Biomarker combination and application thereof in predicting risk of testicular cancer

Abstract

The invention discloses a biomarker combination and application thereof in predicting the risk of testicular cancer. Specifically, the invention discloses application of a biomarker combination in preparation of a kit for predicting and / or diagnosing testicular cancer. Wherein the biomarker combination is composed of 110 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 early testicular cancer risk, provides favorable technical support for prediction of occurrence and development of testicular 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 the risk of testicular cancer. Background Art

[0002] Testicular cancer is the second most common cancer among men aged 20-49, with an incidence of 10.7%. Testicular tumors have a good prognosis, with 95% of early-stage patients surviving for a long time. Advanced tumors have a 5-year survival rate of 80% after comprehensive treatment with radiotherapy, chemotherapy, and high-dose chemotherapy supported by hematopoietic stem cells. Early detection and diagnosis of testicular tumors are very important. Clinically, lesions can be detected early through scrotal translucency tests, B-ultrasound, CT examinations, biopsy and pathological examinations. Regular self-examination can also help detect lesions early. However, these methods have the disadvantages of high false positive rates and late detection. Therefore, there is an urgent need for a highly sensitive and accurate diagnostic method to achieve early cancer screening.

[0003] 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, by collecting body fluid proteomics data and using big data analysis methods to find a tumor risk model with a wide range of applicability and high accuracy, it is helpful to achieve early diagnosis and has important clinical significance for early diagnosis and early treatment of patients. Summary of the invention

[0004] The technical problem to be solved by the present invention is that the prior art lacks biomarkers that can accurately predict the risk of testicular cancer at an early stage, and provides a biomarker combination and its application in predicting the risk of testicular cancer. The biomarker combination of the present invention has the advantages of high sensitivity and high specificity in predicting the risk of early testicular cancer, provides favorable technical support for predicting the occurrence and development of testicular cancer, has broad scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment, etc.

[0005] The present invention solves the above technical problems through the following technical solutions.

[0006] A first aspect of the present invention provides a use of a biomarker combination in the preparation of a kit for predicting and / or diagnosing testicular cancer;

[0007] The biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IG F1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NU It is composed of DT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1.

[0008] A second aspect of the present invention provides a reagent for detecting a biomarker combination, wherein the biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1 , ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR , HPRT1, HSPA6, IGF1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3- 48. IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IG HV4-59, IGHV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, N AXD, NUDT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1.

[0009] 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.

[0010] 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.

[0011] In some embodiments of the present invention, the biomolecule reagent is selected from the group consisting of a primer, a probe and an antibody.

[0012] In some embodiments of the present invention, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

[0013] 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 testicular cancer;

[0014] The biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IG F1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NU It is composed of DT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1.

[0015] In some embodiments of the present invention, the reagent is as described in the second aspect.

[0016] The fourth aspect of the present invention provides a biomarker combination, wherein the biomarker combination is composed of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, E NO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, H PRT1, HSPA6, IGF1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48 , IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGH V4-59, IGHV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NA XD, NUDT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1.

[0017] 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.

[0018] A sixth aspect of the present invention provides a method for detecting testicular cancer for non-diagnostic purposes, the method comprising detecting the expression level of a combination of biomarkers in a sample to be tested;

[0019] The biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IG F1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NU DT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1, and UFC1;

[0020] The expression level is protein expression level and / or mRNA transcription level.

[0021] 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.

[0022] The seventh aspect of the present invention provides a prediction system for testicular 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, and a prediction model is constructed to predict the probability of the sample suffering from testicular cancer and the probability of not suffering from testicular cancer, respectively, and to judge whether the expression level data meets the preset judgment condition to predict the risk of the sample suffering from testicular cancer, and output the prediction result; the judgment condition is that the probability of suffering from testicular cancer is greater than or equal to the probability of not suffering from testicular cancer;

[0023] When the expression level data meets the judgment condition, the output prediction result is "having a risk of testicular cancer"; when the expression level data does not meet the judgment condition, that is, the probability of having testicular cancer is less than the probability of not having testicular cancer, the output prediction result is "not having a risk of testicular cancer";

[0024] The biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IG F1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NU DT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1, and UFC1;

[0025] The expression level is protein expression level and / or mRNA transcription level.

