of the present invention is complementary to the miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO:
1 to 103, or the polynucleotides comprised in the set of the present invention are complementary to the miRNAs comprised in the set, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103, and wherein preferably the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO : 104 to SEQ ID NO : 119,
(ii) the polynucleotide is a fragment of the polynucleotide according to (i), preferably the polynucleotide is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the polynucleotide according to (i), or the polynucleotides comprised in the set are fragments of the polynucleotides comprised in the set according to (i), preferably the polynucleotides comprised in the set are fragments which are between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the polynucleotides comprised in the set according to (i), or
(iii) the polynucleotide has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or the polynucleotides comprised in the set have at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81,
82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
It is particularly preferred that the polynucleotide as defined in (iii) has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or that the polynucleotides comprised in the set as defined in (iii) have at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
In addition, the polynucleotide or polynucleotides as defined in (ii) (i.e. polynucleotide fragment(s)) or (iii) (i.e. polynucleotide variant(s) or polynucleotide fragment variant(s)) is (are) only regarded as a polynucleotide or polynucleotides as defined in (ii) (i.e. polynucleotide fragment(s)) or (iii) (i.e. polynucleotide variant(s) or polynucleotide fragment variant(s)) within the context of the present invention, if it is or they are still capable of binding to, hybridizing with, or detecting a target miRNA of complementary sequence or target miRNAs of complementary sequences, e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119, through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation under stringent hybridization conditions. The skilled person can readily assess whether a polynucleotide or polynucleotides as defined in (ii) (i.e. polynucleotide fragment(s)) or (iii) (i.e. polynucleotide variant(s) or polynucleotide fragment variant(s)) is (are) still capable of binding to, hybridizing with, recognizing or detecting a target miRNA of complementary sequence or target miRNAs of complementary sequences, e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119. Suitable assays to determine whether hybridization under stringent conditions still occurs are well known in the art. However, as an example, a suitable assay to determine whether hybridization still occurs comprises the steps of: (a) incubating the polynucleotide or polynucleotides as defined in (ii) or (iii) attached onto a biochip with the miRNA(s) of complementary sequence(s), e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119, labeled with biotin under stringent hybridization conditions, (b) washing the biochip to remove unspecific bindings, (c) subjecting the biochip to a detection system, and (c) analyzing whether the polynucleotide(s) can still hybridize with the target miRNA(s) of complementary sequence(s), e.g. the respective target miRNA(s) according to SEQ ID NO: 1 to SEQ ID NO: 119. As a positive control, the respective non-mutated and not fragmented polynucleotide as defined in (i) may be used. Preferably stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %,Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
In particularly preferred embodiments, the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the polynucleotide(s) is (are) not for detecting (a) miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
In a second aspect, the present invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, or 99%, sequence identity thereto, and
(ii) comparing said expression profile to a reference, wherein the comparison of said expression profile to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or applying an algorithm or a mathematical function to said expression profile, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
The term "miRNA expression profile", as used herein, represents the expression level of a single miRNA or a collection of expression levels of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or of 103 miRNAs comprised in a set, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Said set of miRNAs may be completed by 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
According to the method of the present invention, the expression profile of a single miRNA or the expression profile of at least two miRNAs comprised in a set is determined in a blood sample from a human patient. This is possible, (i) as the miRNAs disclosed herein are expressed within and/or among cells or tissues and are subsequently released/transferred into circulating blood and/or (ii) as the miRNAs described herein are directly expressed in hemopoietic cells, also known as blood cells, e.g. erythrocytes, leukocytes and/or thrombocytes. Thus, the term "determining an expression profile of miRNAs in a blood sample" does not solely mean that the miRNAs are actually expressed in blood. Said miRNAs may be expressed within and/or among surrounding cells or tissues and may be subsequently released/transferred into circulating blood and/or said miRNAs may be directly expressed in blood, namely in blood cells, e.g. peripheral blood mononuclear cells (PBMCs). Accordingly, the expression levels of miRNAs determined in a blood sample indirectly represent the expression levels of said miRNAs in the cells or tissues from which they originate and/or directly represent the expression levels of miRNAs in blood cells. Preferably, the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
It is preferred that the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
It is more preferred that the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
In preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
Thus, in a preferred embodiment, the present invention provides a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity thereto, and
(ii) comparing said expression profile to a reference, wherein the comparison of said expression profile to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or applying an algorithm or a mathematical function to said expression profile, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
The miRNA expression profiles may be generated by any convenient means for determining miRNA expression levels (see below) and allow the analysis of differential miRNA expression levels between samples, for example, between a sample of a human patient and between (a) sample(s) of (a) control subject(s), e.g. between a sample of a human patient and a sample of a subject known not to suffer from gastric cancer (i.e. being healthy), or between a sample of a human patient and a sample of a subject known to suffer from gastric cancer (i.e. being diseased). Thereby, each miRNA is represented by a numerical value. The higher the value of an individual miRNA, the higher is the expression level of said miRNA. The miRNA expression profile may include expression data for 1, 2, 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 100, or 103 miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
The term "differential expression" of miRNA as used herein, means qualitative and/or quantitative differences in the temporal and/or local miRNA expression patterns within and/or among cells, tissues, or within blood. Thus, a differentially expressed miRNA may qualitatively have its expression altered, including an activation or inactivation in, for example, normal tissue versus diseased tissue. The difference in miRNA expression may also be quantitative, e.g. in that expression is modulated, i.e. either up-regulated, resulting in an increased amount of miRNA, or down-regulated, resulting in a decreased amount of miRNA. The degree to which miRNA expression differs need only be large enough to be quantified via standard characterization techniques, e.g. quantitative hybridization of miRNA, labeled miRNA, or amplified miRNA, quantitative PCR (qPCR) such as real time quantitative PCR (RT qPCR), ELISA for quantitation, next generation sequencing and the like.
The term "a single miRNA or a set comprising at least two miRNAs representative for gastric cancer", as used herein, refers to a fixed defined single miRNA which is known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and is, thus, representative for gastric cancer, or it refers to at least two fixed defined miRNAs comprised in a set which are known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and are, thus, representative for gastric cancer.
The nucleotide sequence of said fixed defined single miRNA or the nucleotide sequences of said at least two fixed defined miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. The above mentioned set may be supplemented by at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%) sequence identity thereto.
Thus, for the analysis of a blood sample such as blood cell sample in step (i) of the method of the present invention, an expression profile of (i) a fixed defined single miRNA which is known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and is, thus, representative for gastric cancer, or (ii) at least two fixed defined miRNAs comprised in a set which are known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and are, thus, representative for gastric cancer, is determined, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
In addition, for further analysis of the blood sample such as blood cell sample in step (a) of the method of the present invention, at least one supplementary miRNA which is known to be differential between subjects having gastric cancer (diseased state) and subjects not having gastric cancer (healthy/control state) and is, thus, also representative for gastric cancer, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto, may be added to the fixed defined set of at least two miRNAs as mentioned above and an expression profile may be determined from this supplemented set, for example, to improve the diagnostic accuracy, specificity and sensitivity in the determination of gastric cancer.
For blood sample such as blood cell sample analysis, it may be required that a polynucleotide (probe) capable of detecting this fixed defined miRNA or polynucleotides (probes) capable of detecting this fixed defined miRNA set is (are) attached to a solid support, substrate, surface, platform, or matrix, e.g. biochip. For example, if the fixed defined set of miRNAs for diagnosing gastric cancer comprises or consists of 30 miRNAs, polynucleotides capable of detecting these 30 miRNAs are attached to a solid support, substrate, surface, platform or matrix, e.g. biochip, in order to perform the diagnostic/prognostic sample analysis.
As mentioned above, an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient is determined in the step (i) of the method of the present invention, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. In preferred embodiments of the method of the present invention, the set comprises, essentially consists of, or consists of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprises/consists of 103 miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
Preferably, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 1 and SEQ ID NO: 2, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 3, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 4, the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 6, the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 7, the nucleotide sequences of the at least 8 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 8, the nucleotide sequences of the at least 9 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 9, the nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 10, the nucleotide sequences of the at least 11 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 11, the nucleotide sequences of the at least 12 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 12, the nucleotide sequences of the at least 13 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 13, the nucleotide sequences of the at least 14 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 14, the nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 15, the nucleotide sequences of the at least 16 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 16, the nucleotide sequences of the at least 17 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 17, the nucleotide sequences of the at least 18 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 18, the nucleotide sequences of the at least 19 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 19, the nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 20, the nucleotide sequences of the at least 21 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 21, the nucleotide sequences of the at least 22 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 22, the nucleotide sequences of the at least 23 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 23, the nucleotide sequences of the at least 24 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 24, the nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 25, the nucleotide sequences of the at least 26 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 26, the nucleotide sequences of the at least 27 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 27, the nucleotide sequences of the at least 28 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 28, the nucleotide sequences of the at least 29 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 29, the nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 30, the nucleotide sequences of the at least 31 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 31, the nucleotide sequences of the at least 32 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 32, the nucleotide sequences of the at least 33 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 33, the nucleotide sequences of the at least 34 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 34, the nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 35, the nucleotide sequences of the at least 36 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 36, the nucleotide sequences of the at least 37 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 37, the nucleotide sequences of the at least 38 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 38, the nucleotide sequences of the at least 39 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 39, the nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 40, the nucleotide sequences of the at least 41 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 41, the nucleotide sequences of the at least 42 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 42, the nucleotide sequences of the at least 43 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 43, the nucleotide sequences of the at least 44 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 44, the nucleotide sequences of the at least 45 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 45, the nucleotide sequences of the at least 46 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 46, the nucleotide sequences of the at least 47 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 47, the nucleotide sequences of the at least 48 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 48, the nucleotide sequences of the at least 49 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 49, the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 50, the nucleotide sequences of the at least 51 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 51, the nucleotide sequences of the at least 52 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 52, the nucleotide sequences of the at least 53 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 53, the nucleotide sequences of the at least 54 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 54, the nucleotide sequences of the at least 55 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 55, the nucleotide sequences of the at least 56 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 56, the nucleotide sequences of the at least 57 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 57, the nucleotide sequences of the at least 58 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 58, the nucleotide sequences of the at least 59 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 59, the nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 60, the nucleotide sequences of the at least 61 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 61, the nucleotide sequences of the at least 62 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 62, the nucleotide sequences of the at least 63 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 63, the nucleotide sequences of the at least 64 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 64, the nucleotide sequences of the at least 65 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 65, the nucleotide sequences of the at least 66 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 66, the nucleotide sequences of the at least 67 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 67, the nucleotide sequences of the at least 68 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 68, the nucleotide sequences of the at least 69 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 69, the nucleotide sequences of the at least 70 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 70, the nucleotide sequences of the at least 71 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 71, the nucleotide sequences of the at least 72 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 72, the nucleotide sequences of the at least 73 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 73, the nucleotide sequences of the at least 74 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 74, the nucleotide sequences of the at least 75 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 75, the nucleotide sequences of the at least 76 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 76, the nucleotide sequences of the at least 77 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 77, the nucleotide sequences of the at least 78 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 78, the nucleotide sequences of the at least 79 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 79, the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 80, the nucleotide sequences of the at least 81 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 81, the nucleotide sequences of the at least 82 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 82, the nucleotide sequences of the at least 83 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 83, the nucleotide sequences of the at least 84 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 84, the nucleotide sequences of the at least 85 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 85, the nucleotide sequences of the at least 86 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 86, the nucleotide sequences of the at least 87 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 87, the nucleotide sequences of the at least 88 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 88, the nucleotide sequences of the at least 89 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 89, the nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 90, the nucleotide sequences of the at least 91 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 91, the nucleotide sequences of the at least 92 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 92, the nucleotide sequences of the at least 93 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 93, the nucleotide sequences of the at least 94 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 94, the nucleotide sequences of the at least 95 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 95, the nucleotide sequences of the at least 96 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 96, the nucleotide sequences of the at least 97 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 97, the nucleotide sequences of the at least 98 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 98, the nucleotide sequences of the at least 99 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 99, the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 100, the nucleotide sequences of the at least 101 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 101, or the nucleotide sequences of the at least 102 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 102, and more preferably, the nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
It is also preferred that the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 2 and SEQ ID NO: 3, the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13, the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20, the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 31, SEQ ID NO: 32, SEQ ID NO: 33, SEQ ID NO: 34, SEQ ID NO: 35, and SEQ ID NO: 36, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 37, SEQ ID NO: 38, SEQ ID NO: 39, SEQ ID NO: 40, SEQ ID NO: 41, and SEQ ID NO: 42, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 43, SEQ ID NO: 44, SEQ ID NO: 45, SEQ ID NO: 46, SEQ ID NO: 47, and SEQ ID NO: 48, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 49, SEQ ID NO: 50, SEQ ID NO: 51, SEQ ID NO: 52, SEQ ID NO: 53, and SEQ ID NO: 54, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 55, SEQ ID NO: 56, SEQ ID NO: 57, SEQ ID NO: 58, SEQ ID NO: 59, and SEQ ID NO: 60, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 61, SEQ ID NO: 62, SEQ ID NO: 63, SEQ ID NO: 64, SEQ ID NO: 65, and SEQ ID NO: 66, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 67, SEQ ID NO: 68, SEQ ID NO: 69, SEQ ID NO: 70, SEQ ID NO: 71, and SEQ ID NO: 72, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 73, SEQ ID NO: 74, SEQ ID NO: 75, SEQ ID NO: 76, SEQ ID NO: 77, and SEQ ID NO: 78, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 79, SEQ ID NO: 80, SEQ ID NO: 81, SEQ ID NO: 82, SEQ ID NO: 83, and SEQ ID NO: 84, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 85, SEQ ID NO: 86, SEQ ID NO: 87, SEQ ID NO: 88, SEQ ID NO: 89, and SEQ ID NO: 90, the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID NO: 91, SEQ ID NO: 92, SEQ ID NO: 93, SEQ ID NO: 94, SEQ ID NO: 95, and SEQ ID NO: 96, or the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID NO: 97, SEQ ID NO: 98, SEQ ID NO: 99, SEQ ID NO: 100, SEQ ID NO: 101, SEQ ID NO: 102 and SEQ ID NO: 103.
