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

Abstract

The present invention discloses a biomarker combination and its application in predicting the risk of esophageal cancer. The application of the biomarker combination in the preparation of a kit for predicting and / or diagnosing esophageal cancer, wherein the biomarker combination consists of 34 biomarkers. The present invention also discloses a reagent for detecting the biomarker combination, a kit comprising the biomarker combination and the reagent, and their applications. The biomarker combination provided by the present invention can be used for risk estimation and detection of esophageal cancer patients, achieving a sensitivity of 100% and a specificity of 100%. It has the advantages of high sensitivity and high specificity, and provides favorable technical support for predicting the occurrence and development of esophageal cancer.

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 esophageal cancer. Background Art

[0002] Esophageal cancer ranks fourth among the causes of death from malignant tumors, with 572,000 cases and 508,000 deaths worldwide each year.

[0003] Clinically, lesions can be detected early through ultrasound, CT scans, and biopsy. Regular self-examination can also help detect lesions early. However, these methods have disadvantages such as high false positive rates and delayed detection.

[0004] Currently, a method for diagnosing esophageal cancer using 44 serum metabolites as biomarkers (Patent Publication No. CN112151121A) has been published, with a sensitivity, specificity, and accuracy of approximately 94%. Given the practical limitations of using metabolites as diagnostic markers, as well as their sensitivity and specificity, a more convenient, more sensitive, and more accurate diagnostic method is urgently needed for early cancer screening.

[0005] Proteomics has played a significant role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and treatment resistance. Proteomic tumor diagnosis, with its advantages of high sensitivity, strong specificity, and clear underlying mechanisms, has been increasingly used for tumor detection in recent years. Furthermore, the research on these tumor markers is often based on a limited amount of experimental data, involving relatively limited cancer types and sample sizes. In recent years, with the continuous development of the proteome, big data on body fluid proteomes has continued to increase. Therefore, by collecting body fluid proteome data and utilizing big data analysis methods to develop a widely applicable and highly accurate tumor risk model, it will facilitate early diagnosis and have important clinical significance for early diagnosis and treatment of patients. Summary of the Invention

[0006] To address the existing lack of a technical solution for predicting esophageal cancer risk with broad applicability, high accuracy, diagnostic sensitivity, and specificity, the present invention provides a biomarker combination and its application in predicting esophageal cancer risk. This biomarker combination exhibits high sensitivity and specificity in predicting early-stage esophageal cancer risk, providing advantageous technical support for predicting the development and progression of esophageal cancer. It has broad scientific research value and greatly facilitates early clinical diagnosis, interventional therapy, and other applications.

[0007] The present invention obtains a group of biomarkers that can predict the risk of esophageal cancer by screening the body fluid proteome. The screening method includes the following steps:

[0008] (1) Collect body fluid samples from healthy individuals and patients with esophageal cancer;

[0009] (2) Protein preparation from body fluid samples of healthy individuals and esophageal cancer patients;

[0010] (3) Detecting protein molecule expression levels in body fluid samples from healthy individuals and esophageal cancer patients;

[0011] (4) Find the proteomic molecules that are highly expressed in the body fluids of tumor patients and construct a classifier to distinguish them.

[0012] To solve the above technical problems, the present invention provides a technical solution: use of a biomarker combination in the preparation of a kit for predicting and / or diagnosing esophageal cancer;

[0013] Among them, the biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

[0014] To solve the above technical problems, the present invention provides a technical solution: a reagent for detecting a biomarker combination, wherein the biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

[0015] As described in the reagent 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 mRNA transcription level.

[0016] The reagent of the present invention is a biomolecule reagent that specifically binds to the biomarker or specifically hybridizes with the nucleic acid encoding the biomarker.

[0017] According to the reagent of the present invention, the biomolecule reagent is selected from the group consisting of primers, probes and antibodies.

[0018] The reagent according to the present invention is a reagent for genome, transcriptome and / or proteome sequencing.

[0019] To solve the above technical problems, the present invention provides a technical solution: use of a reagent for detecting a combination of biomarkers in the preparation of a kit for predicting and / or diagnosing esophageal cancer;

[0020] Among them, the biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

[0021] According to the use described in the present invention, the reagent is as described in the present invention.

[0022] To solve the above technical problems, the present invention provides a technical solution: a biomarker combination, comprising ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

[0023] To solve the above technical problems, the present invention provides a technical solution: a kit comprising the reagents described in the present invention and the biomarker combination described in the present invention.

