A biomarker combination and its application in predicting the risk of testicular cancer

Abstract

The present invention discloses a biomarker combination and its application in predicting the risk of testicular cancer. Specifically, it discloses the application of a biomarker combination in preparing a kit for predicting and / or diagnosing testicular cancer; wherein, the biomarker combination is composed of 110 biomarkers, and the kit contains reagents for detecting the expression levels of the biomarkers in the biomarker combination. 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.

Description

Technical Field

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

[0002] Among men aged 20 - 49, testicular cancer is the second most common cancer, with an incidence rate of 10.7%. The prognosis of testicular tumors is relatively good, and 95% of early-stage patients can survive for a long time. For advanced tumors, after comprehensive treatments such as radiotherapy, chemotherapy, and high-dose chemotherapy supported by hematopoietic stem cells, the 5-year survival rate is 80%. Early detection and diagnosis of testicular tumors are very important. Clinically, lesions can be detected early through scrotal transillumination test, B-ultrasound, CT examination, biopsy, etc. Regular self-examination also helps in early detection of lesions. However, these methods all have disadvantages such as too high false positive rate and relatively late detection time. Therefore, there is an urgent need for a highly sensitive and accurate diagnostic method to achieve early cancer screening.

[0003] Proteomics has played a significant role in revealing the complex molecular events of tumorigenesis, such as tumor occurrence, invasion, metastasis, and treatment resistance. 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 cancers and sample sizes involved are relatively limited. In recent years, with the continuous development of the proteome, the big data of body fluid proteome has been increasing. Therefore, by collecting body fluid proteome data and using big data analysis methods to find a tumor risk model with a wide application range and high accuracy is helpful for early diagnosis and has important clinical significance for early diagnosis and 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. The present invention 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 extensive 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] The first aspect of the present invention provides an application of a biomarker combination in the preparation of a kit for predicting and / or diagnosing testicular cancer;

[0007] Among them, 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, 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, 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] The second aspect of the present invention provides a reagent for detecting a biomarker combination, which 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, 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, IGHV4-61, 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 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 the nucleic acid encoding the biomarker.

[0011] In some embodiments of the present invention, the biomolecular reagent is selected from primers, probes, and antibodies.

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

[0013] The third aspect of the present invention provides the use of a reagent for detecting a biomarker combination in the preparation of a kit for predicting and / or diagnosing testicular cancer;

[0014] 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, 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, IGHV4-61, 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, 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, which 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, 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, IGHV4-61, 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 and UFC1.

[0017] The fifth aspect of the present invention provides a kit, which contains the reagent as described in the second aspect and the biomarker combination as described in the fourth aspect.

[0018] The 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 the biomarker combination in a sample to be tested;

[0019] Among them, 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, 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, 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 the protein expression level and / or the mRNA transcription level.

[0021] In the present invention, the "non-diagnostic purpose" refers to the purpose of scientific research and pathological data statistics, and the applicable scenarios include verifying whether an animal model is successfully constructed, in vitro pharmacodynamic 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 levels of a biomarker combination in a 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 this protein divided by the total iBAQ of all identified proteins in the sample), and a machine learning algorithm based on a generalized linear regression model is preset to construct a prediction model to respectively predict the probability of the sample having testicular cancer and the probability of not having testicular cancer, and judge whether the expression level data meets the preset judgment conditions to predict the risk of the sample having testicular cancer and output a prediction result; the judgment condition is that the probability of having testicular cancer is greater than or equal to the probability of not having testicular cancer;

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

[0024] Among them, 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, 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, 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 the protein expression level and / or the 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 reception or input is completed, which is preset as a machine learning algorithm based on a generalized linear regression model to construct the 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 includes a data collection module, which is used to collect the expression level data of the biomarker combination in the sample to be tested. The expression level data is preferably FOT (Fraction of total, defined as the iBAQ of this 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 testicular cancer.

[0030] The eighth aspect of the present invention provides a computer-readable storage medium, which stores 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, which includes a memory and a processor. 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 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 humoral protein molecules 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 humoral proteome. The screening method includes the following steps:

[0034] (1) Collect humoral samples of healthy people and testicular cancer patients;

[0035] (2) Prepare proteins from the humoral samples of healthy people and testicular cancer patients;

[0036] (3) Detect the expression levels of protein molecules in the humoral samples of healthy people and testicular cancer patients;

[0037] (4) Find the protein group molecules highly expressed specifically in the humors of tumor patients and construct a classifier for discrimination.

