Molecular marker panel for human esophageal squamous cell carcinoma and use thereof

Abstract

The present application relates to a kind of molecular marker group of human esophageal squamous cell carcinoma and its application, the molecular marker group is: the molecular marker group that the esophageal squamous cell carcinoma is divided into differentiation type, immune type, metabolic type, cell stem type: differentiation type: LCE3D, CDSN, KLK5, SPRR2G and DSG1;Immune type: MS4A1, CD79A, CXCL9, MZB1 and IDO1;Metabolic type: GSTA1, ADH7, UGT1A3 and ALDH3A1;Cell stem type: WFDC2, PEG10, SFRP1, LGR6 and VWA2;And the NK cell surface molecular marker group that the esophageal squamous cell carcinoma is divided into prognosis bad and drug insensitivity subtype.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of medical biotechnology, in particular, it relates to a molecular marker group of human esophageal squamous cell carcinoma and application thereof. BACKGROUND

[0002] Esophageal cancer (EC) is a serious and rapidly progressing neoplastic disease, with the highest mortality (509,000 cases per year) and incidence (572,000 new cases per year) of any cancer globally in 2018 (Bray et al., 2018). The global incidence and mortality of EC are expected to continue to increase in the coming decades (Bray et al., 2018; Malhotra et al., 2017). The highest prevalence of EC occurs in Asia and Africa, with the most common subtype being esophageal squamous cell carcinoma (ESCC), while adenocarcinoma is more common in North America and Western Europe (Bray et al., 2018).

[0003] Despite some progress in treatment options, including novel targeted therapies and cancer immunotherapies, the prognosis of ESCC remains poor, with a five-year survival rate of <15% (Abnet et al., 2018; Smyth et al., 2017). The main challenge in the treatment of ESCC is the invasiveness of tumor cells and late diagnosis. Therefore, studying the molecular characteristics of ESCC to identify biomarkers for early diagnosis and key molecular markers that affect disease prognosis is crucial for early intervention and improved treatment strategies.

[0004] Several major international studies have made important progress in identifying the molecular landscape of ESCC and understanding the molecular mechanisms (Cui et al., 2020; Frankell et al., 2019; Sawada et al., 2016; Song et al., 2014; Wu et al., 2014; Yan et al., 2019). They highlighted common dysregulation of RTK / RAS / PI3K and WNT / Notch pathways, frequent mutations in genes such as cell cycle regulation, TP53, FAT1, NOTCH1, KMT2D, NFE2L2, and ZNF750, as well as epigenetic changes in ESCC (Cao et al., 2020). However, genetic events associated with the heterogeneous behavior of ESCC are still poorly understood, resulting in a lack of reliable biomarkers for predicting prognosis or designing effective targeted treatment options for clinical programs. In addition, the precise immune escape mechanisms of ESCC have not been fully revealed, and there is no effective immunotherapy option available for ESCC, even though immunotherapy drugs will be incorporated into the standard systemic treatment options for ESCC in the near future (Kojima et al., 2020).

[0005] Therefore, there is a need for comprehensive multi-omic studies of ESCC to decipher molecular and immune heterogeneity to fully understand the pathogenesis of the disease, discover molecular changes closely related to the prognosis of esophageal cancer, especially for patients from the regions with the highest incidence of ESCC.

[0006] [References]

[0007] 1 Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68, 394-424, doi:10.3322 / caac.21492 (2018).

[0008] 2 Malhotra, G. K. et al. Global trends in esophageal cancer. J Surg Oncol 115, 564-579, doi:10.1002 / jso.24592 (2017).

[0009] 3 Cui, Y. et al. Whole-genome sequencing of 508 patients identifies key molecular features associated with poor prognosis in esophageal squamous cell carcinoma. Cell Res, doi:10.1038 / s41422-020-0333-6 (2020).

[0010] 4 Frankell, A. M. et al. The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic. Nat Genet 51, 506-516, doi:10.1038 / s41588-018-0331-5 (2019).

[0011] 5 Sawada,G.et al.Genomic Landscape of Esophageal Squamous CellCarcinoma in a Japanese Population.Gastroenterology 150,1171-1182,doi:10.1053 / j.gastro.2016.01.035(2016).

[0012] 6 Song,Y.et al.Identification of genomic alterations in oesophagealsquamous cell cancer.Nature 509,91-95,doi:10.1038 / nature13176(2014).