In preferred embodiments, the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a blood cell sample from a human patient.
In more preferred embodiments,
(i) the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in an erythrocyte sample/erythrocyte containing sample from a human patient, (ii) the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a leukocyte sample/leukocyte containing sample from a human patient, and/or
(iii) the expression profile of a (miRNA) set comprising the above mentioned miRNAs representative for gastric cancer is determined in a thrombocyte sample/thrombocyte containing sample from a human patient.
The blood cells may also be PBMCs such as lymphocytes and/or monocytes.
In other preferred embodiments, the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a blood sample, preferably blood cell sample, from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13. For example, the nucleotide sequences of said miRNAs are selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets (signatures) listed in Figure 13.
In more preferred embodiments,
(i) the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in an erythrocyte sample/erythrocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13,
(ii) the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a leukocyte sample/leukocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13, and/or
(iii) the expression profile of a (miRNA) set comprising miRNAs representative for gastric cancer is determined in a thrombocyte sample/thrombocyte containing sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets (signatures) listed in Figure 13.
The blood cells may also be PBMCs such as lymphocytes and/or monocytes.
Preferably, the (miRNA) set as defined above comprises at least one further miRNA, e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s), wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%), sequence identity thereto. Thus, in a preferred embodiment of the method of the present invention, the (miRNA) set comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or 103 miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, and wherein the (miRNA) set comprises (i) at least one further miRNA having the nucleotide sequence according to SEQ ID NO:
104,
(ii) at least 2 further miRNAs having the nucleotide sequence according to SEQ ID NO: 104 and SEQ ID NO: 105,
(iii) at least 3 further miRNAs having the nucleotide sequence according to SEQ ID NO: 104 to SEQ ID NO: 106,
(iv) at least 4 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 107,
(v) at least 5 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 108,
(vi) at least 6 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 109,
(vii) at least 7 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 110,
(viii) at least 8 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 111,
(ix) at least 9 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 112,
(x) at least 10 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 113,
(xi) at least 11 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 114,
(xii) at least 12 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 115,
(xiii) at least 13 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 116,
(xiv) at least 14 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 117, (xv) at least 15 further miRNAs having the nucleotide sequences according to SEQ ID NO: 104 to SEQ ID NO: 118, or
(xvi) at least 16 further miRNAs having the nucleotide sequences according to SEQ ID NO:
104 to SEQ ID NO: 119.
It is particularly preferred that the nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to SEQ ID NO. 13, (x) SEQ ID NO: 10 to SEQ ID NO: 14, or (xi) SEQ ID NO: 1 1 to SEQ ID NO: 15. It is also particularly preferred that the nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 11, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO: 7 to SEQ ID NO: 16, (viii) SEQ ID NO: 8 to SEQ ID NO: 17, (ix) SEQ ID NO: 9 to SEQ ID NO: 18, (x) SEQ ID NO: 10 to SEQ ID NO: 19, or (xi) SEQ ID NO: 11 to SEQ ID NO: 20. The above mentioned sets may also be combined with each other, e.g. (i) with (ii), (ii) with (iii), (iii) with (iv), (iv) with (v), (v) with (vi), (vi) with (vii), or any other possible combination.
It is further particularly preferred that the nucleotide sequences of the miRNAs comprised in the set have (i) SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, (ii) SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16, (iii) SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, and SEQ ID NO: 4, (iv) SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, and SEQ ID NO: 6, (v) SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 17, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 7, and SEQ ID NO: 8, (vi) SEQ ID NO: 23, SEQ ID NO: 5, SEQ ID NO: 107, SEQ ID NO: 35, SEQ ID NO: 38, SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, and SEQ ID NO: 36, (vii) SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, and SEQ ID NO: 15, or (viii) SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, SEQ ID NO: 15, SEQ ID NO: 117, SEQ ID NO: 77, SEQ ID NO: 85, SEQ ID NO: 39, and SEQ ID NO: 8. The above mentioned sets may also be combined with each other, e.g. (i) with (ii), (ii) with (iii), (iii) with (iv), (iv) with (v), (v) with (vi), (vi) with (vii), or any other possible combination.
In a preferred embodiment of the method of the present invention, the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20. It is particularly preferred that the nucleotide sequence of the (single) miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, and SEQ ID NO: 5.
As mentioned above, an expression profile of a (single) miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient, or an expression profile of a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, and preferably comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 further miRNA(s)) representative for gastric cancer in a blood sample such as blood cell sample from a human patient, is determined in the first step of the method of the present invention, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of
(i) a nucleotide sequence according to SEQ ID NO: 1 to SEQ ID NO: 103,
(ii) a nucleotide sequence that is a fragment of the nucleotide sequence according to (i), preferably, a nucleotide sequence that is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the nucleotide sequence according to (i), and
(iii) a nucleotide sequence that has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the nucleotide sequence according to (i) or nucleotide sequence fragment according to (ii), and wherein the nucleotide sequence of the at least one further miRNA comprised in the set is selected from the group consisting of
(i) a nucleotide sequence according to SEQ ID NO: 104 to SEQ ID NO: 119,
(ii) a nucleotide sequence that is a fragment of the nucleotide sequence according to (i), preferably, a nucleotide sequence that is a fragment which is between 1 and 12, more preferably between 1 and 8, and most preferably between 1 and 5 or 1 and 3, i.e. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12, nucleotides shorter than the nucleotide sequence according to (i), and
(iii) a nucleotide sequence that has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity to the nucleotide sequence according to (i) or nucleotide sequence fragment according to (ii).
It is particularly preferred that the nucleotide sequence as defined in (iii) has at least 80%, preferably at least 85%, more preferably at least 90%, and most preferably at least 95% or 99%, i.e. 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99%, sequence identity over a continuous stretch of at least 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more nucleotides, preferably over the whole length, to the nucleotide sequence of the nucleotide according to (i) or nucleotide fragment according to (ii).
In addition, the nucleotide sequence as defined in (ii) (i.e. nucleotide sequence fragment) or (iii) (i.e. nucleotide sequence variant or nucleotide sequence fragment variant) is only regarded as a nucleotide sequence as defined in (ii) (i.e. nucleotide sequence fragment) or (iii) (i.e. nucleotide sequence variant or nucleotide sequence fragment variant) within the context of the present invention, if it can still be bound, hybridized, recognized, or detected by a polynucleotide (probe) of complementary sequence, e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i), through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation under stringent hybridization conditions. The skilled person can readily assess whether a nucleotide sequence as defined in (ii) (i.e. nucleotide sequence fragment) or (iii) (i.e. nucleotide sequence variant or nucleotide sequence fragment variant) can still be bound, hybridized, recognized, or detected by a polynucleotide (probe) of complementary sequence, e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i). Suitable assays to determine whether hybridization under stringent conditions still occurs are well known in the art. However, as an example, a suitable assay to determine whether hybridization still occurs comprises the steps of: (a) incubating a nucleotide sequence as defined in (ii) or (iii) labelled with biotin with a polynucleotide (probe) of complementary sequence, e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i), wherein the polynucleotide (probe) is attached onto a biochip, under stringent hybridization conditions, (b) washing the biochip to remove unspecific bindings, (c) subjecting the biochip to a detection system, and (c) analyzing whether the nucleotide sequence can still be hybridized or detected by a polynucleotide (probe) of complementary sequence, e.g. a polynucleotide (probe) which is complementary to the respective nucleotide sequence as defined in (i). As a positive control, the respective miRNA as defined in (i) may be used. Preferably stringent hybridization conditions include the following: 50% formamide, 5x SSC, and 1% SDS, incubating at 42°C, or, 5x SSC, 1% SDS, incubating at 65°C, with wash in 0.2x SSC, and 0.1% SDS at 65°C; or 6x SSPE, 10 % formamide, 0.01 %,Tween 20, 0.1 x TE buffer, 0.5 mg/ml BSA, 0.1 mg/ml herring sperm DNA, incubating at 42°C with wash in 0.5x SSPE and 6x SSPE at 45°C.
It is preferred that in step (i) of the method of the present invention, a polynucleotide according to the first aspect of the present invention is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient. Thus, for example, in step (i) of the method of the present invention, a polynucleotide is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% identity thereto.
It is particularly preferred that in step (i) of the method of the present invention, a polynucleotide according to the first aspect of the present invention is used for determining an expression profile of a miRNA representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20. It is more particularly preferred that the polynucleotide is in single stranded form and attached to a solid support, substrate, surface, platform or matrix, e.g. biochip, and is incubated with a miRNA of complementary sequence, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10, or is selected from the group consisting of SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20 for determining an expression profile of said miRNA.
It is also preferred that in step (i) of the method of the present invention, a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention is used for determining an expression profile of a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient. Thus, for example, in step (i) of the method of the present invention, a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consi sting of 103 polynucleotides is used for determining an expression profile of a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% identity thereto.
Preferably, the above mentioned set of polynucleotides used in step (i) of the method of the present invention comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for determining an expression profile of at least on further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)) comprised in the above mentioned set of miRNAs, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 1 19, a fragment thereof, and a sequence having at least 80% identity thereto.
In particularly preferred embodiments, the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the expression profile is not determined of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
As mentioned above, in a preferred embodiment, the blood sample is a whole blood sample or a blood fraction sample. Preferably, the blood fraction sample is a blood cell sample, a blood plasma sample or a blood serum sample. More preferably, the blood fraction sample is a blood cell sample. Blood cells also known as hemopoietic cells may also be used, e.g. erythrocytes, leukocytes and/or thrombocytes (see above).
In preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
Blood samples may be collected by any convenient method, as known in the art. It is preferred that 0.1 to 20 ml blood, preferably 0.5 to 10 ml blood, i.e. 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 ml blood, is collected. In another preferred embodiment, the blood sample is obtained from a human patient prior to initiation of therapeutic treatment, during therapeutic treatment and/or after therapeutic treatment. It is particularly preferred that total RNA or subfractions thereof including the miRNA is (are) isolated, e.g. extracted, from the blood sample in order to determine the expression profile of a miRNA or miRNAs comprised in the blood sample.
The inventors of the present invention surprisingly found that miRNAs are not only present in a blood sample but also that miRNAs remain stable and that, thus, blood miRNAs can be used as biomarkers for detecting and/or prognosis of gastric cancer in human patients. Further, the inventors found that the miRNAs present in blood are different from the ones found in tissue of individuals suffering from gastric cancer. Furthermore, the use of blood samples in the method of the present invention for detection and/or prognosis of gastric cancer has a number of advantage, for example, blood miRNAs have a high sensitivity, blood is relatively easy to obtain and even can be collected via routine physical examination, the costs for detection are low, and the samples can easily be preserved (e.g. at - 20°C). Further, blood circulates to all tissues in the body and, therefore, blood is able to reflect the physiological pathology of the whole organism and the detection of blood miRNAs results in an indicator of human health, and according to the present invention, as an indication whether a patient suffers from gastric cancer. Furthermore, this method can widely be used in general screening for gastric cancer. Moreover, the inventors of the present invention surprisingly found that blood is an efficient mean for early diagnosis of gastric cancer. As novel disease markers, blood miRNAs improve the low- specificity and low-sensitivity caused by individual differences which are difficult to overcome with other markers, and notably increase the clinical detection rate of gastric cancer so as to achieve early diagnosis of gastric cancer.
Furthermore, according to the present invention, a first diagnosis of gastric cancer can be performed employing, as disclosed, miRNA-detection in a blood sample such as blood cell sample, followed by a second diagnosis that is based on other methods (e.g. other biomarkers and/or imaging methods).
As mentioned above, in step (i) of the method of the present invention, an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient is determined. The determination may be carried out by any convenient means for determining a RNA expression level or RNA expression levels. For expression profiling, qualitative, semi-quantitative and/or quantitative detection methods may be used. A variety of techniques are well known to the person skilled in the art. It is preferred that the expression profile of the miRNA(s) representative for gastric cancer is determined by nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy or any combination thereof.
