CN113981098A - Biomarker for liver cancer diagnosis and liver cancer prognosis prediction - Google Patents

Abstract

The invention discloses a biomarker for liver cancer diagnosis and liver cancer prognosis prediction, wherein the biomarker is obtained by screening based on a liver cancer diagnosis marker ACE, and has higher efficiency of diagnosing liver cancer and predicting liver cancer prognosis.

Description

Biomarker for liver cancer diagnosis and liver cancer prognosis prediction

Technical Field

The invention belongs to the field of biological medicines, and relates to a biomarker for liver cancer diagnosis and liver cancer prognosis prediction.

Background

The primary liver cancer comprises hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and HCC-ICC mixed type 3 different pathological types, wherein HCC accounts for 85-90% (primary liver cancer diagnosis and treatment standard (2019 edition) infectious disease information, 2020.33(06): p.481-500.). Liver cancer is the 4 th common malignant tumor and 2 nd tumor lethal cause in our country at present, seriously threatens the life and health of our country (ZHEN RS, SUNKX, ZHANGSW, et al. report of cancer diagnosis in China 2015[ J ]. Chin J Oncol, 2019, 41(1):19-28 (in Chinese)). According to the World Health Organization (WHO), the number of new cases of Global liver Cancer in 2018 is about 84.1 ten thousand, and the number of liver Cancer cases in China accounts for 46.7% of the Global cases (BRAYF, FERRAYJ, SOERJOMATARAM I, et al. Global Cancer Statistics 2018: GLOBOCAN observations of intake and mobility for 36cancers in 185 centers [ J ]. CA Cancer JClin, 2018, 68(6): 394; FENG RM, ZOYN, CAO SM, et al. Current Cancer in China: Good bad news from the 2018Global Cancer Statistics [ J ]. Cancer (Commun), 2019, 39(1): 22). Different from western countries and regions, most of liver cancer patients in China have hepatitis B virus infection and liver cirrhosis background caused by the hepatitis B virus infection, most of the patients belong to middle and late stages during diagnosis, the disease condition is complex, the prognosis is poor, and the 5-year overall survival rate of the liver cancer patients in China is less than 15 percent at present (ZENG H, CHEN W, ZHEN R, et al. changing cancer Survival in China dual 2003-15: Apolated analysis of 17 marketing-based cancer registers [ J ]. Lancet Global Health, 2018, 6(5): e555-e 567.). In contrast, patients diagnosed with early stages of liver cancer have a relatively better prognosis, and a 5-year survival rate of greater than 70% (Tsuchiya N.Biomarkers for the early diagnosis of hepatocellular cancer [ J ]. World J Gastroenterol,2015.21(37): p.10573-83.). The radical treatment of early liver cancer can obviously improve the prognosis of patients, and the early diagnosis of liver cancer also becomes a research hotspot.

The current primary liver cancer diagnosis and treatment standard (2019 edition) indicates that screening of high risk groups is the key for improving the curative effect of liver cancer, and the core of the screening is early discovery, early diagnosis and early treatment emphasized by secondary prevention of diseases. Des-gamma-carboxyprothrombin (DCP), also known as vitamin K deficient or antagonist II inducible protein (Prothrombin induced by vitamin-K absence or antagonist-II), is a liver synthesized abnormal Prothrombin without clotting activity, encoded by the ACE gene, and first discovered by Liebman in 1984. DCP was approved for clinical use in China in 2014, and was included in the criteria for primary liver cancer diagnosis and treatment (2017), published by the Committee for health and family planning in the people's republic of China, and recommended for diagnosis of liver cancer (2017, J, 2017, 37 (7): 705-720). Researches on markers related to liver cancer around ACE provides a new means and direction for realizing early liver cancer diagnosis and further realizing early intervention and early treatment.

Disclosure of Invention

To remedy the deficiencies of the prior art, the present invention provides 1) use as a biomarker indicative of liver cancer which can be used for accurate diagnosis or prognosis of liver cancer in a subject; 2) as a biomarker indicative of liver cancer prognosis, which can be used for accurate diagnosis or prognosis of liver cancer in a subject.

In order to achieve the purpose, the invention adopts the following technical scheme:

a first aspect of the invention provides a biomarker for predicting liver cancer, the biomarker comprising a combination of any two or more of the following genes: ADH4, BAMBI, CDCA8, CFHR3, FAM127B, IGSF3, LCAT, LPCAT1, MAGEA6, ME1, MTND4P20, PFN2, PON, S100A9, SLC2A1, SPP1, SQSTM1, TXNRD 1.

Further, the markers comprise at least the following set of characteristic genomes: sig1, Sig1, and Sig 3;

the Sig1 group includes the following genes: CDCA8, SLC2a1, LPCAT1, S100a9, PFN2, SPP 1;

the Sig2 group includes the following genes: CDCA8, SLC2A1, LPCAT1, S100A9, PFN2, SPP1, IGSF3, ADH4, PON1, FAM127B, MTND4P20, TXNRD 1;

the Sig3 group includes the following genes: ADH4, BAMBI, CDCA8, CFHR3, FAM127B, IGSF3, LCAT, LPCAT1, MAGEA6, ME1, MTND4P20, PFN2, PON, S100A9, SLC2A1, SPP1, SQSTM1, TXNRD 1.

In a second aspect, the invention provides the use of a reagent for detecting a biomarker according to the first aspect of the invention in a sample for the preparation of a product for diagnosing or predicting liver cancer.

Further, the reagents include reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using protein immunoassay techniques include antibodies specific for an epitope of the biomarker or functional fragment thereof.

Further, the antibody is a labeled antibody.

Further, the reagent for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using dye technology comprises a dye specific for the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid sequencing techniques include primers that bind to the sequence of the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid hybridization techniques include probes complementary to the sequence of the biomarker or functional fragment thereof.

Further, the probe is a labeled probe.

Further, the sample includes tissue, body fluid.

In a third aspect, the invention provides the use of a reagent for detecting a biomarker according to the first aspect of the invention in a sample for the preparation of a product for predicting prognosis of liver cancer.