[0038] On the basis of conforming to common knowledge in the art, the above preferred conditions can be combined arbitrarily to obtain various preferred examples of the present invention.

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

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

[0041] The biomarker combination of the present invention is used for the 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 extensive scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the area under the ROC curve of the biomarker combination in the training set.

[0043] Figure 2 It is a schematic diagram of the area under the ROC curve of the biomarker combination in the test set.

[0044] Figure 3 It is a schematic diagram of the area under the ROC curve of the biomarker combination in the independent validation set.

[0045] Figure 4 It is a schematic diagram of the structure of the system for predicting the risk of testicular cancer.

[0046] Figure 5 It is a schematic diagram of the structure of the electronic device. DETAILED DESCRIPTION OF THE INVENTION

[0047] The present invention will be further described below by way of examples, but the present invention is not limited to the scope of the described examples. The experimental methods without specific conditions in the following examples are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0048] The plasma samples in the examples included 114 normal subjects and 46 testicular cancer patients. The design and implementation of this study have been approved and supervised by an ethical vote, and written informed consent has been obtained from all patients.

[0049] Example 1 Screening and Validation of the Combination of Biomarkers for Predicting the Risk of Testicular Cancer

[0050] 1.1 Isolation of Plasma

[0051] Collect whole blood samples, place them in EDTA anticoagulant tubes, invert and mix well, then use a 4°C low-temperature centrifuge to centrifuge at 1,600×g for 10 min. After centrifugation, collect the supernatant (plasma) into a new EP tube, centrifuge at 16,000×g for 10 min to remove cell debris, aliquot the plasma into centrifuge tubes, and store at -80°C for later use.

[0052] 1.2 Pretreatment of Plasma Samples

[0053] Add 50 mM ammonium bicarbonate at a concentration of 100 μL to 2 μL of plasma sample, vortex for 1 min, heat and incubate the sample at 95 °C for 4 min to thermally denature the protein. After cooling 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 digestion. Desalt the peptide sample after enzymatic digestion, dry it by evaporation, and store it at -80 °C until mass spectrometry detection.

[0054] 1.3 Mass Spectrometry Detection of Plasma Samples

[0055] Use an Orbitrap Fusion Lumos triple quadrupole high-resolution mass spectrometry system (Thermo Fisher Scientific, Rockford, USA) in tandem with a high-performance liquid chromatography system (EASY-nLC 1200, Thermo Fisher) for detection and obtain the mass spectrometry data of the whole protein corresponding to the peptide sample. The specific operation is as follows:

[0056] Adopt nanoflow liquid chromatography, and the chromatographic column is a self-made C18 chromatographic column (150 μm ID × 8 cm, 1.9 μm / packing). The column oven temperature is 60 °C. Reconstitute the dry powder peptide with the loading buffer (aqueous solution of 0.1% formic acid), and after loading, separate it through the chromatographic column, elute with a linear 6–30% mobile phase B (ACN and 0.1% formic acid) at 600 nL / min, and use a 10-min liquid phase gradient combined with data-independent acquisition (DIA) mass spectrometry detection method. The DIA mass spectrometry detection parameters are set as follows: the ion mode is positive ion; the resolution of the first-stage mass spectrometry is 30K, the maximum injection time is 20 ms, the AGC Target is 3e6, and the scanning range is 300-1400 m / z; the resolution of the second-stage scan is 15K, 30 variable isolation windows are obtained, and the collision energy is 27%. The liquid chromatography-tandem mass spectrometry system is 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 and consists of multiple functional modules such as a user login interface, raw data, identification and quantification, data analysis, and knowledge mining. DIA data were searched against the UniProt human protein database (updated on December 17, 2019, with 20,406 entries) using DIANN (v12.1). The mass difference of precursor ions was set to 20 ppm, and the mass difference of product ions was set to 50 mmu. Up to two missed cleavage sites were allowed. The search engine set carbamidomethylation of cysteine as a fixed modification and N-acetylation and oxidation of methionine as variable modifications. The precursor ion charge range was set to +2, +3, and +4. The False Discovery Rate (FDR) was set to 1%. The results of DIA data were merged into the reference library using SpectraST software. A total of 327 libraries were used as the reference library.

