WO2023128419A1 - Procédé de dépistage du cancer colorectal et des polypes colorectaux ou des adénomes avancés et son application - Google Patents

Procédé de dépistage du cancer colorectal et des polypes colorectaux ou des adénomes avancés et son application Download PDF

Info

Publication number
WO2023128419A1
WO2023128419A1 PCT/KR2022/020461 KR2022020461W WO2023128419A1 WO 2023128419 A1 WO2023128419 A1 WO 2023128419A1 KR 2022020461 W KR2022020461 W KR 2022020461W WO 2023128419 A1 WO2023128419 A1 WO 2023128419A1
Authority
WO
WIPO (PCT)
Prior art keywords
seq
nos
genes
primers
colorectal cancer
Prior art date
Application number
PCT/KR2022/020461
Other languages
English (en)
Korean (ko)
Inventor
양효석
장연희
황다솜
Original Assignee
주식회사 이노제닉스
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020220163338A external-priority patent/KR20230104517A/ko
Application filed by 주식회사 이노제닉스 filed Critical 주식회사 이노제닉스
Publication of WO2023128419A1 publication Critical patent/WO2023128419A1/fr

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer

Definitions

  • the present invention relates to a method for screening colon cancer and colon polyps or advanced adenomas and a kit used for the method.
  • Colorectal cancer is a malignant tumor that occurs in the colon and rectum, which constitute the large intestine.
  • colorectal cancer is the third most common cancer among all cancers, with 1.9 million new cases worldwide, and 935,000 people died from colorectal cancer, ranking third in the mortality rate due to cancer. It is a major cancer
  • the 5-year relative survival rate of colorectal cancer is significantly lowered according to the degree of progression of the cancer. In Stage I, the 5-year relative survival rate reaches 90%, but in Stage IV, the 5-year survival rate rapidly decreases to 14%. However, only 37% of cases are diagnosed at Stage I because most of them do not show symptoms in Stage I. Therefore, early diagnosis of colorectal cancer through regular screening is important in increasing the survival rate of colorectal cancer.
  • the selection-cancerization process refers to the process in which normal epithelial cells of the colon progress to colorectal cancer via non-advanced adenoma and advanced adenoma.
  • Progressive adenomas are adenomas that are highly likely to develop into colorectal cancer. Histologically, they are larger than 10 mm, contain more than 25% villous components, have high-grade dysplastic lesions, or have three or more adenomas.
  • a 13-year follow-up study revealed that subjects with advanced adenomas were 2.7 times more likely to develop colorectal cancer and 2.6 times more likely to die from colorectal cancer than subjects with control and non-advanced adenomas. Therefore, in order to lower the incidence of colorectal cancer, it is important to detect and remove advanced adenomas at the stage.
  • colonoscopy and fecal occult blood test are performed for the diagnosis of colorectal cancer.
  • Colonoscopy can examine the entire large intestine at once and can remove adenomas or some early cancers found during the examination. Through a meta-analysis, it has been reported that colonoscopy reduces both the incidence and mortality of colorectal cancer by about 70%.
  • Colonoscopy requires a bowel preparation process before examination, and the degree of bowel preparation has a very important effect on the accuracy and quality of the examination.
  • intestinal perforation which can occur as a complication, appears with a frequency of about 0.09%. Therefore, as a regular colorectal cancer screening test targeting a large population, the number of doctors that can be performed is limited, the patient's compliance may be poor due to pain during examination, and discomfort in pretreatment, and complications may occur relatively often.
  • Fecal occult blood test is a method of diagnosis by detecting bleeding from a mass in the large intestine in the stool. It is a non-invasive method, has no special side effects, is relatively easy to implement, and has the advantage of low cost. Compared to those who did not perform fecal occult blood testing, those who underwent fecal occult blood testing had a 10-40% lower mortality rate.
  • the sensitivity of the fecal occult blood test for colorectal cancer and advanced adenoma was 56-74% and 23-31%, respectively, and the specificity was 90-95%. Since bleeding from colon tumors is often intermittent, the accuracy of the test may vary depending on whether or not the specimen is properly collected, and the compliance of test subjects in using a stool sample may be low.
  • the present invention solves the above problems and has been made by the above necessity, and an object of the present invention is to use a quantitative reverse transcription polymerization reaction based on a blood sample that is relatively easy to extract and an artificial intelligence prediction model produced through the result. It is to provide an information provision method for developing a molecular diagnostic test method for colorectal cancer and advanced adenoma or colon polyp with high sensitivity and specificity.
  • Another object of the present invention is a molecule of colorectal cancer and colorectal polyps or advanced adenomas with high sensitivity and specificity using an artificial intelligence prediction model produced through quantitative reverse transcription polymerization based on a blood sample that is relatively easy to extract and the result. It is to provide a composition for diagnostic testing.
  • the present invention is a colorectal cancer group and colorectal cancer high-risk group containing primers or probes involved in measuring the relative expression levels of IL1B, LTF, TNFSF13B, ITIH4, CXCL11, MAPK6, GK, and MCAM genes as active ingredients , It provides a composition capable of distinguishing between a low-risk group and a normal group.
  • the primers and probes are SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, SEQ ID NOs: 40 to 42, SEQ ID NOs: 43 to 45, and sequences It is preferably composed of the sequence shown in Nos. 56 to 58, but is not limited thereto.
  • the normal group is a case in which there are no lesions in the colon through colonoscopy
  • the low-risk group is a case in which there are less than three low-risk adenomas
  • the high-risk group is a case in which three or more low-risk adenomas are present through colonoscopy, and a high-risk group is present. It is preferable to include one or more adenomas and carcinoma in situ, but is not limited thereto.
  • the present invention is a colorectal cancer group containing primers or probes involved in measuring the relative expression levels of CES1, IL1B, TNFSF13B, ITIH4, CXCL11, MAPK6, GK, and MCAM genes as an active ingredient, and colorectal cancer high-risk, low-risk and normal A composition capable of classifying groups is provided.
  • the primers and probes are SEQ ID NOs: 4 to 6, SEQ ID NOs: 7 to 9, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 31 to 33, SEQ ID NOs: 34 to 36, SEQ ID NOs: 40 to 42, SEQ ID NOs: 43 to 45 and SEQ ID NOs: 56 to 58, but is not limited thereto.
  • the present invention provides a composition capable of distinguishing a high-risk group from a colorectal cancer high-risk group, a low-risk group, and a normal group, comprising primers or probes involved in measuring the relative expression levels of IL1B, LTF, TNFSF13B, ITIH4, CXCL11, and MAPK6 genes as an active ingredient to provide.
  • the primers and probes preferably consist of the sequences shown in SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, and SEQ ID NOs: 40 to 42, but therefor Not limited.
  • the present invention a) measuring the relative expression levels of IL1B, LTF, TNFSF13B, ITIH4, CXCL11, MAPK6, GK, and MCAM genes using primers and probes through polymerase chain reaction,