Further, the reagents include reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using protein immunoassay techniques include antibodies specific for an epitope of the biomarker or functional fragment thereof.

Further, the antibody is a labeled antibody.

Further, the reagent for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using dye technology comprises a dye specific for the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid sequencing techniques include primers that bind to the sequence of the biomarker or functional fragment thereof.

Further, the reagents for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid hybridization techniques include probes complementary to the sequence of the biomarker or functional fragment thereof.

Further, the probe is a labeled probe.

Further, the sample includes tissue, body fluid.

Further, the kit further comprises instructions for diagnosing or prognosing liver cancer.

In a fourth aspect, the invention provides a product for diagnosing or predicting the prognosis of liver cancer, the product comprising reagents for detecting the biomarkers of the first aspect of the invention.

Further, the product comprises a chip and a kit.

Further, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit.

Further, the kit also comprises instructions for diagnosing or predicting liver cancer/liver cancer prognosis.

A fifth aspect of the invention provides a system comprising:

a sample;

one or more probes and/or stains that bind to a biomarker and/or a homologous sequence thereof according to the first aspect of the invention; and

one or more devices capable of quantifying the presence, absence and/or amount of at least one probe or stain that binds to a biomarker and/or homologous sequence thereof according to the first aspect of the invention.

A sixth aspect of the present invention provides a system/apparatus for diagnosing whether a subject has or is at risk of developing liver cancer and predicting prognosis of liver cancer, comprising:

an analysis unit adapted to measure the amount of a biomarker according to the first aspect of the invention in a sample of a subject; and

an evaluation unit comprising a stored reference and a data processor having implemented an algorithm for comparing the amount of the biomarker measured by the analysis unit with the stored reference, thereby diagnosing liver cancer or the presence of a risk of developing liver cancer.

A seventh aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the system/apparatus of the sixth aspect of the present invention.

An eighth aspect of the present invention provides a method of screening for markers predictive of liver cancer, the method comprising:

1) constructing an interaction protein network of the liver cancer diagnosis gene;

2) screening network proteins closely related to liver cancer;

3) grouping according to the screened network proteins;

4) screening for differentially expressed genes according to the grouping described in 3).

The method further comprises the step of carrying out single factor analysis on the genes in the step 4) and screening the genes related to survival.

The method further performs multifactorial regression analysis on the survival-related genes to screen for markers for prognosis.

Further, the liver cancer diagnostic genes include ARG1, GPC3, AFP, ACE, CD10, HSPA 4.

Further, the liver cancer diagnosis gene is ACE.

Further, the median of the network protein expression level was used for grouping in step 3).

The invention has the advantages and beneficial effects that:

the invention screens the biomarker for accurately predicting liver cancer based on the liver cancer diagnosis gene ACE, and the biomarker has higher diagnosis sensitivity and specificity.

The invention provides a method for screening biomarkers for predicting liver cancer based on diagnostic genes, and the markers screened by the method have higher diagnostic efficiency.

Drawings

FIG. 1 is a PPI diagram of ACE;

FIG. 2 is a ROC plot of differential genes, wherein FIG. 2A is RHOC; FIG. 2B is RHOA;

FIG. 3 is a diagram of differential genome using the effective value of p;

FIG. 4 is a graph of diagnostic performance of different groupings, wherein FIG. 4A is a DT ROC plot of Sig 1; FIG. 4B is a graph of RF ROC for Sig 1; FIG. 4C is a SVM ROC plot of Sig 1; FIG. 4D is a DT ROC plot of Sig 2; FIG. 4E is a graph of RF ROC for Sig 2; FIG. 4F is a SVM ROC plot of Sig 2; FIG. 4G is a DT ROC plot of Sig 3; FIG. 4H is a RF ROC plot of Sig 3; FIG. 4I is a SVM ROC plot of Sig 3;

fig. 5 is a graph of the performance of different groups for predicting prognosis of liver cancer, wherein fig. 5A is a graph of survival curve of Sig1 for predicting prognosis of liver cancer, fig. 5B is a graph of survival curve of Sig2 for predicting prognosis of liver cancer, and fig. 5C is a graph of survival curve of Sig3 for predicting prognosis of liver cancer.

Detailed Description

The invention researches genes strongly related to liver cancer by extensive and intensive research based on an ACE gene network of 11 genes, and discovers a characteristic genome of a 2-gene. The invention aims to fully utilize the potential value of ACE as a marker of liver cancer to develop effective characteristic genes to predict liver cancer and prognosis of the liver cancer. Differentially expressed genes associated with the characteristic genome of the 2-gene were found in the clinical databases of the inventors. And further, from the differentially expressed genes, 18 characteristic genomes and a plurality of subgroups were constructed. These characteristic genomes are very effective in predicting liver cancer and prognosis of liver cancer.

The term "and/or" as used herein in phrases such as "a and/or B" is intended to include both a and B; a or B; a (alone); and B (alone). Likewise, the term "and/or" as used in phrases such as "A, B and/or C" is intended to encompass each of the following embodiments: A. b and C; A. b or C; a or C; a or B; b or C; a and C; a and B; b and C; a (alone); b (alone); and C (alone).

The term "biomarker" refers to a biological molecule present in an individual at different concentrations that can be used to predict the cancer status of the individual. Biomarkers can include, but are not limited to, nucleic acids, proteins, and variants and fragments thereof. A biomarker may be DNA comprising all or part of a nucleic acid sequence encoding the biomarker, or the complement of such a sequence. Biomarker nucleic acids useful in the present invention are considered to include DNA and RNA comprising all or part of any nucleic acid sequence of interest.

In particular embodiments of the invention, the biomarkers include genes and their encoded proteins and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed biomarkers, as well as any form of biomarker that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of the biomarkers.