Nucleic acid amplification may be performed using real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR). The real time polymerase chain reaction (RT-PCR) is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs). It is particularly suitable for detecting low abundance miRNAs. The real time quantitative polymerase chain reaction (RT qPCR), however, allows the analysis of a single miRNA as well as a complex set of miRNAs (e.g. a set of at least 2 to 103 miRNAs such as a set of 50, 60, 70, 80, 90, or 100 miRNAs) comprised in a blood sample such as blood cell sample from a human patient, e.g. a single miRNA or a set comprising at least two miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (e.g. a set of 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103).
The aforesaid real time polymerase chain reaction (RT-PCR) may include the following steps: (i) extracting the total RNA from a blood (e.g. whole blood, particularly blood cell, serum, or plasma) sample of a human patient and obtaining cDNA samples by RNA reverse transcription (RT) reaction using miRNA-specific primers, or collecting a blood (e.g. whole blood, particularly blood cell, serum, or plasma) sample of a human patient and conducting reverse transcriptase reaction using miRNA-specific primers with the blood (e.g. whole blood, particularly blood cell, serum, or plasma) sample being a buffer so as to prepare cDNA samples, (ii) designing miRNA-specific cDNA forward primers and providing universal reverse primers to amplify the cDNA via polymerase chain reaction (PCR), (iii) adding a fluorescent probe to conduct PCR, and (iv) detecting the miRNA(s) level in the blood (e.g. whole blood, serum, or plasma) sample.
A variety of kits and protocols to determine an expression profile by real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR) are available. For example, reverse transcription of miRNAs may be performed using the TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems) according to manufacturer's recommendations. Briefly, miRNA may be combined with dNTPs, MultiScribe reverse transcriptase and the primer specific for the target miRNA. The resulting cDNA may be diluted and may be used for PCR reaction. The PCR may be performed according to the manufacturer's recommendation (Applied Biosystems). Briefly, cDNA may be combined with the TaqMan assay specific for the target miRNA and PCR reaction may be performed using ABI7300.
Nucleic acid hybridization may be performed using a microarray/biochip or in situ hybridization. In situ hybridization is preferred for the analysis of a single miRNA or a set comprising a low number of miRNAs (e.g. a set of at least 2 to 50 miRNAs such as a set of 2, 5, 10, 20, 30, or 40 miRNAs). The microarray/biochip, however, allows the analysis of a single miRNA as well as a complex set of miRNAs (e.g. a set of at least 2 to 103 miRNAs such as a set of at least 50, 60, 70, 80, 90, or 100 miRNAs) comprised in a blood sample such as blood cell sample from a human patient, e.g. a single miRNA or a set comprising at least two miRNAs, wherein the nucleotide sequence of said miRNA or said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103 (e.g. a set of 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103).
For nucleic acid hybridization, for example, the polynucleotides (probes) according to the first aspect of the present invention with complementarity to the corresponding miRNAs to be detected are attached to a solid phase to generate a microarray/biochip (e.g. 103 polynucleotides (probes) which are complementary to the 103 miRNAs having SEQ ID NO: 1 to SEQ ID NO: 103 comprised in a set). Said microarray/biochip is then incubated with miRNAs, isolated (e.g. extracted) from a blood sample such as blood cell sample of a human patient, which may be labelled, e.g. fluorescently labelled, or unlabelled. Upon hybridization of the labelled miRNAs to the complementary polynucleotide sequences on the microarray/biochip, the success of hybridisation may be controlled and the intensity of hybridization may be determined via the hybridisation signal of the label in order to determine the expression level of each tested miRNA in said blood sample such as blood cell sample. Preferably, (i) the nucleic acid hybridization is performed using a microarray/biochip, or using in situ hybridization, and/or (ii) the nucleic acid amplification is performed using real-time PCR.
Thus, it is preferred that in step (i) of the method of the present invention, an expression profile of a set comprising, essentially consisting of, or consisting of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides (probes), or comprising/consi sting of 103 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
It is also particularly preferred that in step (i) of the method of the present invention, an expression profile of a set comprising at least 5 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 5 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 5 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 5, (ii) SEQ ID NO: 2 to SEQ ID NO: 6, (iii) SEQ ID NO: 3 to SEQ ID NO: 7, (iv) SEQ ID NO: 4 to SEQ ID NO: 8, (v) SEQ ID NO: 5 to SEQ ID NO: 9, (vi) SEQ ID NO: 6 to SEQ ID NO: 10, (vii) SEQ ID NO: 7 to SEQ ID NO: 11, (viii) SEQ ID NO: 8 to SEQ ID NO: 12, (ix) SEQ ID NO: 9 to SEQ ID NO. 13, (x) SEQ ID NO: 10 to SEQ ID NO: 14 or (xi) SEQ I'D NO: 11 to SEQ ID NO: 15, or that in step (i) of the method of the present invention, an expression profile of a set comprising at least 10 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 10 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 10 miRNAs comprised in the set have (i) SEQ ID NO: 1 to SEQ ID NO: 10, (ii) SEQ ID NO: 2 to SEQ ID NO: 1 1, (iii) SEQ ID NO: 3 to SEQ ID NO: 12, (iv) SEQ ID NO: 4 to SEQ ID NO: 13, (v) SEQ ID NO: 5 to SEQ ID NO: 14, (vi) SEQ ID NO: 6 to SEQ ID NO: 15, (vii) SEQ ID NO: 7 to SEQ ID NO: 16, (viii) SEQ ID NO: 8 to SEQ ID NO: 17, (ix) SEQ ID NO: 9 to SEQ ID NO: 18, (x) SEQ ID NO: 10 to SEQ ID NO: 19, or (xi) SEQ ID NO: 11 to SEQ ID NO: 20.
It is further particularly preferred that in step (i) of the method of the present invention, an expression profile of a set comprising at least 50 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 50 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 50. It is more preferred that in step (i) of the method of the present invention, an expression profile of a set comprising at least 80 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 80 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 80. It is also more preferred that in step (i) of the method of the present invention, an expression profile of a set comprising at least 100 miRNAs representative for gastric cancer is determined by nucleic acid hybridization using a microarray/biochip which comprises a set comprising at least 100 polynucleotides (probes) that are complementary to the miRNAs, wherein the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 100.
Furthermore, according to the present invention, the miRNA or the set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample such as blood cell sample from a human patient may be established on one experimental platform (e.g. microarray), while for routine diagnosis/prognosis another experimental platform (e.g. qPCR) may be chosen.
Subsequent to the determination of an expression profile (data) of a (single) miRNA representative for gastric cancer as defined above, or of a set comprising at least two miRNAs representative for gastric cancer as defined above in a blood sample such as blood cell sample from a human patient in step (i) of the method according to the present invention, said expression profile (data) is compared to a reference in step (ii) of the method according to the present invention, wherein the comparison of said expression profile (data) to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or an algorithm or a mathematical function is applied to said expression profile (data) in step (ii) of the method of the present invention, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
The reference may be any reference which allows for the diagnosis and/or prognosis of gastric cancer, e.g. an indicated value or values, and/or the algorithm or mathematical function may be any algorithm or mathematical function which allows for the diagnosis and/or prognosis of gastric cancer.
The term "(clinical) condition" (biological state or health state), as used herein, means a status of a subject that can be described by physical, mental or social criteria. It includes so- called "healthy" and "diseased" conditions. For the definition of "healthy" and "diseased" conditions it is referred to the international classification of diseases (ICD) of the WHO (http://wwwint/classifications/icd/en/index.html). When the expression profile (data) determined in a human patient is compared to a reference of one known (clinical) condition or when an algorithm or a mathematical function obtained from a reference of one known (clinical) condition is applied to the expression profile (data) determined in a human patient according to preferred embodiments of the method of the present invention, it is understood that said condition is gastric cancer (i.e. diseased condition), or that said condition is no gastric cancer (i.e. healthy/healthiness). When the expression profile (data) determined in a human patient is compared to a reference of at least two known (clinical) conditions or when an algorithm or a mathematical function obtained from a reference of at least two known (clinical) conditions is applied to the expression profile (data) determined in a human patient according to other preferred embodiments of the method of the present invention, it is understood that this is possible for all (clinical) conditions that can be defined and is not limited to a comparison of a diseased versus healthy comparison and extends to multiway comparisons, under the proviso that one known (clinical) condition is gastric cancer (i.e. diseased condition). For example, the expression profile (data) determined in a human patient may be compared to a reference of two known (clinical) conditions, which are gastric cancer and no gastric cancer, or an algorithm or a mathematical function obtained from a reference of two known (clinical) conditions, which are gastric cancer and no gastric cancer, may be applied to the expression profile (data) determined in a human patient.
It should be noted that the term "reference expression profile (data)", as used herein, means an expression profile (data) of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i), or an expression profile (data) of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i), but means an expression profile which is obtained from a (control) subject(s) with a known clinical condition, e.g. gastric cancer, or no gastric cancer.
The terms "essentially correspond(s)" or "essentially identical", as used in the context (of the second aspect) of the present invention, mean that the miRNA(s) of step (i) and the miRNA(s) of the reference expression profile may slightly differ in their nucleotide sequence, whereas said difference is so marginal that it may still allow for the diagnosis and/or prognosis of gastric cancer. Preferably, the nucleotide sequence(s) of the miRNA(s) of step (i) and the nucleotide sequence(s) of the miRNA(s) of the reference expression profile differ in 1 to 5, more preferably in 1 to 3, and most preferably in 1 to 2 nucleotides, i.e. in 1, 2, 3, 4, or 5 nucleotides. It is preferred that said difference resides in 1 to 5, more preferably 1 to 3, and most preferably 1 to 2 nucleotide mutations, i.e. 1, 2, 3, 4, or 5 nucleotide mutations (e.g. substitutions, additions, insertions, and/or deletions). This is caused by the fact that the miRNAs within the human species may differ. It is preferred that the miRNA(s) of step (i) and the miRNA(s) of the reference expression profile do not differ in their nucleotide sequence, i.e. are identical. It is particularly preferred that the miRNA(s) of step (i) and the miRNA(s) of the reference expression profile are derived from subject/patients of the same gender and/or similar age/phase of life.
Preferably, both the reference expression profile and the expression profile of step (i) are determined in the same type of blood sample, for example, blood serum sample, blood plasma sample, or blood cell (e.g. erythrocytes, leukocytes and/or thrombocytes) sample. It is understood that the reference expression profile is not necessarily obtained from a single (control) subject, e.g. a subject known to be affected by gastric cancer, or a subject known to be not affected by gastric cancer, but may be an average reference expression profile of a plurality of (control) subjects, e.g. subjects known to be affected by gastric cancer, or subjects known to be not affected by gastric cancer, e.g. at least 2 to 40 subjects, more preferably at least 10 to 25 subjects, and most preferably at least 15 to 20 subjects, i.e. at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, or 40 subjects. Preferably, both the reference expression profile and the expression profile of step (i) are obtained from a subject/patient of the same gender (e.g. female or male) and/or of a similar age/phase of life (e.g. infant, young child, juvenile, adult).
Thus, in a preferred embodiment of the method of the present invention, the reference is a reference expression profile (data) of at least one subject, preferably the reference is an average expression profile (data) of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39, or 40 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e. healthy/healthiness), wherein the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
For example, said reference may be a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy) or known to be affected by gastric cancer (i.e. diseased), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
The comparison of the expression profile of the human patient to be diagnosed and/or prognosed to the (average) reference expression profile (data), may then allow for diagnosing and/or prognosing of gastric cancer (step (ii)).
Considering the above, diagnosing preferably means comparing the expression profile
(data) of a human patient determined in step (i) to the (average) reference expression profile (data) as mentioned above to decide, if the at least one known clinical condition, which is gastric cancer, or which is no gastric cancer (i.e. healthy), is present in said patient. Prognosing preferably means comparing the expression profile (data) of a human patient determined in step (i) to the (average) reference expression profile (data) as mentioned above to decide, if the at least one known clinical condition, which is gastric cancer, or which is no gastric cancer (i.e. healthy), will likely be present in said patient.
For example, the human patient may be diagnosed as not suffering from gastric cancer (i.e. being healthy), or as suffering from gastric cancer (i.e. being diseased). Further, for example, the human patient may be prognosed as not developing gastric cancer (i.e. staying healthy), or as developing gastric cancer (i.e. getting diseased).
Diagnosing/prognosing of gastric cancer based on (a) reference expression profile (data) as reference may take place as follows: For instance, (i) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of at least 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, at least 2 fold higher (up-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy), the human patient tested is diagnosed as suffering from gastric cancer (i.e. being diseased) or prognosed as likely developing gastric cancer (i.e. getting diseased), or (ii) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, at least 2 fold lower (down-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy), the human patient tested is diagnosed as suffering from gastric cancer (i.e. being diseased) or prognosed as likely developing gastric cancer (i.e. getting diseased).