[0026] 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.

[0027] In some embodiments of the present invention, the sample to be tested is a plasma sample.

[0028] 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).

[0029] In some embodiments of the present invention, the prediction system is a system for predicting early stage testicular cancer.

[0030] 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.

[0031] 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.

[0032] The present invention establishes a tumor risk model through body fluid protein molecules used for testicular cancer screening, which helps to achieve early diagnosis of testicular cancer.

[0033] The present invention obtains a group of biomarkers capable of predicting the risk of testicular cancer by screening the body fluid proteome, and the screening method comprises the following steps:

[0034] (1) Collect body fluid samples from healthy subjects and testicular cancer patients;

[0035] (2) Protein preparation of body fluid samples from healthy subjects and testicular cancer patients;

[0036] (3) Detecting the expression levels of protein molecules in body fluid samples of healthy subjects and testicular cancer patients;

[0037] (4) Find the proteomic molecules that are highly expressed specifically in the body fluids of tumor patients and construct a classifier to distinguish them.

[0038] 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.

[0039] The reagents and raw materials used in the present invention are commercially available.

[0040] The positive and progressive effects of the present invention are:

[0041] The biomarker combination of the present invention is used for risk prediction and detection of testicular cancer, has the advantages of high sensitivity and high specificity, provides favorable technical support for predicting the occurrence and development of testicular cancer, has broad scientific research value and provides great convenience for early clinical diagnosis, intervention treatment, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the area under the ROC curve of the marker combination in the training set.

[0043] Figure 2 Schematic diagram of the area under the ROC curve of the marker combination in the test set.

[0044] Figure 3 Schematic diagram of the area under the ROC curve of the marker combination in the independent validation set.

[0045] Figure 4 Schematic diagram of the system for predicting testicular cancer risk.

[0046] Figure 5 A schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0047] 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.

[0048] The embodiment includes 114 normal people and 46 testicular cancer patients' plasma samples. The design and implementation of this study have been approved and supervised by ethics voting, and written informed consent has been obtained from all patients.

[0049] Example 1 Screening and validation of a combination of biomarkers for predicting testicular cancer risk

[0050] 1.1 Separation of plasma

[0051] 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.

[0052] 1.2 Plasma sample pretreatment

[0053] Add 100uL 50mM ammonium bicarbonate to 2μL plasma sample, vortex mix for 1min, heat and incubate the sample at 95℃ for 4min to denature the protein, cool to room temperature, add 2μg trypsin to the system, oscillate at 37℃ for 18h, and then add 10μL ammonia water to the system to stop enzymatic hydrolysis. Desalt the peptide samples after enzymatic hydrolysis, drain, and freeze at -80℃ until mass spectrometry detection.

[0054] 1.3 Mass spectrometry detection of plasma samples

[0055] 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:

[0056] 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 the 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.

[0057] 1.4 Data Analysis

[0058] 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.

[0059] 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. We calculated the peak area values ​​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 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.

[0060] The Firmiana selected in this embodiment is preset as a machine learning algorithm based on a generalized linear regression model to build a prediction model to predict the probability of a sample suffering from testicular cancer and the probability of not suffering from testicular cancer. The code for building the prediction model is:

[0061]

[0062]

[0063] 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 110 protein molecular markers (ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1 , HPR, HPRT1, HSPA6, IGF1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IGH V4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NUDT21, NUP205 The relative expression levels of NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1, UFC1) were used to draw the ROC curve (Receiver Operating Curve) and calculate the AUC (Area Under the ROC Curve). The training set included 22 positive cases and 56 negative cases, with AUC = 1.00, a diagnostic sensitivity of 95.45%, and a specificity of 98.21% (see Figure 1 ). The test set included 10 positive cases and 24 negative cases, AUC = 1.00, diagnostic sensitivity 100.00%, and specificity 100.00% (see Figure 2 ). The independent validation set included 14 positive cases and 34 negative cases, with AUC = 1.00, diagnostic sensitivity 100.00%, and specificity 100.00% (see 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. The FOT values ​​of 110 protein markers in the training set, test set, and independent validation set are shown in Tables 1-10, 11-20, and 21-30.

