Biomarker combination and application thereof in predicting gastric cancer risk

Abstract

The invention discloses a biomarker composition and application thereof in predicting gastric cancer risk. Specifically, the invention discloses application of a biomarker combination in preparation of a kit for predicting and / or diagnosing gastric cancer. Wherein the biomarker combination is composed of 145 kinds of biomarkers, and the kit comprises a reagent for detecting the expression level of the biomarkers in the biomarker combination. The biomarker combination has the advantages of high sensitivity and high specificity in prediction of gastric cancer risk, provides favorable technical support for prediction of occurrence and development of gastric cancer, has wide scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment and the like.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and diagnosis, and specifically relates to a biomarker combination and its application in predicting gastric cancer risk. Background Art

[0002] East Asian countries are high-incidence countries of gastric cancer, monopolizing the top few countries in terms of gastric cancer incidence. According to the latest data, the top three countries in terms of gastric cancer incidence are South Korea, Mongolia and Japan.

[0003] Clinically, lesions can be detected early through B-ultrasound, CT examination, biopsy and pathological examination, and regular self-examination can also help to detect lesions early. However, these methods have disadvantages such as high false positive rate and late detection time. Therefore, a highly sensitive and accurate diagnostic method is urgently needed to achieve early cancer screening.

[0004] Proteomics plays an important role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis and tolerance to treatment. Proteomic tumor diagnosis has the advantages of high sensitivity, strong specificity and clear background mechanism, and has been increasingly used in tumor detection in recent years. Moreover, the research on these tumor markers is often based on a certain amount of experimental data, and the types of cancer involved and the sample size are relatively limited. In recent years, with the continuous development of proteomics, the big data of body fluid proteomics has continued to increase. Therefore, it is urgent to use big data analysis methods to find a tumor risk model with a wide range of applications and high accuracy, which can help achieve early diagnosis and has important clinical significance for early diagnosis and early treatment of patients. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the prior art lacks biomarkers that can accurately predict the risk of gastric cancer at an early stage, and provides a biomarker combination and its application in predicting the risk of gastric cancer. The biomarker combination of the present invention has the advantages of high sensitivity and high specificity in predicting the risk of early gastric cancer, provides favorable technical support for predicting the occurrence and development of gastric cancer, has broad scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment, etc.

[0006] The present invention solves the above technical problems through the following technical solutions.

[0007] The first aspect of the present invention provides a use of a biomarker combination in preparing a kit for predicting and / or diagnosing gastric cancer;

[0008] The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CS RP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, G P1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM 7. MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PS MB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6.

[0009] A second aspect of the present invention provides a reagent for detecting a biomarker combination, wherein the biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CSRP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSC N1, GAPDH, GIT1, GP1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM7, MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, P CBP3, PDCD10, PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRK It is composed of AG1, PSMB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6.

[0010] In some embodiments of the present invention, the reagent is used to detect the expression level of the biomarker combination; the expression level is the protein expression level and / or the mRNA transcription level.

[0011] In some preferred embodiments of the present invention, the reagent is a biomolecular reagent that specifically binds to the biomarker, or specifically hybridizes with a nucleic acid encoding the biomarker.

[0012] In some embodiments of the present invention, the biomolecule reagent is selected from the group consisting of a primer, a probe and an antibody.

[0013] In some embodiments of the present invention, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

[0014] A third aspect of the present invention provides a use of a reagent for detecting a combination of biomarkers in the preparation of a kit for predicting and / or diagnosing gastric cancer;

[0015] The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CS RP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, G P1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM 7. MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PS MB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6.

[0016] In some embodiments of the present invention, the reagent is as described in the second aspect.

[0017] A fourth aspect of the present invention provides a biomarker combination, the combination of the biomarker combination consisting of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN 2. CORO1C, CRP, CSRP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1 , GAPDH, GIT1, GP1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MA PK8IP3, MCM7, MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PC BP3, PDCD10, PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKA The protein is composed of G1, PSMB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6.

[0018] The fifth aspect of the present invention provides a kit, which comprises the reagent as described in the second aspect and the biomarker combination as described in the fourth aspect.

[0019] A sixth aspect of the present invention provides a method for detecting gastric cancer for non-diagnostic purposes, the method comprising detecting the expression level of a combination of biomarkers in a sample to be tested;

[0020] The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CS RP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, G P1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM 7. MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PS MB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1, and YKT6;

[0021] The expression level is protein expression level and / or mRNA transcription level.

[0022] In the present invention, the "non-diagnostic purpose" refers to the purpose of scientific research and pathological data statistics, and applicable scenarios include verifying whether the animal model is successfully constructed, in vitro drug efficacy experiments, epidemiological statistics of tumors, etc.

