Biomarker combination and application thereof in predicting breast cancer risk

Abstract

The invention discloses a biomarker combination and application thereof in prediction of breast cancer risk, and particularly discloses application of the biomarker combination in preparation of breast cancer prediction products, the biomarker combination is composed of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1 The marker combination provided by the invention can be used for risk estimation and detection of breast cancer tumor patients, has the advantages of high sensitivity and high specificity, and provides favorable technical support for prediction of occurrence and development of breast cancer. A corresponding prediction device developed by using the marker combination can provide great convenience for early clinical diagnosis or intervention treatment and the like.

Description

Technical Field

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

[0002] In 2020, breast cancer replaced lung cancer as the most common cancer in the world. Clinically, lesions can be detected early through B-ultrasound, CT scans, biopsy and pathological examinations, and regular self-examination can also help detect lesions early. However, these methods have disadvantages such as high false positive rates and late detection. Therefore, a highly sensitive and accurate diagnostic method is urgently needed to achieve early cancer screening.

[0003] Proteomics plays a significant role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and resistance 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, it is an urgent problem to find a tumor risk model with a wide range of applications and high accuracy to achieve early diagnosis and early treatment of patients. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a biomarker combination and its application in predicting breast cancer risk. The protein molecular marker combination of the present invention has significant changes in expression levels in body fluid samples of tumor patients and healthy people. Therefore, the marker combination provided in the present invention can be used as a risk estimation and detection for breast cancer patients, and has the advantages of high sensitivity and high specificity, providing favorable technical support for predicting the occurrence and development of breast cancer. The development of a corresponding prediction device using the above marker combination can provide great convenience for early clinical diagnosis or intervention treatment.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides an application of a biomarker combination in the preparation of a product for predicting breast cancer, wherein the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD.

[0006] In a preferred embodiment, the product for predicting breast cancer is a product for predicting early stage breast cancer.

[0007] In a second aspect, the present invention provides a reagent for detecting a biomarker combination, wherein the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD.

[0008] In a preferred embodiment, 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.

[0009] In a preferred embodiment, the reagent is a biomolecular reagent that specifically binds to the biomarker or specifically hybridizes with the nucleic acid encoding the biomarker.

[0010] In a preferred embodiment, the biomolecule reagent is selected from the group consisting of primers, probes and antibodies.

[0011] In a preferred embodiment, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

[0012] A third aspect of the present invention provides an application of a reagent for detecting a biomarker combination in the preparation of a kit for predicting breast cancer; wherein the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD.

[0013] In a preferred embodiment, the reagent is as described in the first aspect of the present invention.

[0014] In a preferred embodiment, the kit for predicting breast cancer is a kit for predicting early stage breast cancer.

[0015] A fourth aspect of the present invention provides a biomarker combination, which is composed of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD.

[0016] The fifth aspect of the present invention provides a kit, which comprises the reagent as described in the second aspect of the present invention or the biomarker combination as described in the fourth aspect of the present invention.

[0017] A sixth aspect of the present invention provides a method for predicting breast cancer for non-diagnostic purposes, the method comprising detecting the expression level of a combination of biomarkers in a sample to be tested;

[0018] wherein the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1, and UROD;

[0019] The expression level is protein expression level and / or mRNA transcription level.

[0020] In a preferred embodiment, the prediction of breast cancer is the prediction of early stage breast cancer.

[0021] A seventh aspect of the present invention provides a breast cancer risk prediction system, 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;

[0022] The analysis and judgment module processes the expression level data through Firmiana software, 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), 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 breast cancer and the probability of not suffering from breast cancer, respectively, and to judge whether the expression level data meets the preset judgment conditions to predict the risk of the sample suffering from breast cancer, and output the prediction result; the judgment condition is that the probability of suffering from breast cancer is greater than or equal to the probability of not suffering from breast cancer;

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

[0024] Wherein, the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD; and the expression level is protein expression level and / or mRNA transcription level.

[0025] 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.

[0026] In a preferred embodiment, the sample to be tested is a plasma sample.