[0064] Table 1 FOT values ​​of 110 protein markers in the training set

[0065]

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[0068] Table 2 FOT values ​​of 110 protein markers in the training set

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[0072] Table 3 FOT values ​​of 110 protein markers in the training set

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[0075] Table 4 FOT values ​​of 110 protein markers in the training set

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[0079] Table 5 FOT values ​​of 110 protein markers in the training set

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[0083] Table 6 FOT values ​​of 110 protein markers in the training set

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[0086] Table 7 FOT values ​​of 110 protein markers in the training set

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[0090] Table 8 FOT values ​​of 110 protein markers in the training set

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[0094] Table 9 FOT values ​​of 110 protein markers in the training set

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[0097] Table 10 FOT values ​​of 110 protein markers in the training set

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[0101] Table 11 FOT values ​​of 110 protein markers in the test set

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[0104] Table 12 FOT values ​​of 110 protein markers in the test set

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[0107] Table 13 FOT values ​​of 110 protein markers in the test set

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[0109] Table 14 FOT values ​​of 110 protein markers in the test set

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[0112] Table 15 FOT values ​​of 110 protein markers in the test set

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[0115] Table 16 FOT values ​​of 110 protein markers in the test set

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[0118] Table 17 FOT values ​​of 110 protein markers in the test set

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[0121] Table 18 FOT values ​​of 110 protein markers in the test set

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[0124] Table 19 FOT values ​​of 110 protein markers in the test set

[0125]

[0126] Table 20 FOT values ​​of 110 protein markers in the test set

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[0128] Table 21 FOT values ​​of 110 protein markers in the independent validation set

[0129]

[0130]

[0131]

[0132] Table 22 FOT values ​​of 110 protein markers in the independent validation set

[0133]

[0134]

[0135] Table 23 FOT values ​​of 110 protein markers in the independent validation set

[0136]

[0137]

[0138] Table 24 FOT values ​​of 110 protein markers in the independent validation set

[0139]

[0140]

[0141] Table 25 FOT values ​​of 110 protein markers in the independent validation set

[0142]

[0143]

[0144] Table 26 FOT values ​​of 110 protein markers in the independent validation set

[0145]

[0146]

[0147] Table 27 FOT values ​​of 110 protein markers in the independent validation set

[0148]

[0149]

[0150] Table 28 FOT values ​​of 110 protein markers in the independent validation set

[0151]

[0152]

[0153] Table 29 FOT values ​​of 110 protein markers in the independent validation set

[0154]

[0155]

[0156] Table 30 FOT values ​​of 110 protein markers in the independent validation set

[0157]

[0158]

[0159] For unknown samples, the expression levels of the above biomarkers are substituted into the model to obtain the testicular cancer risk prediction of the sample and output the result. When the probability of having testicular cancer is greater than or equal to the probability of not having testicular cancer, the output prediction result is "having a testicular cancer risk"; when the probability of having testicular cancer is less than the probability of not having testicular cancer, the output prediction result is "not having a testicular cancer risk".

[0160] The above results show that 110 protein molecular markers in the plasma of tumor patients (ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FC N1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, H SPA6, IGF1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3- 53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NUDT21 , NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1, UFC1) can be used to predict tumor risk.

[0161] Example 2 System for predicting testicular cancer risk

[0162] The system 61 for predicting the risk of testicular cancer includes a data processing module 52 and a judgment and output module 53, and also includes a data collection module 51 ( Figure 4 ).

[0163] The data collection module 51 is used to collect the expression level data of the biomarker combination in the patient's testicular cancer tissue sample and transmit it to the data processing module.

[0164] 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.

[0165] The judgment and output module 53 is used to judge whether the calculation result meets the preset judgment condition, that is, the risk probability of testicular cancer is greater than or equal to the predicted risk probability of not having testicular cancer, so as to predict the risk of testicular cancer and output the prediction result; wherein, in the judgment and output module, when the expression level data meets the judgment condition that the risk probability of testicular cancer is greater than or equal to the predicted risk probability of not having testicular cancer, the output prediction result is "having testicular cancer risk"; when the expression level data does not meet the judgment condition that the risk probability of testicular cancer is less than the predicted risk probability of not having testicular cancer, the output prediction result is "not having testicular cancer risk".