In the converse case, (i) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of at least 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, not at least 2 fold higher (up-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of a human subject known not to suffer from gastric cancer (i.e. being healthy), the human patient tested is diagnosed as not suffering from gastric cancer (i.e. being healthy) or prognosed as likely not developing gastric cancer (i.e. staying healthy), or (ii) if the miRNA(s) of step (i) (e.g. a single miRNA or a set of 2 miRNAs) in the expression profile of a human patient to be diagnosed for gastric cancer is (are), for example, not at least 2 fold lower (down-regulated) compared to said miRNA(s) (e.g. a single miRNA or a set of at least 2 miRNAs) in the reference expression profile of human subject known not to suffer from gastric cancer (i.e. being healthy), the patient tested (e.g. human or animal) is diagnosed as not suffering from gastric cancer (i.e. being healthy) or prognosed as likely not developing gastric cancer (i.e. staying healthy).
It should be noted that a human patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer, may possibly suffer from another disease not tested/known, or a human patient that is prognosed as staying healthy, i.e. likely not developing gastric cancer, may possibly developing another disease not tested/known.
Preferably, the algorithm or mathematical function is obtained from a reference expression profile (data) of at least one subject, preferably of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e. healthy/healthiness), wherein the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i), or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
For example, said algorithm or mathematical function may be obtained from a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy), or known to be affected by gastric cancer (i.e. diseased), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
It is particularly preferred that the algorithm or mathematical function is obtained from reference expression profiles (data) of at least two subjects, preferably of at least 3 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with at least two known clinical conditions, preferably at least 2 to 5, more preferably at least 2 to 4 (i.e. at least 2, 3, 4, or 5) known clinical conditions, which are gastric cancer and any other known clinical condition(s), wherein the reference expression profiles are the profiles of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs of step (i).
Preferably, the two known clinical conditions are gastric cancer and no gastric cancer.
For example, said algorithm or mathematical function may be obtained from reference expression profiles of at least two subjects, at least one subject known to suffer from gastric cancer (i.e. diseased) and at least one subject known not to suffer from gastric cancer (i.e. healthy), wherein the reference expression profile is the profile of a single miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that corresponds (are identical), to the nucleotide sequences of the miRNAs of step (i).
Preferably, the above mentioned algorithm or mathematical function is obtained from reference expression profiles of the same number of (control) subjects (e.g. subjects known to be healthy or diseased). For example, the algorithm or mathematical function may be obtained from reference expression profiles of 10 subjects known to suffer from gastric cancer (positive control) and 10 subjects known not to suffer from gastric cancer (negative control). The algorithm or mathematical function may also be obtained from reference expression profiles of 20 subjects known to suffer from gastric cancer (positive control) and 20 subjects known not to suffer from gastric cancer (negative control).
It is preferred that the algorithm or mathematical function is obtained using a machine learning approach. Machine learning approaches may include but are not limited to supervised or unsupervised analysis: classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches), Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal probit regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression), Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA), Adaptations, extensions, and combinations of the previously mentioned approaches.
The inventors of the present invention surprisingly found that the application of a machine learning approach (e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means) leads to the obtainment of an algorithm or a mathematical function that is trained by the reference expression profile(s) (data) mentioned above and that this allows (i) a better discrimination between the at least two (e.g. 2 or 3) known clinical conditions (the at least two statistical classes) or (ii) a better decision, whether the at least one known clinical condition (the at least one statistical class) is present. In this way, the performance for diagnosing/prognosing of individuals suffering from gastric cancer can be increased (see also experimental section for details).
Preferably, the machine learning approach involves the following steps:
(i) inputting the reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer, and
(ii) computing an algorithm or a mathematical function based on said reference expression profile(s) that is suitable to distinguish between the (likely) clinical condition of gastric cancer and any other (likely) clinical condition(s), preferably the clinical condition of no gastric cancer, or to decide if the clinical condition of gastric cancer or no gastric cancer is present or will likely be present in said patient.
It should be noted that item (ii) encompasses both that the computed algorithm or mathematical function is suitable to distinguish between the clinical condition of gastric cancer and any other clinical condition(s), preferably the clinical condition of no gastric cancer, and that the computed algorithm or mathematical function is suitable to distinguish between the likely clinical condition of gastric cancer and any other likely clinical condition(s), preferably the clinical condition of no gastric cancer. In this respect "likely" means that it is to be expected that the patient will develop said clinical condition(s).
Thus, in a preferred embodiment, the machine learning approach involves the following steps:
(i) inputting the reference expression profile of at least one subject, preferably the reference expression profiles of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with one known clinical condition which is gastric cancer, or no gastric cancer, and
(ii) computing an algorithm or a mathematical function based on said reference expression profile, preferably based on said reference expression profiles, that is suitable to decide if the clinical condition of gastric cancer, or no gastric cancer is present or will likely be present in said patient.
Thus, in another preferred embodiment, the machine learning approach involves the following steps:
(i) inputting the reference expression profiles of at least two subjects , preferably of at least 3 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with at least two known clinical conditions (only one known clinical condition per subject) which are gastric cancer and any other known clinical condition(s), preferably the known clinical condition of no gastric cancer, and
(ii) computing an algorithm or a mathematical function based on said reference expression profiles that is suitable to distinguish between the (likely) clinical condition of gastric cancer, and any other (likely) clinical condition(s), preferably the clinical condition of no gastric cancer.
The application of the algorithm or mathematical function as mentioned above to the expression profile of the human patient to be diagnosed and/or prognosed may then allow for diagnosing and/or prognosing of gastric cancer.
Considering the above, diagnosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide, if the at least one known clinical condition, which is gastric cancer (i.e. diseased condition), or which is no gastric cancer (i.e. healthy condition), is present in said patient. Prognosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide, if the at least one known clinical condition, which is gastric cancer (i.e. diseased condition), or which is no gastric cancer (i.e. healthy condition), will likely be present in said patient.
For example, the human patient may be diagnosed as not suffering from gastric cancer (i.e. being healthy), or as suffering from gastric cancer (i.e. being diseased). Further, for example, the human patient may be prognosed as not developing gastric cancer (i.e. staying healthy), or as developing gastric cancer (i.e. getting diseased).
Furthermore, considering the above, diagnosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide which of the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer, is (are) present in said patient, or to distinguish between the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer. Prognosing preferably means applying the algorithm or mathematical function as mentioned above to the expression profile of a human patient to decide which of the at least two known clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer, will likely be present in said patient, or to distinguish between the at least two likely clinical conditions, which are gastric cancer and any other known clinical condition(s), preferably the clinical condition of no gastric cancer.
For example, if the at least two known clinical conditions are gastric cancer (i.e. diseased condition) and no gastric cancer (i.e. healthy condition), the human patient may be diagnosed as suffering from gastric cancer (i.e. being diseased), or as not suffering from gastric cancer (i.e. being healthy). If the at least two known clinical conditions are gastric cancer (i.e. diseased condition) and no gastric cancer (i.e. healthy condition), the human patient may be prognosed as developing gastric cancer (i.e. getting diseased), or as not developing gastric cancer (i.e. staying healthy).
Diagnosing/prognosing of gastric cancer based on an algorithm or a mathematical function may take place as follows: For instance, if the algorithm or mathematical function, which is obtained from a reference expression profile of at least one subject with the known clinical condition of gastric cancer, is applied to the expression profile (data) of a human patient, the human patient is classified as suffering from gastric cancer (i.e. being diseased), if the resulting score is below a specified threshold, or if the algorithm or mathematical function, which is obtained from a reference expression profile of at least one subject with the known clinical condition of no gastric cancer, is applied to the expression profile (data) of the human patient, the human patient is classified as not suffering from gastric cancer (i.e. being healthy), if the resulting score is above a specified threshold.
Further, if the algorithm or mathematical function, which is obtained from a reference expression profile of at least one subject with the known clinical condition of gastric cancer and from a reference expression profile of at least one subject with the known clinical condition of no gastric cancer, is applied to the expression profile (data) of a human patient, the human patient is classified as suffering from gastric cancer (i.e. being diseased), if the resulting score is below a specified threshold, and the human patient is classified as not suffering from gastric cancer i.e. being healthy), if the resulting score is above a specified threshold.
Preferably, machine learning approaches are employed to develop/obtain algorithms or mathematical functions for diagnosing and/or prognosing of clinical conditions such as gastric cancer. Support vector machines (SVMs) are a set of related supervised learning methods which are preferably used for classification and regression. For example, given a set of training examples (e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer), each marked as belonging to one of one category (e.g. condition of gastric cancer or no gastric cancer) or to one of two categories (e.g. condition of gastric cancer and no gastric cancer), an SVM algorithm builds a model that predicts whether a new example (e.g. sample from a human patient to be tested) falls into one category or the other (e.g. condition of gastric cancer or no gastric cancer). A SVM model is a representation of the training examples (e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer) as points in space, mapped so that the training examples of the separate categories (e.g. condition of gastric cancer or no gastric cancer) are divided by a clear gap that is as wide as possible. New examples (e.g. sample from a human patient to be tested) are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on (e.g. gastric cancer or no gastric cancer). More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. A good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
Classifying data is a preferred task in machine learning. For example, considering some given data points each belong to one (e.g. gastric cancer or no gastric cancer) of two classes (e.g. gastric cancer and no gastric cancer), the goal is to decide which class a new data point (e.g. achieved from a human patient) will be in. In the preferred case of support vector machines, a data point is viewed as a p-dimensional vector (a list of p numbers), and the question is, whether it is possible to separate such points with a p-1 -dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes (e.g. condition of gastric cancer or no gastric cancer). Thus, the hyperplane should be chosen so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier.
The formalization of the support vector machine is exemplary summarised below.
For example, given are training data D (e.g. reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer), a set of n subject samples (e.g. subjects known to suffer from gastric cancer and/or any other known clinical condition, for example, no gastric cancer) of the form v = ((¾ Q) I ¾ ir , a {—l, ijj i
where the c, is either 1 or -1, indicating the class labels (e.g. gastric cancer and/or any other known clinical condition(s), for example, no gastric cancer) to which the miRNA biomarker intensities x belongs. Each xi is a /^-dimensional real vector (with p being the number of miRNA biomarkers). The task is know to find the maximum-margin hyperplane that divides the points having c, = 1 from those having c, = - 1. Any hyperplane can be written as the set of points X satisfying w ■ x — b = 0,
where "." denotes the dot product. The vector w is a normal vector: it is perpendicular to the
ft
hyperplane. The parameter I determines the offset of the hyperplane from the origin along the normal vector W.
W and b should be chosen to maximize the margin, or distance between the parallel hyperplanes that are as far apart as possible while still separating the data. These hyperplanes can be described by the equations w■ x— b— 1
and w■ x— b— ----- 1 ,
Note that if the training data are linearly separable, the two hyperplanes of the margin can be selected in a way that there are no points between them and then by trying to maximize their distance. For example, by using geometry, the distance between these two hyperplanes is liwli , so ll^' 11 should be minimized. To also prevent data points falling into the margin, the following constraint should be added: for each i either
W ■ X.j ----- h > 1 for X.j of the first class (e.g. gastric cancer) or
W ■ Xj— b " — 1 for X j of the second class (no gastric cancer). This can be rewritten as:
Cj (w■ x b) > 1, for all 1 < i < n. (1)
This can be put together to get the optimization problem: Minimize (in w;
|l w subject to (for any ¾ ? y¾)
Q (W ' X|— h) > 1. The optimization problem presented in the preceding section is difficult to solve because it depends on ||w||, the norm of w, which involves a square root. It is, however, possible to alter the t
equation by substituting ||w|| with 2 ^ Π (the factor of 1/2 being used for mathematical convenience) without changing the solution (the minimum of the original and the modified equation have the same w and b). This is a quadratic programming (QP) optimization problem. More clearly:
1
9. subject to (for any ¾ ~ ^ ? ■ > q ( w■ X — h) > 1. The previous problem may be expressed by means of non-negative Lagrange multipliers a; as min { - ||w||2— o¾ [q (W■ xj— b)— 1] } but this would led to an incorrect result. The reason is the following: suppose that a family of hyperplanes which divide the points can be found; then all ( w ' x* """ """ ^ — Hence the minimum by sending all a; to +00 could be found, and this minimum would be reached for all the members of the family, not only for the best one which can be chosen solving the original problem.
Nevertheless the previous constrained problem can be expressed as
Figure imgf000061_0001
looking for a saddle point. In doing so all the points which can be separated as Lj ( W · Xj— b) — 1 0 ^0 not mar er since the corresponding a; must be set to zero.
This problem can be solved by standard quadratic programming techniques and programs. The solution can be expressed by terms of linear combination of the training vectors as
Figure imgf000061_0002
Only a few a; will be greater than zero. The corresponding ¾ are exactly the support vectors, which lie on the margin and satisfy Li (w " χϊ " ~~ From this it can be derived that the support vectors also satisfy
Figure imgf000061_0003
which allows one to define the offset b. In practice, it is more robust to average over all Nsv support vectors:
Figure imgf000062_0001
Writing the classification rule in its unconstrained dual form reveals that the maximum margin hyperplane and therefore the classification task is only a function of the support vectors, the training data that lie on the margin.