[0023] The seventh aspect of the present invention provides a prediction system for gastric cancer risk, the prediction system comprising a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be tested, and transmits the expression level data to the analysis and judgment module; the analysis and judgment module processes the expression level data through Firmiana software, the expression level data is preferably FOT (Fraction of total, defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample), and is preset as a machine learning algorithm based on a generalized linear regression model, to construct a prediction model, respectively predict the probability of a sample suffering from gastric cancer and the probability of not suffering from gastric cancer, to judge whether the expression level data meets the preset judgment condition, so as to predict the risk of the sample suffering from gastric cancer, and output the prediction result; the judgment condition is that the probability of suffering from gastric cancer is greater than or equal to the probability of not suffering from gastric cancer;

[0024] When the expression level data meets the judgment condition, the output prediction result is "having gastric cancer risk"; when the expression level data does not meet the judgment condition, that is, the probability of having gastric cancer is less than the probability of not having gastric cancer, the output prediction result is "not having gastric cancer risk";

[0025] The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CS RP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, G P1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM 7. MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PS MB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1, and YKT6;

[0026] The expression level is protein expression level and / or mRNA transcription level.

[0027] In some embodiments of the present invention, the prediction system is used to process the expression level data through Firmiana software after the receiving or input is completed, and a machine learning algorithm based on a generalized linear regression model is preset to construct a prediction system.

[0028] In some embodiments of the present invention, the sample to be tested is a plasma sample.

[0029] In some embodiments of the present invention, the prediction system further comprises a data collection module, which is used to collect expression level data of the biomarker combination in the sample to be tested, and the expression level data is preferably FOT (Fraction of total, defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample).

[0030] In some embodiments of the present invention, the prediction system is a system for predicting early stage gastric cancer.

[0031] The eighth aspect of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the functions of the prediction system as described in the seventh aspect of the present invention, or implement the steps of the method as described in the sixth aspect of the present invention.

[0032] The ninth aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the functions of the prediction system as described in the seventh aspect of the present invention, or to implement the steps of the method as described in the sixth aspect of the present invention.

[0033] The present invention establishes a tumor risk model through body fluid protein molecules used for gastric cancer screening, which helps to achieve early diagnosis of gastric cancer.

[0034] The present invention obtains a group of biomarkers capable of predicting the risk of gastric cancer by screening body fluid proteomes. The screening method comprises the following steps:

[0035] (1) Collect body fluid samples from healthy people and gastric cancer patients;

[0036] (2) Protein preparation from body fluid samples of healthy people and gastric cancer patients;

[0037] (3) Detecting the expression levels of protein molecules in body fluid samples of healthy people and gastric cancer patients;

[0038] (4) Find the proteomic molecules that are highly expressed specifically in the body fluids of tumor patients and construct a classifier to distinguish them.

[0039] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0040] The reagents and raw materials used in the present invention are commercially available.

[0041] The positive and progressive effects of the present invention are:

[0042] The above-mentioned protein molecular markers provided by the present invention have been found through experiments to have significant changes in expression levels in body fluid samples of tumor patients and healthy people. Therefore, the body fluid protein molecular markers provided by the present invention can be used for risk estimation and detection of tumor patients, and have the advantages of high sensitivity and high specificity, providing favorable technical support for predicting the occurrence and development of gastric cancer.

[0043] The development of corresponding prediction devices based on protein molecular markers of body fluid samples of healthy people and gastric cancer patients has broad scientific research value and provides great convenience for early clinical diagnosis, intervention treatment, etc. The sensitivity and specificity of the 145 marker combinations provided by the present invention reached 100%. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Schematic diagram of the area under the ROC curve of the marker combination in the training set.

[0045] Figure 2 Schematic diagram of the area under the ROC curve of the marker combination in the test set.

[0046] Figure 3 Schematic diagram of the area under the ROC curve of the marker combination in the external validation set.

[0047] Figure 4 Schematic diagram of the system for predicting gastric cancer risk.

[0048] Figure 5 A schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0049] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples. The experimental methods in the following examples without specifying specific conditions are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0050] The present invention includes plasma samples from 114 normal people and 49 gastric cancer patients. The design and implementation of this study have been approved and supervised by ethics committees, and written informed consent has been obtained from all patients.

[0051] Example 1 Screening and validation of a combination of biomarkers for predicting gastric cancer risk

[0052] 1.1 Separation of plasma

[0053] Collect whole blood samples in EDTA anticoagulant tubes, mix by inversion, and centrifuge at 1,600×g for 10 min in a 4°C low-temperature centrifuge. Collect the supernatant (plasma) into new EP tubes after centrifugation at 16,000×g for 10 min to remove cell debris. Dispense the plasma into centrifuge tubes and freeze at -80°C for later use.

