CN114214409A - Biomarker for esophageal cancer typing and application thereof - Google Patents

Biomarker for esophageal cancer typing and application thereof Download PDF

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CN114214409A
CN114214409A CN202111590459.8A CN202111590459A CN114214409A CN 114214409 A CN114214409 A CN 114214409A CN 202111590459 A CN202111590459 A CN 202111590459A CN 114214409 A CN114214409 A CN 114214409A
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刘鑫
贾富建
刘康
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Abstract

The invention relates to a biomarker for esophageal cancer typing and application thereof, and relates to the technical field of medical detection. The biomarker comprises more than 5 genes such as GAS7, LRP1B, MAP2K4, FOXP1 and WWOX. By utilizing the biological markers, the diagnosis of the esophageal squamous cell carcinoma and the esophageal adenocarcinoma is carried out, the diagnosis force AUC can reach 0.9447 when 5 markers are used, the diagnosis force AUC can reach 0.9789 when 10 markers are used, the diagnosis force AUC can reach 0.9714 when 20 markers are used, the diagnosis force AUC can reach 0.9489 when all the markers are used, the diagnosis force is excellent, a molecular level-based method for distinguishing two pathological subtypes is provided, mutual verification is provided for pathological diagnosis results, the diagnosis result of a case is ensured to be correct, and subsequent accurate treatment is facilitated.

Description

Biomarker for esophageal cancer typing and application thereof
Technical Field
The invention relates to the technical field of medical detection, in particular to a biomarker for esophageal cancer typing and application thereof.
Background
Esophageal cancer is the 8 th most common cancer and 6 th leading cause of death all over the world, and in China, the incidence rate of esophageal cancer is reduced in recent years, but the mortality rate is always the fourth place. Esophageal cancer is a malignant disease with poorer prognosis, and the total 5-year survival rate of the esophageal cancer in each stage is about 14 percent. Histologically, esophageal cancer is classified as a tumor of epithelial and non-epithelial origin. More than 90% of esophageal cancers are Squamous Cell Carcinomas (SCC) or adenocarcinoma. In terms of histological types, esophageal cancer mainly comprises squamous cell carcinoma accounting for more than 90 percent of the total esophageal cancer in China, mainly comprises adenocarcinoma accounting for about 70 percent of the total esophageal cancer in America and Europe, but the pathological form of the esophageal cancer is changed later, the esophageal adenocarcinoma comprising gastroesophageal junction cancer (GEJ) is remarkably increased in Western countries and America, and currently, the esophageal cancer accounts for 50 percent of the pathological form of the esophageal cancer. Smoking and heavy drinking are important factors causing esophageal squamous carcinoma.
Treatment regimens for esophageal cancer depend on the judgment of the pathotype. Meanwhile, in addition to conventional chemoradiotherapy means, esophageal cancer treatment has now begun to advance into the era of precision medicine: patients who break through the progress of treatment at a later stage, usually need to be tested for genetic variation and, depending on the individual condition, use corresponding targeted drugs, such as Her2 inhibitors, EGFR inhibitors, PDL-1 immunotherapy, mTOR inhibitors, AXL inhibitors, C-MET inhibitors.
The limited tissue samples and the need for increasingly more therapeutic targeting markers greatly increases the current diagnostic needs, and studies of histological diagnostic reproducibility have shown intra-and inter-pathologist variability in the decision: the wrong result of pathological judgment, poorly differentiated tumors, contradictory immunohistochemical results and the like present challenges to the precise medical accuracy of current esophageal cancer.
Meanwhile, the latest 2021 year-old national solid tumor somatic cell high-throughput sequencing (big panel) detection interstitial assessment report shows that the national detection laboratory qualification rate is only 49.2%, the qualification rate of genome or exon sequencing is expected to be lower, and the data greatly shows that the quality control of the patient for formulating a therapeutic drug recommendation based on pathology and gene detection is urgently needed to be improved.