  • the primers and probes used in a) are SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, Consisting of the sequences set forth in SEQ ID NOs: 40 to 42, SEQ ID NOs: 43 to 45, and SEQ ID NOs: 56 to 58,
  • the primers and probes used in b) are SEQ ID NOs: 4 to 6, SEQ ID NOs: 7 to 9, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 31 to 33, SEQ ID NOs: 34 to 36, and SEQ ID NOs: 40 to 40. 42, SEQ ID NOs: 43 to 45, and SEQ ID NOs: 56 to 58,
  • the primers and probes used in c) are the sequences shown in SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, and SEQ ID NOs: 40 to 42. It is preferable that it has been made, but it is not limited thereto.
  • the present invention provides a) primers or probe sets involved in measuring the relative expression levels of IL1B, LTF, TNFSF13B, ITIH4, CXCL11, MAPK6, GK, and MCAM genes,
  • a kit for screening for colorectal cancer and colorectal polyps including primers or probe sets involved in measuring the relative expression levels of IL1B, LTF, TNFSF13B, ITIH4, CXCL11, and MAPK6 genes is provided.
  • the primers and probes used in a) are SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, Consists of the sequences set forth in SEQ ID NOs: 40 to 42, SEQ ID NOs: 43 to 45, and SEQ ID NOs: 56 to 58;
  • the primers and probes used in b) are SEQ ID NOs: 4 to 6, SEQ ID NOs: 7 to 9, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 31 to 33, SEQ ID NOs: 34 to 36, and SEQ ID NOs: 40 to 40. 42, SEQ ID NOs: 43 to 45, and SEQ ID NOs: 56 to 58,
  • the primers and probes used in c) are the sequences shown in SEQ ID NOs: 7 to 9, SEQ ID NOs: 13 to 15, SEQ ID NOs: 16 to 18, SEQ ID NOs: 22 to 24, SEQ ID NOs: 34 to 36, and SEQ ID NOs: 40 to 42. It is preferable that it has been made, but it is not limited thereto.
  • the present invention is CCR1, CES1, IL1B, ITGA2, LTF, TNFSF13B, PTGES, ITIH4, TUG1, NME1, PTGS2, CXCL11, MAPK6, GK, KRT19, EpCAM, MCAM, PPARG, ANKHD1-EIF4EBP3, GPR15, MMP23B, TAS2R10,
  • a composition for screening for colorectal cancer and advanced adenoma comprising primers or probes involved in measuring the relative expression levels of TYMS, FOXA2, MKi67, ERBB2, NPTN, SNAI2, TERT and VIM genes as active ingredients.
  • the primers and probes preferably consist of the sequences shown in SEQ ID NOs: 1 to 91, but all mutant sequences that achieve the effect of the present invention through one or more substitutions, deletions, additions, etc. to the sequences are also included in the scope of the present invention.
  • the present invention is CCR1, CES1, IL1B, ITGA2, LTF, TNFSF13B, PTGES, ITIH4, TUG1, NME1, PTGS2, CXCL11, MAPK6, GK, KRT19, EpCAM, MCAM, PPARG, ANKHD1-EIF4EBP3, GPR15, MMP23B, TAS2R10,
  • a kit for screening for colorectal cancer and advanced adenoma comprising primers or probes involved in measuring the relative expression levels of TYMS, FOXA2, MKi67, ERBB2, NPTN, SNAI2, TERT and VIM genes as active ingredients.
  • the primers and probes preferably consist of the sequences shown in SEQ ID NOs: 1 to 91, but all mutant sequences that achieve the effect of the present invention through one or more substitutions, deletions, additions, etc. to the sequences are also included in the scope of the present invention.
  • the present invention is CCR1, CES1, IL1B, ITGA2, LTF, TNFSF13B, PTGES, ITIH4, TUG1, NME1, PTGS2, CXCL11, MAPK6, GK, KRT19, EpCAM, Measuring the relative expression levels of MCAM, PPARG, ANKHD1-EIF4EBP3, GPR15, MMP23B, TAS2R10, TYMS, FOXA2, MKi67, ERBB2, NPTN, SNAI2, TERT and VIM genes
  • a method for providing information for prediction or diagnosis of colorectal cancer or advanced adenoma is provided.
  • the expression level is performed using primers and probes, and the primers and probes preferably consist of the sequences shown in SEQ ID NOs: 1 to 91, but one or more substitutions, deletions, or additions to the sequences are preferred. All mutant sequences that achieve the effect of the present invention through the like are also included in the scope of the present invention.
  • the subject when one or more of the following 1) to 3) is confirmed, the subject is determined to have colorectal cancer:
  • CCR1, CES1, GK, IL1B, KRT19, LTF, PPARG, PTGES, PTGS2, TAS2R10, TNFSF13B and TYMS genes were compared with the corresponding genes or those encoded by the genes in normal control subject samples. If expression level changes compared to protein levels are seen;
  • ANKHD1-EIF4EBP3, CCR1, MCAM, MMP23B, TAS2R10, TNFSF13B, TUG1 and TYMS genes or protein levels encoded by the genes are compared with the level of the gene or the protein encoded by the gene in a sample of an individual with advanced adenoma to show a change in expression level.
  • the following 1) or 2) is additionally confirmed to determine the pathological characteristics of an individual having colorectal cancer:
  • CXCL11 and PTGS2 genes or the protein levels encoded by the genes are compared with the corresponding genes or the protein levels encoded by the genes of an individual sample having a low TNM stage colorectal cancer, and the change in expression level if present, the subject is judged to have high TNM stage colorectal cancer; or
  • the present invention provides blood gene marker combinations (Table 1) for the purpose of screening for colorectal cancer and advanced adenoma composed of the following gene groups.
  • the present invention provides an artificial intelligence algorithm-based classification model for colorectal cancer and advanced adenoma screening tests prepared by substituting the expression levels of the 30 markers.
  • primer and probe sequences are provided to indicate the relative expression levels of corresponding biomarkers in blood.
  • the present invention provides an artificial intelligence prediction model for colorectal cancer, advanced adenoma, and/or colorectal polyps screening test prepared by substituting the expression levels of the 18 markers.
  • Total RNA A method for isolating a commonly used full-length RNA (Total RNA) and a method for synthesizing cDNA therefrom can be performed through a known method, and a detailed description of this process can be found in Joseph Sambrook et al., Molecular Cloning, A Laboratory Manual. , Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (2001); and Noonan, K.F. etc. are disclosed and may be incorporated by reference into the present invention.
  • the primers of the present invention can be chemically synthesized using the phosphoramidite solid support method, or other well-known methods. Such nucleic acid sequences can also be modified using a number of means known in the art.
  • Non-limiting examples of such modifications include methylation, "capping", substitution of one or more homologues of a natural nucleotide, and modifications between nucleotides, such as uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphotriesters, phosphoramidates, carbamates, etc.) or to charged associations (eg phosphorothioates, phosphorodithioates, etc.).
  • a nucleic acid can contain one or more additional covalently linked moieties, such as proteins (eg, nucleases, toxins, antibodies, signal peptides, ly-L-lysine, etc.), intercalants (eg, acridine, psoralen, etc.). ), chelating agents (eg, metals, radioactive metals, iron, oxidizing metals, etc.), and alkylating agents.
  • proteins eg, nucleases, toxins, antibodies, signal peptides, ly-L
  • a nucleic acid sequence of the present invention may also be modified with a label capable of providing, directly or indirectly, a detectable signal.
  • labels include radioactive isotopes, fluorescent molecules, and biotin.
  • the amplified target sequence (CCR1, GAPDH gene, etc.) may be labeled with a detectable labeling substance.
  • the label material may be a material that emits fluorescence, phosphorescence, chemiluminescence, or radioactivity, but is not limited thereto.
  • the labeling material may be fluorescein, phycoerythrin, rhodamine, lissamine, Cy-5 or Cy-3.
  • a radioactive isotope such as 32P or 35 S