Figure imgf000062_0002
Using the fact, that l!wll ¾! and substituting it can show shown that the dual of the SVM reduces to the following optimization problem:
Maximize (in a; )
L( ) = C¾Qj Q ¾ fc (¾. Xj )
Figure imgf000062_0003
subject to (for any ¾— 1, ,
Ot-i > 0,
and to the constraint from the minimization in b
Figure imgf000062_0004
Here the kernel is defined by * X*■ xj7 x* " x .
The a terms constitute a dual representation for the weight vector in terms of the training
Figure imgf000062_0005
For simplicity reasons, sometimes it is required that the hyperplane passes through the origin of the coordinate system. Such hyperplanes are called unbiased, whereas general hyperplanes not necessarily passing through the origin are called biased. An unbiased hyperplane can be enforced by setting b = 0 in the primal optimization problem. The corresponding dual is identical to the dual given above without the equality constraint
Si
Figure imgf000062_0006
Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning. Here, in addition to the training set Ί), the learner is also given a set
7T = {X*[x* e Mp}*=1 of test examples to be classified. Formally, a transductive support vector machine is defined by the following primal optimization problem:
Minimize (in w; {J-€ )
Figure imgf000063_0001
subject to (for any 1, . . "''and any J ~~ 1 &)
Figure imgf000063_0002
and c*€ {-1, 1}.
Transductive support vector machines were introduced by Vladimir Vapnik in 1998.
SVMs belong to a family of generalized linear classifiers. They can also be considered a special case of Tikhonov regularization. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers.
In 1995, Corinna Cortes and Vladimir Vapnik suggested a modified maximum margin idea that allows for mislabeled examples. If there exists no hyperplane that can split the "yes" and "no" examples, the Soft Margin method will choose a hyperplane that splits the examples as cleanly as possible, while still maximizing the distance to the nearest cleanly split examples. The method introduces slack variables, ¾, which measure the degree of misclassification of the datum x,
Figure imgf000063_0003
The objective function is then increased by a function which penalizes non-zero ξ,, and the optimization becomes a trade off between a large margin, and a small error penalty. If the penalty function is linear, the optimization problem becomes:
Figure imgf000064_0001
subject to (for any ¾" ~ 1 : ' ')
f¾ (w - Xi - 6) > 1 & > 0.
This constraint in (2) along with the objective of minimizing iwlii can be solved using Lagrange multipliers as done above. One has then to solve the following problem
Figure imgf000064_0002
with <¾ ; /¾≥ 0.
The key advantage of a linear penalty function is that the slack variables vanish from the dual problem, with the constant C appearing only as an additional constraint on the Lagrange multipliers. For the above formulation and its huge impact in practice, Cortes and Vapnik received the 2008 ACM Paris Kanellakis Award. Non-linear penalty functions have been used, particularly to reduce the effect of outliers on the classifier, but unless care is taken, the problem becomes non-convex, and thus it is considerably more difficult to find a global solution.
The original optimal hyperplane algorithm proposed by Vladimir Vapnik in 1963 was a linear classifier. However, in 1992, Bernhard Boser, Isabelle Guyon and Vapnik suggested a way to create non-linear classifiers by applying the kernel trick (originally proposed by Aizerman et al.[4] ) to maximum-margin hyperplanes. The resulting algorithm is formally similar, except that every dot product is replaced by a non-linear kernel function. This allows the algorithm to fit the maximum-margin hyperplane in a transformed feature space. The transformation may be non- linear and the transformed space high dimensional; thus though the classifier is a hyperplane in the high-dimensional feature space, it may be non-linear in the original input space.
If the kernel used is a Gaussian radial basis function, the corresponding feature space is a Hilbert space of infinite dimension. Maximum margin classifiers are well regularized, so the infinite dimension does not spoil the results. Some common kernels include, · Polynomial (homogeneous): χϊ ; xj) (xi ' xj)
• Polynomial (inhomogeneous): Λ 3 ' Λ* ■- .■>*
• Radial Basis Function: xi- xj) = exP("""7llxS ^ xjll ) for γ > 0 • Gaussian Radial basis function:
• Hyperbolic tangent: (xi : xj j
Figure imgf000065_0001
every) K > 0 and c < 0
The kernel is related to the transform !rj (xi) by the equation (xi? xj) ~ *iH U *
Figure imgf000065_0002
The value w is also in the transformed space, with ¾ ~~ j °* i 'Ψ (x*) 'Dot products with w for classification can again be computed by the kernel trick, i.e. w " ^(x) ∑ri i-l¾t.j^(x^, x) However, there does not in general exist a value w' such that w ' vKx/— " , X; .
After the W and £ are determined employing the above mentioned methods these can be used for classifying new datasets (e.g. a expression profile of a patient with p miRNA biomarkers and corresponding xi intensity values).
As mentioned above, the method of the present invention is for diagnosing gastric cancer in a human patient. Preferably, the diagnosis comprises (i) determining the occurrence/presence of gastric cancer, (ii) monitoring the course of gastric cancer, (iii) staging of gastric cancer, (iv) measuring the response of a patient with gastric cancer to therapeutic intervention, and/or (v) segmentation of a patient suffering from gastric cancer. Further, the method of the present invention is for prognosis of gastric cancer in a human patient. Preferably, the prognosis comprises (i) identifying of a patient who has a risk to develop gastric cancer, (ii) predicting/estimating the occurrence, preferably the severity of occurrence, of gastric cancer, and/or (iii) predicting the response of a patient with gastric cancer to therapeutic intervention.
Like highlighted above, the reference miRNA expression profiles may be classified using machine learning approaches in order to compute accuracy, specificity, and sensitivity for the diagnosis and/or prognosis of gastric cancer (see experimental section for more details). Examples of miRNA sets (signatures) that performed best for the diagnosis of gastric cancer according to their accuracy, specificity, and sensitivity are sets of miRNAs having SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11 (see Figure 3), having SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16 (see Figure 4), having SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20 (see Figure 5), having SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, and SEQ ID NO: 4 (see Figure 6), having SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, and SEQ ID NO: 6 (see Figure 7), having SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 17, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 7, and SEQ ID NO: 8 (see Figure 8), having SEQ ID NO: 23, SEQ ID NO: 5, SEQ ID NO: 107, SEQ ID NO: 35, SEQ ID NO: 38, SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, and SEQ ID NO: 36 (see Figure 9), having SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, and SEQ ID NO: 15 (see Figure 10), or having SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, SEQ ID NO: 15, SEQ ID NO: 117, SEQ ID NO: 77, SEQ ID NO: 85, SEQ ID NO: 39, and SEQ ID NO: 8 (see Figure 11)
Preferably, the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) stored in a database, e.g. an internet database, a centralized, or a decentralized database. It is also preferred that the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) stored on a data carrier, e.g. electronically data carrier. It is further preferred that the reference (e.g. (average) reference expression profile (data)) and/or the algorithm or mathematical function is (are) comprised in a computer program stored on an electronically data carrier.
In a third aspect, the present invention provides means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention. Thus, for example, the means for diagnosing and/or prognosing of gastric cancer comprise a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consisting of 103 polynucleotides, for detecting a set comprising, essentially consisting of, or consisting of at least 2 miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. It is preferred that said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
Preferably, the means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a solid support, substrate, surface, platform or matrix comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention. Thus, for example, the means for diagnosing and/or prognosing of gastric cancer comprise a solid support, substrate, surface, platform or matrix comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consisting of 103 polynucleotides, for detecting a set comprising, essentially consisting of, or consisting of at least 2 miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consi sting of 103 miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. It is preferred that said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Preferably, the above mentioned polynucleotide(s) is (are) attached to or immobilized on the solid support, substrate, surface, platform or matrix. It is possible to include appropriate controls for non-specific hybridization on the solid support, substrate, surface, platform or matrix.
More preferably, said means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a microarray/biochip comprising a polynucleotide (probe) or a set comprising, essentially consisting of, or consisting of at least two polynucleotides (probes) according to the first aspect of the present invention. Thus, for example, the means for diagnosing and/or prognosing of gastric cancer comprise a microarray/biochip comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consisting of 103 polynucleotides, for detecting a set comprising, essentially consisting of, or consisting of at least 2 miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs, wherein the nucleotide sequence of said miRNA or nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. It is preferred that said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Preferably, the above mentioned polynucleotide(s) is (are) attached to or immobilized on the microarray/biochip. It is possible to include appropriate controls for non-specific hybridization on the microarray/biochip.
The polynucleotide(s) (probe(s)) may also be comprised as polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants in the means for diagnosing and/or prognosing of gastric cancer. For example, polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants may be comprised in the solid support, substrate, surface, platform or matrix, preferably microarray/biochip. Said polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants may be attached or linked to the solid support, substrate, surface, platform or matrix, preferably microarray/biochip. As to the definition of said polynucleotide fragments, polynucleotide variants, or polynucleotide fragment variants and as to the preferred polynucleotide (probe) or sets of polynucleotides (probes), it is referred to the first aspect of the present invention.
The terms "biochip" or "microarray", as used herein, refer to a solid phase comprising an attached or immobilized polynucleotide described herein as probe or a set (plurality) of polynucleotides described herein attached or immobilized as probes. The polynucleotide probes may be capable of hybridizing to a target sequence, such as a complementary miRNA or miRNA* sequence, under stringent hybridization conditions. The polynucleotide probes may be attached or immobilized at spatially defined locations on the solid phase. One or more than one nucleotide (probe) per target sequence may be used. The polynucleotide probes may either be synthesized first, with subsequent attachment to the biochip, or may be directly synthesized on the biochip. The solid phase may be a material that may be modified to contain discrete individual sites appropriate for the attachment or association of the polynucleotide probes and is amenable to at least one detection method. Representative examples of solid phase materials include glass and modified or functionalized glass, plastics (including acrylics, polystyrene and copolymers of styrene and other materials, polypropylene, polyethylene, polybutylene, polyurethanes, Teflon, etc.), polysaccharides, nylon or nitrocellulose, resins, silica or silica-based materials including silicon and modified silicon, carbon, metals, inorganic glasses and plastics. The solid phase may allow optical detection without appreciably fluorescing. The solid phase may be planar, although other configurations of solid phase may be used as well. For example, polynucleotide probes may be placed on the inside surface of a tube, for flow- through sample analysis to minimize sample volume. Similarly, the solid phase may be flexible, such as flexible foam, including closed cell foams made of particular plastics. The solid phase of the biochip and the probe may be modified with chemical functional groups for subsequent attachment of the two. For example, the biochip may be modified with a chemical functional group including, but not limited to, amino groups, carboxyl groups, oxo groups or thiol groups. Using these functional groups, the probes may be attached using functional groups on the probes either directly or indirectly using a linker. The polynucleotide probes may be attached to the solid support by either the 5' terminus, 3' terminus, or via an internal nucleotide. The polynucleotide probe may also be attached to the solid support non-covalently. For example, biotinylated polynucleotides can be made, which may bind to surfaces covalently coated with streptavidin, resulting in attachment. Alternatively, polynucleotide probes may be synthesized on the surface using techniques such as photopolymerization and photolithography. In the context of the present invention, the terms "biochip" and "microarray" are interchangeable used.
The terms "attached" or "immobilized", as used herein, refer to the binding between the polynucleotide and the solid support/phase and may mean that the binding between the polynucleotide probe and the solid support is sufficient to be stable under conditions of binding, washing, analysis and removal. The binding may be covalent or non-covalent. Covalent bonds may be formed directly between the polynucleotide and the solid support or may be formed by a cross linker or by inclusion of specific reactive groups on either the solid support or the polynucleotide, or both. Non-covalent binding may be electrostatic, hydrophilic and hydrophobic interactions or combinations thereof. Immobilization or attachment may also involve a combination of covalent and non-covalent interactions.
More preferably, said means for diagnosing and/or prognosing of gastric cancer comprise, essentially consist of, or consist of a set of beads or microspheres comprising a polynucleotide (probe) or a set comprising at least two polynucleotides (probes) according to the first aspect of the present invention. Thus, for example, the means for diagnosing and/or prognosing of gastric cancer comprise a set of beads or microspheres comprising a polynucleotide (probe) for detecting a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 polynucleotides (probes), preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 polynucleotides, or comprising/consi sting of 103 polynucleotides, for detecting a set comprising, essentially consisting of, or consisting of at least 2 miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs, wherein the nucleotide sequence of said miRNA or nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. It is preferred that said polynucleotide set comprises at least one further polynucleotide (e.g. 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, or 16 polynucleotide(s)) for detecting the above mentioned miRNA set comprising at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80%) sequence identity thereto.