Disclosure of Invention
Aiming at the problems, the invention provides a biomarker for esophageal cancer typing, obtains the biomarker for the typing diagnosis of esophageal squamous cell carcinoma and esophageal adenocarcinoma through different expression maps of variant genes in esophageal squamous cell carcinoma vs adenocarcinoma of the esophageal cancer, provides a distinguishing method for two pathological subtypes based on molecular level, and provides mutual verification for pathological diagnosis results, thereby ensuring correct case diagnosis results and facilitating subsequent accurate treatment.
In order to achieve the above objects, the present invention provides a biomarker for esophageal cancer typing, comprising at least 5 of the following genes: TP, NFE2L, NOTCH, RUNX1T, LRP1, ERBB, MUC, PTPRD, PREX, ERBB, CDKN2, NBEA, APC, ARID1, SMAD, ROBO, PTPRC, NRG, RGPD, TNFAIP, PDGFRB, WWOX, SMAD, SRC, MAFB, PTPRT, SETBP, PML, IDH, BLM, FES, CHD, FLCN, MAP2K, SMAD, ASXL, GAS, MAP2K, TCF, SDC, CRTC, PTK, SS18L, GNAS, NFATC, SALL, 1, FOXP, SRGAP, VHL, PIK3, ROA, TBTBL 1XR, NCKIPSD, MINNND, FBLN, MLF, ROBO, SOX, MB21 XPD, SOC, GMPS, SETD, GMPLTP, MUTP 3, ATR, TBETP, NCK, CACCITP, MCBTR, EPTR, MGPR, TMB, TMBER, TMB, TMCP, MCPR, TMB, MCPR, TMB, MCPR, MCTF, MCPR, MCTF, TMB, MCTF, PBF, MCTF, PBF, MCTF, PBF, PBR, PBF, MCTF, PBF, MCTF, PBR, MCTF, PBR, MCTF, MCR, PBR, MCR, PBR, MCR.
The inventor analyzes through a TCGA database, and finds that the genetic map of the esophageal squamous carcinoma with higher gene mutation frequency than that of esophageal adenocarcinoma is derived from the following genes: TP53, NFE2L2, NOTCH1, RUNX1T 1; the genetic map of the esophageal adenocarcinoma with higher frequency of gene mutation than that of esophageal squamous cell carcinoma is derived from the following genes: LRP1B, ERBB4, MUC16, PTPRD, PREX2, ERBB2, CDKN2A, NBEA, APC, ARID1A, SMAD4, ROBO2, PTPRC, NRG1, RGPD3, TNFAIP3, PDGFRB; the genetic map of the esophageal squamous carcinoma with higher frequency of copy number variation than that of esophageal adenocarcinoma is derived from the following genes: LRP1B, FOXP1, SRGAP3, VHL, PIK3CA, RHOA, TBL1XR1, NCKIPSD, MITF, FANCD2, FBLN2, MLF1, ROBO2, SOX2, MB21D2, XPC, GMPS, SETD2, WWTR1, MAP3K13, TP63, TFRC, BAP1, MECOM, EPHA3, RAF1, MLH1, CTNNB1, IGF2BP2, ETV5, MUC4, CACNA1D, PPARG, MYD88, EIF4A2, BCL6, PBTGFBR 1, TGFBR2, ERBB4, LPP, ATR, CCR 4; the genetic map of the esophageal adenocarcinoma with higher frequency of copy number variation than that of esophageal squamous cell carcinoma is derived from the following genes: WWOX, SMAD4, SRC, MAFB, PTPRT, SETBP1, PML, IDH2, BLM, FES, CHD2, FLCN, MAP2K1, SMAD3, ASXL1, GAS7, MAP2K4, TCF12, SDC4, CRTC3, PTK6, SS18L1, GNAS, NFATC2, SALL 4; the gene is used as a biomarker, and the diagnostic power AUC can reach 0.9489.