  • the amplification product is synthesized and radioactive is incorporated into the amplification product, so that the amplification product can be radioactively labeled.
  • One or more oligonucleotide primer sets used to amplify the target sequence may be used.
  • Labeling is performed by various methods commonly practiced in the art, such as the nick translation method, the random priming method (Multiprime DNA labeling systems booklet, “Amersham” (1989)), and the kination method (Maxam & Gilbert, Methods in Enzymology, 65:499 (1986)).
  • the label provides a signal that can be detected by fluorescence, radioactivity, chromometry, gravimetry, X-ray diffraction or absorption, magnetism, enzymatic activity, mass analysis, binding affinity, hybridization radiofrequency, nanocrystals.
  • the expression level is measured at the mRNA level through RT-PCR.
  • novel primer pairs and fluorescently labeled probes that specifically bind to the CCR1 and GAPDH genes are required, and in the present invention, corresponding primers and probes specified by specific nucleotide sequences can be used, but are not limited thereto , Anything that can specifically bind to these genes to provide a detectable signal to perform RT-PCR can be used without limitation.
  • FAM and Quen (Quencher) mean fluorescent dyes.
  • the RT-PCR method applied to the present invention may be performed through a known process commonly used in the art.
  • the step of measuring the mRNA expression level may be used without limitation as long as it is a method capable of measuring the normal mRNA expression level, and may be performed through radioactivity measurement, fluorescence measurement, or phosphorescence measurement depending on the type of probe label used, but is limited thereto. It doesn't work.
  • the fluorescence measurement method is to label the 5'-end of the primer with Cy-5 or Cy-3 and perform real-time RT-PCR to label the target sequence with a detectable fluorescent label. And the fluorescence thus labeled can be measured using a fluorescence meter.
  • the radioactivity measurement method is to add a radioactive isotope such as 32 P or 35 S to the PCR reaction solution during RT-PCR to label the amplification product, and then use a radioactivity measurement instrument, for example, a Geiger counter or Radioactivity can be measured using a liquid scintillation counter.
  • a radioactivity measurement instrument for example, a Geiger counter or Radioactivity can be measured using a liquid scintillation counter.
  • a fluorescence-labeled probe is attached to the PCR product amplified through the RT-PCR to emit fluorescence of a specific wavelength, and at the same time as amplification, the fluorescence of the genes of the present invention is measured in the fluorescence meter of the PCR device.
  • the mRNA expression level is measured in real time, and the measured value is calculated and visualized through a PC, so that the inspector can easily check the expression level.
  • the screening kit may be a kit for diagnosing colorectal cancer and colorectal polyps, characterized in that it includes essential elements necessary for carrying out a reverse transcription polymerase reaction.
  • the reverse transcription polymerase reaction kit may include each primer pair specific for the gene of the present invention.
  • the primer is a nucleotide having a sequence specific to the nucleic acid sequence of each marker gene, and may have a length of about 7 bp to 50 bp, more preferably about 10 bp to 30 bp.
  • reverse transcription polymerase reaction kits include a test tube or other suitable container, reaction buffer (with varying pH and magnesium concentration), deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNAse, RNAse inhibitors, DEPC-water, sterile water, and the like.
  • reaction buffer with varying pH and magnesium concentration
  • dNTPs deoxynucleotides
  • enzymes such as Taq-polymerase and reverse transcriptase, DNAse, RNAse inhibitors, DEPC-water, sterile water, and the like.
  • kit of the present invention may further include a user guide describing optimal reaction performance conditions.
  • the guide is a printed matter that explains how to use the kit, eg, how to prepare a buffer solution, suggested reaction conditions, and the like.
  • the guide may include a brochure in the form of a pamphlet or leaflet, a label affixed to the kit, and instructions on the surface of the package containing the kit.
  • the guide may include information disclosed or provided through an electronic medium such as the Internet.
  • the term "information provision method for diagnosing colon cancer and colon polyps" is a preliminary step for diagnosis and provides objective basic information necessary for diagnosis of cancer, and clinical judgment or opinion of a doctor is excluded.
  • the term "information provision method for screening for colorectal cancer and advanced adenoma" is a preliminary step for diagnosis and provides objective basic information necessary for diagnosis of cancer, and clinical judgment or opinion of a doctor is excluded.
  • primer refers to a short nucleic acid sequence having a short free 3-terminal hydroxyl group capable of forming base pairs with a complementary template and serving as a starting point for copying the template strand.
  • Primers can initiate DNA synthesis in the presence of reagents for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in an appropriate buffer and temperature.
  • the primers of the present invention are sense and antisense nucleic acids having sequences of 7 to 50 nucleotides specific to each marker gene.
  • a primer may incorporate additional features that do not alter the basic properties of the primer that serve as the starting point of DNA synthesis.
  • probe is a single-stranded nucleic acid molecule and contains a sequence complementary to a target nucleic acid sequence.
  • real-time RT-PCR refers to reverse transcription of RNA into complementary DNA (cDNA) using reverse transcriptase and using cDNA as a template containing target primers and labels It is a molecular biological polymerization method that amplifies a target using a target probe and quantitatively detects a signal generated from the label of the target probe on the amplified target at the same time.
  • a data mining method capable of diagnosing colon cancer and colon polyps through information learning can be used for diagnosing or predicting colon cancer and colon polyps of the present invention, and in particular, it can be effectively improved through AI analysis. Therefore, a method capable of measuring the relative expression levels of diagnostic markers for colon cancer and colon polyps and/or an AI analysis method may be preferably used in the method for diagnosing or predicting colon cancer and colon polyps of the present invention.
  • AI analysis when AI analysis is used for colorectal cancer and colorectal polyps prediction models, various interpretable models can be used without limitation, and linear regression, logistic regression, neural network analysis, decision tree, decision rule, rule fit, support vector machine A model such as is applicable without limitation, and in a preferred embodiment of the present invention, logistic regression analysis, decision tree, neural network analysis and support vector machine are used in particular.
  • the prediction model of the present invention may include a colorectal cancer and colon polyps diagnosis unit, a classification unit, and a weighting unit.
  • the colon-related disease classification unit may perform a process of classifying colon cancer and colon polyps using a neural network as a classifier, and the weighting unit assigns a weight to the classification result, thereby detecting colorectal cancer and colon polyps can be screened.
  • Neural network analysis refers to a system that constructs one or more layers to make a decision based on a plurality of data.