[0027] In a preferred embodiment, the prediction system also includes a data collection module, which is used to collect expression level data of the biomarker combination in the sample breast tissue, 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).

[0028] In a preferred embodiment, the prediction system is a system for predicting the early stages of breast cancer.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] The reagents and raw materials used in the present invention are commercially available.

[0033] The positive and progressive effects of the present invention are:

[0034] The protein molecular marker combination of the present invention has significant changes in expression levels in body fluid samples of tumor patients and healthy people. Therefore, the marker combination provided in the present invention can be used as a risk prediction and detection for breast cancer patients, with the advantages of high sensitivity and high specificity, providing favorable technical support for predicting the occurrence and development of breast cancer. The development of a corresponding prediction device using the above marker combination can provide great convenience for early clinical diagnosis or intervention treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of the area under the ROC curve of the marker combination in the training set.

[0036] Figure 2 Schematic diagram of the area under the ROC curve of the marker combination in the test set.

[0037] Figure 3 Schematic diagram of the area under the ROC curve of the marker combination in the external validation set.

[0038] Figure 4 Schematic diagram of the structure of the system for predicting breast cancer risk.

[0039] Figure 5 A schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0040] The present invention includes plasma samples from 114 normal people and 101 breast cancer patients. The design and implementation of this study have been approved and supervised by ethics committees, and written informed consent has been obtained from all patients.

[0041] 1. Separation of plasma

[0042] 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.

[0043] 2. Pretreatment of plasma samples

[0044] 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.

[0045] 3. Mass spectrometry detection of plasma samples

[0046] 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:

[0047] 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 a loading buffer (0.1% formic acid in water), loaded and separated by a chromatographic column, eluted with a linear 6-30% mobile phase B (ACN and 0.1% formic acid) at 600nL / min, and a 10-min liquid phase gradient was combined with a data-independent acquisition (DIA) mass spectrometry detection method. The DIA mass spectrometry detection parameters were set as follows: the ion mode was positive ion; the primary mass spectrometry resolution was 30K, the maximum injection time was 20ms, the AGC Target was 3e6, and the scanning range was 300-1400m / z; the secondary scanning resolution was 15K, 30 variable isolation windows were acquired, and the collision energy was 27%. The liquid chromatography tandem mass spectrometry system was controlled by Xcalibur software for data acquisition.

[0048] 4. Data Analysis

[0049] All data were processed using Firmiana. 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 was 20ppm, and the mass difference of the daughter ion was 50mmu. A maximum of two missed cleavage sites were allowed. The search engine set cysteine ​​carbamidomethylation as a fixed modification and methionine N-acetylation and oxidation as variable modifications. The parent ion charge range was set to +2, +3 and +4. The false discovery rate (FDR) was 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.

[0050] The quantification 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 the label-free intensity-based absolute quantification (iBAQ) method. We calculated the peak area values ​​as a fraction of the corresponding protein. The total fraction (FOT) is used to represent the standardized abundance of a specific protein in the 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.

[0051] 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 breast cancer and the probability of not suffering from breast cancer. The code for building the prediction model is:

[0052] from sklearn.linear model import LogisticRegressionCV

[0053] import joblib

[0054] import pandas as pd

[0055] tumor_types = ['***','***','***','***','***'] #*** refers to the 37 protein molecule markers described in the present invention

[0056] for tumor_type in tumor_types:

[0057] df_train=pd.read_csv('{}_train.csv'.format(tumor_type))

[0058] df_test=pd.read_csv('{}_test.csv'.format(tumor_type))

[0059] df_val=pd.read_csv('{}_validation.csv'.format(tumor_type))

[0060] X_train = df_train.iloc[:,2:]

[0061] y_train = df_train.iloc[:,1]

[0062] X_test = df_test.iloc[:,2:]

[0063] y_test = df_test.iloc[:,1]

[0064] X_val = df_val.iloc[:,2:]

[0065] y_val = df_val.iloc[:,1]

[0066] model=LogisticRegressionCV()

[0067] model.fit(X_train,y_train)