In one embodiment, the biomarkers that are typed for frequency of occurrence of gene mutations include at least 5 of the following genes: TP53, NFE2L2, NOTCH1, RUNX1T1, LRP1B, ERBB4, MUC16, PTPRD, PREX2, ERBB2, CDKN2A, NBEA, APC, ARID1A, SMAD4, ROBO2, PTPRC, NRG1, RGPD3, TNFAIP3, frpdgb; the biomarkers based on the occurrence frequency of copy number variation comprise at least 5 of the following genes: LRP1B, FOXP1, SRGAP3, VHL, PIK3CA, RHOA, TBL1XR1, NCKIPSD, MITF, FANCD2, FBLN2, MLF 2, ROBO2, SOX2, MB21D2, XPC, GMPS, SETD2, WWTR 2, MAP3K 2, TP 2, TFRC, BAP 2, MECOM, EPHA 2, RAF 2, MLH 2, CTNNB 2, IGF2BP2, ETV 2, MUC 2, CACNA 12, PPARG, MYD 2, MEIF 4A2, BCL 2, PBFB3672, TGFBR2, ERBB2, LPP, ATR 2, WW 36OX, SMFB 2, PREI 2, SDCT 2, TBSC 2, SMTBAS 2, SMTCK 2, SATC 2, SMTCF 2, SATC 2, SARG 2, FLTC 2, SARG 2, SATCK 2, TFSC 2, TFS 2, TFSC 2, TFAS 2, TFSC 2, TFS 2, TFSC 2, TFS 2, TFSC 2, TFAS 2, TFS 2, TFSC 2, TFAS 2, TFSC 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFSC 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFS 2, TF.
In one embodiment, the biomarkers include the following genes: GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
By adopting the 5 genes as the biomarkers, the diagnostic power AUC can reach 0.9447.
In one embodiment, the biomarkers include the following genes: TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
By adopting the 10 genes as the biomarkers, the diagnostic power AUC can reach 0.9789.
In one embodiment, the biomarkers include the following genes: ETV5, SMAD4, ROBO2, TFRC, TBL1XR1, ERBB4, GMPS, PIK3CA, PPARG, RAF1, TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
By adopting the 20 genes as the biomarkers, the diagnostic power AUC can reach 0.9714.
The invention also provides application of the biomarker in developing and/or preparing a diagnostic product for esophageal squamous carcinoma and esophageal adenocarcinoma typing.
In one embodiment, the biomarker is used as a biomarker in the detection of a biological sample taken from: at least one of blood or tissue.
In one embodiment, the biological sample is detected by a detection method selected from the group consisting of: sequencing technology, microarray hybridization technology, or PCR technology.
In one embodiment, the sequencing technique is selected from the group consisting of: sanger sequencing technology, high-throughput sequencing technology, pyrosequencing technology, sequencing-by-synthesis technology, single-molecule sequencing technology, nanopore sequencing technology, semiconductor sequencing technology, connection sequencing technology, hybridization sequencing technology, digital gene expression technology, next-generation sequencing technology, single-molecule sequencing-by-synthesis technology, massively parallel sequencing technology, clonal single-molecule array technology, shotgun sequencing technology, Maxim Gilbert sequencing technology, primer walking technology, or sequencing technology based on PacBio, SOLiD, ion Torrent or nanopore platforms.
In one embodiment, the microarray hybridization technique is a SNP microarray technique.
In one embodiment, the PCR technique is selected from: KASP typing method, ligase detection reaction typing method or Taqman probe method.
The invention also provides a kit for esophageal cancer typing diagnosis, which comprises a reagent for detecting the biomarkers in a biological sample.
The invention also provides a system for esophageal cancer typing diagnosis, which comprises:
an analysis device: the system is used for obtaining the genetic variation condition of the biomarker in a biological sample of a subject to be diagnosed and inputting the genetic variation condition into an evaluation model for typing evaluation;
an output device: for outputting the above evaluation result.
In one embodiment, the evaluation model is established by: and obtaining a plurality of biological samples of esophageal adenocarcinoma and esophageal squamous carcinoma, sequencing to obtain the gene mutation condition of the biomarker, and establishing a typing model by using a random forest model to obtain the biological sample.