[0054] 1.2 Plasma sample pretreatment

[0055] Add 100 μL of 50 mM ammonium bicarbonate to 2 μL of plasma sample, vortex mix for 1 min, heat and incubate the sample at 95 °C for 4 min to denature the protein, cool to room temperature, add 2 μg of trypsin to the system, oscillate at 37 °C for 18 h, and then add 10 μL of ammonia water to stop the enzymatic hydrolysis. Desalt the peptide samples after enzymatic hydrolysis, drain, and freeze at -80 °C until mass spectrometry detection.

[0056] 1.3 Mass spectrometry detection of plasma samples

[0057] The Orbitrap Fusion Lumos three-in-one high-resolution mass spectrometry system (Thermo Fisher Scientific, Rockford, USA) was used in series with a high-performance liquid chromatography system (EASY-nLC 1200, Thermo Fisher) to obtain the mass spectrometry data of the whole protein corresponding to the peptide sample. The specific operation was as follows:

[0058] Nanoflow liquid chromatography was used, and the chromatographic column was a homemade C18 chromatographic column (150 μm ID×8 cm, 1.9 μm / Filler). The column oven temperature was 60°C. The dry powder peptide was reconstituted with loading buffer (0.1% formic acid in water), loaded and separated by chromatographic column, eluted with 600nL / min linear 6–30% mobile phase B (ACN and 0.1% formic acid), and a 10-min liquid phase gradient was combined with data independent acquisition (DIA) mass spectrometry detection. The DIA mass spectrometry detection parameters were set as follows: ion mode was positive ion; primary mass spectrometry resolution was 30K, maximum injection time was 20ms, AGC Target was 3e6, and scanning range was 300-1400m / z; secondary scanning resolution was 15K, 30 variable isolation windows were acquired, and collision energy was 27%. The liquid chromatography tandem mass spectrometry system was controlled by Xcalibur software for data acquisition.

[0059] 1.4 Data Analysis

[0060] All data were processed using Firmiana (V1.0). Firmiana is a workflow based on the Galaxy system, consisting of multiple functional modules such as user login interface, raw data, identification and quantification, data analysis and knowledge mining. The DIA data were searched using DIANN (v12.1) against the UniProt human protein database (updated on 2019.12.17, 20406 entries). The mass difference of the parent ion is 20ppm, and the mass difference of the daughter ion is 50mmu. A maximum of two missed cleavage sites are allowed. The search engine sets cysteine ​​carbamidomethylation as a fixed modification and methionine N-acetylation and oxidation as variable modifications. The parent ion charge range is set to +2, +3 and +4. The false discovery rate (FDR) is set to 1%. The results of the DIA data were merged into the reference library using SpectraST software. A total of 327 libraries were used as reference libraries.

[0061] The quantitative results of the identified peptides were recorded as the average of the peak areas of the chromatographic fragment ions in all reference spectral libraries. Protein quantification was performed using a label-free intensity-based absolute quantification (iBAQ) method. The peak area value was calculated as a portion of the corresponding protein. The total fraction (FOT) is used to represent the standardized abundance of a specific protein in a sample. FOT is defined as the iBAQ of a protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and a 1% FDR were selected. The FOT of each protein was calculated and the FOT of each protein was input into the generalized linear regression model as protein expression data.

[0062] The Firmiana selected in this embodiment is preset as a machine learning algorithm based on a generalized linear regression model, and a prediction model is constructed (the FOT values ​​of the training set are shown in Tables 1-12, the FOT values ​​of the test set are shown in Tables 13-24, and the FOT values ​​of the external validation set are shown in Tables 25-36), respectively predicting the probability of the sample suffering from gastric cancer and the probability of not suffering from gastric cancer. The code for constructing the prediction model is:

[0063] from sklearn.linear model import LogisticRegressionCV

[0064] import joblib

[0065] import pandas as pd

[0066] tumor_types = ['***','***','***','***','***'] #*** refers to the 145 protein molecule markers described in this invention

[0067] for tumor_type in tumor_types:

[0068] df_train=pd.read_csv('{}_train.csv'.format(tumor_type))

[0069] df_test=pd.read_csv('{}_test.csv'.format(tumor_type))

[0070] df_val=pd.read_csv('{}_validation.csv'.format(tumor_type))

[0071] X_train = df_train.iloc[:,2:]

[0072] y_train = df_train.iloc[:,1]

[0073] X_test = df_test.iloc[:,2:]

[0074] y_test = df_test.iloc[:,1]

[0075] X_val = df_val.iloc[:,2:]

[0076] y_val = df_val.iloc[:,1]

[0077] model=LogisticRegressionCV()

[0078] model.fit(X_train,y_train)

[0079] joblib.dump(model,f'{tumor_type}_model.joblib');