[0068] joblib.dump(model,f'{tumor_type}_model.joblib');

[0069] The relative expression levels of 37 protein molecular markers (ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD) in plasma samples of breast cancer patients were plotted and the Area Under the ROC curve (ROC) was calculated. Curve), where the training set includes 49 positive cases and 56 negative cases, AUC = 1.00, diagnostic sensitivity 100.00%, and specificity 100.00% (see Figure 1 ). The test set included 21 positive cases and 24 negative cases, with AUC = 0.98, diagnostic sensitivity 97%, specificity 100%, positive predictive value 94%, and negative predictive value 100% (see Figure 2 ). The external validation set included 31 positive cases and 34 negative cases, with AUC = 0.96, diagnostic sensitivity 96.77%, and specificity 82.35%. (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 37 protein markers in the training set, test set, and external validation set are shown in Tables 1-3, 4-6, and 7-9.

[0070] For unknown samples, the expression levels of the above biomarkers are substituted into the model to obtain the breast cancer risk prediction of the sample and output the result. When the probability of having breast cancer is greater than or equal to the probability of not having breast cancer, the output prediction result is "having breast cancer risk"; when the probability of having breast cancer is less than the probability of not having breast cancer, the output prediction result is "not having breast cancer risk".

[0071] From the above results, it can be seen that the combination of 37 protein molecular markers in the plasma of cancer patients can be used to predict tumor risk.

[0072] Table 1 FOT values ​​of 37 protein markers in the training set

[0073]

[0074]

[0075]

[0076] Table 2 FOT values ​​of 37 protein markers in the training set

[0077]

[0078]

[0079]

[0080] Table 3 FOT values ​​of 37 protein markers in the training set

[0081]

[0082]

[0083]

[0084]

[0085] Table 4 FOT values ​​of 37 protein markers in the test set

[0086]

[0087]

[0088] Table 5 FOT values ​​of 37 protein markers in the test set

[0089]

[0090]

[0091] Table 6 FOT values ​​of 37 protein markers in the test set

[0092]

[0093]

[0094] Table 7 FOT values ​​of 37 protein markers in the external validation set

[0095]

[0096]

[0097] Table 8 FOT values ​​of 37 protein markers in the external validation set

[0098]

[0099]

[0100]

[0101] Table 9 FOT values ​​of 37 protein markers in the external validation set

[0102]

[0103]

[0104] Example 2 System for predicting breast cancer risk

[0105] The system 61 for predicting breast cancer risk includes a data processing module 52 and a judgment and output module 53, and also includes a data collection module 51 ( Figure 4 ).

[0106] The data collection module 51 is used to collect the expression level data of the biomarker combination in the patient's breast cancer tissue sample and transmit it to the data processing module.

[0107] 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.

[0108] 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 breast cancer is greater than or equal to the predicted risk probability of not having breast cancer, so as to predict the risk of breast 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 breast cancer is greater than or equal to the predicted risk probability of not having breast cancer, the output prediction result is "having breast cancer risk"; when the expression level data does not meet the judgment condition that the risk probability of breast cancer is less than the predicted risk probability of not having breast cancer, the output prediction result is "not having breast cancer risk".

[0109] Embodiment 3 Electronic device

[0110] 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 breast cancer risk in Embodiment 1 of the present invention can be implemented when the processor executes the computer program.

[0111] Figure 5 The hardware structure diagram of this embodiment is shown, and the electronic device 4 specifically includes:

[0112] 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:

[0113] The bus 93 includes a data bus, an address bus, and a control bus.

[0114] 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 .

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Embodiment 4 Computer readable storage medium

[0120] The embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method for predicting breast cancer risk in Embodiment 1 of the present invention are implemented.

[0121] 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.

[0122] In a possible implementation manner, the present invention may 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 breast cancer risk in Embodiment 1 of the present invention.

[0123] 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.

[0124] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, but not to limit it.