Compared with the prior art, the invention has the following beneficial effects:
the biomarker for esophageal cancer typing and the application thereof are obtained through different expression maps of variant genes in esophageal squamous cell carcinoma vs adenocarcinoma of esophageal cancer, so that the typing diagnosis of esophageal squamous cell carcinoma and esophageal adenocarcinoma is realized, a distinguishing method for two pathological subtypes based on molecular level can be provided, mutual verification is provided for pathological diagnosis results, the diagnosis result of a case is ensured to be correct, and the follow-up accurate treatment is facilitated.
Drawings
FIG. 1 is a flow chart for modeling an esophageal cancer model in example 2;
FIG. 2 is a graph showing AUC in the esophageal cancer triple classification model using 88 markers in example 2;
FIG. 3 is a graph showing the AUC of the esophageal cancer triage model using 20 markers in example 2;
FIG. 4 is a graph showing the AUC of the esophageal cancer triage model using 10 markers in example 2;
fig. 5 is a graph showing AUC of the esophageal cancer triple classification model using 5 markers in example 2.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Defining:
esophageal squamous carcinoma: refers to a subtype of esophageal cancer that develops primarily from squamous cell dysplasia.
Esophageal adenocarcinoma: refers to a subtype of esophageal cancer that has evolved primarily from normal squamous epithelium of the esophagus via Barrett's Esophagus (BE) or incomplete intestinal metaplasia.
TCGA: refers to a database collectively known as The Cancer Genome Atlas, which contains data for 30+ tumors, derived from The National Cancer Institute (NCI) and The National human Genome Institute (NHGRI) initiated Cancer Genome Atlas (The Cancer Genome Atlas, TCGA) program, with The website https:// www.cbioportal.org/.
The source is as follows:
reagents, materials and equipment used in the embodiment are all commercially available sources unless otherwise specified; unless otherwise specified, all the experimental methods are routine in the art.
Example 1
And (3) primarily screening the variant gene markers for the pathological subtype typing of the esophageal cancer based on a TCGA public database.
The screening method is as follows.
1. Tumor tissue whole exome sequencing data from esophageal cancer patients were obtained from the TCGA database.
In this example, full exon sequencing data of 184 esophageal cancer patients (88 adenocarcinoma and 96 squamous carcinoma) were downloaded, and seven different software were used: MuTect, VarScan, MuSE, Radia and somaticSniper, and respectively detecting SNP mutation sites; InDels is detected by VarScan, Pindel and Inelocator respectively, and finally, at least two mutation software are adopted to simultaneously call the mutation sites in the sample as candidate mutation sites.
2. Differential analysis was performed on the adenocarcinoma group dataset and the squamous carcinoma group dataset.
The mutant genes (SNV and CNV) with p less than or equal to 0.05 are selected as potential markers by fisher test analysis, and the potential markers are shown in the table.
TABLE 1 SNV Table
Figure BDA0003428894670000051
Figure BDA0003428894670000061
TABLE 2 CNV Table
Figure BDA0003428894670000071
Figure BDA0003428894670000081
3. Cancer-associated genes and WWOX genes were selected according to COMIC CGC (Cancer Gene Census, v94) Gene annotation, totaling 88 genes as model markers, 88 markers: TP, NFE2L, NOTCH, RUNX1T, LRP1, ERBB, MUC, PTPRD, PREX, ERBB, CDKN2, NBEA, APC, ARID1, SMAD, ROBO, PTPRC, NRG, RGPD, TNFAIP, PDGFRB, WWOX, SMAD, SRC, MAFB, PTPRT, SETBP, PML, IDH, BLM, FES, CHD, FLCN, MAP2K, SMAD, ASXL, GAS, MAP2K, TCF, SDC, CRTC, PTK, SS18L, GNAS, NFATC, SALL, 1, FOXP, SRGAP, VHL, PIK3, ROA, TBTBL 1XR, NCKIPSD, MINNND, FBLN, MLF, ROBO, SOX, MB21 XPD, SOC, GMPS, SETD, GMPLTP, MUTP 3, ATR, TBETP, NCK, CACCITP, MCBTR, EPTR, MGPR, TMB, TMBER, TMB, TMCP, MCPR, TMB, MCPR, TMB, MCPR, MCTF, MCPR, MCTF, TMB, MCTF, PBF, MCTF, PBF, MCTF, PBF, PBR, PBF, MCTF, PBF, MCTF, PBR, MCTF, PBR, MCTF, MCR, PBR, MCR, PBR, MCR.