[0080] The experiment found that the expression levels of some proteins in the body fluid samples of tumor patients and healthy people were significantly changed. The expression levels of 145 protein molecular markers (ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, etc.) in the plasma samples of gastric cancer patients were analyzed. , CHGB, CLIC1, CNN2, CORO1C, CRP, CSRP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKB P1A, FSCN1, GAPDH, GIT1, GP1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A , MAPK8IP3, MCM7, MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3 , PDCD10, PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PSMB8, The relative expression levels of PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6 were used to draw ROC curves (Receiver Operating Curve) and calculate AUC (Area Under the ROC Curve).The training set included 24 positive cases and 55 negative cases, with AUC = 0.99, diagnostic sensitivity 83.33%, and specificity 100.00% (see, Figure 1 ); the test set included the remaining 10 positive cases and 25 negative cases, AUC = 0.99, diagnostic sensitivity 90.00%, specificity 92.00% (see Figure 2 ); the external validation set included 15 positive cases and 34 negative cases, AUC = 0.96, diagnostic sensitivity 80.00%, specificity 94.12% ( Figure 3 ). For the analysis method, see Karimollah Hajian-Tilaki, Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, Caspian J Intern Med 2013; 4(2): 627-635. For an unknown sample, the expression level of the above biomarkers is substituted into the model to obtain the gastric cancer risk prediction of the sample and output the result. When the probability of having gastric cancer is greater than or equal to the probability of not having gastric cancer, the output prediction result is "having gastric cancer risk"; when the probability of having gastric cancer is less than the probability of not having gastric cancer, the output prediction result is "not having gastric cancer risk".

[0081] The above results show that 145 protein molecular markers in the plasma of tumor patients (ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, COR O1C, CRP, CSRP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH , GIT1, GP1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM7, MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PSMB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6) can be used in combination to predict tumor risk.

[0082] Table 1 FOT values ​​of 145 protein markers in the training set

[0083]

[0084]

[0085]

[0086] Table 2 FOT values ​​of 145 protein markers in the training set

[0087]

[0088]

[0089]

[0090] Table 3 FOT values ​​of 145 protein markers in the training set

[0091]

[0092]

[0093]

[0094] Table 4 FOT values ​​of 145 protein markers in the training set

[0095]

[0096]

[0097] Table 5 FOT values ​​of 145 protein markers in the training set

[0098]

[0099]

[0100]

[0101] Table 6 FOT values ​​of 145 protein markers in the training set

[0102]

[0103]

[0104]

[0105] Table 7 FOT values ​​of 145 protein markers in the training set

[0106]

[0107]

[0108] Table 8 FOT values ​​of 145 protein markers in the training set

[0109]

[0110]

[0111] Table 9 FOT values ​​of 145 protein markers in the training set

[0112]

[0113]

[0114]

[0115] Table 10 FOT values ​​of 145 protein markers in the training set

[0116]

[0117]

[0118]

[0119] Table 11 FOT values ​​of 145 protein markers in the training set

[0120]

[0121]

[0122] Table 12 FOT values ​​of 145 protein markers in the training set

[0123]

[0124]

[0125] Table 13 FOT values ​​of 145 protein markers in the test set

[0126]

[0127]

[0128] Table 14 FOT values ​​of 145 protein markers in the test set

[0129]

[0130]

[0131] Table 15 FOT values ​​of 145 protein markers in the test set

[0132]

[0133]

[0134] Table 16 FOT values ​​of 145 protein markers in the test set

[0135]

[0136] Table 17 FOT values ​​of 145 protein markers in the test set

[0137]

[0138]

[0139] Table 18 FOT values ​​of 145 protein markers in the test set

[0140]

[0141]

[0142] Table 19 FOT values ​​of 145 protein markers in the test set

[0143]

[0144]

[0145] Table 20 FOT values ​​of 145 protein markers in the test set

[0146]

[0147]

[0148] Table 21 FOT values ​​of 145 protein markers in the test set

[0149]

[0150] Table 22 FOT values ​​of 145 protein markers in the test set

[0151]

[0152]

[0153] Table 23 FOT values ​​of 145 protein markers in the test set

[0154]

[0155]

[0156] Table 24 FOT values ​​of 145 protein markers in the test set

[0157]

[0158]

[0159] Table 25 FOT values ​​of 145 protein markers in the external validation set

[0160]

[0161]

[0162] Table 26 FOT values ​​of 145 protein markers in the external validation set

[0163]

[0164]

[0165] Table 27 FOT values ​​of 145 protein markers in the external validation set

[0166]

[0167]

[0168] Table 28 FOT values ​​of 145 protein markers in the external validation set

[0169]

[0170]

[0171] Table 29 FOT values ​​of 145 protein markers in the external validation set