[0166] Embodiment 3 Electronic device

[0167] 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 the risk of testicular cancer in Embodiment 1 of the present invention can be implemented when the processor executes the computer program.

[0168] Figure 5 The hardware structure diagram of this embodiment is shown, and the electronic device 4 specifically includes:

[0169] 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:

[0170] The bus 93 includes a data bus, an address bus, and a control bus.

[0171] 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 .

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Embodiment 4 Computer readable storage medium

[0177] 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 the risk of testicular cancer in Embodiment 1 of the present invention are implemented.

[0178] 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.

[0179] In a possible implementation manner, 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 testicular cancer risk in Example 1 of the present invention.

[0180] 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.

[0181] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, rather than to limit it.

[0182] Biomarker name: (refer to NCBI or genecards database)

[0183] ADPRS: ADP-ribosylserine hydrolase, Gene ID: 54936

[0184] ALDH4A1: aldehyde dehydrogenase 4family member A1, Gene ID: 8659

[0185] ALDH5A1: aldehyde dehydrogenase 5family member A1, Gene ID: 7915

[0186] ANXA10: annexin A10, Gene ID: 11199

[0187] ATP6AP2: ATPase H+transporting accessory protein 2, Gene ID: 10159

[0188] B3GAT3: beta-1,3-glucuronyltransferase 3, Gene ID: 26229

[0189] BLVRB: biliverdin reductase B, Gene ID: 645

[0190] C3orf38: chromosome 3open reading frame 38, Gene ID: 285237

[0191] CA1: carbonic anhydrase 1, Gene ID: 759

[0192] CAST: calpastatin, Gene ID: 831

[0193] CAT:catalase,Gene ID:847

[0194] CDC37:cell division cycle 37,Gene ID:11140

[0195] CDC42BPA:CDC42 binding protein kinase alpha,Gene ID:8476

[0196] COL18A1:collagen type XVIII alpha 1chain,Gene ID:80781

[0197] CTSL:cathepsin L,Gene ID:1514

[0198] CYFIP1:cytoplasmic FMR1 interacting protein 1,Gene ID:23191

[0199] DCN:decorin,Gene ID:1634

[0200] DDT:D-dopachrome tautomerase,Gene ID:1652

[0201] DHX16:DEAH-box helicase 16,Gene ID:8449

[0202] DHX29:DExH-box helicase 29,Gene ID:54505

[0203] DLG1:discs large MAGUK scaffold protein 1,Gene ID:1739

[0204] EIF5A2:eukaryotic translation initiation factor 5A2,Gene ID:56648

[0205] EIF5AL1:eukaryotic translation initiation factor 5A like 1,Gene ID:143244EIF5B:Eukaryotic Translation Initiation Factor 5B,Gene ID:9669

[0206] ELFN1:extracellular leucine rich repeat and fibronectin type IIIdomain containing 1,Gene ID:392617ENO2:enolase 2,Gene ID:2026