[0024] To solve the above technical problems, the present invention provides a technical solution: a method for detecting esophageal cancer for non-diagnostic purposes, the method comprising detecting the expression level of a biomarker combination in a sample to be tested;

[0025] wherein the biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2, and SPG21;

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

[0027] 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.

[0028] To solve the above technical problems, the present invention provides a technical solution: a prediction system for esophageal cancer risk, the prediction system comprising: a detection module and an analysis and judgment module;

[0029] 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;

[0030] The analysis and judgment module processes the expression level data through Firmiana software, wherein 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 to a machine learning algorithm based on a generalized linear regression model to construct a prediction model to predict the probability of the sample suffering from esophageal cancer and the probability of not suffering from esophageal cancer, respectively, and judge whether the expression level data meets a preset judgment condition to predict the risk of the sample suffering from esophageal cancer, and output a prediction result; the judgment condition is that the probability of suffering from esophageal cancer is greater than or equal to the probability of not suffering from esophageal cancer;

[0031] When the expression level data meets the judgment condition, the prediction result is output as "having esophageal cancer risk"; when the expression level data does not meet the judgment condition, that is, the probability of having esophageal cancer is lower than the probability of not having esophageal cancer, the prediction result is output as "not having esophageal cancer risk";

[0032] wherein the biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2, and SPG21;

[0033] The expression level is protein expression level and / or mRNA transcription level.

[0034] 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.

[0035] According to the prediction system of the present invention, the sample to be tested is a human plasma sample.

[0036] As described in the prediction system of the present invention, the prediction system also includes a data collection module for collecting data on the expression levels 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).

[0037] To solve the above technical problems, the present invention provides a technical solution: a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the functions of the prediction system as described in the present invention, or implement the steps of the method as described in the present invention.

[0038] In the present invention, 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 thereof.

[0039] In a possible embodiment, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to perform the functions of the prediction system as described in the present invention, or to implement the steps of the method as described in the present invention.

[0040] 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 the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0041] To solve the above technical problems, the present invention provides a technical solution: an electronic device, which can be expressed in the form of a computing device (for example, a server 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 realize the functions of the prediction system as described in the present invention, or to realize the steps of the method as described in the present invention.

[0042] The electronic device can further communicate with one or more external devices (e.g., a keyboard, a pointing device, etc.). Such communication can be performed via an input / output (I / O) interface. Furthermore, the electronic device can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. As is known in the art, other hardware and / or software modules can be used in conjunction with electronic devices, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0043] The division of features and functions of the units / modules or sub-units / modules of the electronic device described in the present invention may be specific according to the conventional knowledge in the art.

[0044] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0045] The reagents and raw materials used in the present invention are commercially available.

[0046] The positive progress effect of the present invention is:

[0047] Experiments have shown that the protein molecular markers provided by the present invention have significant expression differences in body fluid samples from cancer patients and healthy controls. Therefore, the body fluid protein molecular markers provided by the present invention can be used for risk assessment and detection of cancer patients, with the advantages of high sensitivity and high specificity, providing favorable technical support for predicting the occurrence and development of esophageal cancer.

[0048] The development of a predictive device based on protein molecular markers from body fluid samples of healthy individuals and esophageal cancer patients has broad scientific research value and greatly facilitates early clinical diagnosis and therapeutic intervention. The 34-marker combination provided by this invention achieves 100% sensitivity and 100% specificity. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the area under the ROC curve of the marker combination in the training set.

[0050] Figure 2 Schematic diagram of the area under the ROC curve of the marker combination in the test set.

[0051] Figure 3 Schematic diagram of the area under the ROC curve of the marker combination in the external validation set.

[0052] Figure 4 Schematic diagram of the system for predicting esophageal cancer.

[0053] Figure 5 A schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0054] The present invention is further illustrated by way of examples below, but the present invention is not limited to the scope of the examples. Experimental methods in the following examples where specific conditions are not specified were performed according to conventional methods and conditions, or selected according to the product specifications.

[0055] The present invention includes plasma samples from 114 normal subjects and 44 patients with esophageal cancer. 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.

[0056] Example 1 Screening and Validation of a Combination of Biomarkers for Predicting Esophageal Cancer Risk

[0057] 1.1 Separation of plasma

[0058] Whole blood samples were collected in EDTA anticoagulant tubes, mixed by inversion, and centrifuged at 1,600 × g for 10 min in a 4°C low-temperature centrifuge. The supernatant (plasma) was collected into new EP tubes and centrifuged at 16,000 × g for 10 min to remove cell debris. The plasma was aliquoted into centrifuge tubes and frozen at -80°C for later use.