[0172]

[0173]

[0174] Table 30 FOT values ​​of 145 protein markers in the external validation set

[0175]

[0176]

[0177] Table 31 FOT values ​​of 145 protein markers in the external validation set

[0178]

[0179]

[0180] Table 32 FOT values ​​of 145 protein markers in the external validation set

[0181]

[0182]

[0183] Table 33 FOT values ​​of 145 protein markers in the external validation set

[0184]

[0185]

[0186] Table 34 FOT values ​​of 145 protein markers in the external validation set

[0187]

[0188]

[0189] Table 35 FOT values ​​of 145 protein markers in the external validation set

[0190]

[0191]

[0192] Table 36 FOT values ​​of 145 protein markers in the external validation set

[0193]

[0194]

[0195] Example 2 System for predicting gastric cancer risk

[0196] The system 61 for predicting gastric cancer risk includes a data processing module 52 and a judgment and output module 53, and also includes a data collection module 51 ( Figure 4 ).

[0197] The data collection module 51 is used to collect the expression level data of the biomarker combination in the patient's gastric cancer tissue sample and transmit it to the data processing module.

[0198] The data processing module 52 is used to analyze the received or inputted expression level data of the biomarker combination according to the data analysis method described in Example 4 to obtain a calculation result. The expression level data of the biomarker combination can be collected by the data collection module 51, or the expression level data of the biomarker combination can be obtained from other sources.

[0199] The judgment and output module 53 is used to judge whether the calculation result meets the preset judgment condition, that is, the probability of gastric cancer risk is greater than or equal to the predicted probability of not having gastric cancer risk, so as to predict the gastric cancer risk and output the predicted result; wherein, in the judgment and output module, when the expression level data meets the judgment condition that the probability of gastric cancer risk is greater than or equal to the predicted probability of not having gastric cancer risk, the output prediction result is "having gastric cancer risk"; when the expression level data does not meet the judgment condition that the probability of gastric cancer risk is less than the predicted probability of not having gastric cancer risk, the output prediction result is "not having gastric cancer risk".

[0200] Embodiment 3 Electronic device

[0201] This embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method for predicting gastric cancer risk in Embodiment 1 of the present invention can be implemented when the processor executes the computer program.

[0202] Figure 5 The hardware structure diagram of this embodiment is shown, and the electronic device 4 specifically includes:

[0203] At least one processor 91, at least one memory 92, and a bus 93 for connecting different system components (including the processor 91 and the memory 92), wherein:

[0204] The bus 93 includes a data bus, an address bus, and a control bus.

[0205] The memory 92 includes a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read-only memory (ROM) 923 .

[0206] Memory 92 also includes a program / utility 925 having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0207] The processor 91 executes various functional applications and data processing by running the computer programs stored in the memory 92, such as the data analysis method of embodiment 1 of the present invention.

[0208] The electronic device 9 can further communicate with one or more external devices 94 (e.g., keyboards, pointing devices, etc.). Such communication can be performed through an input / output (I / O) interface 95. In addition, the electronic device 9 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 through a bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0209] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0210] Embodiment 4 Computer readable storage medium

[0211] The embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for predicting gastric cancer risk in Embodiment 1 of the present invention are implemented.

[0212] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0213] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the method for predicting gastric cancer risk in Example 1 of the present invention.

[0214] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0215] Finally, the above specific implementation methods are only used to illustrate the technical solution of the present invention, but not to limit it. Biomarker name: (refer to NCBI or genecards database)

[0216] ABLIM1: actin binding LIM protein 1, Gene ID: 3983ABRACL: ABRA C-terminallike, Gene ID: 58527

[0217] ACTA1: actin alpha 1, Gene ID: 58

[0218] ACTA2: actin alpha 2, Gene ID: 59

[0219] ACTB: actin beta, Gene ID: 60

[0220] ACTBL2: actin beta like 2, Gene ID: 345651

[0221] ACTC1: Actin Alpha Cardiac Muscle 1, Gene ID: 70

[0222] ACTG1: Actin Gamma 1, Gene ID: 71

[0223] ACTG2: Actin Gamma 2, Gene ID: 72

[0224] ACTR3B: actin related protein 3B, Gene ID: 57180

[0225] ACTR3C: actin related protein 3C, Gene ID: 653857

[0226] ALDH9A1:aldehyde dehydrogenase 9family member A1,Gene ID:223ANKHD1:ankyrin repeat and KH domain containing 1,Gene ID:54882ANKRD17:ankyrin repeatdomain 17,Gene ID:26057ARHGDIB:Rho GDP dissociation inhibitor beta,Gene ID:397