[0013] 7 Wu,C.et al.Joint analysis of three genome-wide association studiesof esophageal squamous cell carcinoma in Chinese populations.Nat Genet 46,1001-1006,doi:10.1038 / ng.3064(2014).

[0014] 8 Yan,T.et al.Multi-region sequencing unveils novel actionabletargets and spatial heterogeneity in esophageal squamous cell carcinoma.NatCommun 10,1670,doi:10.1038 / s41467-019-09255-1(2019).

[0015] 9 Cao,W.et al.Multi-faceted epigenetic dysregulation of geneexpression promotes esophageal squamous cell carcinoma.Nat Commun 11,3675,doi:10.1038 / s41467-020-17227-z(2020).

[0016] 10 Kojima, T. et al. Randomized Phase III KEYNOTE-181 Study of Pembrolizumab Versus Chemotherapy in Advanced Esophageal Cancer. J Clin Oncol, JCO2001888, doi:10.1200 / JCO.20.01888(2020). Summary of the Invention

[0017] This invention first relates to the application of a group of molecular markers in the preparation of diagnostic reagent kits, characterized in that,

[0018] The diagnostic kit classifies esophageal squamous cell carcinoma (ESCC) into four molecular subtypes: differentiated, immunogenic, metabolic, and stemness.

[0019] The molecular markers and their relationship with the molecular typing of the ESCC are as follows:

[0020] Differentiated: High expression of LCE3D, CDSN, KLK5, SPRR2G and DSG1;

[0021] Immunogenic: MS4A1, CD79A, CXCL9, MZB1 and IDO1 are highly expressed;

[0022] Metabolic: GSTA1, ADH7, UGT1A3 and ALDH3A1 are highly expressed;

[0023] Stem cells: WFDC2, PEG10, SFRP1, LGR6 and VWA2 are highly expressed.

[0024] This invention also relates to the application of a group of molecular markers in the preparation of diagnostic reagent kits, characterized in that,

[0025] The diagnostic kit described herein is used to identify subtypes of esophageal squamous cell carcinoma (ESCC) with poor prognosis and / or drug insensitivity.

[0026] The molecular markers mentioned are: NK cell surface markers or stem cell markers;

[0027] Preferably, the molecular marker is any combination of the following genes: XCL1, XCL2, CD160, and LGR6;

[0028] The identification is defined as: detecting high expression of the aforementioned molecular marker.

[0029] This invention also relates to a detection kit for identifying stemness subtypes of esophageal squamous cell carcinoma (ESCC), the kit comprising detection reagents for detecting the expression levels of WFDC2, SFRP1, LGR6, VWA2, and XCL1 genes, preferably qRT-PCR, with primers for each gene as follows:

[0030] WFDC2

[0031] SEQ ID NO.1: Upstream primer: 5'-CTGCCCAATGATAAGGAGGGT-3'

[0032] SEQ ID NO.2: Downstream primer: 5'-TTGCGGCAGCATTTCATCTG-3'

[0033] VWA2

[0034] SEQ ID NO.3: Upstream primer: 5'-CTGCACACTGTCCCTTCTACA-3'

[0035] SEQ ID NO.4: Downstream primer: 5'-GGTAGCCGTCCAGTCCTTCT-3'

[0036] SFRP1

[0037] SEQ ID NO.5: Upstream primer: 5'-TGGCCCGAGATGCTTAAGTG-3'

[0038] SEQ ID NO.6: Downstream primer: 5'-CCTCAGTGCAAACTCGCTGG-3'

[0039] LGR6

[0040] SEQ ID NO.7: Upstream primer: 5'-TGGGAAGACCAAGGTTGACAC-3'

[0041] SEQ ID NO.8: Downstream primer: 5'-AGAGAGACGCAGCTCCTCCAA-3'

[0042] XCL1

[0043] SEQ ID NO.9: Upstream primer: 5'-TGCTCTCTCACTGCATACATTG-3'

[0044] SEQ ID NO.10: Downstream primer: 5'-TGGTGTAGGTCTTGATTCTGCT-3'.

[0045] The present invention also relates to a drug for treating esophageal squamous cell carcinoma (ESCC).

[0046] The esophageal squamous cell carcinoma (ESCC) subtype described is a subtype with high expression of NK cell surface markers;

[0047] The drug is a drug that blocks XCL1, XCL2, and CD160; preferably, the drug is LCL-161, whose structure is shown in the following formula:

[0048]

[0049] Preferably, the drug further includes necessary pharmaceutical excipients.