As used herein, the term "sample" refers to a biological sample obtained or derived from a source of interest as described herein. In some embodiments, the source of interest comprises an organism, such as an animal or human. In some embodiments, the biological sample comprises a biological tissue or fluid. In some embodiments, the biological sample may be or comprise bone marrow; blood; blood cells; ascites fluid; tissue or fine needle biopsy samples; a body fluid containing cells; free floating nucleic acids; sputum; saliva; (ii) urine; cerebrospinal peritoneal fluid; pleural fluid; feces; lymph; a skin swab; orally administering the swab; a nasal swab; washings or lavages, such as catheter lavages or bronchoalveolar lavages; (ii) an aspirate; scraping scraps; bone marrow specimen; a tissue biopsy specimen; a surgical specimen; feces, other body fluids, secretions and/or excretions; and/or cells therein, and the like. In some embodiments, the biological sample is or comprises cells obtained from an individual. In some embodiments, the sample is a "primary sample" obtained directly from a source of interest by any suitable means. For example, in some embodiments, the primary biological sample is obtained by a method selected from the group consisting of: biopsies (e.g., fine needle aspirates or tissue biopsies), surgical tissue, collection of bodily fluids (e.g., blood, lymph, stool, etc.), and the like. In some embodiments, as will be apparent from the context, the term "sample" refers to a preparation obtained by processing (e.g., by removing one or more components of a primary sample and/or by adding one or more reagents to a primary sample). For example, filtration using a semipermeable membrane. Such "processed samples" may comprise, for example, nucleic acids or proteins extracted from the sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and/or purification of certain components, and the like.

Whether the level of the biomarker in the biological sample derived from the test subject differs from the level of the biomarker present in a normal subject can be determined by comparing the level of the biomarker in the sample from the test subject to a suitable control. The skilled person can select an appropriate control for the assay in question. For example, a suitable control can be a biological sample derived from a known subject (e.g., a subject known to be a normal subject without cancer). If a suitable control is obtained from a normal subject, a statistically significant difference in the level of the biomarker in the test subject relative to the suitable control indicates that the subject has liver cancer. In one embodiment, the difference in the level of the biomarker is an increase. A suitable control may also be a reference standard. The reference standard serves as a reference level for comparison, such that the test sample can be compared to the reference standard to infer the liver cancer status of the subject. The reference standard can represent the level of one or more biomarkers in a known subject (e.g., a subject known to be a normal subject or a subject known to have liver cancer). Likewise, the reference standard can represent the level of one or more biomarkers in a known population of subjects (e.g., a population of subjects known to be normal subjects or a population of subjects known to have liver cancer). For example, a reference standard can be obtained by pooling samples from multiple individuals and determining the levels of biomarkers in the pooled samples, thereby generating a standard in an average population. Such reference standards represent the average level of a biomarker in a population of individuals. For example, a reference standard can also be obtained by averaging the levels of biomarkers determined to be present in individual samples obtained from a plurality of individuals. Such criteria also represent the average level of a biomarker in a population of individuals. The reference standard can also be a collection of values, each value representing the level of a biomarker in a known subject in a population of individuals. In certain embodiments, the test sample can be compared to a collection of such values to infer the liver cancer status of the subject. In certain embodiments, the reference standard is an absolute value. In such embodiments, the test sample can be compared to absolute values to infer the liver cancer status of the subject. In one embodiment, the comparison between the levels of one or more biomarkers in the sample relative to a suitable control is performed by executing a software classification algorithm. In some embodiments, the expression of one or a combination of biomarkers is increased, wherein the increased expression is about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% or more greater than the expression of the same biomarker in a normal sample. In some embodiments, the expression of one or a combination of biomarkers is increased, wherein the increased expression is about 2X, 3X, 4X, 5X, 6X, 7X, 8X, 9X or about 10X or more expression compared to the expression of the same one or combination of biomarkers in a normal sample.

The term "reference" refers to a biomarker whose level can be used to compare the level of the biomarker in a test sample. In one embodiment of the invention, the reference comprises a housekeeping gene, such as beta-globin, alcohol dehydrogenase or any other housekeeping gene, the level or expression of which does not vary according to the disease state of the cell containing the marker. In another embodiment, all assayed biomarkers or a subset thereof can be used as a reference.

The terms "polynucleotide" and "nucleic acid molecule" are used interchangeably herein and refer to a polymer of nucleotides of any length and include DNA and RNA. The polynucleotide may be a deoxyribonucleotide, a ribonucleotide, a modified nucleotide or base, and/or analogs thereof, or any substrate that can be incorporated into a polymer by a DNA or RNA polymerase.

The terms "polypeptide" and "peptide" and "protein" are used interchangeably herein and refer to a polymer of amino acids of any length. The polymer may be linear or branched, it may comprise modified amino acids, and it may be interrupted by non-amino acids. The term also encompasses amino acid polymers that have been modified either naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation to a labeling component. Also included within this definition are, for example, polypeptides containing one or more amino acid analogs (including, for example, unnatural amino acids), as well as other modifications known in the art. It is to be understood that because the polypeptides of the invention may be based on antibodies or fusion proteins, in certain embodiments, the polypeptides may occur as single chains or related chains (e.g., dimers).

The term "subject" refers to any animal (e.g., a mammal), including, but not limited to, humans, non-human primates, dogs, cats, rodents, and the like. Further, the subject is a human subject. The terms "subject", "individual" and "patient" are used interchangeably herein. Thus, the terms "subject," "individual," and "patient" encompass individuals having cancer (e.g., liver cancer), including those who have undergone or undergone resection (surgery) to remove candidates for cancerous tissue.

Determining levels of biomarkers

The level of one or more biomarkers in a biological sample can be determined by any suitable method. Any reliable method may be used to measure the level or amount in the sample. Generally, mRNA can be detected and quantified from a sample (including fractions thereof), such as a sample of isolated RNA, by various known methods including, for example, amplification-based methods (e.g., Polymerase Chain Reaction (PCR), real-time polymerase chain reaction (RT-PCR), quantitative polymerase chain reaction (qPCR), rolling circle amplification, etc.), hybridization-based methods (e.g., hybridization arrays (e.g., microarrays), NanoString analysis, Northern Blot analysis, branched dna (bdna) signal amplification, in situ hybridization, etc.), and sequencing-based methods (e.g., next generation sequencing methods, e.g., using Illumina or iontorrentt platform). Other exemplary techniques include Ribonuclease Protection Assay (RPA) and mass spectrometry.