[0227] ARHGEF10L:Rho guanine nucleotide exchange factor 10like,Gene ID:55160ARMC8:Armadillo Repeat Containing 8,Gene ID:25852ARPC5:actin relatedprotein 2 / 3complex subunit 5,Gene ID:10092BHMT2:betaine--homocysteine S-methyltransferase 2,Gene ID:23743BIN2:bridging integrator 2,Gene ID:51411

[0228] CALM1:calmodulin 1,Gene ID:801

[0229] CALM2:calmodulin 2,Gene ID:805

[0230] CALM3:calmodulin 3,Gene ID:808

[0231] CALML3:calmodulin like 3,Gene ID:810

[0232] CAPN1:calpain 1,Gene ID:823

[0233] CAVIN2:caveolae associated protein 2,Gene ID:8436CFL1:cofilin 1,GeneID:1072

[0234] CHGB:chromogranin B,Gene ID:1114

[0235] CLIC1:chloride intracellular channel 1,Gene ID:1192CNN2:calponin 2,Gene ID:1265

[0236] CORO1C:coronin 1C,Gene ID:23603

[0237] CRP:C-reactive protein,Gene ID:1401

[0238] CSRP1:cysteine and glycine rich protein 1,Gene ID:1465

[0239] CYFIP1:cytoplasmic FMR1 interacting protein 1,Gene ID:23191DLG1:discslarge MAGUK scaffold protein 1,Gene ID:1739DSC3:desmocollin 3,Gene ID:1825

[0240] DTNB:dystrobrevin beta,Gene ID:1838

[0241] DUSP3:dual specificity phosphatase 3,Gene ID:1845

[0242] EIF5A2:eukaryotic translation initiation factor 5A2,Gene ID:56648

[0243] EIF5AL1:eukaryotic translation initiation factor 5A like 1,Gene ID:143244EIF5B:eukaryotic translation initiation factor 5B,Gene ID:9669ELMOD2:ELMO domain containing 2,Gene ID:255520

[0244] EML4:EMAP like 4,Gene ID:27436

[0245] ENO1:enolase 1,Gene ID:2023

[0246] ENO2:enolase 2,Gene ID:2026

[0247] ENO3:enolase 3,Gene ID:2027

[0248] FHL1:four and a half LIM domains 1,Gene ID:2273

[0249] FKBP1A:FKBP prolyl isomerase 1A,Gene ID:2280

[0250] FSCN1:fascin actin-bundling protein 1,Gene ID:6624

[0251] GAPDH:glyceraldehyde-3-phosphate dehydrogenase,Gene ID:2597

[0252] GIT1:GIT ArfGAP 1,Gene ID:28964

[0253] GP1BB:glycoprotein Ib platelet subunit beta,Gene ID:2812GP6:glycoprotein VI platelet,Gene ID:51206

[0254] GSTO1:glutathione S-transferase omega 1,Gene ID:9446

[0255] HBE1:hemoglobin subunit epsilon 1,Gene ID:3046

[0256] HNRNPA0:heterogeneous nuclear ribonucleoprotein A0,Gene ID:10949

[0257] HSPA6:heat shock protein family A(Hsp70)member 6,Gene ID:3310

[0258] HSPA7:heat shock protein family A(Hsp70)member 7(pseudogene),Gene ID:3311IGF1:insulin like growth factor 1,Gene ID:3479

[0259] ILK:integrin linked kinase,Gene ID:3611

[0260] IMPDH1:inosine monophosphate dehydrogenase 1,Gene ID:3614ISLR:immunoglobulin superfamily containing leucine rich repeat,Gene ID:3671ITGA2B:integrin subunit alpha 2b,Gene ID:3674

[0261] ITIH3:inter-alpha-trypsin inhibitor heavy chain 3,Gene ID:3699ITIH5:inter-alpha-trypsin inhibitor heavy chain 5,Gene ID:80760LGALSL:galectinlike,Gene ID:29094

[0262] LRG1:leucine rich alpha-2-glycoprotein 1,Gene ID:116844MAP1A:microtubule associated protein 1A,Gene ID:4130

[0263] MAPK8IP3:mitogen-activated protein kinase 8 interacting protein 3,Gene ID:23162MCM7:minichromosome maintenance complex component 7,Gene ID:4176

[0264] MECP2:methyl-CpG binding protein 2,Gene ID:4204

[0265] MIF:macrophage migration inhibitory factor,Gene ID:4282

[0266] MMP10:matrix metallopeptidase 10,Gene ID:4319

[0267] MRPL37:mitochondrial ribosomal protein L37,Gene ID:51253

[0268] MSL1:MSL complex subunit 1,Gene ID:339287

[0269] MTHFD2:methylenetetrahydrofolate dehydrogenase(NADP+dependent)2,GeneID:10797MTPN:myotrophin,Gene ID:136319