[0125] Full name of biomarker (refer to genecards database)

[0126] ADPRS: ADP-Ribosylserine Hydrolase

[0127] ARMC8: Armadillo Repeat Containing 8

[0128] ASCC3:Activating Signal Cointegrator 1Complex Subunit 3

[0129] B3GAT3: Beta-1,3-Glucuronyltransferase 3

[0130] BLVRB: Biliverdin Reductase B

[0131] CA1:Carbonic Anhydrase 1

[0132] CDC37: Cell Division Cycle 37

[0133] DLG1: Discs Large MAGUK Scaffold Protein 1

[0134] DSC3: Desmocollin 3

[0135] DUSP3: Dual Specificity Phosphatase 3

[0136] EIF5AL1: Eukaryotic Translation Initiation Factor 5A Like 1

[0137] EWSR1: EWS RNA Binding Protein 1

[0138] GCNT3:Glucosaminyl(N-Acetyl)Transferase 3

[0139] H3-4:H3.4 Histone

[0140] H3C1:H3 Clustered Histone 1

[0141] H3C15:H3 Clustered Histone 15

[0142] HBB:Hemoglobin Subunit Beta

[0143] HBE1:Hemoglobin Subunit Epsilon 1

[0144] HNRNPA0:Heterogeneous Nuclear Ribonucleoprotein A0

[0145] HSPA6:Heat Shock Protein Family A(Hsp70)Member 6

[0146] ITGA6:Integrin Subunit Alpha 6

[0147] MSL1:MSL Complex Subunit 1

[0148] NUP155:Nucleoporin 155

[0149] PDCD10:Programmed Cell Death 10

[0150] PLBD2:Phospholipase B Domain Containing 2

[0151] PXDN:Peroxidasin

[0152] RDH10:Retinol Dehydrogenase 10

[0153] RECK:Reversion Inducing Cysteine Rich Protein With Kazal Motifs

[0154] RUVBL1:RuvB Like AAA ATPase 1

[0155] SAMHD1:SAM And HD Domain Containing Deoxynucleoside TriphosphateTriphosphohydrolase 1

[0156] SLC30A7:Solute Carrier Family 30 Member 7

[0157] SMARCA5:SWI / SNF-Related Matrix-Associated Actin-Dependent RegulatorOf Chromatin Subfamily A Member 5

[0158] SPG21:Spastic Paraplegia 21

[0159] TCIRG1:T Cell Immune Regulator 1

[0160] TRIOBP:TRIO and F-actin binding protein

[0161] UFC1:Ubiquitin-Fold Modifier Conjugating Enzyme 1

[0162] UROD:Uroporphyrinogen Decarboxylase。

Claims

1. A reagent for detecting a combination of biomarkers, characterized in that: The biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1, and UROD; 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, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD.

3. A kit, characterized in that: The kit comprises the reagent according to claim 1 or 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 breast cancer.

5. A breast 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 breast cancer and the probability of not suffering from breast cancer, respectively, judges whether the expression level data meets the preset judgment condition, so as to predict the risk of the sample suffering from breast cancer, and outputs the prediction result; the judgment condition is that the probability of suffering from breast cancer is greater than or equal to the probability of not suffering from breast cancer; When the expression level data meets the judgment condition, the prediction result is output as "having a risk of breast cancer"; when the expression level data does not meet the judgment condition, that is, the probability of having breast cancer is less than the probability of not having breast cancer, the prediction result is output as "not having a risk of breast cancer"; Wherein, the biomarker combination consists of ADPRS, ARMC8, ASCC3, B3GAT3, BLVRB, CA1, CDC37, DLG1, DSC3, DUSP3, EIF5AL1, EWSR1, GCNT3, H3-4, H3C1, H3C15, HBB, HBE1, HNRNPA0, HSPA6, ITGA6, MSL1, NUP155, PDCD10, PLBD2, PXDN, RDH10, RECK, RUVBL1, SAMHD1, SLC30A7, SMARCA5, SPG21, TCIRG1, TRIOBP, UFC1 and UROD; the expression level is protein expression level and / or mRNA transcription level; Preferably, the sample to be tested is a 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 breast tissue.

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

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

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