[0059] The quantitative results of the identified peptides were recorded as the average of the chromatographic fragment ion peak areas in all reference spectral libraries. Protein quantification was performed using label-free intensity-based absolute quantification (iBAQ) method. We calculated the peak area values as a fraction of the corresponding protein. The total score (FOT) was used to represent the normalized abundance of a specific protein in the sample. FOT was defined as the iBAQ of the 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 used as the protein expression data and input into the generalized linear regression model.

[0060] In this example, the selected Firmiana was preset with a machine learning algorithm based on the generalized linear regression model to construct a prediction model to predict the probability of the sample having testicular cancer and the probability of not having testicular cancer, respectively. The code for constructing the prediction model is:

[0061]

[0062]

[0063] Experimental findings show that there are significant changes in the expression levels of some proteins in the body fluid samples of cancer patients and healthy individuals. ROC curves (Receiver Operating Curve) were plotted for the relative expression levels of 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, IGHV4-61, 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) in the plasma samples of testicular cancer patients, and the AUC (Area Under the ROC Curve) was calculated. The training set included 22 positive cases and 56 negative cases, with AUC = 1.00, diagnostic sensitivity of 95.45%, and specificity of 98.21% (see Figure 1 ). The test set included 10 positive cases and 24 negative cases, with AUC = 1.00, diagnostic sensitivity of 100.00%, and specificity of 100.00% (seeFigure 2 ) The independent validation set included 14 positive cases and 34 negative cases, with an AUC of 1.00, a diagnostic sensitivity of 100.00%, and a specificity of 100.00% (see Figure 3 ). For the analysis method, refer to 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, Tables 11-20, and Tables 21-30.

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

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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] FOT values of 110 protein markers in the training set

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

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

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

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

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

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

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

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

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

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

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

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

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

[0125]

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

[0127]

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

[0129]

[0130]

[0131]

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

[0133]

[0134]

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

[0136]

[0137]

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

[0139]

[0140]

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

[0142]

[0143]

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

[0145]

[0146]

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

[0148]

[0149]

[0150] 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 an unknown sample, substitute the expression levels of the above biomarkers into the model to obtain the testicular cancer risk prediction for 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 predicted result is output as "at risk of testicular cancer"; when the probability of having testicular cancer is less than the probability of not having testicular cancer, the predicted result is output as "not at risk of testicular cancer".

[0160] As can be seen from the above results, the combination of 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, IGHV4-61, 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) in the plasma of cancer patients can be used to predict cancer risk.

[0161] Example 2 System for Predicting Testicular Cancer Risk

[0162] System 61 for Predicting Testicular Cancer Risk: Data Processing Module 52 and Judgment and Output Module 53, further including Data Collection Module 51 ( Figure 4 ).

[0163] Data Collection Module 51 is used to collect the expression level data of the biomarker combination in the testicular cancer tissue sample of the patient and transmit it to the Data Processing Module.

[0164] The data processing module 52 is used to analyze the expression level data of the received or input biomarker combination according to the data analysis method described in Embodiment 4 to obtain a calculation result. Among them, 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 suffering from testicular cancer is greater than or equal to the risk prediction probability of not suffering from testicular cancer, so as to predict the risk of testicular cancer and output a prediction result; among them, in the judgment and output module, when the expression level data meets the judgment condition that the risk probability of suffering from testicular cancer is greater than or equal to the risk prediction probability of not suffering from testicular cancer, the output prediction result is "at risk of testicular cancer"; when the expression level data does not meet the judgment condition that the risk probability of suffering from testicular cancer is less than the risk prediction probability of not suffering from testicular cancer, the output prediction result is "not at risk of testicular cancer".

[0166] Embodiment 3 Electronic device

[0167] This embodiment provides an electronic device, which can be presented in the form of a computing device (for example, it can be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting the risk of testicular cancer in Embodiment 1 of the present invention can be implemented.

[0168] Figure 5 The schematic diagram of the hardware structure of this embodiment is shown. 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 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] The memory 92 further includes a program / utilities 925 having a set (at least one) of program modules 924. Such program modules 924 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0173] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the data analysis method in Embodiment 1 of the present invention.