  • the input layer is a layer that inputs relative expression level information of gene markers as data into a neural network analysis model
  • the output layer determines the presence or absence of colorectal cancer and colon polyp disease patients based on various input information. It is a layer that gives results that can be done.
  • the hidden layer is a layer that proceeds with the process of determining whether or not there is a patient by assigning weights to various criteria (gene mutation information).
  • the method for predicting colorectal cancer and colorectal polyps using an AI analysis technique estimates a neural network analysis model having the number of hidden nodes using an MLP neural network.
  • the neural network model with the highest accuracy estimated from each model is determined as the final neural network model for colorectal disease prediction.
  • the AI analysis may be composed of an input layer, a hidden layer, and an output layer, and the neural network analysis model through the neural network analysis step may be a neural network model having several hidden nodes in several hidden layers.
  • the present invention is helpful in screening for colorectal cancer and colorectal polyps by substituting the expression patterns of genetic markers expressed in blood into an artificial intelligence algorithm using blood samples that are relatively easy to extract. can give
  • the present invention can help screen for colorectal cancer and advanced adenoma by using a relatively easy-to-extract blood sample and substituting the expression patterns of genetic markers expressed in blood into an artificial intelligence algorithm.
  • Figure 2 is the number of samples by group in which the experiment and analysis were performed
  • 3 is a primer probe nucleotide sequence prepared for detecting a genetic biomarker
  • Figure 9 shows the results of t-test statistical analysis for each group using all samples (selection of biomarkers for Model A production),
  • Figure 11 shows the results of t-test statistical analysis by group using negative samples in Model A and B (selection of biomarkers for Model C production),
  • Model 15 is a schematic diagram of the final result of sequentially applying Models A, B, and C;
  • 16 is a process of building an artificial intelligence algorithm-based classification model and verifying model performance
  • Examples 1 to 5 are for screening colon cancer and colon polyps, and 18 gene markers [C-C motif chemokine receptor 1 (CCR1), Carboxylesterase 1 (CES1), Interleukin 1 beta (IL1B), Integrin alpha 2 ( ITGA2), Lactotransferrin (LTF), Tumor necrosis factor superfamily 13b (TNFSF13B), Prostaglandin E synthase (PTGES), Inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), Taurine upregulated gene1 (TUG1), Nucleoside diphosphate kinase 1 (NME1) ), Prostaglandin-endoperoxide synthase 2 (PTGS2), C-X-C motif chemokine 11 (CXCL11), Mitogen-activated protein kinase 6 (MAPK6), Glycerol kinase (GK), Keratin 19 (KRT19), Epithelial cell adhesion molecule (EpCAM), Melanoma
  • Examples 6 to 10 are for diagnosis of colorectal cancer and its precancerous stage, and five new markers ( ANKHD1-EIF4EBP3 Readthrough (ANKHD1-EIF4EBP3), G Protein-Coupled Receptor 15 (GPR15) , Matrix Metallopeptidase 23B (MMP23B), Taste 2 Receptor Member 10 (TAS2R10), and Thymidylate Synthetase (TYMS)] were added to perform experiments on 23 genetic markers,
  • the number of samples analyzed was 112 in the colorectal cancer group, 178 in the advanced adenoma group, 104 in the non-advanced adenoma group, and 203 in the control group.
  • Examples 11 to 15 are for colorectal cancer and advanced adenoma screening, and 7 new markers (Forkhead box A2 (FOXA2), Marker Of proliferation Ki-67 (MKi67), Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2), Neuroplastin (NPTN), Snail family transcriptional repressor 2 (SNAI2), Telomerase reverse transcriptase (TERT) and Vimentin (VIM)) were added and experiments were performed on 30 genetic markers. , The number of samples analyzed was 148 in the colorectal cancer group, 197 in the advanced adenoma group, and 143 in the control group.
  • FOXA2 Formhead box A2
  • Ki67 Marker Of proliferation Ki-67
  • ERBB2 Receptor Tyrosine Kinase 2 ERBB2
  • NPTN Neuroplastin
  • SNAI2 Snail family transcriptional repressor 2
  • TERT Telomerase reverse transcriptase
  • VAM Viment
  • Circulating tumor cells may exist in the blood in colorectal cancer or advanced adenoma, a precursor of colorectal cancer, and 7 genes ( FOXA2, MKi67, MUC1, NPTN, SNAI2, TERT, VIM ) were used as targets, and the relative expression levels of the corresponding genes were compared by group using blood from normal, advanced adenoma, and colorectal cancer groups.
  • 7 genes FOXA2, MKi67, MUC1, NPTN, SNAI2, TERT, VIM
  • the relative expression level (2 - ⁇ Cq ) of the target gene was calculated using the Cq value of the target gene using the Cq value of the GAPDH gene.
  • the fold change value was obtained from the relative expression level ratio of the normal group to the colorectal cancer group, and the p -value of the difference between the two groups was obtained through Student's t -test analysis.
  • the fold change value was calculated as the relative expression level ratio of the normal group to the advanced glandular group, and the p -value of the difference between the two groups was obtained through Student's t -test analysis.
  • the fold change value was calculated as the ratio of the relative expression level of the advanced adenoma group to the colorectal cancer group, and the p -value of the difference between the two groups was obtained through Student's t-test analysis.
  • the relative expression levels of FOXA2, MKi67, MUC1, NPTN, SNAI2, TERT, and VIM genes in the advanced adenoma group and colorectal cancer group compared to the normal group showed a statistically significant difference with a p -value of 0.05 or less.
  • a statistically significant difference was confirmed with a p -value of 0.05 or less (Table 2).
  • Table 2 is a comparison of differences in relative expression between groups. Accordingly, 3) genes that were significant in distinguishing between colorectal cancer and advanced adenoma in the study (Examples 11-15) and a total of 30 genes to which the above 7 genes were added. was used to construct a classification model for the purpose of screening for colorectal cancer and advanced adenoma.
  • Table 3 is a list of primer and probe sequences for all 30 markers used in the present invention.
  • Table 3 shows the base sequences of each primer and probe used in the present invention.
  • high-risk groups including those with 3 or more low-risk adenomas, 1 or more high-risk adenomas, and those with carcinoma in situ
  • Total RNA is isolated from a blood sample collected with a Tempus tube using the Tempus blood RNA isolation kit (Applied Biosystems®).
  • Example 3 Construction of cDNA from isolated total RNA and real-time PCR
  • ABSI thermocycler
  • Real time PCR reaction was performed using CFX96 (Biorad), and the reaction temperature conditions are as follows. After 95°C, 3 minutes, 95°C, 3 seconds - 60°C, 30 seconds were repeated 40 times. Each time the annealing process (60 ° C, 30 seconds) was performed, a process of measuring fluorescence was added to measure the fluorescence value that increased for each number of times.
  • Table 3 shows the base sequences of each primer and probe used in the present invention.
  • the relative expression level (2 - ⁇ Cq ) of the target gene is calculated using the Cq value of the target gene.
  • Example 5 Production of predictive model for diagnosis of colon cancer and colon polyps
  • Genetic biomarkers for substitution in the diagnosis prediction model for colorectal cancer and colon polyps are selected, and the relative expression levels of the selected genetic biomarkers are substituted to produce a diagnosis prediction model for colon cancer and colon polyps.