[0050] The beneficial effects of this invention are as follows:

[0051] (1) We performed a comprehensive genomic and deep transcriptomic analysis of tumors matched with normal tissue in untreated ESCC patients who were followed up for more than four years after surgical resection.

[0052] (2) We explored transcriptomic subtypes and different immune microenvironments and discovered novel intrinsic tumor immune escape mechanisms. These data were then combined to provide a reliable set of prognostic biomarkers.

[0053] (3) Our study broadens our knowledge of the molecular and histological diversity of ESCC and provides new potential therapeutic targets for the treatment of ESCC. Attached Figure Description

[0054] Figure 1 Transcriptional typing and identification of ESCC

[0055] 1A, NMF clustering results of whole-genome expression data of 120 cases of esophageal squamous cell carcinoma;

[0056] 1B, each subtype sample has different cytological characteristics;

[0057] 1C. Survival analysis results for each subtype;

[0058] 1D. Groups with high expression of stem cell characteristic genes had poor survival.

[0059] Figure 2 Immune cell marker typing and correlation analysis of ESCC

[0060] 2A. Immunophenotyping results;

[0061] The expression levels of XCL1 and XCL2 genes in samples from the 2B and C3 subtypes were significantly higher than those in the other two subtypes;

[0062] High levels of 2C, B cells, and NK cells are associated with poor prognosis in esophageal cancer;

[0063] Significant differences were found in the prognosis of ESCC patients differentiated by NK cell markers in the 2D, local (China), and TCGA sample sets.

[0064] Among the 2E and C3 immune types, the Stemness transcriptome genotype had the highest proportion;

[0065] In patient samples of 2F and C3 immune types, the expression of NK cell marker genes such as XCL1, XCL2, and CD160 and LGR6, a representative gene of stem cell type, showed a significant positive correlation.

[0066] 2G. Immunohistochemical analysis was performed on the co-expression of LGR6, XCL1 and CD160 in tumor specimens.

[0067] Figure 3 Analysis of XCL1 gene expression and drug sensitivity in esophageal squamous cell carcinoma

[0068] The median expression values ​​of 3A and XCL1 can divide esophageal squamous cell carcinoma cell lines into two categories;

[0069] 3B and XCL1 high-expression cell lines are insensitive to 5-fluorouracil;

[0070] 3C. Overexpression of XCL1 in XCL1-low expression cell lines significantly reduced sensitivity to 5-fluorouracil.

[0071] 3D, the results of activity screening of drugs in XCL1-overexpressing cell lines. Detailed Implementation

[0072] Example 1: Whole transcriptome analysis, typing, prognostic analysis and validation of esophageal squamous cell carcinoma (ESCC)

[0073] We collected surgical specimens from 120 cases of esophageal squamous cell carcinoma (ethics review number from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University: 2019-KY-51) and performed whole transcriptome sequencing to ensure that each sample produced more than 5Gb of data (number of bases). The sequencing data were then used to re-encode the human genome version 37 transcriptome using Salmon software, and each gene was counted and quantified.

[0074] The quantified gene data from each sample were merged into a matrix, and only the 1,500 genes with the largest mean absolute deviation among individuals were retained. Non-negative matrix factorization (NMF) clustering analysis was performed, and the optimal clustering result was determined by the maximum state correlation coefficient and the validation results on three datasets. Feature gene selection involved comparing the current subtype with other samples. Differential gene analysis was performed using limma software to identify genes highly expressed in the subtype, requiring a fold change greater than 1. Gene enrichment analysis (GSEA) was then performed on the differentially expressed genes for each subtype to obtain the gene set most relevant to that subtype (p < 0.001).

[0075] The results are as follows Figure 1 As shown in Figure A, NMF clustering of whole-genome expression data from 120 cases of esophageal squamous cell carcinoma was used to classify them into four subtypes. Based on gene enrichment analysis of each subtype, the relevant gene sets were obtained, and the four subtypes were named as follows:

[0076] Differentiated type, represented by genes such as LCE3D, CDSN, KLK5, SPRR2G and DSG1;

[0077] Immunogenic type, represented by genes such as MS4A1, CD79A, CXCL9, MZB1, and IDO1;

[0078] Metabolic, represented by genes such as GSTA1, ADH7, UGT1A3, and ALDH3A1; and

[0079] Stemness is represented by genes such as WFDC2, PEG10, SFRP1, LGR6, and VWA2.

[0080] Hematoxylin-eosin (HE) stained slides can reveal different cytological characteristics in samples of different subtypes. Figure 1 B).