Preferably, the above mentioned polynucleotide(s) is (are) attached to or immobilized on the beads or microspheres, e.g. via a covalent or non-covalent linkage (see above). Preferably, said beads or microspheres are made of a synthetic material, e.g. polystyrene, polyethylene or polypropylene. It is preferred that said beads or microspheres have a mean diameter of between 2 to 20 microns, preferably 4 to 10 microns, most preferably 5 to 7 microns, i.e. 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 microns. It is also preferred that said beads or microspheres are internally dyed, preferably with red and infrared fluorophores. For example, the bead or microsphere setting from Luminex may be used.
In a fourth aspect, the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
(i) means for determining an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs, representative for gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80%> sequence identity thereto, and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier. Preferably, the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
It is preferred that the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
It is more preferred that the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
In preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
Thus, in a preferred embodiment, the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising
(i) means for determining an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, or 102 miRNAs, or comprising/consisting of 103 miRNAs, representative for gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
In another preferred embodiment, the present invention provides a kit for diagnosing and/or prognosing of gastric cancer comprising (i) means for determining an expression profile of a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample, preferably blood cell sample, such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient, wherein the nucleotide sequences of said miRNAs are selected from one or more sets listed in Figure 13, and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
For example, the nucleotide sequences of said miRNAs may be selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more sets listed in Figure 13.
As to further preferred embodiments of miRNA sets and blood samples such as blood cell samples, it is referred to the first and second aspect of the present invention.
Preferably, the above mentioned (miRNA) set comprises at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)), wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
In particularly preferred embodiments, the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the means are not for determining the expression profile of miRNA(s) having (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
Said means may be a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention; means according to the third aspect of the present invention; primers suitable to perform reverse transcriptase reaction and/or real time polymerase chain reaction such as quantitative polymerase chain reaction; and/or means for conducting next generation sequencing.
It is preferred that said kit comprises means according to the third aspect of the present invention. Thus, said kit preferably comprises means for diagnosing and/or prognosis of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect of the present invention, or more preferably comprises means for diagnosing and/or prognosis of gastric cancer, wherein said means comprise a microarray/biochip, or a set of beads or microspheres comprising a polynucleotide or a set comprising at least two polynucleotides according to the first aspect of the present invention.
It is particularly preferred that said kit comprises (ia) a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according to the first aspect of the present invention, and
(ib) optionally means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample, e.g. blood serum, blood plasma, or blood cell sample, and/or means to immobilize the polynucleotide(s) on a solid support or matrix, e.g. biochip,
for determining an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
It is more preferred that said kit comprises
(ia) a solid support, substrate, surface, platform or matrix comprising a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according of the first aspect of the present invention, and
(ib) optionally at least one of the means selected from the group consisting of: means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample, means for input/injection of a blood sample, means for holding the solid support, substrate, platform or matrix comprising the polynucleotide(s) (probe(s)), means for labelling the isolated miRNA (e.g. NTP/biotin-NTP), means to carry out hybridization, means to carry out enzymatic reactions (e.g. exonuclease I and/or Klenow enzyme), means for washing steps, means for detecting the hybridization signal, and means for analysing the detected hybridization signal,
for determining an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Preferably, the above mentioned polynucleotide or the polynucleotides comprised in a set is (are) attached to or immobilized on the solid support, substrate, surface, platform or matrix.
It is most preferred that said kit comprises
(ia) a microarray/biochip comprising a polynucleotide or a set comprising, essentially consisting of, or consisting of at least two polynucleotides according of the first aspect of the present invention, and
(ib) optionally at least one of the means selected from the group consisting of: means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample, means for input/injection of a blood sample, means for holding the microarray/biochip comprising the polynucleotide(s) (probe(s)), means for labelling the isolated miRNA (e.g. NTP/biotin-NTP), means to carry out hybridization, means to carry out enzymatic reactions (e.g. exonuclease I and/or Klenow enzyme), means for washing steps, means for detecting the hybridization signal, and means for analysing the detected hybridization signal,
for determining an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least two miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Preferably, the above mentioned polynucleotide or the polynucleotides comprised in a set is (are) attached to or immobilized on a microarray/biochip.
It is further particularly preferred that said kit comprises
(ia) a miRNA-specific primer for reverse transcription of miRNA in miRNA-specific cDNA for a single miRNA or at least two miRNA-specific primers for reverse transcription of miRNAs in miRNA-specific cDNAs for at least 2 miRNAs, preferably for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or 103, comprised in a set of miRNAs, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, and
(ib) preferably, a primer set comprising a forward primer which is specific for the cDNA obtained from the miRNA and an universal reverse primer for amplifying the cDNA obtained from the miRNA via real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR) for the single cDNA obtained from the miRNA or at least two primer sets comprising a forward primer which is specific for the single cDNA obtained from the miRNA and an universal reverse primer for amplifying the cDNA obtained from the miRNA via real time polymerase chain reaction (RT-PCR) such as real time quantitative polymerase chain reaction (RT qPCR) for at least 2, preferably for at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or 103 cDNAs obtained from the miRNAs comprised in the set of miRNAs, wherein said cDNA is complementary to the nucleotide sequence of the miRNA or said cDNAs are complementary to the nucleotide sequences of the miRNAs selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, and
(ic) optionally at least one of the means selected from the group consisting of: means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample, additional means to carry out the reverse transcriptase reaction (miRNA in cDNA) (e.g. reverse transcriptase (RT) enzyme, puffers, dNTPs, RNAse inhibitor), additional means to carry out real time polymerase chain reaction (RT-PCR) such as real time quantitative PCR (RT qPCR) (e.g. enzymes, puffers, water), means for labelling (e.g. fluorescent label and/or quencher), positive controls for reverse transcriptase reaction and real time PCR, and means for analysing the real time polymerase chain reaction (RT-PCR) result, for determining an expression profile of a miRNA or a set comprising, essentially consisting of, or consisting of at least 2, preferably comprising, essentially consisting of, or consisting of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 ,62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, or 103 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and more preferably for determining an expression profile of at least one further miRNA (e.g. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 miRNA(s)) comprised in said miRNA set, wherein the nucleotide sequence of the at least one further miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
The primer as defined in (ia) above may also be an oligo-dT primer, e.g. if the miRNA comprises a polyA tail (e.g. as a result of a miRNA elongation, for example, subsequent to RNA extraction) or a miRNA specific looped RT primer.
It is also preferred that said kit comprises means (e.g. a setting) for conducting next generation sequencing in order to determine an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. Said kit optionally comprises means selected from the group consisting of: means to collect a blood sample from a human patient, means for conserving the RNA-fraction within the blood probe, and means to extract total RNA (or fractions thereof, e.g. miRNA) from a blood sample.
It is further preferred that said kit comprises means (e.g. a setting or settings) for conducting a real time polymerase chain reaction (RT-PCR), preferably a real time quantitative polymerase chain reaction (RT-qPCR), in order to determine an expression profile of a (single) miRNA or a set comprising, essentially consisting of, or consisting of at least 2 miRNAs representative for gastric cancer in a blood sample such as blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto. The setting for conduction a RT-PCR reaction, preferably a RT-qPCR reaction, may be combined with a setting for conducting a reverse transcription reaction, or may be two separate settings, meaning that both reactions, namely the reverse transcription reaction and RT-PCR reaction, preferably the RT-qPCR reaction, can be run together in one setting or separately in two settings.
As to the definition of the biological sample, miRNA fragment, miRNA variant, or miRNA fragment variant mentioned above and as to the preferred (single) miRNA or sets of miRNAs determined by the means of (i), it is referred to the second aspect of the present invention.
The above mentioned kits may optionally comprise at least one reference and/or algorithm or mathematical function comprised on at least one data carrier. A comparison to the at least one reference may allow for the diagnosis and/or prognosis of gastric cancer and/or an application of the at least one algorithm or mathematical function may allow for the diagnosis and/or prognosis of gastric cancer.
The at least one reference may be any reference which allows for the diagnosis and/or prognosis of gastric cancer, e.g. an indicated value or values, and/or the at least one algorithm or mathematical function may be any algorithm or mathematical function which allows for the diagnosis and/or prognosis of gastric cancer.
It is preferred that said reference is a reference expression profile (data) of at least one subject, preferably the reference is an average expression profile (data) of at least 2 to 40 subjects, more preferably at least 10 to 25 subjects, and most preferably at least 15 to 20 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e. healthy /healthiness), wherein the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
Said reference may be a reference expression profile of at least one subject known to be not affected by gastric cancer (i.e. healthy), or known to be affected by gastric cancer (i.e. diseased).
The term "essentially corresponds (essentially identical)", as used in the context (of the fourth aspect) of the present invention, means that the miRNA(s) which expression profile is determinable by the means of (i) and the miRNA(s) of the reference expression profile may slightly differ in their nucleotide sequence, whereas said difference is so marginal that it may still allow for the diagnosis and/or prognosis of gastric cancer. Preferably, the nucleotide sequence(s) of the miRNA(s) which expression profile is determinable by the means of (i) and the nucleotide sequence(s) of the miRNA(s) of the reference expression profile differ in 1 to 5, more preferably in 1 to 3, and most preferably in 1 to 2 nucleotides, i.e. in 1, 2, 3, 4, or 5 nucleotides. It is preferred that said difference resides in 1 to 5, more preferably 1 to 3, and most preferably 1 to 2 nucleotide mutations, i.e. 1, 2, 3, 4, or 5 nucleotide mutations (e.g. substitutions, additions, insertions, and/or deletions).
Preferably, it is indicated in which type of blood sample (e.g. whole blood or blood fraction such as blood cells, serum or plasma) and/or from which (control) subject(s) (e.g. gender and/or age or stage of life), the (average) reference expression profile (data), which is (are) provided with the kit, has (have) been determined. It is preferred that the expression profile (data) of the human patient will be determined in the same type of blood sample such as blood cell sample and/or will be obtained from a human patient of the same gender and/or similar age or stage of life.
Preferably, the algorithm or mathematical function is obtained from a reference expression profile (data) of at least one subject, preferably of at least 2 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with one known clinical condition which is gastric cancer, or which is no gastric cancer (i.e. healthy/healthiness), wherein the reference expression profile is the profile of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i), or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
It is also preferred that the algorithm or mathematical function is obtained from reference expression profiles (data) of at least two subjects, preferably of at least 3 to 40 subjects, more preferably of at least 10 to 25 subjects, and most preferably of at least 15 to 20 subjects, i.e. of at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 subjects, with at least two known clinical conditions (only one known clinical condition per subject), preferably at least 2 to 5, more preferably at least 2 to 4 (i.e. at least 2, 3, 4, or 5) known clinical conditions, which are gastric cancer and any other known clinical condition(s), wherein the reference expression profiles are the profiles of a (single) miRNA that has a nucleotide sequence that essentially corresponds (is essentially identical), preferably that corresponds (is identical), to the nucleotide sequence of the miRNA which expression profile is determinable by the means of (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that essentially correspond (are essentially identical), preferably that correspond (are identical), to the nucleotide sequences of the miRNAs which expression profile is determinable by the means of (i).
It is preferred that the two known clinical conditions are gastric cancer and no gastric cancer.
It is further preferred that the algorithm or mathematical function is obtained using a machine learning approach. The inventors of the present invention surprisingly found that the application of a machine learning approach (e.g. t-test, AUC, support vector machine, hierarchical clustering, or k-means) leads to the obtainment of an algorithm or a mathematical function that is trained by the reference expression profile(s) (data) mentioned above (see also second aspect of the present invention).
As mentioned above, the kit may optionally comprise at least one reference and/or algorithm or mathematical function comprised on at least one data carrier. Said data carrier may be a graphically data carrier such as an information leaflet or an information sheet (e.g. for comparing tested single reference miRNA biomarkers with the expression profile data of a human patient to be diagnosed and/or prognosed) or an electronically data carrier such as a floppy disk, a compact disk (CD), or a digital versatile disk (DVD) (e.g. for comparing tested sets of miRNA biomarkers with the expression profile data of a human patient to be diagnosed and/or prognosed). Said reference, preferably said (average) reference expression profile (data), and/or said algorithm or mathematical function may further be comprised in a computer program which is saved on an electronically data carrier.
The kit may alternatively comprise an access code comprised on a data carrier which allows the access to a database, e.g. an internet database, a centralized, or a decentralized database, where (i) the reference, preferably the (average) reference expression profile (data), and/or the algorithm or mathematical function is (are) comprised, or (ii) where a computer program comprising the reference, preferably the (average) reference expression profile (data), and/or the algorithm or mathematical function can be downloaded.
More than one reference and/or more than one algorithm or mathematical function, e.g. 2, 3, 4, 5, or more references and/or algorithms or mathematical functions, may be comprised on one or more data carrier(s). For example, the kit may comprise (i) references (data), preferably (average) reference expression profile(s) (data), which may be comprised on an information leaflet and/or on a compact disk (CD), e.g. two expression profiles (data), for example, one from a subject(s) known to be healthy and one from a subject(s) known to have gastric cancer, and/or (ii) algorithms or mathematical functions, which may be comprised on a compact disc (CD) and/or on a digital versatile disk (DVD), e.g. two algorithms or mathematical functions, for example, one obtained from a reference expression profile of a subject(s) known to be healthy and one obtained from a reference expression profile of a subject(s) known to have gastric cancer.