  • the SPSS statistical analysis package was used, and for the production of colorectal cancer and colorectal polyps diagnosis prediction models by substituting the relative expression levels of the selected genetic biomarkers. Statistical analysis was performed using the R package.
  • colorectal cancer and colorectal polyps diagnosis prediction models was performed by decision tree (DT), logistic regression (LR), neural network (NN), and support vector machine (SVM), but is not limited thereto.
  • the artificial intelligence prediction model is created by substituting the results of a training set composed of a part of the total sample results. After constructing a validation set with samples not included in the training set, the accuracy of the model built with the training set is verified by substituting the results of the validation set. In this case, accuracy means how accurate the prediction of the prediction model is.
  • a total of four types of models (DT, NR, NN, and SVM) were produced, and the results using the training set and validation set were repeated 1000 times.
  • the model with the highest accuracy confirmed by the Validation set appears as the final result.
  • the type with the highest sensitivity or specificity of the total set (491 in total) including all samples was selected.
  • Model A is constructed by substituting the relative expression levels of a total of 8 corresponding genetic markers.
  • an SVM model was selected that differentiates the colorectal cancer group from the high-risk/low-risk/normal group with a sensitivity of 92.9% and a specificity of 65.0%.
  • Model A has high sensitivity to distinguish between the colorectal cancer group, but up to 40% of the remaining groups are classified as colorectal cancer groups. Therefore, a model that can distinguish between the colorectal cancer group and the rest of the groups should be created again using samples that are positive in model A.
  • Model B is produced by substituting the relative expression levels of a total of 9 corresponding genetic markers.
  • an SVM model was selected that differentiates the colorectal cancer group from the high-risk/low-risk/normal group with a sensitivity of 94.9% and a specificity of 87.9%.
  • models A and B include a high-risk group that requires colonoscopy, a model that distinguishes the high-risk group from the low-risk group/normal group must be created.
  • Model C is constructed by substituting the relative expression levels of a total of six corresponding gene markers.
  • an SVM model was selected that distinguished the high-risk group from the low-risk group/normal group with a sensitivity of 91.3% and specificity of 81.9%.
  • the biomarkers used in the models A, B and C are as follows.
  • Table 4 shows the classification of subjects and the number of samples according to the results of colonoscopy.
  • Total RNA is isolated from a blood sample collected with a Tempus tube using the Tempus blood RNA isolation kit (Applied Biosystems®).
  • Example 8 cDNA construction and qPCR from isolated total RNA
  • a thermocycler Applied Biosystems
  • THUNDERBIRD® Probe qPCR Mix (TOYOBO), Forward / Reverse Primer, and 1 uL of Probe (10 pmole/uL), add 2 ⁇ L of synthesized cDNA, and add ultrapure water to make the final volume 20 ⁇ l. Mix.
  • the qPCR reaction was performed using CFX96 (Biorad), and the reaction temperature conditions were as follows. After 95°C, 3 minutes, 95°C, 3 seconds - 60°C, 30 seconds were repeated 40 times. Each time the annealing process (60 ° C, 30 seconds) was performed, a process of measuring fluorescence was added to measure the fluorescence value that increased for each number of times. A constant fluorescence value was set as the threshold, and the Cq value, which is the number of cycles at the time of reaching the threshold, was derived.
  • the relative expression level (2 - ⁇ Cq) of the target gene is calculated using the Cq value of the target gene.
  • the list of genes targeted is as follows (Table 5).
  • Table 5 is a list of target blood genetic markers
  • Example 10 Establishment of a classification model for the purpose of screening for colorectal cancer and advanced adenoma by substituting the relative expression level of the target gene
  • An artificial intelligence algorithm-based classification model was constructed using the H2O package (version 3.32.1.3) of Statistical R software (version 3.6.3).
  • the production of colorectal cancer and advanced adenoma diagnosis prediction models was based on deep neural network (DNN), generalized linear model (GLM), and random forest (RF) algorithms, and additionally several types of models (GLM, RF, DNN, GBM, stacked ensemble (SE)), but is not limited thereto.
  • an artificial intelligence algorithm-based classification model that can distinguish between a normal group and a colorectal cancer group and an advanced cancer group was constructed, and the performance of the built model was evaluated using the test set. do.
  • a 5-fold cross-validation technique is applied so that the training set is divided into 5 areas to learn the model and at the same time verify the performance of the model using each area to provide a high-performance model. wanted to build.
  • the performance of the artificial intelligence classification model was judged through the AUROC and AUPRC values of the training set and test set based on the AUROC and AUPRC values, which are representative performance indicators of the classification model. Among them, the model with the best performance was selected based on the performance of the new test set that was not used for model learning.
  • the AUROC and AUPRC values of the DNN, GBM, and RF models built based on each algorithm and the SE model built through AutoML are as follows (Table 6). As a result, the AUROC and AUPRC indicators were the highest in the SE model based on the test set.
  • Model training set Test set AUROC AUPRC AUROC AUPRC DNN 0.87 0.88 0.75 0.73 GLM 0.78 0.79 0.75 0.71 RF 0.80 0.79 0.76 0.76 SE 1.00 1.00 0.80 0.80
  • Table 6 shows AUROC and AUPRC performance indicators in the training set and test set. As a result, as shown in Table 7, the sensitivity for classifying the colorectal cancer group was 89.3% and the sensitivity for classifying the advanced adenoma group was 74.5%. The specificity to distinguish the control group was 72.0%.
  • Table 7 shows the sensitivity and specificity results for each group of the SE model.
  • Table 8 shows the classification of subjects and the number of specimens according to colonoscopy results.
  • Total RNA is isolated from a blood sample collected with a Tempus tube using the Tempus blood RNA isolation kit (Applied Biosystems®).
  • Example 13 cDNA construction and qPCR from isolated total RNA
  • a thermocycler Applied Biosystems
  • THUNDERBIRD® Probe qPCR Mix (TOYOBO), Forward / Reverse Primer, and 1 uL of Probe (10 pmole/uL), add 2 ⁇ L of synthesized cDNA, and add ultrapure water to make the final volume 20 ⁇ l. Mix.
  • the qPCR reaction was performed using CFX96 (Biorad), and the reaction temperature conditions were as follows. After 95°C, 3 minutes, 95°C, 3 seconds - 60°C, 30 seconds were repeated 40 times. Each time the annealing process (60 ° C, 30 seconds) was performed, a process of measuring fluorescence was added to measure the fluorescence value that increased by number of times. A constant fluorescence value was set as the threshold, and the Cq value, which is the number of cycles at the time of reaching the threshold, was derived.
  • the relative expression level (2 -*?*Cq ) of the target gene is calculated using the Cq value of the target gene.
  • the relative expression amount ratio of the colorectal cancer group compared to the normal group the relative expression amount ratio of the advanced glandular group compared to the normal group, and the relative expression amount ratio of the colorectal cancer group compared to the advanced glandular group were calculated and shown in the table below. can be expressed as (Table 9).