Amplification-based methods

There are many amplification-based methods for detecting the level of biomarker nucleic acid sequences, including, but not limited to, PCR, RT-PCR, qPCR, and rolling circle amplification. Other amplification-based techniques include, for example, ligase chain reaction, multiplex ligatable probe amplification, In Vitro Transcription (IVT), strand displacement amplification, transcription-mediated amplification, RNA (Eberwine) amplification, and other methods known to those skilled in the art.

Hybridization-based methods

Biomarkers can be detected using hybridization-based methods including, but not limited to, hybridization arrays (e.g., microarrays), NanoString assays, Northern Blot assays, branched dna (bdna) signal amplification, and in situ hybridization.

Microarrays can be used to simultaneously measure the expression levels of a large number of biomarkers. Microarrays can be fabricated using a variety of techniques, including printing on a slide with a fine-tipped needle, photolithography with a pre-fabricated mask, photolithography with a dynamic micro-mirror device, ink-jet printing, or electrochemistry on a micro-electrode array. Microfluidic TaqMan low density arrays based on microfluidic qRT-PCR reaction arrays may also be used, as well as related microfluidic qRT-PCR based methods.

The image may be scanned using an Axon B-4000 scanner and Gene-Pix Pro 4.0 software or other suitable software. Non-positive spots after background subtraction were removed as well as outliers detected by the ESD procedure. The resulting signal intensity values were normalized to the median value for each chip and then used to obtain the geometric mean and standard error for each biomarker. Each signal can be converted to log base 2 and subjected to a single sample t-test. Independent hybridization of each sample can be performed on the chip, spotting multiple times for each biomarker to increase the robustness of the data.

Several types of microarrays can be used, including, but not limited to, a spotted oligonucleotide microarray, a preformed oligonucleotide microarray or a spotted long oligonucleotide array.

In some embodiments, biomarker expression is determined by assays known to those of skill in the art including, but not limited to, multi-analyte profiling assays, enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, western blot assays, immunofluorescence assays, enzyme immunoassays, immunoprecipitation assays, chemiluminescence assays, immunohistochemistry assays, dot blot assays, or slot blot assays. In some embodiments, wherein an antibody is used in the assay, the antibody is detectably labeled. Antibody labels may include, but are not limited to, immunofluorescent labels, chemiluminescent labels, phosphorescent labels, enzyme labels, radioactive labels, avidin/biotin, colloidal gold particles, colored particles, and magnetic particles. In some embodiments, the expression of the biomarker is determined by an IHC assay.

In some embodiments, the expression of a biomarker is determined using an agent that specifically binds to the biomarker. Any molecular entity that exhibits specific binding to a biomarker can be used to determine the level of the biomarker protein in a sample. Specific binding agents include, but are not limited to, antibodies, antibody fragments, antibody mimetics, and polynucleotides (e.g., aptamers, etc.). The skilled artisan understands that the degree of specificity desired is determined by the particular assay used to detect the biomarker protein, and in some embodiments the disclosure relates to a system comprising a solid support (such as an ELISA plate, gel, bead or column comprising an antibody, antibody fragment, antibody mimetic, and/or polynucleotide capable of binding T3p or a salt thereof).

As used herein, the term "antibody" refers to an immunoglobulin molecule that recognizes and specifically binds a target, such as a protein, polypeptide, peptide, carbohydrate, polynucleotide, lipid, or combination of the foregoing, through at least one antigen binding site. As used herein, the term encompasses intact polyclonal antibodies, intact monoclonal antibodies, single chain antibodies, antibody fragments (such as Fab, Fab ', F (ab')2, and Fv fragments), single chain Fv (scfv) antibodies, multispecific antibodies (such as bispecific antibodies), monospecific antibodies, monovalent antibodies, chimeric antibodies, humanized antibodies, human antibodies, fusion proteins comprising an antigen binding site of an antibody, and any other modified immunoglobulin molecule comprising an antigen binding site, so long as the antibody exhibits the desired biological binding activity. The antibody can be any of the five major classes of immunoglobulins: IgA, IgD, IgE, IgG, and IgM, or subclasses (isotypes) thereof (e.g., IgG1, IgG2, IgG3, IgG4, IgA1, and IgA 2). The different classes of immunoglobulins have different and well-known subunit structures and three-dimensional configurations. Antibodies may be naked or conjugated to other molecules, including but not limited to toxins and radioisotopes.

The term "antibody fragment" refers to a portion of an intact antibody and refers to the epitope variable region of an intact antibody. Examples of antibody fragments include, but are not limited to, Fab ', F (ab')2, and Fv fragments, linear antibodies, single chain antibodies, and multispecific antibodies formed from antibody fragments. As used herein, an "antibody fragment" comprises at least one antigen binding site or epitope binding site. The term "variable region" of an antibody refers to the variable region of an antibody light chain or the variable region of an antibody heavy chain, alone or in combination. The variable region of a heavy or light chain is typically composed of four Framework Regions (FRs) connected by three Complementarity Determining Regions (CDRs), also referred to as "hypervariable regions". The CDRs in each chain are held together in close proximity by the framework regions and contribute to the formation of the antigen-binding site of the antibody.

The term "monoclonal antibody" refers to a homogeneous population of antibodies that are involved in the highly specific recognition and binding of a single antigenic determinant or epitope. This is in contrast to polyclonal antibodies which typically comprise a mixture of different antibodies directed against a variety of different antigenic determinants. The term "monoclonal antibody" encompasses intact and full-length monoclonal antibodies as well as antibody fragments (e.g., Fab ', F (ab')2, Fv), single chain (scFv) antibodies, fusion proteins comprising an antibody portion, and any other modified immunoglobulin molecule comprising an antigen binding site. In addition, "monoclonal antibody" refers to such antibodies prepared by a number of techniques including, but not limited to, hybridoma production, phage selection, recombinant expression, and transgenic animals.

Sequencing-based method

If available, advanced sequencing methods may also be used. For example, Illumina can be used to detect biomarkers. Next generation Sequencing (e.g., Sequencing-By-Synthesis or TruSeq methods using, for example, the HiSeq, HiScan, genoanalyzer, or MiSeq system (Illumina, Inc., san. ca)). Biomarkers can also be detected using Ion current sequencing (Ion Torrent Systems, inc., guliford, connecticut) or other suitable semiconductor sequencing methods.