Example 2
For the potential markers obtained in example 1, clinical samples were analyzed and validated.
The verification process is as follows.
1. Obtaining a tissue sample: 184 specimens of relevant FFPE sections of pathology identified by relevant experts as esophageal cancer (96 squamous carcinomas, 88 squamous carcinomas) were collected from river-south university.
2. Sample sequencing analysis: FFPE tissue samples were subjected to whole genome sequencing analysis by a third party (clear biotechnology).
3. Establishing a model: using the information of all target markers in example 1, performing detection and judgment on independent verification sets, namely tissue samples of 96 cases of squamous carcinoma and 88 cases of adenocarcinoma patients, performing modeling analysis by using a random forest model, wherein a modeling flow is shown in fig. 1, segmentation is performed according to a ratio of 7:3, 20 times of repetition are performed, the AUC of the model is up to 0.9489, and model parameters are as follows: n _ estimators is 300, max _ features is log2, criterion is entropy, min _ samples _ leaf is 3, and class _ weight is balanced. As shown in fig. 2.
4. Preference was given to 88 markers: by utilizing a random forest model for modeling analysis, tissue samples of 96 cases of squamous carcinoma patients and 88 cases of adenocarcinoma patients are detected and judged according to the proportion of 7:3, repeating for 20 times, establishing the characteristic importance of the model according to the step 3, and selecting the optimal combination of the first 20 MARKER, namely ETV5, SMAD4, ROBO2, TFRC, TBL1XR1, ERBB4, GMPS, PIK3CA, PPARG, RAF1, TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1 and WWOX, wherein the AUC of the model obtained by using the 20 MARKER is as high as 0.9714, as shown in FIG. 3.
5. Preference was given to 20 markers: by utilizing a random forest model for modeling analysis, tissue samples of 96 cases of squamous carcinoma patients and 88 cases of adenocarcinoma patients are detected and judged according to the proportion of 7:3, repeating for 20 times, selecting the optimal combination of the first 10 MARKER according to the characteristic importance of the model in the step 3, namely TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1 and WWOX, and obtaining the model AUC up to 0.9789 by using the 10 MARKER, as shown in FIG. 4.
6. Preference was given to 10 markers: by utilizing a random forest model for modeling analysis, tissue samples of 96 cases of squamous carcinoma patients and 88 cases of adenocarcinoma patients are detected and judged according to the proportion of 7:3, repeating for 20 times, selecting the optimal combination of the first 5 MARKER according to the characteristic importance of the model in the step 3, namely GAS7, LRP1B, MAP2K4, FOXP1 and WWOX, and obtaining the AUC of the model up to 0.9447 by using the above 5 MARKERs, as shown in FIG. 5.
Example 3
28 samples which are clinically judged to be esophageal cancer are selected, 10 MARKER combined models established in the embodiment 2 are adopted for analysis, the analysis results are compared with the judgment results of clinical experts, and the results are shown in the following table.
Table 3 clinical verification results
Figure BDA0003428894670000091
Figure BDA0003428894670000101
From the results, the biomarker of the invention is adopted to accurately judge the classification of squamous cell carcinoma or adenocarcinoma in small cell lung cancer by using the model, and the consistency with expert judgment is more than 91.3%.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A biomarker for the typing of esophageal cancer, comprising at least 5 of the following genes: TP, NFE2L, NOTCH, RUNX1T, LRP1, ERBB, MUC, PTPRD, PREX, ERBB, CDKN2, NBEA, APC, ARID1, SMAD, ROBO, PTPRC, NRG, RGPD, TNFAIP, PDGFRB, WWOX, SMAD, SRC, MAFB, PTPRT, SETBP, PML, IDH, BLM, FES, CHD, FLCN, MAP2K, SMAD, ASXL, GAS, MAP2K, TCF, SDC, CRTC, PTK, SS18L, GNAS, NFATC, SALL, 1, FOXP, SRGAP, VHL, PIK3, ROA, TBTBL 1XR, NCKIPSD, MINNND, FBLN, MLF, ROBO, SOX, MB21 XPD, SOC, GMPS, SETD, GMPLTP, MUTP 3, ATR, TBETP, NCK, CACCITP, MCBTR, EPTR, MGPR, TMB, TMBER, TMB, TMCP, MCPR, TMB, MCPR, TMB, MCPR, MCTF, MCPR, MCTF, TMB, MCTF, PBF, MCTF, PBF, MCTF, PBF, PBR, PBF, MCTF, PBF, MCTF, PBR, MCTF, PBR, MCTF, MCR, PBR, MCR, PBR, MCR.