[0059] 1.2 Plasma sample pretreatment

[0060] To 2 μL of plasma sample, 100 μL of 50 mM ammonium bicarbonate was added and vortexed for 1 minute. The sample was heated at 95°C for 4 minutes to denature the protein. After cooling to room temperature, 2 μg of trypsin was added to the system. The system was shaken at 37°C for 18 hours, and then 10 μL of ammonia was added to stop the enzymatic hydrolysis. The peptide samples after enzymatic hydrolysis were desalted, dried, and frozen at -80°C until mass spectrometry analysis.

[0061] 1.3 Mass spectrometry detection of plasma samples

[0062] The Orbitrap Fusion Lumos three-in-one high-resolution mass spectrometry system (Thermo Fisher Scientific, Rockford, USA) was used in conjunction with a high-performance liquid chromatography system (EASY-nLC 1200, Thermo Fisher) to obtain mass spectrometry data of the whole protein corresponding to the peptide sample. The specific operation was as follows:

[0063] Nanoflow liquid chromatography was used, and the chromatographic column was a homemade C18 column (150 μm ID×8 cm, 1.9 μm / The column oven temperature was 60°C. The dry powdered peptide was reconstituted in loading buffer (0.1% formic acid in water) and applied to the column for separation. Elution was performed at 600 nL / min using a linear 6–30% mobile phase B (ACN and 0.1% formic acid). A 10-min liquid phase gradient was used with data-independent acquisition (DIA) mass spectrometry detection. DIA mass spectrometry detection parameters were as follows: positive ionization mode; primary mass spectrometry resolution of 30K, maximum injection time of 20 ms, AGC target of 3e6, scan range of 300–1400 m / z; secondary scan resolution of 15K, acquisition of 30 variable isolation windows, and collision energy of 27%. Data acquisition was performed on the liquid chromatography-tandem mass spectrometry system controlled by Xcalibur software.

[0064] 1.4 Data Analysis

[0065] 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.

[0066] 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. Peak area values ​​were calculated as a fraction of the corresponding protein. The total fraction (FOT) was used to represent the normalized abundance of a specific protein in the sample. FOT was defined as the iBAQ of a protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and a 1% FDR were selected. The FOT of each protein was calculated and the FOT of each protein was input into the generalized linear regression model as protein expression data.

[0067] The Firmiana algorithm selected in this example is a machine learning algorithm based on a generalized linear regression model. A prediction model is constructed to predict the probability of a sample having esophageal cancer and the probability of not having esophageal cancer. The code for constructing the prediction model is:

[0068]

[0069] The experiment found that the expression levels of some proteins in the body fluid samples of tumor patients and healthy people were significantly different. The relative expression levels of 34 protein molecular markers (ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2, SPG21) in the plasma samples of esophageal cancer patients were plotted to calculate the area under the ROC curve (Receiver Operating Curve). Curve), where the training set includes 22 positive cases and 55 negative cases, AUC = 1.00, diagnostic sensitivity 100.00%, and specificity 100.00% (see Figure 1 ); the test set includes 9 positive cases and 24 negative cases, AUC = 1.00, diagnostic sensitivity 100%, specificity 100% (see Figure 2 ); the external validation set included 13 positive cases and 35 negative cases, with AUC = 0.94, diagnostic sensitivity 76.92%, and specificity 94.29% (see Figure 3 For modeling and analysis methods, see Hajian-Tilaki K. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med. 2013; 4(2): 627-635. The FOT values ​​of the 34 protein markers in the training set, test set, and external validation set are shown in Tables 2-4, 5-7, and 8-10, respectively.

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

[0071] Table 1 Protein information list of all markers

[0072]

[0073]

[0074] The Gene IDs of the markers in Table 1 can be retrieved from the biological database National Center for Biotechnology Information (https: / / www.ncbi.nlm.nih.gov / ).

[0075] From the above results, it can be seen that the combination of 34 protein molecular markers (ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2, SPG21) in the plasma of cancer patients can be used to predict cancer risk.

[0076] Table 2 FOT values ​​of 34 protein markers in the training set

[0077]

[0078]

[0079] Table 3 FOT values ​​of 34 protein markers in the training set

[0080]

[0081]

[0082] Table 4 FOT values ​​of 34 protein markers in the training set

[0083]

[0084]

[0085]

[0086] Table 5 FOT values ​​of 34 protein markers in the test set

[0087]

[0088]

[0089] Table 6 FOT values ​​of 34 protein markers in the test set

[0090]

[0091] Table 7 FOT values ​​of 34 protein markers in the test set

[0092]

[0093]

[0094] Table 8 FOT values ​​of 34 protein markers in the external validation set

[0095]

[0096]

[0097] Table 9 FOT values ​​of 34 protein markers in the external validation set

[0098]

[0099]

[0100] Table 10 FOT values ​​of 34 protein markers in the external validation set

[0101]

[0102]

[0103] Example 2 System for predicting esophageal cancer risk

[0104] The system 61 for predicting esophageal cancer includes a detection module 51 and an analysis and judgment module 52, and also includes a data collection module 53 ( Figure 4 ).