Other detection tools

The biomarkers can be quantified using RNase profiling (mapping) using mass spectrometry. The isolated RNA may be enzymatically digested with an RNA endonuclease (RNase) having high specificity (e.g., RNase T1, which cleaves 3' to all unmodified guanosine residues) prior to analysis of the isolated RNA by MS or tandem MS (MS/MS) methods. The first method developed used reverse phase HPLC coupled directly to ESI-MS to perform on-line chromatographic separation of endonuclease digests. The presence of post-transcriptional modifications can be revealed by mass shifts from those expected based on the RNA sequence. Ions of abnormal mass/charge values can then be isolated for tandem MS sequencing, thereby locating the sequence position of the post-transcriptionally modified nucleoside.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has also been used as an analytical method to obtain information about post-transcriptionally modified nucleosides. MALDI-based methods can be distinguished from ESI-based methods by separation steps. In MALDI-MS, mass spectrometry is used to separate biomarkers.

Other methods for biomarker detection and measurement include, for example, strand invasion assays (Third Wave Technologies, Inc.), Surface Plasmon Resonance (SPR), cDNA, MTDNA (metal DNA; Advance Technologies, sas cartoon, sas, tsucker), and single molecule methods such as those developed by US Genomics. Multiple biomarkers can be detected in microarray format using a new method that combines surface enzyme reactions and nanoparticle amplification SPR imaging (SPRI). Surface reaction of poly (a) polymerase produces a poly (a) tail on a biomarker hybridized to a Locked Nucleic Acid (LNA) microarray. The DNA-modified nanoparticles were then adsorbed to the poly (a) tail and detected with SPRI. This ultrasensitive nanoparticle amplified SPRI method can be used for biomarker analysis at the attamole level.

Detecting amplified or non-amplified biomarkers

In certain embodiments, labels, dyes or labeled probes and/or primers are used to detect amplified or unamplified biomarkers. Based on the sensitivity of the detection method and the abundance of the target, the skilled person will recognise which detection methods are suitable. Depending on the sensitivity of the detection method and the abundance of the target, amplification may or may not be required prior to detection. One skilled in the art will recognize that detection methods for biomarker amplification are preferred.

Probes or primers may include standard (A, T or U, G and C) bases, or modified bases. Modified bases include, but are not limited to, AEGIS bases. In certain aspects, the bases are linked by natural phosphodiester bonds or different chemical linkages. Different chemical bonds include, but are not limited to, peptide bonds or Locked Nucleic Acid (LNA) bonds.

In certain embodiments, one or more primers in an amplification reaction may comprise a label. In still further embodiments, the different probes or primers comprise detectable labels that are distinguishable from each other. In some embodiments, a nucleic acid, such as a probe or primer, may be labeled with two or more distinguishable labels.

In some aspects, the label is attached to one or more probes and has one or more of the following properties: (i) providing a detectable signal; (ii) interact with the second label to modify a detectable signal provided by the second label, e.g., FRET (fluorescence resonance energy transfer); (iii) stable hybridization, e.g., formation of duplexes; and (iv) providing a member of a binding complex or affinity group, e.g., affinity, antibody-antigen, ionic complex, hapten-ligand (e.g., biotin-avidin). In still other aspects, the use of labels can be accomplished using any of a number of known techniques employing known labels, bonds, linkers, reagents, reaction conditions, and analytical and purification methods.

Biomarkers can be detected by direct or indirect methods. In direct detection methods, one or more biomarkers are detected by a detectable label linked to a nucleic acid molecule. In such methods, the biomarker may be labeled prior to binding to the probe. Thus, binding is detected by screening for labeled biomarkers bound to the probe. The probe is optionally attached to a bead (bead) in the reaction volume.

In certain embodiments, the nucleic acid is detected by direct binding to a labeled probe, and the probe is subsequently detected. In one embodiment of the invention, nucleic acids, such as amplified biomarkers, are detected using FIexMAP microspheres (Luminex) conjugated to probes to capture the desired nucleic acids. Some methods may involve, for example, detection with a fluorescently labeled modified polynucleotide probe or detection of branched dna (bdna).

In some embodiments, the expression of the biomarkers is determined using a PCR-based assay comprising primers and/or probes specific for each biomarker. As used herein, the term "probe" refers to any molecule capable of selectively binding to a particular intended target biomolecule. In some embodiments, the term "probe" herein refers to any molecule that can bind to or be associated with any substrate and/or reaction product and/or protease disclosed herein, either indirectly or directly, covalently or non-covalently, and which association or binding can be detected using the methods disclosed herein. In some embodiments, the probe is a fluorescent probe, an antibody, or an absorbance-based probe. In the case of absorbance-based probes, the chromophore pNA (p-nitroaniline) can be used as a probe for detecting and/or quantifying the target nucleic acid sequence disclosed herein. In some embodiments, a probe may be a nucleic acid sequence comprising a fluorescent molecule or substrate that becomes fluorescent upon exposure to an enzyme, and the nucleic acid sequence is complementary to a fragment of one nucleic acid sequence.

The term "primer" or "probe" encompasses an oligonucleotide having a specific sequence or an oligonucleotide having a specific sequence. In other embodiments, the nucleic acid is detected by an indirect detection method. For example, biotinylated probes can be combined with streptavidin-conjugated dyes to detect bound nucleic acids. The streptavidin molecules bind the biotin labels on the amplified biomarkers, and the bound biomarkers are detected by detecting dye molecules attached to the streptavidin molecules. In one embodiment, the streptavidin-conjugated dye molecule comprises PHYCOLINK. Streptavidin R-phycoerythrin (PROzyme). Other conjugated dye molecules are known to those skilled in the art.

Markers include, but are not limited to: luminescent, light scattering, and light absorbing compounds that produce or quench a detectable fluorescent, chemiluminescent, or bioluminescent signal. In some embodiments, a dual-labeled fluorescent probe comprising a reporter fluorophore and a quencher fluorophore is used. It will be appreciated that pairs of fluorophores with different emission spectra are selected so that they can be readily distinguished. In certain embodiments, the label is a hybridization stabilizing moiety that is used to enhance, stabilize or affect hybridization of the duplex, e.g., an intercalator and an intercalating dye.