[0174] The electronic device 9 can further communicate with one or more external devices 94 (such as a keyboard, pointing device, etc.). Such communication can be carried out through the input / output (I / O) interface 95. Moreover, the electronic device 9 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 through the bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination 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 of the above-described units / modules can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0176] Embodiment 4 Computer-readable storage medium

[0177] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and 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 realized.

[0178] Among them, the more specific forms that the readable storage medium can adopt can include but are not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, 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 program code, and when the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps of the method for predicting the risk of testicular cancer in Embodiment 1 of the present invention.

[0180] Among them, the program code for implementing the present invention can be written in any combination of one or more programming languages, and the program code can be executed entirely on the user device, partially on the user device, executed as an independent software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0181] Finally, the above specific implementation methods are only used to illustrate the technical solutions of the present invention, rather than limiting it.

[0182] Biomarker name: (reference can be made to the NCBI or genecards database)

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

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

[0185] ALDH5A1: aldehyde dehydrogenase 5 family 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 3 open 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 1 chain, 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 III domain containing 1, Gene ID: 392617; ENO2: 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, Gene ID: 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: 28426 IGHV3-43D: immunoglobulin heavy variable 3-43D, Gene ID: 103106901 IGHV3-48: immunoglobulin heavy variable 3-48, Gene ID: 28424 IGHV3-53: immunoglobulin heavy variable 3-53, Gene ID: 28420 IGHV3-66: immunoglobulin heavy variable 3-66, Gene ID: 28412 IGHV3-7: immunoglobulin heavy variable 3-7, Gene ID: 28452 IGHV3-72: immunoglobulin heavy variable 3-72, Gene ID: 28410 IGHV3-74: immunoglobulin heavy variable 3-74, Gene ID: 28408 IGHV3-9: immunoglobulin heavy variable 3-9, Gene ID: 28451 IGHV4-30-4: immunoglobulin heavy variable 4-30-4, Gene ID: 28397 IGHV4-34: immunoglobulin heavy variable 4-34, Gene ID: 28395 IGHV4-38-2: immunoglobulin heavy variable 4-38-2, Gene ID: 28389 IGHV4-39: immunoglobulin heavy variable 4-39, Gene ID: 28394 IGHV4-59: immunoglobulin heavy variable 4-59, Gene ID: 28392 IGHV4-61: immunoglobulin heavy variable 4-61, Gene ID: 28391 IGKV1-33: immunoglobulin kappa variable 1-33, Gene ID: 28933 IGKV1D-33: immunoglobulin kappa variable 1D-33,GeneID: 28896 IGKV3-7: immunoglobulin kappa variable 3-7, Gene ID: 28915 IGLV3-25: immunoglobulin lambda variable 3-25, Gene ID: 28793 IGLV3-27: immunoglobulin lambda variable 3-27, Gene ID: 28791 LDHB: lactate dehydrogenase B, Gene ID: 3945 LSM5: LSM5 homolog, Gene ID: 23658,

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

[0235] NAT10: N-acetyltransferase 10, Gene ID: 55226 NAXD: NAD(P)HX dehydratase, Gene ID: 55739 NUDT21: 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: 25796 PHF21A: PHD finger protein 21A, Gene ID: 51317 PLBD2: phospholipase B domain containing 2, Gene ID: 196463 PMVK: 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 regulator of 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 The protein is composed of 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.

2. The reagent according to claim 1, characterized in that 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.

3. The reagent according to claim 2, characterized in that The reagent is a biomolecular reagent that specifically binds to the biomarker, or specifically hybridizes with the nucleic acid encoding the biomarker; and / or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

4. The reagent according to claim 3, characterized in that The biomolecule reagent is selected from the group consisting of primers, probes and antibodies.

5. A biomarker combination, 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 The protein is composed of 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.

6. A kit, characterized in that: The kit comprises the reagent according to any one of claims 1 to 4 and the biomarker combination according to claim 5.

7. Use of the reagent according to any one of claims 1 to 4, the biomarker combination according to claim 5, or the kit according to claim 6 in the preparation of a product for predicting and / or diagnosing testicular cancer.

8. 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 The protein expression level refers to the protein expression level and / or the mRNA transcription level.

9. The prediction system according to claim 8, characterized in that 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.

10. 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 8 or 9 can be realized.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The processor is configured to execute the computer program to implement the functions of the prediction system according to claim 8 or 9.

Images