  • Table 9 compares the relative expression of 30 genes between groups.
  • Example 15 Establishment of a classification model for the purpose of screening colorectal cancer and advanced adenoma by substituting the relative expression level of target genes
  • An artificial intelligence algorithm-based classification model was constructed using the H2O package (version 3.32.1.3) of Statistical R software (version 3.6.3).
  • the production of colorectal cancer and advanced adenoma diagnosis prediction models was based on Deep neural network (DNN), Generalized linear model (GLM), Gradient boosting machine (GBM), and Random forest (RF) algorithms, and additionally several types of models (GLM, RF, DNN, GBM, stacked ensemble (SE)) was performed by grafting Automated machine learning (AutoML) method to build a model suitable for data, but is not limited thereto.
  • an artificial intelligence algorithm-based classification model that can distinguish between a normal group and a colorectal cancer group and an advanced cancer group was constructed, and the performance of the built model was evaluated using the test set.
  • FIG. 12 When building a model using a training set, a 5-fold cross-validation technique is applied so that the training set is divided into 5 areas to learn the model and at the same time verify the performance of the model using each area to provide a high-performance model. It was intended to build (FIG. 16).
  • the performance of the artificial intelligence classification model was judged through the AUROC and AUPRC values of the training set and test set based on the AUROC and AUPRC values, which are representative performance indicators of the classification model. Among them, the model with the best performance was selected based on the performance of the new test set that was not used for model learning.
  • the AUROC and AUPRC values of the DNN, GBM, GLM, and RF models built based on each algorithm and the SE model built through AutoML are as follows (Table 9). As a result, the AUC and AUPRC indicators were the highest based on the test set in the GBM model and the SE model built through AutoML.
  • Model training set Test set AUROC AUPRC AUROC AUPRC GLM 0.93 0.98 0.92 0.98 DNN 0.95 0.98 0.91 0.97 RF 0.93 0.98 0.96 0.99 GBM 1.00 1.00 0.97 0.99 SE 1.00 1.00 0.97 0.99
  • Table 10 shows the results of the AUROC and AUPRC indicators in the training and test sets for each model, the test set results for each group of the GBM model and SE model.
  • the sensitivity for distinguishing the colorectal cancer group was 94.6%
  • the sensitivity for distinguishing the advanced adenoma group was 97.5%
  • the specificity for distinguishing the normal group was 80.6% (Table 11).
  • the sensitivity was 91.9%
  • the sensitivity to distinguish the advanced adenoma group was 95.1%
  • the specificity to distinguish the normal group was 80.6% (Table 12). Therefore, the GBM model showing higher sensitivity was finally selected.
  • Table 11 shows the sensitivity and specificity results for each group of the GBM model.
  • Table 12 shows the sensitivity and specificity results for each group of the SE model.
  • colorectal cancer or advanced adenoma a precursor of colorectal cancer, circulating tumor cells may exist in the blood, and 10 genes ( EpCAM, ERBB2, FOXA2, KRT19, MCAM, MKi67, NPTN, SNAI2, TERT, VIM ) as a target, the relative expression level by group was calculated, and an artificial intelligence algorithm-based model was constructed to distinguish colorectal cancer or advanced adenoma from the normal group.
  • Table 13 is the classification of subjects and the number of samples according to the results of colonoscopy
  • Total RNA is isolated from a blood sample collected with a Tempus tube using the Tempus blood RNA isolation kit (Applied Biosystems®).
  • a thermocycler Applied Biosystems
  • THUNDERBIRD®Probe qPCR Mix (TOYOBO), Forward / Reverse Primer, and 1 uL of Probe (10 pmole/uL), add 2 ⁇ L of synthesized cDNA, and add ultrapure water to make the final volume 20 ⁇ l. Mix.
  • the qPCR reaction was performed using CFX96 (Biorad), and the reaction temperature conditions were as follows. After 95°C 3 minutes, 95°C 3 seconds - 60°C 30 seconds were repeated 40 times. Each time the annealing process (60 ° C, 30 seconds) was performed, a process of measuring fluorescence was added to measure the fluorescence value that increased for each number of times. A constant fluorescence value was set as the threshold, and the Cq value, which is the number of cycles at the time of reaching the threshold, was derived.
  • the relative expression level (2 - ⁇ Cq ) of the target gene is calculated using the Cq value of the target gene.
  • the list of genes targeted is as follows (Table 14).
  • An artificial intelligence algorithm-based classification model was constructed using the H2O package (version 3.32.1.3) of Statistical R software (version 3.6.3).
  • the production of colorectal cancer and advanced adenoma diagnosis prediction models was based on Deep neural network (DNN), Generalized linear model (GLM), Gradient boosting machine (GBM), and Random forest (RF) algorithms, and additionally several types of models (GLM, RF, DNN, GBM, stacked ensemble (SE)) was performed by grafting Automated machine learning (AutoML) method to build a model suitable for data, but is not limited thereto.
  • an artificial intelligence algorithm-based classification model that can distinguish between a normal group and a colorectal cancer group and an advanced cancer group was constructed, and the performance of the built model was evaluated using the test set. do.
  • a 5-fold cross-validation technique is applied so that the training set is divided into 5 areas to learn the model and at the same time verify the performance of the model using each area to provide a high-performance model. wanted to build.
  • the performance of the artificial intelligence classification model was judged through the AUROC and AUPRC values of the training set and test set based on the AUROC and AUPRC values, which are representative performance indicators of the classification model. Among them, the model with the best performance was selected based on the performance of the new test set that was not used for model learning.
  • the AUROC and AUPRC values of the DNN, GBM, and RF models built based on each algorithm and the GBM model built through AutoML are as follows (Table 3). As a result, the AUROC and AUPRC indicators were the highest in the GBM model based on the test set.
  • Model training set Test set AUROC AUPRC AUROC AUPRC GLM 0.91 0.96 0.86 0.96 DNN 0.99 1.00 0.92 0.97 RF 0.90 0.96 0.94 0.98 GBM 1.00 1.00 0.94 0.98 GBM (AutoML) 0.98 0.99 0.91 0.97
  • Table 15 shows AUROC and AUPRC performance indicators in the training set and test set.
  • the sensitivity to distinguish the colorectal cancer group was 78.4%
  • the sensitivity to distinguish the advanced adenoma group was 88.9%
  • the specificity to distinguish the normal group was 80.6%.
  • Table 16 shows the sensitivity and specificity results for each group of the GBM model.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Immunology (AREA)
  • Organic Chemistry (AREA)
  • Molecular Biology (AREA)
  • Pathology (AREA)
  • Analytical Chemistry (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Microbiology (AREA)
  • Urology & Nephrology (AREA)
  • Physics & Mathematics (AREA)
  • Biotechnology (AREA)
  • Genetics & Genomics (AREA)
  • Oncology (AREA)
  • Hospice & Palliative Care (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Biochemistry (AREA)
  • Hematology (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Cell Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biophysics (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