[0207] FCN1:ficolin 1,Gene ID:2219

[0208] FKBP1A:FKBP prolyl isomerase 1A,Gene ID:2280

[0209] GAPDH:glyceraldehyde-3-phosphate dehydrogenase,Gene ID:2597

[0210] GOLGA4:golgin A4,Gene ID:2803

[0211] H3-4:H3.4 histone,cluster member,Gene ID:8290

[0212] H3C1:H3 clustered histone 1,Gene ID:8350

[0213] H3C15:H3 clustered histone 15,Gene ID:333932

[0214] HBA1:hemoglobin subunit alpha 1,Gene ID:3039

[0215] HBB:hemoglobin subunit beta,Gene ID:3043

[0216] HBD:hemoglobin subunit delta,GeneID:3045

[0217] HBE1:hemoglobin subunit epsilon 1,Gene ID:3046

[0218] HBZ:hemoglobin subunit zeta,Gene ID:3050

[0219] HMGCS1:3-hydroxy-3-methylglutaryl-CoA synthase 1,Gene ID:3157

[0220] HNRNPA0:heterogeneous nuclear ribonucleoprotein A0,Gene ID:10949

[0221] HP:haptoglobin,Gene ID:3240

[0222] HPCAL1:hippocalcin like 1,Gene ID:3241

[0223] HPR:haptoglobin-related protein,Gene ID:3250

[0224] HPRT1:hypoxanthine phosphoribosyltransferase 1,Gene ID:3251

[0225] HSPA6:heat shock protein family A(Hsp70)member 6,Gene ID:3310

[0226] IGF1:insulin like growth factor 1,Gene ID:3479

[0227] IGHV3-11:immunoglobulin heavy variable 3-11,Gene ID:28450

[0228] IGHV3-13:immunoglobulin heavy variable 3-13,Gene ID:28449

[0229] IGHV3-15:immunoglobulin heavy variable 3-15,Gene ID:28448

[0230] IGHV3-20:immunoglobulin heavy variable 3-20,Gene ID:28445

[0231] IGHV3-21:immunoglobulin heavy variable 3-21,Gene ID:28444

[0232] IGHV3-23:immunoglobulin heavy variable 3-23,Gene ID:28442

[0233] IGHV3-43:immunoglobulin heavy variable 3-43,Gene ID:28426IGHV3-43D:immunoglobulin heavy variable 3-43D,Gene ID:103106901IGHV3-48:immunoglobulinheavy variable 3-48,Gene ID:28424IGHV3-53:immunoglobulin heavy variable 3-53,Gene ID:28420IGHV3-66:immunoglobulin heavy variable 3-66,Gene ID:28412IGHV3-7:immunoglobulin heavy variable 3-7,Gene ID:28452IGHV3-72:immunoglobulinheavy variable 3-72,Gene ID:28410IGHV3-74:immunoglobulin heavy variable 3-74,Gene ID:28408IGHV3-9:immunoglobulin heavy variable 3-9,Gene ID:28451IGHV4-30-4:immunoglobulin heavy variable 4-30-4,Gene ID:28397IGHV4-34:immunoglobulinheavy variable 4-34,Gene ID:28395IGHV4-38-2:immunoglobulin heavy variable 4-38-2,Gene ID:28389IGHV4-39:immunoglobulin heavy variable 4-39,Gene ID:28394IGHV4-59:immunoglobulin heavy variable 4-59,Gene ID:28392IGHV4-61:immunoglobulin heavy variable 4-61,Gene ID:28391IGKV1-33:immunoglobulin kappavariable 1-33,Gene ID:28933IGKV1D-33:immunoglobulin kappa variable 1D-33,GeneID:28896IGKV3-7:immunoglobulin kappa variable 3-7,Gene ID:28915IGLV3-25:immunoglobulin lambda variable 3-25,Gene ID:28793IGLV3-27:immunoglobulinlambda variable 3-27,Gene ID:28791LDHB:lactate dehydrogenase B,Gene ID:3945LSM5:LSM5 homolog,Gene ID:23658,

[0234] MAP4:microtubule associated protein 4,Gene ID:4134MCU:mitochondrialcalcium uniporter,Gene ID:90550MSL1:MSL complex subunit 1,Gene ID:339287MTPN:myotrophin,Gene ID:136319

[0235] NAT10:N-acetyltransferase 10,Gene ID:55226NAXD:NAD(P)HX dehydratase,Gene ID:55739NUDT21:nudix hydrolase 21,Gene ID:11051

[0236] NUP205:nucleoporin 205,Gene ID:23165

[0237] NUP93:nucleoporin 93,Gene ID:9688

[0238] PGLS:6-phosphogluconolactonase,Gene ID:25796PHF21A:PHD finger protein21A,Gene ID:51317PLBD2:phospholipase B domain containing 2,Gene ID:196463PMVK:phosphomevalonate kinase,Gene ID:10654