2. The biomarker according to claim 1, wherein the biomarker based on the typing of the occurrence frequency of gene mutation comprises at least 5 of the following genes: TP53, NFE2L2, NOTCH1, RUNX1T1, LRP1B, ERBB4, MUC16, PTPRD, PREX2, ERBB2, CDKN2A, NBEA, APC, ARID1A, SMAD4, ROBO2, PTPRC, NRG1, RGPD3, TNFAIP3, frpdgb; the biomarkers based on the occurrence frequency of copy number variation comprise at least 5 of the following genes: LRP1B, FOXP1, SRGAP3, VHL, PIK3CA, RHOA, TBL1XR1, NCKIPSD, MITF, FANCD2, FBLN2, MLF 2, ROBO2, SOX2, MB21D2, XPC, GMPS, SETD2, WWTR 2, MAP3K 2, TP 2, TFRC, BAP 2, MECOM, EPHA 2, RAF 2, MLH 2, CTNNB 2, IGF2BP2, ETV 2, MUC 2, CACNA 12, PPARG, MYD 2, MEIF 4A2, BCL 2, PBFB3672, TGFBR2, ERBB2, LPP, ATR 2, WW 36OX, SMFB 2, PREI 2, SDCT 2, TBSC 2, SMTBAS 2, SMTCK 2, SATC 2, SMTCF 2, SATC 2, SARG 2, FLTC 2, SARG 2, SATCK 2, TFSC 2, TFS 2, TFSC 2, TFAS 2, TFSC 2, TFS 2, TFSC 2, TFS 2, TFSC 2, TFAS 2, TFS 2, TFSC 2, TFAS 2, TFSC 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFSC 2, TFS 2, TFAS 2, TFS 2, TFAS 2, TFS 2, TF.
3. The biomarker of claim 1, comprising the following genes: GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
4. The biomarker of claim 1, comprising the following genes: TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
5. The biomarker of claim 1, comprising the following genes: ETV5, SMAD4, ROBO2, TFRC, TBL1XR1, ERBB4, GMPS, PIK3CA, PPARG, RAF1, TP63, FLCN, MB21D2, MITF, SOX2, GAS7, LRP1B, MAP2K4, FOXP1, and WWOX.
6. Use of the biomarker according to any one of claims 1 to 5 in the development and/or manufacture of a diagnostic product for the typing of esophageal squamous carcinoma and esophageal adenocarcinoma.
7. The use according to claim 6, wherein the biomarker is used as a biomarker in the detection of a biological sample taken from the group consisting of: at least one of blood or tissue.
8. A kit for use in a differential diagnosis of esophageal cancer, comprising reagents for detecting the biomarkers of any one of claims 1-5 in a biological sample.
9. A system for genotyping and diagnosing esophageal cancer, the system comprising:
an analysis device: the method is used for obtaining the genetic variation condition of the biomarker of any one of claims 1 to 5 in a biological sample of a subject to be diagnosed, and inputting the genetic variation condition into an evaluation model for typing evaluation;
an output device: for outputting the above evaluation result.
10. The system of claim 9, wherein the evaluation model is established by: and obtaining a plurality of biological samples of esophageal adenocarcinoma and esophageal squamous carcinoma, sequencing to obtain the gene mutation condition of the biomarker, and establishing a typing model by using a random forest model to obtain the biological sample.
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