Diagnosis of

The biomarkers described herein can be used alone or in combination in diagnostic tests to assess the liver cancer status of a subject. The liver cancer status includes the presence or absence of liver cancer. The liver cancer status may also include monitoring the course of liver cancer, e.g., monitoring disease progression. Based on the liver cancer status of the subject, additional procedures may be indicated, including, for example, additional diagnostic tests or therapeutic procedures.

The ability of a diagnostic test to correctly predict a disease state is typically measured in terms of the accuracy of the assay, the sensitivity of the assay, the specificity of the assay, or the "area under the curve" (AUC, e.g., the area under the Receiver Operating Characteristic (ROC) curve). As used herein, accuracy is a measure of the fraction of misclassified samples. The accuracy degree may be calculated as, for example, the total number of correctly classified samples in the test population divided by the total number of samples. Sensitivity is a measure of "true positives" that are predicted to be positive by the test and can be calculated as the number of correctly identified liver cancer samples divided by the total number of liver cancer samples. Specificity is a measure of "true negatives" that are predicted to be negative by the test, and can be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under the receiver operating characteristic curve, which is a plot of sensitivity versus false positive rate (1-specificity). The greater the AUC, the more powerful the predicted value tested. Other useful measures of test utility include both "positive predictive value," which is the percentage of actual positives that test positive, and "negative predictive value," which is the percentage of actual negatives that test negative. In a preferred embodiment, the levels of one or more biomarkers in samples derived from subjects having different liver cancer states exhibit statistically significant differences relative to normal subjects of at least p 0.05, e.g., p 0.05, p 0.01, p 0.005, p 0.001, etc., as determined relative to an appropriate control. In other preferred embodiments, diagnostic tests using the biomarkers described herein, alone or in combination, exhibit an accuracy of at least about 75%, e.g., an accuracy of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, a diagnostic test using the biomarkers described herein, alone or in combination, exhibits a specificity of at least about 75%, e.g., a specificity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, a diagnostic test using the biomarkers described herein, alone or in combination, exhibits a sensitivity of at least about 75%, e.g., a sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, diagnostic tests using the biomarkers described herein, alone or in combination, exhibit a specificity and sensitivity of at least about 75% each, e.g., at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100% (e.g., at least about 80% specificity and at least about 80% sensitivity, or e.g., at least about 80% specificity and at least about 95% sensitivity).

Each biomarker is present differently in a biological sample derived from a subject having liver cancer than a normal subject, and thus each biomarker alone can be used to facilitate determination of liver cancer in a test subject. Such methods involve determining the level of a biomarker in a sample derived from the subject. Determining the level of the biomarker in the sample may comprise measuring, detecting or determining the level of the biomarker in the sample using any suitable method (e.g., the methods described herein). Determining the level of the biomarker in the sample may further comprise examining the results of the measurement, detecting, or determining the level of the biomarker in the sample. The method may also involve comparing the level of the biomarker in the sample to a suitable control. A change in biomarker level relative to that in a normal subject assessed using a suitable control is indicative of the liver cancer status of the subject. A diagnostic amount of a biomarker can be used, which indicates that above or below the diagnostic amount, the subject is classified as having a particular liver cancer status. For example, if a biomarker is upregulated in a sample derived from an individual having liver cancer as compared to a normal individual, a measurement above a diagnostic cut-off provides a diagnosis of liver cancer. As is well known in the art, adjusting the particular diagnostic cut-off used in an assay allows one to adjust the sensitivity and/or specificity of the diagnostic assay as desired. A particular diagnostic cutoff value may be determined, for example, by measuring the amount of a biomarker in a statistically significant number of samples from subjects with different liver cancer states and plotting the cutoff value with a desired level of accuracy, sensitivity, and/or specificity. In certain embodiments, the diagnostic cutoff may be determined with the aid of a classification algorithm.

While biomarkers alone may be useful in diagnostic applications for liver cancer, as shown herein, the combination of biomarkers may provide a higher predictive value of liver cancer status than biomarkers when used alone. In particular, detecting multiple biomarkers may increase the accuracy, sensitivity, and/or specificity of a diagnostic test. The invention includes individual biomarkers and biomarker combinations listed in these tables, and their use in the methods and kits described herein.

In some embodiments, data generated using samples such as "known samples" may then be used to "train" the classification model. A "known sample" is a sample that has been previously classified, e.g., as a sample from a normal subject or from a subject with liver cancer. The data derived from the spectra and used to form the classification model may be referred to as a "training data set". Once trained, the classification model may identify patterns in the data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This is useful, for example, in predicting whether a particular biological sample is associated with a particular biological condition (e.g., diseased or not).

Any suitable statistical classification (or "learning") method may be used to form a classification model that attempts to classify a body of data based on objective parameters present in the data. In supervised classification, training data containing examples of known classes is presented to a learning mechanism that learns one or more sets of relationships that define each known class. The new data may then be applied to a learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., Multiple Linear Regression (MLR), Partial Least Squares (PLS) regression, and Principal Component Regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART classification and regression trees), artificial neural networks such as back propagation networks, discriminant analysis (e.g., Bayesian classifier (Bayesian classifier) or fisher analysis (Fischer analysis)), logical classifiers, and support vector classifiers (support vector machines).

In other embodiments, the created classification model may be formed using unsupervised learning methods. Unsupervised classification attempts to learn classification based on similarities in the training dataset, without pre-classifying the spectra from which the training dataset is derived. Unsupervised learning methods include cluster analysis. Cluster analysis attempts to divide the data into "clusters" or groups, which ideally should have members that are very similar to each other and to members of other clusters. Similarity is then measured using some distance metric that measures the distance between data items and clusters together data items that are close to each other.

The classification model may be formed and used on any suitable digital computer. Suitable digital computers include micro (mini) or mainframe computers using any standard or proprietary operating system, such as a Unix, WINDOWS, or LINUX based operating system.