[0239] POGZ:pogo transposable element derived with ZNF domain,Gene ID:23126

[0240] PRDX2:peroxiredoxin 2,Gene ID:7001

[0241] PSMA8:proteasome 20S subunit alpha 8,Gene ID:143471

[0242] PTPRE:protein tyrosine phosphatase receptor type E,Gene ID:5791

[0243] RAB39A:RAB39A,member RAS oncogene family,Gene ID:54734

[0244] RNF40:ring finger protein 40,Gene ID:9810

[0245] RPAP1:RNA polymerase II associated protein 1,Gene ID:26015

[0246] RPS4Y1:ribosomal protein S4 Y-linked 1,Gene ID:6192

[0247] S100A4:S100 calcium binding protein A4,Gene ID:6275

[0248] SCRN1:secernin 1,Gene ID:9805

[0249] SDC4:syndecan 4,Gene ID:6385

[0250] SEC24C:SEC24 homolog C,Gene ID:9632

[0251] SFTPB:surfactant protein B,Gene ID:6439

[0252] SH3BGRL3:SH3 domain binding glutamate rich protein like 3,Gene ID:83442

[0253] SLC2A1:solute carrier family 2 member 1,Gene ID:6513

[0254] SMARCA5:SWI / SNF related,matrix associated,actin dependent regulatorof chromatin,subfamily a,member 5,Gene ID:8467

[0255] ST13P4:ST13,Hsp70 interacting protein pseudogene 4,Gene ID:145165

[0256] ST13P5:ST13,Hsp70 interacting protein pseudogene 5,Gene ID:144106

[0257] TRA2B:transformer 2 beta homolog,Gene ID:6434

[0258] TRIOBP:TRIO and F-actin binding protein,Gene ID:11078

[0259] TXN:thioredoxin,Gene ID:7295

[0260] UBXN1:UBX domain protein 1,Gene ID:51035

[0261] UFC1:ubiquitin-fold modifier conjugating enzyme 1,Gene ID:51506。

Claims

1. A reagent for detecting a combination of biomarkers, characterized in that: The biomarker panel consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GA PDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IGF1 , IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IG HV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IGH V4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NUD T21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1, and UFC1; 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 ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A , GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, I GF1, IGHV3-11, IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IG HV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NU It is composed of DT21, NUP205, NUP93, PGLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1.

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 testicular cancer.

5. A testicular cancer risk prediction system, 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 testicular cancer and the probability of not suffering from testicular cancer, respectively, judges whether the expression level data meets the preset judgment condition, so as to predict the risk of the sample suffering from testicular cancer, and outputs the prediction result; the judgment condition is that the probability of suffering from testicular cancer is greater than or equal to the probability of not suffering from testicular cancer; When the expression level data meets the judgment condition, the output prediction result is "having a risk of testicular cancer"; when the expression level data does not meet the judgment condition, that is, the probability of having testicular cancer is less than the probability of not having testicular cancer, the output prediction result is "not having a risk of testicular cancer"; The biomarker combination consists of ADPRS, ALDH4A1, ALDH5A1, ANXA10, ATP6AP2, B3GAT3, BLVRB, C3orf38, CA1, CAST, CAT, CDC37, CDC42BPA, COL18A1, CTSL, CYFIP1, DCN, DDT, DHX16, DHX29, DLG1, EIF5A2, EIF5AL1, EIF5B, ELFN1, ENO2, FCN1, FKBP1A, GAPDH, GOLGA4, H3-4, H3C1, H3C15, HBA1, HBB, HBD, HBE1, HBZ, HMGCS1, HNRNPA0, HP, HPCAL1, HPR, HPRT1, HSPA6, IGF1, IGHV3- 11. IGHV3-13, IGHV3-15, IGHV3-20, IGHV3-21, IGHV3-23, IGHV3-43, IGHV3-43D, IGHV3-48, IGHV3-53, IGHV3-66, IGHV 3-7, IGHV3-72, IGHV3-74, IGHV3-9, IGHV4-30-4, IGHV4-34, IGHV4-38-2, IGHV4-39, IGHV4-59, IGHV4-61, IGKV1-33, IGKV1D-33, IGKV3-7, IGLV3-25, IGLV3-27, LDHB, LSM5, MAP4, MCU, MSL1, MTPN, NAT10, NAXD, NUDT21, NUP205, NUP93, P GLS, PHF21A, PLBD2, PMVK, POGZ, PRDX2, PSMA8, PTPRE, RAB39A, RNF40, RPAP1, RPS4Y1, S100A4, SCRN1, SDC4, SEC24C, SFTPB, SH3BGRL3, SLC2A1, SMARCA5, ST13P4, ST13P5, TRA2B, TRIOBP, TXN, UBXN1 and UFC1; 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.

Images