La présente invention concerne un procédé de dépistage du cancer colorectal et des polypes colorectaux et des adénomes avancés et une application associée, et fournit un procédé de dépistage du cancer colorectal et des polypes colorectaux utilisant 18 marqueurs génétiques de la présente invention et une application associée, ainsi qu'un procédé de dépistage du cancer colorectal et des adénomes avancés, utilisant 30 marqueurs génétiques de la présente invention et une application associée. La présente invention peut aider à dépister le cancer colorectal, les polypes colorectaux ou les adénomes avancés en utilisant des échantillons sanguins relativement faciles à extraire et en appliquant des modèles d'expression de marqueurs génétiques exprimés dans le sang à un algorithme d'intelligence artificielle.
PCT/KR2022/020461 2021-12-31 2022-12-15 Procédé de dépistage du cancer colorectal et des polypes colorectaux ou des adénomes avancés et son application WO2023128419A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20210193852 2021-12-31
KR10-2021-0193852 2021-12-31
KR10-2022-0163338 2022-11-29
KR1020220163338A KR20230104517A (ko) 2021-12-31 2022-11-29 대장암 및 대장 용종 또는 진행 선종의 선별 방법 및 그 응용

Publications (1)

Publication Number Publication Date
WO2023128419A1 true WO2023128419A1 (fr) 2023-07-06

Family

ID=86999714

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2022/020461 WO2023128419A1 (fr) 2021-12-31 2022-12-15 Procédé de dépistage du cancer colorectal et des polypes colorectaux ou des adénomes avancés et son application

Country Status (1)