The training data set and the classification model may be embodied in computer code executed or used by a digital computer. The computer code may be stored on any suitable computer readable medium, including optical or magnetic disks, magnetic sticks, tapes, etc., and may be written in any suitable computer programming language, including C, C + +, visual basic, etc.

The learning algorithm can be used for developing a classification algorithm for biomarkers of liver cancer. The classification algorithm, in turn, can be used in diagnostic tests by providing diagnostic values (e.g., cut-off points) for the biomarkers used alone or in combination.

Reagent kit

The present invention provides kits for diagnosing liver cancer in a subject for determining levels of biomarkers (wherein the sequences optionally comprise uracil in place of one, more than one, or all of the disclosed thymines), and combinations thereof. The kit may comprise materials and reagents suitable for selectively detecting the presence of a biomarker or a panel of biomarkers for diagnosing liver cancer in a sample derived from a subject. For example, in one embodiment, the kit can include reagents that specifically hybridize to the biomarkers. Such reagents may be nucleic acid molecules in a form suitable for detecting a biomarker, e.g., probes or primers. The kit may include reagents for performing an assay to detect one or more biomarkers, e.g., reagents that may be used to detect one or more biomarkers in a qPCR reaction. The kit may also include a microarray for detecting one or more biomarkers.

In further embodiments, the kit may contain instructions for appropriate operating parameters in the form of labels or product inserts. For example, the instructions may include information or guidance on how to collect the sample, how to determine the level of one or more biomarkers in the sample, or how to correlate the level of one or more biomarkers in the sample with the liver cancer status of the subject.

In another embodiment, the kit may contain one or more containers with a biomarker sample to be used as a reference standard, a suitable control, or for calibration of an assay to detect a biomarker in a test sample.

System/apparatus

The invention relates to a system/device for diagnosing whether a subject has liver cancer or is at risk of having liver cancer and predicting liver cancer prognosis, which comprises:

an analysis unit adapted to measure the amount of a biomarker according to the invention in a sample of a subject; and

an evaluation unit comprising a stored reference and a data processor having implemented an algorithm for comparing the amount of the biomarker measured by the analysis unit with the stored reference, thereby diagnosing liver cancer or the presence of a risk of developing liver cancer.

A device as applied herein shall at least comprise the above-mentioned units. The units of the device are operatively connected to each other. How the units are operatively linked will depend on the type of unit contained in the device. For example, in case a tool for automatic quantitative measurement of biomarkers is applied in the analysis unit, the data obtained by said automatic operation unit may be processed by the evaluation unit, e.g. by a computer program running on a computer as data processor, in order to facilitate the diagnosis. In one embodiment, the data processor performs a comparison of the amount of the biomarker to a reference.

Further, in this case, the unit is constituted by a single device. However, the analysis unit and the evaluation unit may also be physically separate. In this case, operational connection (operational connection) may be realized via wired and wireless connection between units allowing data transmission. The wireless connection may use a wireless lan (wlan) or the internet. The wired connection may be achieved by optical and non-optical cable connections between the units. The cable for wired connection is further suitable for high-throughput data transmission.

The present invention will be described in further detail with reference to the accompanying drawings and examples. The following examples are intended to illustrate the invention only and are not intended to limit the scope of the invention. The experimental procedures, in which specific conditions are not specified in the examples, are generally carried out under conventional conditions or conditions recommended by the manufacturers.

1. Construction of PPI network for ACE

Based on string database, construct PPI network map around ACE, thereby obtaining a gene set of ACE, AGT, AGTR1, AGTR2, BDKRB2, KNG1, MME, NR3C2, REN, RHOA, RHOC, see fig. 1.

2. Screening of network protein closely related to liver cancer

RNA sequencing data (FPKM value) of gene expression of liver cancer and clinical information are downloaded from UCSC Xena (https:// gdc.xenahubs.net) and processed as follows: deleting samples without clinical follow-up information and samples with unknown survival time, less than 0 day and no survival state; performing gene annotation on the data sample; removing the duplicate, taking the average value and carrying out power conversion; the final included samples were 50 normal samples and 368 cancer samples.

Dividing the sample into a normal group and a cancer group, drawing an ROC curve of PPI network genes by using a 'pROC' packet in R, selecting genes closely related to liver cancer, and screening the standard: AUC >0.85, and the genes with larger AUC values are screened.

The ROC curve and AUC values of the genes are respectively shown in figure 2, RHOC and RHOA are closely related to liver cancer, wherein the AUC value of the liver cancer diagnosed by RHOC is 0.891, the specificity is 0.783, and the sensitivity is 0.880; the AUC value of RHOA for diagnosing liver cancer is 0.931, the specificity is 0.870 and the sensitivity is 0.900.

3. Grouping and screening of differentially expressed genes

Dividing cancer samples into high and low groups according to the median of the expression data of the 2 genes of RHOC and RHOA, intersecting the 2 high groups obtained according to the median of the expression data of the 2 genes, defining all high expression groups as high expression groups, defining other high expression groups as low expression groups, obtaining a high expression group sample of 107 and a low expression group sample of 261.

Based on the grouping of high and low expression, differential expression analysis is carried out by using a 'limma' packet in the R language, and differential expression genes are screened, wherein the screening standard is as follows: FDR < 0.05.

Screening results show that 646 genes show significant differences, wherein 376 genes are significantly up-regulated, and 270 genes are significantly down-regulated.

4. One factor analysis

Genes showing significant difference in high-low expression groups are subjected to single-factor analysis by using a "survivval" package and a "survivor" package in R, and genes related to survival are screened according to the following screening criteria: p < 0.01.

The screening results showed 286 genes associated with survival.

5. LASSO Cox regression analysis

LASSO Cox analysis is carried out on the genes related to survival by using survival and glmnet in R, a regression model is constructed, and a prognostic gene signature is constructed by linear combination of LASSO Cox regression model coefficients and mRNA expression levels.

The results of the regression analysis are shown in Table 1, and a total of 18 gene regression models were obtained.