[0270] MYL12B:myosin light chain 12B,Gene ID:103910

[0271] MYL3:myosin light chain 3,Gene ID:4634

[0272] MYL6B:myosin light chain 6B,Gene ID:140465

[0273] NAXD:NAD(P)HX dehydratase,Gene ID:55739

[0274] NCALD:neurocalcin delta,Gene ID:83988

[0275] NIT2:nitrilase family member 2,Gene ID:56954

[0276] NUP155:nucleoporin 155,Gene ID:9631

[0277] ORM1:orosomucoid 1,Gene ID:5004

[0278] PARVB:parvin beta,Gene ID:29780

[0279] PCBP1:poly(rC)binding protein 1,Gene ID:5093

[0280] PCBP3:poly(rC)binding protein 3,Gene ID:54039

[0281] PDCD10:programmed cell death 10,Gene ID:11235

[0282] PDLIM1:PDZ and LIM domain 1,Gene ID:9124

[0283] PEX5:peroxisomal biogenesis factor 5,Gene ID:5830

[0284] PFN1:profilin 1,Gene ID:5216

[0285] PGAM2:phosphoglycerate mutase 2,Gene ID:5224

[0286] PGK2:phosphoglycerate kinase 2,Gene ID:5232

[0287] PKLR:pyruvate kinase L / R,Gene ID:5313

[0288] PKM:pyruvate kinase M1 / 2,Gene ID:5315

[0289] PMVK:phosphomevalonate kinase,Gene ID:10654

[0290] POSTN:periostin,Gene ID:10631

[0291] POTEE:POTE ankyrin domain family member E,Gene ID:445582POTEF:POTEankyrin domain family member F,Gene ID:728378POTEI:POTE ankyrin domain familymember I,Gene ID:653269POTEJ:POTE ankyrin domain family member J,Gene ID:653781POTEKP:POTE ankyrin domain family member K,pseudogene,Gene ID:440915PPCS:phosphopantothenoylcysteine synthetase,Gene ID:79717PPP2R2D:protein phosphatase 2 regulatory subunit Bdelta,Gene ID:55844

[0292] PRDX5:peroxiredoxin 5,Gene ID:25824

[0293] PRKAG1:protein kinase AMP-activated non-catalytic subunit gamma 1,Gene ID:5571PSMB8:proteasome 20S subunit beta 8,Gene ID:5696

[0294] PTGIS:prostaglandin I2 synthase,Gene ID:5740

[0295] PTMA:prothymosin alpha,Gene ID:5757

[0296] PUDP:pseudouridine 5'-phosphatase,Gene ID:8226

[0297] RAB1B:RAB1B,member RAS oncogene family,Gene ID:81876RARRES2:retinoicacid receptor responder 2,Gene ID:5919RCN1:reticulocalbin 1,Gene ID:5954

[0298] RDH10:retinol dehydrogenase 10,Gene ID:157506

[0299] RECK:reversion inducing cysteine rich protein with kazal motifs,GeneID:8434RPL5:ribosomal protein L5,Gene ID:6125

[0300] RPS27A:ribosomal protein S27a,Gene ID:6233

[0301] RPS7:ribosomal protein S7,Gene ID:6201

[0302] S100A9:S100 calcium binding protein A9,Gene ID:6280

[0303] SAA1:serum amyloid A1,Gene ID:6288

[0304] SAA2:serum amyloid A2,Gene ID:6289

[0305] SF3A3:splicing factor 3a subunit 3,Gene ID:10946

[0306] SFTPB:surfactant protein B,Gene ID:6439

[0307] SKP1:S-phase kinase associated protein 1,Gene ID:6500

[0308] SMC1A:structural maintenance of chromosomes 1A,Gene ID:8243SMC2:structural maintenance of chromosomes 2,Gene ID:10592SPG21:SPG21 abhydrolasedomain containing,maspardin,Gene ID:51324

[0309] SYNE1:spectrin repeat containing nuclear envelope protein 1,Gene ID:23345SYNE2:spectrin repeat containing nuclear envelope protein 2,Gene ID:23224SYNM:synemin,Gene ID:23336

[0310] TAGLN2:transgelin 2,Gene ID:8407

[0311] TBXAS1:thromboxane A synthase 1,Gene ID:6916

[0312] TMSB4X:thymosin beta 4 X-linked,Gene ID:7114

[0313] TNFAIP2:TNF alpha induced protein 2,Gene ID:7127

[0314] TPM4:tropomyosin 4,Gene ID:7171

[0315] TUBB8B:tubulin beta 8B,Gene ID:260334

[0316] TXN:thioredoxin,Gene ID:7295

[0317] UBA52:ubiquitin A-52 residue ribosomal protein fusion product 1,GeneID:7311UBB:ubiquitin B,Gene ID:7314