Said two or more references (e.g. reference expression profiles) and/or algorithms or mathematical functions may be accessible by on or more access codes as mentioned above which may alternatively be comprised in the kit (see above).
As to the reference or preferred embodiments of the reference (e.g. reference expression profile(s) (data)) and as to the algorithm or mathematical function, it is also referred to the second aspect of the present invention (see above).
In a further aspect, the present invention relates to a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) providing a polynucleotide according to the first aspect of the present invention for detecting a miRNA representative for gastric cancer in a blood sample from a human patient or a set comprising at least two polynucleotides according to the first aspect of the present invention for detecting a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient,
wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) preferably selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto,
(ii) using the polynucleotide(s) provided in (i) for determining an miRNA expression profile in a blood sample from a human patient with an unknown clinical condition,
(iii) comparing said expression profile to a reference and/or applying an algorithm or a mathematical function to said expression profile,
(iv) diagnosing or prognosing the clinical condition of the human patient on the basis of said comparison and/or said application.
Preferably, the blood sample is a whole blood sample or a blood fraction sample. More preferably, the blood fraction sample is a blood cell (also known as hemopoietic cell) sample, a blood plasma sample, or a blood serum sample.
It is preferred that the blood cells are erythrocytes, leukocytes and/or thrombocytes, i.e. (i) erythrocytes, (ii) leukocytes, (iii) thrombocytes, (iv) erythrocytes and leukocytes, (v) erythrocytes and thrombocytes, (vi) leukocytes and thrombocytes, and (vii) erythrocytes, leukocytes and thrombocytes.
It is more preferred that the leukocytes are granulocytes cells and/or lymphoid cells. It is most preferred that the granulocytes are neutrophil, eosinophil (or acidophil) and/or basophil cells. It is most preferred that the lymphoid cells are lymphocytes and/or monocytes. The lymphocytes and monocytes belong to the class of peripheral blood mononuclear cells (PBMCs).
In preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction, an erythrocyte containing blood cell fraction and/or a thrombocyte containing blood cell fraction. In more preferred embodiments, the blood cell sample is a leukocyte containing blood cell fraction.
Thus, in a preferred embodiment, the present invention relates to a method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) providing a polynucleotide according to the first aspect of the present invention for detecting a miRNA representative for gastric cancer in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient or a set comprising at least two polynucleotides according to the first aspect of the present invention for detecting a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient,
wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) preferably selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto,
(ii) using the polynucleotide(s) provided in (i) for determining an miRNA expression profile in a blood cell sample such as an erythrocyte, a leukocyte and/or a thrombocyte sample from a human patient with an unknown clinical condition,
(iii) comparing said expression profile to a reference and/or applying an algorithm or a mathematical function to said expression profile,
(iv) diagnosing or prognosing the clinical condition of the human patient on the basis of said comparison and/or said application.
The term "human patient with an unknown clinical condition" refers to a human patient, which may suffer from gastric cancer (i.e. diseased patient), or which may not suffer from gastric cancer (i.e. healthy patient). It is also possible to determine, whether the human patient to be prognosed will develop the above mentioned disease as the inventors of the present invention surprisingly found that miRNAs representative for gastric cancer are already present in the blood sample such as blood cell sample before gastric cancer occurs or during the early stage of gastric cancer. It should be noted that a human patient that is diagnosed as being healthy, i.e. not suffering from gastric cancer, may possibly suffer from another disease not tested/known.
As to the definition of said miRNA fragments, miRNA variants, or miRNA fragment variants, it is referred to the second aspect of the present invention. As to the reference and preferred embodiments of said reference, the algorithm or mathematical function, the preferred embodiments of the blood sample such as blood cell sample and the definition of an expression profile, it is referred to the second aspect of the present invention.
In another further aspect, the present invention relates to a miRNA or a set comprising at least two miRNAs as biomarker(s) for the diagnosis and/or prognosis of gastric cancer, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103. In a preferred embodiment, the present invention relates to a set comprising at least two miRNAs as biomarkers for the diagnosis and/or prognosis of gastric cancer, wherein the nucleotide sequences of said miRNAs are selected from one or more sets listed in Figure 13. In another preferred embodiment, the above mentioned miRNA set comprises at least one further miRNA, wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to 119.
For diagnosis and/or prognosis of gastric cancer, a blood sample such as a whole blood sample or a blood fraction sample (e.g. a blood cell, plasma or serum sample) from a human patient is preferably used. As to further preferred embodiments of miRNA sets and blood samples such as blood cells samples, it is referred to the first and second aspect of the present invention.
In particularly preferred embodiments, the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 115 and/or SEQ ID NO: 116. In particularly more preferred embodiments, the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 4 and/or SEQ ID NO: 5. In other particularly more preferred embodiments, the miRNA(s) as biomarker(s) for the diagnosis and/or prognosis of gastric cancer do/does not have (a) nucleotide sequence(s) according to SEQ ID NO: 115 and/or SEQ ID NO: 116.
It is also particularly preferred that the blood sample such s blood cell sample as mentioned in the aspects above does not comprise isolated exosomes.
In summary, the present invention is composed of the following:
1. A polynucleotide for detecting a miRNA or a set comprising at least two polynucleotides for detecting a set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
2. The polynucleotide or set comprising the polynucleotides of item 1, wherein (i) the polynucleotide is complementary to the miRNA according to item 1, or the polynucleotides comprised in the set are complementary to the miRNAs comprised in the set according to item 1,
(ii) the polynucleotide is a fragment of the polynucleotide according to (i), or the polynucleotides comprised in the set are fragments of the polynucleotides comprised in the set according to (i), or
(iii) the polynucleotide has at least 80% sequence identity to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or the polynucleotides comprised in the set have at least 80% sequence identity to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
3. The polynucleotides of items 1 or 2, wherein the set comprises at least 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 miRNAs, or comprises 103 miRNAs, and wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103.
4. The polynucleotides of items 1 to 3, wherein
(i) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID
NO: 1 and SEQ ID NO: 2,
(ii) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 3,
(iii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 4,
(iv) the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 5,
(v) the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 7,
(vi) the nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 10,
(vii) the nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 15,
(viii) the nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 20,
(ix) the nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 25, (x) the nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 30,
(xi) the nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 35,
(xii) the nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 40,
(xiii) the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 50,
(xiv) the nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 60,
(xv) the nucleotide sequences of the at least 70 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 70,
(xvi) the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 80,
(xvii) the nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 90,
(xviii) the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 100, or
(xxiv) the nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
5. The polynucleotides of items 1 to 3, wherein
(i) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID
NO: 2 and SEQ ID NO: 3,
(ii) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID
NO: 3 and SEQ ID NO: 4,
(iii) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7,
(iv) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10,
(v) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13,
(vi) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16,
(vii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID
NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20, (viii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24, or
(ix) the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID
NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30.
6. The polynucleotides of items 1 to 5, wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
7. The polynucleotide of items 1 or 2, wherein the nucleotide sequence of the miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID
NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10.
8. A method for diagnosing and/or prognosing of gastric cancer comprising the steps of:
(i) determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and
(ii) comparing said expression profile to a reference, wherein the comparison of said expression profile to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or applying an algorithm or a mathematical function to said expression profile, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
9. The method of item 8, wherein the set comprises at least 3, 4, 5, 7, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 miRNAs, or comprises 103 miRNAs, and wherein the nucleotide sequences of said miRNAs are selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
10. The method of items 8 or 9, wherein
(i) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID
NO: 1 and SEQ ID NO: 2,
(ii) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 3,
(iii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 4, (iv) the nucleotide sequences of the at least 5 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 5,
(v) the nucleotide sequences of the at least 7 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 7,
(vi) the nucleotide sequences of the at least 10 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 10,
(vii) the nucleotide sequences of the at least 15 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 15,
(viii) the nucleotide sequences of the at least 20 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 20,
(ix) the nucleotide sequences of the at least 25 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 25,
(x) the nucleotide sequences of the at least 30 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 30,
(xi) the nucleotide sequences of the at least 35 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 35,
(xii) the nucleotide sequences of the at least 40 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 40,
(xiii) the nucleotide sequences of the at least 50 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 50,
(xiv) the nucleotide sequences of the at least 60 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 60,
(xv) the nucleotide sequences of the at least 70 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 70,
(xvi) the nucleotide sequences of the at least 80 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 80,
(xvii) the nucleotide sequences of the at least 90 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 90,
(xviii) the nucleotide sequences of the at least 100 miRNAs comprised in the set have SEQ ID
NO: 1 to SEQ ID NO: 100, or
(xix) the nucleotide sequences of the 103 miRNAs comprised in the set have SEQ ID NO: 1 to SEQ ID NO: 103.
11. The method of items 8 or 9, wherein
(i) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID
NO: 2 and SEQ ID NO: 3, (ii) the nucleotide sequences of the at least 2 miRNAs comprised in the set have SEQ ID NO: 3 and SEQ ID NO: 4,
(iii) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 5, SEQ ID NO: 6, and SEQ ID NO: 7,
(iv) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10,
(v) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 11, SEQ ID NO: 12, and SEQ ID NO: 13,
(vi) the nucleotide sequences of the at least 3 miRNAs comprised in the set have SEQ ID
NO: 14, SEQ ID NO: 15, and SEQ ID NO: 16,
(vii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID
NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20,
(viii) the nucleotide sequences of the at least 4 miRNAs comprised in the set have SEQ ID
NO: 21, SEQ ID NO: 22, SEQ ID NO: 23, and SEQ ID NO: 24, or
(ix) the nucleotide sequences of the at least 6 miRNAs comprised in the set have SEQ ID
NO: 25, SEQ ID NO: 26, SEQ ID NO: 27, SEQ ID NO: 28, SEQ ID NO: 29, and SEQ ID NO: 30.
12. The method of items 8 to 1 1, wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
13. The method of item 8, wherein the nucleotide sequence of the miRNA is selected from the group consisting of SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, and SEQ ID NO: 10.
14. A method of items 8 to 13, wherein in step (i) a polynucleotide or a set comprising at least two polynucleotides according to items 1 to 7 is used for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient.
15. The method of items 8 to 14, wherein the expression profile of the miRNA(s) representative for gastric cancer is determined by nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy or any combination thereof.
16. The method of item 15, wherein
(i) the nucleic acid hybridization is performed using a microarray/biochip, or using in situ hybridization, and/or (ii) the nucleic acid amplification is performed using real-time PCR.
17. The method of items 8 to 16, wherein the algorithm or mathematical function is obtained from a reference expression profile of at least one subject with one known clinical condition which is gastric cancer, or which is no gastric cancer, wherein the reference expression profile is the profile of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
18. The method of items 8 or 16, wherein the algorithm or mathematical function is obtained from reference expression profiles of at least two subjects with at least two known clinical conditions (only one known clinical condition per subject) which are gastric cancer and any other known clinical condition(s), wherein the reference expression profiles are the profiles of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
19. The method of item 18, wherein the two known clinical conditions are gastric cancer and no gastric cancer.
20. The method of items 8 to 19, wherein the algorithm or mathematical function is obtained using a machine learning approach.
21. The method of item 20, wherein the machine learning approach involves the following steps:
(i) inputting the reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer, and
(ii) computing an algorithm or a mathematical function based on said reference expression profile(s) that is suitable to distinguish between the (likely) clinical condition of gastric cancer and any other (likely) clinical condition(s), preferably the clinical condition of no gastric cancer, or to decide if the clinical condition of gastric cancer or no gastric cancer is present or will likely be present in said patient.
22. The method of items 8 to 21, wherein the reference is a reference expression profile of at least one subject with one known clinical condition which is gastric cancer, or which is no gastric cancer, wherein the reference expression profile is the profile of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i). 23. The method of items 8 to 22, wherein said blood sample is a whole blood sample or a blood fraction sample.
24. The method of item 23, wherein the blood fraction sample is a blood cell sample, a blood plasma sample or a blood serum sample.
25. The method of items 8 to 24, wherein the diagnosis comprises
(i) determining the occurrence/presence of gastric cancer,
(ii) monitoring the course of gastric cancer,
(iii) staging of gastric cancer,
(iv) measuring the response of a patient with gastric cancer to therapeutic intervention, and/or
(v) segmentation of a patient suffering from gastric cancer.
26. The method of items 8 to 24, wherein the prognosis comprises
(i) identifying of a patient who has a risk to develop gastric cancer,
(ii) predicting/estimating the occurrence, preferably the severity of occurrence, of gastric cancer, and/or
(iii) predicting the response of a patient with gastric cancer to therapeutic intervention.
27. Means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to items 1 to 7.
28. The means of item 27, wherein said means comprise a biochip, or a set of beads or microspheres comprising a polynucleotide or a set comprising at least two polynucleotides according to items 1 to 7.