TABLE 1 prognostic genes

Gene	Regression coefficient
		ADH4	-0.00774
BAMBI	0.00703
		CDCA8	0.25835
CFHR3	-0.02501
		FAM127B	0.00784
IGSF3	0.002954
		LCAT	-0.0292
LPCAT1	0.004821
		MAGEA6	0.040844
ME1	0.031595
		MTND4P20	-0.10539
PFN2	0.026258
		PON1	-0.03801
S100A9	0.062054
		SLC2A1	0.008163
SPP1	0.005281
		SQSTM1	0.065108
TXNRD1	0.049255

6. Classification of marker subgroups

The 18 genes were further subdivided into different subgroups, Sig1, Sig2, Sig3, depending on the effectiveness determined by the p-value. The grouping situation is shown in fig. 3.

7. Prediction of liver cancer by a subset of markers

Based on the grouping of normal diseases, a model is constructed in R by using a machine learning method for 3 subgroups respectively to predict the diagnosis effectiveness of the marker on the disease, wherein 3 models of RF, SVM and DT are constructed for each subgroup.

The results are shown in fig. 4, the AUC of the liver cancer predicted by DT, RF and SVM models constructed by Sig1 group is 0.889, 0.988 and 0.990 respectively; the AUC of liver cancer predicted by DT, RF and SVM models constructed by Sig2 group is 0.901, 0.980 and 0.986 respectively; the AUC of liver cancer predicted by DT, RF and SVM models constructed by Sig3 group is 0.846, 0.978 and 0.990 respectively; different subgroups of liver cancer can effectively predict liver cancer, and the liver cancer has higher sensitivity and specificity.

8. Prediction of liver cancer prognosis by marker subgroups

The R software packages of 'survivval', 'surviviner' and 'ggplot 2' are adopted to carry out survival analysis on the three subgroups.

Results as shown in fig. 5, different subgroups can be used to predict prognosis of liver cancer (P < 0.0001).

The description of the embodiments is only intended to serve for understanding the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.

Claims (10)

1. A biomarker for predicting liver cancer, comprising a combination of any two or more of the following genes: ADH4, BAMBI, CDCA8, CFHR3, FAM127B, IGSF3, LCAT, LPCAT1, MAGEA6, ME1, MTND4P20, PFN2, PON, S100A9, SLC2A1, SPP1, SQSTM1, TXNRD 1;

preferably, the markers comprise at least the following set of characteristic genomes: sig1, Sig1, and Sig 3;

the Sig1 group includes the following genes: CDCA8, SLC2a1, LPCAT1, S100a9, PFN2, SPP 1;

the Sig2 group includes the following genes: CDCA8, SLC2A1, LPCAT1, S100A9, PFN2, SPP1, IGSF3, ADH4, PON1, FAM127B, MTND4P20, TXNRD 1;

the Sig3 group includes the following genes: ADH4, BAMBI, CDCA8, CFHR3, FAM127B, IGSF3, LCAT, LPCAT1, MAGEA6, ME1, MTND4P20, PFN2, PON, S100A9, SLC2A1, SPP1, SQSTM1, TXNRD 1.

2. Use of a reagent for detecting a biomarker according to claim 1 in a sample for the manufacture of a product for diagnosing or prognosing liver cancer.

3. Use of a reagent for detecting a biomarker according to claim 1 in a sample for the preparation of a product for predicting prognosis of liver cancer.

4. Use according to claim 2 or 3, wherein the reagents comprise reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample by digital imaging techniques, protein immunization techniques, dye techniques, nucleic acid sequencing techniques, nucleic acid hybridization techniques, chromatography techniques, mass spectrometry techniques;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using protein immunoassay comprises an antibody specific for an epitope of the biomarker or a functional fragment thereof;

preferably, the antibody is a labeled antibody;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using dye technology comprises a dye specific for the biomarker or functional fragment thereof;

preferably, the reagents for detecting the presence, absence and/or amount of a biomarker or functional fragment thereof in a sample using nucleic acid sequencing techniques comprise primers that bind to the sequence of the biomarker or functional fragment thereof;

preferably, the reagent for detecting the presence, absence and/or amount of a biomarker or a functional fragment thereof in a sample using nucleic acid hybridization techniques comprises a probe that is complementary to the sequence of the biomarker or functional fragment thereof;

preferably, the probe is a labeled probe.

5. Use according to claim 2 or 3, wherein the sample comprises tissue, body fluid.

6. A product for diagnosing or predicting liver cancer/prognosis, comprising reagents for detecting the biomarkers of claim 1;

preferably, the product comprises a chip, a kit;

preferably, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit;

preferably, the kit further comprises instructions for diagnosing or predicting liver cancer/prognosis.

7. A system, comprising:

a sample;

one or more probes and/or stains that bind to the biomarker and/or cognate sequence thereof of claim 1; and

one or more devices capable of quantifying the presence, absence and/or amount of at least one probe or stain that binds to the biomarker and/or cognate sequence thereof of claim 1.

8. A system/apparatus for diagnosing whether a subject has, or is at risk for developing, liver cancer and predicting prognosis of liver cancer, comprising:

an analysis unit adapted to measure the amount of the biomarker of claim 1 in a sample of a subject; and

an evaluation unit comprising a stored reference and a data processor having implemented an algorithm for comparing the amount of the biomarker measured by the analysis unit with the stored reference, thereby diagnosing liver cancer or the presence of a risk of developing liver cancer.

9. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the system/apparatus of claim 8.

10. A method of screening for markers predictive of liver cancer, comprising:

1) constructing an interaction protein network of the liver cancer diagnosis gene;

2) screening network proteins closely related to liver cancer;

3) grouping according to the screened network proteins;

4) screening the differential expression genes according to the grouping in 3);

preferably, the method further comprises performing one-way analysis on the genes in step 4) to screen genes related to survival;

preferably, the method further comprises performing a multifactorial regression analysis of the survival-related gene, screening for a marker for prognosis;

preferably, the liver cancer diagnostic genes include ARG1, GPC3, AFP, ACE, CD10, HSPA 4;

preferably, the liver cancer diagnosis gene is ACE;

preferably, the median of the network protein expression level is used for grouping in step 3).

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