[0105] The detection module 51 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 52.

[0106] The analysis and judgment module 52 processes the expression level data through the Firmiana software, which is preset to a machine learning algorithm based on a generalized linear regression model, to construct a prediction model to predict the probability of the sample suffering from esophageal cancer and the probability of not suffering from esophageal cancer, respectively, to judge whether the expression level data meets the preset judgment conditions to predict the risk of the sample suffering from esophageal cancer and output the prediction result; the judgment condition is that the probability of suffering from esophageal cancer is greater than or equal to the probability of not suffering from esophageal cancer.

[0107] When the expression level data meets the judgment conditions, the output prediction result is "having the risk of esophageal cancer"; when the expression level data does not meet the judgment conditions, that is, the probability of having esophageal cancer is less than the probability of not having esophageal cancer, the output prediction result is "not having the risk of esophageal cancer".

[0108] The data collection module 53 is used to collect data on the expression levels of the biomarker combinations in the sample to be tested.

[0109] Example 3 Electronic Equipment

[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 when the processor executes the computer program, the method for predicting esophageal cancer in Example 1 of the present invention can be implemented.

[0111] Figure 5 The hardware structure diagram of this embodiment is shown. The electronic device 9 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 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., a keyboard, pointing device, etc.). Such communication can be performed via an input / output (I / O) interface 95. Furthermore, the electronic device 9 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via a bus 93. It should be understood that, although not shown in the figures, 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 (RAID) systems, tape drives, and data backup storage systems.

[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, depending on the embodiment 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 and embodied by multiple units / modules.

[0119] Example 4 Computer-readable storage medium

[0120] An 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 esophageal cancer 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 thereof.

[0122] In a possible embodiment, the present invention can also be implemented in the form of a program product, which includes 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 esophageal cancer in Example 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 the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0124] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, rather than to limit it.

Claims

1. A reagent for detecting a combination of biomarkers for esophageal cancer, characterized in that: The esophageal cancer biomarker panel consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

2. The reagent according to claim 1, wherein The reagent is used to detect the expression level of the esophageal cancer biomarker combination, and the expression level is the protein expression level and / or the mRNA transcription level.

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

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

5. A biomarker combination for esophageal cancer, characterized in that: The esophageal cancer biomarker panel consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21.

6. A kit, characterized in that The kit comprises the reagent according to claim 1 and the esophageal cancer biomarker combination according to claim 5.

7. Use of the reagent according to claim 1 or the kit according to claim 6 in the preparation of a product for predicting and / or diagnosing esophageal cancer.

8. A system for predicting the risk of esophageal cancer, characterized in that: The prediction system includes: Detection module and analysis and judgment module; The detection module detects the expression level of the esophageal cancer 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 to a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from esophageal cancer and the probability of not suffering from esophageal cancer, respectively, determines whether the expression level data meets a preset judgment condition, so as to predict the risk of the sample suffering from esophageal cancer, and outputs a prediction result; the judgment condition is that the probability of suffering from esophageal cancer is greater than or equal to the probability of not suffering from esophageal cancer; When the expression level data meets the judgment condition, the prediction result is output as "having esophageal cancer risk"; when the expression level data does not meet the judgment condition, that is, the probability of having esophageal cancer is less than the probability of not having esophageal cancer, the prediction result is output as "not having esophageal cancer risk"; Among them, the esophageal cancer biomarker combination consists of ACTB, ACTG1, ALDH9A1, ARMC8, B3GAT3, CRP, DLG1, DSC3, DUSP3, EIF2B1, EIF5A2, EIF5AL1, GLO1, GP1BB, GSTO1, H1-10, H3-4, H3C1, H3C15, HSPA6, MMP10, MYL6B, MYOF, NAXD, NCALD, PCBP3, PLBD2, RCN1, RECK, RUVBL1, SAA1, SF3A3, SMC2 and SPG21; the expression level is protein expression level and / or mRNA transcription level.

9. The prediction system according to claim 8, wherein: The sample to be tested is a human plasma sample; and / or, the prediction system further includes a data collection module for collecting data on the expression level of the esophageal cancer 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