29. A kit for diagnosing and/or prognosing of gastric cancer comprising
(i) means for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto; and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
30. The kit of item 29, wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
31. The kit of items 29 or 30, wherein said kit comprises the means of items 27 or 28. BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1: MiRNAs for diagnosis or prognosis of gastric cancer. Experimental data obtained for analysis of miRNAs according to SEQ ID NO: 1 to SEQ ID NO: 119. Experimental details: SEQ ID NO: sequence identification number, miRNA: identifier of the miRNA according to miRBase, median gl : median intensity obtained from microarray analysis for healthy controls, median g2: median intensity obtained from microarray analysis for individuals with gastric cancer, qmedian: ratio of median gl/median g2, logqmedian: log of qmedian, ttest rawp: p-value obtained when applying t-test, ttest adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment, AUC: Area under the curve, limma rawp: p-value obtained when applying limma-test, limma adjp: adjusted p-value in order to reduce false discovery rate by Benjamini-Hochberg adjustment.
Figure 2: List of miRNAs including sequences (SEQ ID NO: 1 to SEQ ID NO: 119) found to be relevant for diagnosis and/or prognosis of gastric cancer.
Figure 3: Diagnostic miRNA-signature employing SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11; with VI = accuracy (85%), V2 = specificity (92%), V3 = sensitivity (78%).
Figure 4: Diagnostic miRNA-signature employing SEQ ID NO: 6, SEQ ID NO: 7, SEQ
ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, and SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, and SEQ ID NO: 16; with VI = accuracy (84%), V2 = specificity (92%), V3 = sensitivity (77%).
Figure 5: Diagnostic miRNA-signature employing SEQ ID NO: 11, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 16, SEQ ID NO: 17, SEQ ID NO: 18, SEQ ID NO: 19, and SEQ ID NO: 20; with VI = accuracy (81%), V2 = specificity (93%), V3 = sensitivity (69%).
Figure 6: Diagnostic miRNA-signature employing SEQ ID NO: 104, SEQ ID NO: 105, SEQ ID NO: 1, SEQ ID NO: 106, SEQ ID NO: 2, SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 1 10, and SEQ ID NO: 4; with VI = accuracy (85%), V2 = specificity (100%), V3 = sensitivity (77%).
Figure 7: Diagnostic miRNA-signature employing SEQ ID NO: 107, SEQ ID NO: 108, SEQ ID NO: 16, SEQ ID NO: 109, SEQ ID NO: 110, SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, and SEQ ID NO: 6; with VI = accuracy (91%), V2 = specificity (100%), V3 = sensitivity (84%). Figure 8: Diagnostic miRNA-signature employing SEQ ID NO: 4, SEQ ID NO: 3, SEQ ID NO: 111, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 17, SEQ ID NO: 112, SEQ ID NO: 113, SEQ ID NO: 7, and SEQ ID NO: 8; with VI = accuracy (88%), V2 = specificity (100%), V3 = sensitivity (76%).
Figure 9: Diagnostic miRNA-signature employing SEQ ID NO: 23, SEQ ID NO: 5, SEQ
ID NO: 107, SEQ ID NO: 35, SEQ ID NO: 38, SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, and SEQ ID NO: 36; with VI = accuracy (93%), V2 = specificity (93%), V3 = sensitivity (93%).
Figure 10: Diagnostic miRNA-signature employing SEQ ID NO: 4, SEQ ID NO: 50, SEQ ID NO: 37, SEQ ID NO: 6, SEQ ID NO: 48, SEQ ID NO: 36, SEQ ID NO: 1 18, SEQ ID NO: 89, SEQ ID NO: 112, and SEQ ID NO: 15; with VI = accuracy (89%), V2 = specificity (92%), V3 = sensitivity (85%).
Figure 11: Diagnostic miRNA-signature employing SEQ ID NO: 36, SEQ ID NO: 118, SEQ ID NO: 89, SEQ ID NO: 112, SEQ ID NO: 15, SEQ ID NO: 117, SEQ ID NO: 77, SEQ ID NO: 85, SEQ ID NO: 39, and SEQ ID NO: 8; with VI = accuracy (89%), V2 = specificity (93%), V3 = sensitivity (85%).
Figure 12: Diagram describing the general approach for determining miRNA signatures for use as biomarkers in the diagnosis and/or prognosis of gastric cancer.
Figure 13: Summary of preferred diagnostic miRNA-signatures (sets) with their specific accuracy, sensitivity and specificity. Acc = accuracy, Spec = specificity, Sens = sensitivity, NO. = Signature number.
EXAMPLES
The Examples are designed in order to further illustrate the present invention and serve a better understanding. They are not to be construed as limiting the scope of the invention in any way.
Materials and Methods Samples
All blood donors participating in this study have given their written informed consent. The patient samples have been prepared at Heidelberg University. Besides the samples of diseased patients, also control samples were provided. MiRNA extraction and microarray screening
Blood of patients has been extracted as previously described [1]. In brief, 2.5 to 5 ml blood was extracted in PAXgene Blood RNA tubes (BD, Franklin Lakes, New Jersey USA) and centrifuged at 5000 x g for 10 min at room temperature. The miRNeasy kit (Qiagen GmbH, Hilden) was used to isolate total RNA including miRNA from the resuspended pellet according to manufacturer's instructions. The eluted RNA was stored at -70°C.
All samples were analyzed with the Geniom RT Analyzer (febit biomed GmbH, Heidelberg, Germany) at the in-house genomic service department using the Geniom Biochip miRNA Homo sapiens. Each array contains 7 replicates of about 863 miRNAs and miRNA star sequences as annotated in the Sanger miRBase releases 12.0 to 14. On-chip sample labeling with biotin was carried out by microfluidic-based primer extension labeling of miRNAs (MPEA [2]). Following hybridization for 16 hours at 42°C, the biochip was washed and a program for signal enhancement was carried out. All steps from sample loading to miRNA detection were processed without any manual intervention and inside the machine. The detection pictures were evaluated using the Geniom Wizard Software. For each feature, the median signal intensity was calculated. Following a background correction step, the median of the 7 replicates of each miRNA was computed. To normalize the data across different arrays, quantile normalization [3] was applied and all further analyses were carried out using the normalized and background subtracted intensity values. Since the miRBase has been upgraded twice in the past year from version 12.0 to 14, we used for the final data analysis the 863 miRNAs that were consistently present in all three versions. Statistical analysis
To estimate the value of single miRNAs, t-tests (unpaired, two-tailed) were carried out. The resulting p-values have been adjusted for multiple testing by Benjamini-Hochberg adjustment [4, 5].
In addition to this single biomarker analysis, we performed supervised classification of samples by using Support Vector Machines (SVM [6]) as implemented in the R el 071 package [7]. As parameters, we evaluated different kernel methods including linear, polynomial (degree 2 to 5), sigmoid and radial basis function kernels. The cost parameter was sampled from 0.01 to 10 in decimal powers. As subset selection technique, a filter approach based on t-test was carried out. In each iteration, the s miRNAs with lowest p-values were computed on the training set in each fold of a standard 10-fold cross validation, where s was sampled in regular intervals between 2 and 300. The respective subset was used to train the SVM and to carry out the prediction of the test samples in the cross validation. To compute probabilities for classes instead of class labels, a regression approach based on the output of the support vectors has been applied. To test for overtraining, non-parametric permutation tests have been applied. All computations were carried out using R [7], a freely available language for statistical tasks
REFERENCES Keller A, Leidinger P, Borries A, Wendschlag A, Wucherpfennig F, Scheffler M, Huwer H, Lenhof FIP, Meese E: miRNAs in lung cancer - studying complex fingerprints in patient's blood cells by microarray experiments. BMC Cancer 2009, 9:353.
Vorwerk S, Ganter K, Cheng Y, Hoheisel J, Stahler PF, Beier M: Microfluidic-based enzymatic on-chip labeling of miRNAs. N Biotechnol 2008, 25(2-3): 142-149.
Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics
2003, 19(2): 185-193.
Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res 2001, 125(l-2):279-284.
Hochberg Y: A sharper bonferroni procedure for multiple tests of significance. Biometrica 1988, 75: 185-193.
Vapnik V: The nature of statistical learning theory., 2nd edition edn. New York: Spinger; 2000.
Team R: R: A Language and Environment for Statistical Computing. In. Vienna: R Foundation for Statistical Computing; 2008.

Claims

A polynucleotide for detecting a miRNA or a set comprising at least two polynucleotides for detecting a set comprising at least two miRNAs for diagnosing and/or prognosing of gastric cancer in a blood cell sample from a human patient, wherein the nucleotide sequence of the miRNA or the nucleotide sequences of the miRNAs comprised in the set is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103.
The polynucleotide or set comprising the polynucleotides of claim 1, wherein
(i) the polynucleotide is complementary to the miRNA according to claim 1, or the polynucleotides comprised in the set are complementary to the miRNAs comprised in the set according to claim 1,
(ii) the polynucleotide is a fragment of the polynucleotide according to (i), or the polynucleotides comprised in the set are fragments of the polynucleotides comprised in the set according to (i), or
(iii) the polynucleotide has at least 80% sequence identity to the polynucleotide sequence of the polynucleotide according to (i) or polynucleotide fragment according to (ii), or the polynucleotides comprised in the set have at least 80% sequence identity to the polynucleotide sequences of the polynucleotides comprised in the set according to (i) or polynucleotide fragments comprised in the set according to (ii).
The polynucleotides of claims 1 or 2, wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119.
A method for diagnosing and/or prognosing of gastric cancer comprising the steps of: (i) determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto, and (ii) comparing said expression profile to a reference, wherein the comparison of said expression profile to said reference allows for the diagnosis and/or prognosis of gastric cancer, and/or applying an algorithm or a mathematical function to said expression profile, wherein the application of said algorithm or mathematical function to said expression profile allows for the diagnosis and/or prognosis of gastric cancer.
The method of claim 4, wherein the set comprises at least one further miRNA, and wherein the nucleotide sequence of said miRNA is selected from the group consisting of SEQ ID NO: 104 to SEQ ID NO: 119, a fragment thereof, and a sequence having at least 80% sequence identity thereto.
The method of claims 4 or 5, wherein the algorithm or mathematical function is obtained from a reference expression profile of at least one subject with one known clinical condition which is gastric cancer, or which is no gastric cancer, wherein the reference expression profile is the profile of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or is the profile of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
The method of claims 4 or 5, wherein the algorithm or mathematical function is obtained from reference expression profiles of at least two subjects with at least two known clinical conditions which are gastric cancer and any other known clinical condition(s), wherein the reference expression profiles are the profiles of a miRNA that has a nucleotide sequence that corresponds to the nucleotide sequence of the miRNA of step (i) or are the profiles of a set comprising at least two miRNAs that have nucleotide sequences that correspond to the nucleotide sequences of the miRNAs of step (i).
The method of claim 7, wherein the two known clinical conditions are gastric cancer and no gastric cancer.
The method of claims 4 to 8, wherein the algorithm or mathematical function is obtained using a machine learning approach.
10. The method of claim 9, wherein the machine learning approach involves the following steps:
(i) inputting the reference expression profile(s) of (a) subject(s) with the known clinical condition of gastric cancer and/or with any other known clinical condition(s), preferably with the known clinical condition of no gastric cancer, and
(ii) computing an algorithm or a mathematical function based on said reference expression profile(s) that is suitable to distinguish between the (likely) clinical condition of gastric cancer and any other (likely) clinical condition(s), preferably the clinical condition of no gastric cancer, or to decide if the clinical condition of gastric cancer or no gastric cancer is present or will likely be present in said patient.
11. Means for diagnosing and/or prognosing of gastric cancer comprising a polynucleotide or a set comprising at least two polynucleotides according to claims 1 to 3.
12. The means of claim 11, wherein said means comprise
(i) a biochip,
(ii) a set of beads, or
(iii) microspheres
comprising a polynucleotide or a set comprising at least two polynucleotides according to claims 1 to 3.
13. A kit for diagnosing and/or prognosing of gastric cancer comprising
(i) means for determining an expression profile of a miRNA or a set comprising at least two miRNAs representative for gastric cancer in a blood cell sample from a human patient, wherein the nucleotide sequence of said miRNA or the nucleotide sequences of said miRNAs is (are) selected from the group consisting of SEQ ID NO: 1 to SEQ ID NO: 103, a fragment thereof, and a sequence having at least 80% sequence identity thereto; and
(ii) optionally at least one reference and/or algorithm or mathematical function comprised on at least one data carrier.
14. The kit of claim 13, wherein said kit comprises the means of claims 11 or 12.
The polynucleotide(s) of claims 1 to 3, the method of claims 4 to 10, or the kit of claims 13 or 14, wherein the blood cell is an erythrocyte, a leukocyte and/or a thrombocyte.
PCT/EP2011/062326 2010-07-20 2011-07-19 Complex mirna sets as novel biomarkers for gastric cancer WO2012010584A1 (en)

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