[0318] UBC:ubiquitin C,Gene ID:7316

[0319] VPS13A:vacuolar protein sorting 13 homolog A,Gene ID:23230

[0320] VWF:von Willebrand factor,Gene ID:7450

[0321] XPNPEP1:X-prolyl aminopeptidase 1,Gene ID:7511

[0322] YKT6:YKT6 v-SNARE homolog,Gene ID:10652。

Claims

1. A reagent for detecting a combination of biomarkers, characterized in that: The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CSRP1 , CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, GP1 BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM7, MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD1 0. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PSM B8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1, and YKT6; Preferably, the reagent is used to detect the expression level of the biomarker combination, and the expression level is the protein expression level and / or the mRNA transcription level; More preferably, the reagent is a biomolecular reagent that specifically binds to the biomarker, or specifically hybridizes with the nucleic acid encoding the biomarker, and the biomolecular reagent is preferably selected from primers, probes and antibodies; and / or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

2. A biomarker combination, characterized in that: The combination of biomarkers consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CS RP1, CYFIP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, G P1BB, GP6, GSTO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM 7. MECP2, MIF, MMP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD 10. PDLIM1, PEX5, PFN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PS MB8, PTGIS, PTMA, PUDP, RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6.

3. A kit, characterized in that: The kit comprises the reagent according to claim 1 and the biomarker combination according to claim 2.

4. Use of the reagent according to claim 1, the biomarker combination according to claim 2, or the kit according to claim 3 in the preparation of a product for predicting and / or diagnosing gastric cancer.

5. A system for predicting gastric cancer risk, characterized in that: The prediction system includes a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be tested, and transmits the expression level data to the analysis and judgment module; the analysis and judgment module processes the expression level data through Firmiana software, which is preset as a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from gastric cancer and the probability of not suffering from gastric cancer, respectively, judges whether the expression level data meets the preset judgment conditions, so as to predict the risk of the sample suffering from gastric cancer, and outputs the prediction result; The judgment condition is that the probability of having gastric cancer is greater than or equal to the probability of not having gastric cancer; When the expression level data meets the judgment condition, the output prediction result is "having gastric cancer risk"; when the expression level data does not meet the judgment condition, that is, the probability of having gastric cancer is less than the probability of not having gastric cancer, the output prediction result is "not having gastric cancer risk"; The biomarker combination consists of ABLIM1, ABRACL, ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG1, ACTG2, ACTR3B, ACTR3C, ALDH9A1, ANKHD1, ANKRD17, ARHGDIB, ARHGEF10L, ARMC8, ARPC5, BHMT2, BIN2, CALM1, CALM2, CALM3, CALML3, CAPN1, CAVIN2, CFL1, CHGB, CLIC1, CNN2, CORO1C, CRP, CSRP1, CYF IP1, DLG1, DSC3, DTNB, DUSP3, EIF5A2, EIF5AL1, EIF5B, ELMOD2, EML4, ENO1, ENO2, ENO3, FHL1, FKBP1A, FSCN1, GAPDH, GIT1, GP1BB, GP6, G STO1, HBE1, HNRNPA0, HSPA6, HSPA7, IGF1, ILK, IMPDH1, ISLR, ITGA2B, ITIH3, ITIH5, LGALSL, LRG1, MAP1A, MAPK8IP3, MCM7, MECP2, MIF, M MP10, MRPL37, MSL1, MTHFD2, MTPN, MYL12B, MYL3, MYL6B, NAXD, NCALD, NIT2, NUP155, ORM1, PARVB, PCBP1, PCBP3, PDCD10, PDLIM1, PEX5, P FN1, PGAM2, PGK2, PKLR, PKM, PMVK, POSTN, POTEE, POTEF, POTEI, POTEJ, POTEKP, PPCS, PPP2R2D, PRDX5, PRKAG1, PSMB8, PTGIS, PTMA, PUDP , RAB1B, RARRES2, RCN1, RDH10, RECK, RPL5, RPS27A, RPS7, S100A9, SAA1, SAA2, SF3A3, SFTPB, SKP1, SMC1A, SMC2, SPG21, SYNE1, SYNE2, SYNM, TAGLN2, TBXAS1, TMSB4X, TNFAIP2, TPM4, TUBB8B, TXN, UBA52, UBB, UBC, VPS13A, VWF, XPNPEP1 and YKT6, and the expression level is the protein expression level and / or mRNA transcription level; Preferably, the sample to be tested is a human plasma sample; and / or, the prediction system further comprises a data collection module, wherein the data collection module is used to collect expression level data of the biomarker combination in the sample to be tested.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the functions of the prediction system according to claim 5 can be realized.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: The processor is configured to execute the computer program to implement the functions of the prediction system according to claim 5.

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