Country Link
WO (1) WO2023128419A1 (fr)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007112330A2 (fr) * 2006-03-24 2007-10-04 Diadexus, Inc. Compositions et méthodes pour détecter, pronostiquer et traiter un cancer du côlon
KR20080102360A (ko) * 2005-12-23 2008-11-25 퍼시픽 에지 바이오테크놀로지 엘티디. 직장결장암용 예후 예측
KR20110052642A (ko) * 2008-07-18 2011-05-18 오라제닉스, 인코포레이티드 결장직장암의 검출 및 치료를 위한 조성물
JP2012517607A (ja) * 2009-02-20 2012-08-02 オンコノム，インコーポレイテッド 大腸癌の診断及び予後判定のための用具セット及び方法
JP2013533977A (ja) * 2010-07-14 2013-08-29 コモンウェルス サイエンティフィック アンド インダストリアル リサーチ オーガニゼイション 大腸癌の診断
WO2014068124A1 (fr) * 2012-11-05 2014-05-08 Diagnoplex Sa Combinaisons de biomarqueurs pour tumeurs colorectales
KR20170094786A (ko) * 2014-12-11 2017-08-21 위스콘신 얼럼나이 리서어치 화운데이션 대장암의 검출 및 치료를 위한 방법
WO2020225426A1 (fr) * 2019-05-08 2020-11-12 Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Examen de dépistage du cancer colorectal et procédé de détection précoce
KR20220116931A (ko) * 2021-02-16 2022-08-23 연세대학교 원주산학협력단 대장암 및 대장 용종 선별 방법 및 그 응용

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20080102360A (ko) * 2005-12-23 2008-11-25 퍼시픽 에지 바이오테크놀로지 엘티디. 직장결장암용 예후 예측
WO2007112330A2 (fr) * 2006-03-24 2007-10-04 Diadexus, Inc. Compositions et méthodes pour détecter, pronostiquer et traiter un cancer du côlon
KR20110052642A (ko) * 2008-07-18 2011-05-18 오라제닉스, 인코포레이티드 결장직장암의 검출 및 치료를 위한 조성물
JP2012517607A (ja) * 2009-02-20 2012-08-02 オンコノム，インコーポレイテッド 大腸癌の診断及び予後判定のための用具セット及び方法
JP2013533977A (ja) * 2010-07-14 2013-08-29 コモンウェルス サイエンティフィック アンド インダストリアル リサーチ オーガニゼイション 大腸癌の診断
WO2014068124A1 (fr) * 2012-11-05 2014-05-08 Diagnoplex Sa Combinaisons de biomarqueurs pour tumeurs colorectales
KR20170094786A (ko) * 2014-12-11 2017-08-21 위스콘신 얼럼나이 리서어치 화운데이션 대장암의 검출 및 치료를 위한 방법
WO2020225426A1 (fr) * 2019-05-08 2020-11-12 Deutsches Krebsforschungszentrum Stiftung des öffentlichen Rechts Examen de dépistage du cancer colorectal et procédé de détection précoce
KR20220116931A (ko) * 2021-02-16 2022-08-23 연세대학교 원주산학협력단 대장암 및 대장 용종 선별 방법 및 그 응용

Similar Documents

Publication Publication Date Title
WO2012081898A2 (fr) Marqueur destiné à établir un pronostic du cancer de l'estomac et procédé d'établissement d'un pronostic du cancer de l'estomac
WO2011055916A2 (fr) Procédé de détection de la méthylation de gènes marqueurs de méthylation spécifiques du cancer du côlon pour le diagnostic du cancer du côlon
WO2009113771A1 (fr) Méthode de dépistage du cancer du poumon au moyen de gènes marqueurs de méthylation spécifiques du cancer du poumon
WO2014073785A1 (fr) Procédé permettant de détecter les polypes gastriques et le cancer gastrique à l'aide d'un gène marqueur du polype gastrique et de la méthylation spécifique du cancer gastrique
WO2012070861A2 (fr) Biomarqueur de méthylation spécifique du cancer de l'estomac pour le diagnostic du cancer de l'estomac
WO2018169145A1 (fr) Système de prédiction de pronostic post-chirurgie ou de compatibilité vis-à-vis de médicaments anticancéreux de patients atteints d'un cancer gastrique avancé
WO2014027831A1 (fr) Marqueur de pronostic et de diagnostic du cancer de la vessie
WO2016171474A1 (fr) Procédé d'analyse de biomolécule à l'aide de biomolécule externe comme matériau étalon, et kit à cet effet
WO2012037128A2 (fr) Procédés et kits pour la détection du mélanome
WO2013147330A1 (fr) Système de prédiction de pronostic du cancer gastrique avancé localement
Lario et al. microRNA profiling in duodenal ulcer disease caused by Helicobacter pylori infection in a Western population
WO2021075797A2 (fr) Composition pour le diagnostic de cancer du foie à l'aide de modifications de la méthylation de cpg dans des gènes spécifiques et son utilisation
WO2014168346A1 (fr) Méthode de dépistage du cancer de la vessie au moyen d'un gène marqueur épigénétique spécifique du cancer de la vessie
WO2021230663A1 (fr) Procédé de prédiction de pronostic de patients atteints d'un cancer du sein précoce
Gentilini et al. GeneScanning analysis of Ig/TCR gene rearrangements to detect clonality in canine lymphomas
WO2024043743A1 (fr) Composition pour l'amplification du gène flt3 et ses utilisations
WO2023128419A1 (fr) Procédé de dépistage du cancer colorectal et des polypes colorectaux ou des adénomes avancés et son application
WO2016108403A1 (fr) Utilisation du gène rsph9 comme marqueur diagnostique/pronostique du cancer de la vessie
WO2021182881A1 (fr) Multiples biomarqueurs pour le diagnostic du cancer du sein et utilisation associée
WO2015060609A2 (fr) Procédé et appareil d'analyse de biomolécules à l'aide d'oligonucléotide
WO2018194280A1 (fr) Procédé de détection de méthylation du gène syndécane 2 (sdc2)
WO2023128429A1 (fr) Procédé de dépistage du cancer colorectal et de l'adénome avancé, et son application
WO2010047561A2 (fr) Trousse de diagnostic pour predire le pronostic du cancer de la vessie invasif ou invasif non musculaire, procede de prediction du pronostic du cancer de la vessie, et procede de criblage de medicament contre le cancer de la vessie
WO2021107713A1 (fr) Procédé de diagnostic de la sclérose latérale amyotrophique sur la base d'un marqueur de mutation du gène lats1
WO2011142646A2 (fr) Procédé de détection de hpv (papillomavirus humain) et de son génotype

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22916562

Country of ref document: EP

Kind code of ref document: A1