[0081] To examine the prognostic value among different subtypes, 120 patients were followed up, and follow-up information for 109 patients was ultimately obtained. Kaplan-Meier curve analysis and Cox multivariate analysis were performed, combining clinicopathological characteristics such as age, sex, smoking, alcohol consumption, tumor stage, and grade, to determine whether there were survival differences between the analyses. The survival analysis results are as follows: Figure 1 As shown in Figure C, the results indicate significant differences in prognosis among the various subtypes, with patients of the stem cell subtype having the shortest long-term survival.

[0082] To validate the prognostic value of stem cell subtypes, four representative genes, WFDC2, VWA2, SFRP1, and LGR6, and GAPDH were selected as reference genes to design qRTPCR primers (primer sequence structure). Gene expression quantification was performed on another sample set of 65 samples Gene Each gene in each sample underwent three PCR reactions, and the gene CT value was the average of the three experiments (raw CT data from qRTPCR results are shown in Table 2). The difference between the CT value of each gene and GAPDH expression was calculated to obtain the -delta CT value as the expression level of each gene. The expression levels of the four genes were summed to represent the cell stem type value of a sample. Based on the cell stem type value, the samples were divided into high and low groups, and Kaplan-Meier curve analysis and Cox multivariate survival analysis were performed to determine the relationship between the value and survival.

[0083] Table 1. Q-PCR expression data of representative genes for stem cell type and primer list for control genes.

[0084] Upstream primer sequence Downstream primer sequence WFDC2 5'-CTGCCCAATGATAAGGAGGGT-3' 5'-TTGCGGCAGCATTTCATCTG-3' VWA2 5'-CTGCACACTGTCCCTTCTACA-3' 5'-GGTAGCCGTCCAGTCCTTCT-3' SFRP1 5'-TGGCCCGAGATGCTTAAGTG-3' 5'-CCTCAGTGCAAACTCGCTGG-3' LGR6 5'-TGGGAAGACCAAGGTTGACAC-3' 5'-AGAGAGACGCAGCTCCTCCAA-3' GAPDH 5'-GACTGTGGATGGCCCCTCCGG-3' 5'-AGGTGGAGGAGTGGGTGTCGC-3' Figure 1

[0085] We performed RT-PCR quantitative analysis on the expression of stem cell characteristic genes in different sample sets and their prognostic correlation. We found that the group with high expression of stem cell gene had poor survival. Figure 2 D), which shows a significant statistical difference.

[0086] Table 2. Q-PCR expression levels of representative genes for dry skin type in 65 samples.

[0087]

[0088]

[0089]

[0090] Example 2: Immunophenotyping and Analysis of Esophageal Squamous Cell Carcinoma (ESCC)

[0091] Many different gene signatures have been used to analyze the tumor microenvironment and immune cells, but their performance varies significantly. We selected six different methods—Timer, MCP-count, Danaher, xCell, Davoli, and Rooney—to evaluate the immune microenvironment of esophageal squamous cell carcinoma (ESCC) cohorts (120 samples) from Example 1. Correlation analysis with immunohistochemical results determined the ESCC immune cell types. Then, a consensus clustering method was used to cluster the ESCC immune microenvironment, with parameters set as hierarchical clustering, Pearson correlation distance, and 50 resampling. After comprehensively comparing the predictive capabilities of various software for ESCC immune cells, we determined that the Danaher-based method, combined with Davoli, was the optimal method for predicting CD4+ T cells, ultimately identifying 13 relevant immune cell types. Cluster analysis of esophageal squamous cell carcinoma using these three types of immune cells revealed that 120 local esophageal squamous cell carcinoma samples could be divided into three immune subtypes: C1 (hot immune type), C2 (intermediate immune type), and C3 (cold immune type). The immunophenotyping results are as follows: Figure 2 As shown in Figure A, the optimal clustering classification result was determined by both the consensus matrix and the clustering tracking plot (based on transcriptomic data). Immune cell clustering was determined using Pearson correlation and mean clustering methods. This immunophenotyping result was also validated using MCP-counter immune cell characterization.

[0092] XCL1 and XCL2 are marker genes for NK cells. We compared the distribution differences of XCL1 and XCL2 among the three identified immune subtypes using the Wilcoxon rank-sum test to determine if they were consistent with the immune subtype clustering results. The results are as follows... Figure 2 As shown in Figure B, the expression levels of XCL1 and XCL2 genes in the C3 subtype samples were significantly higher than in the other two subtypes. This suggests that we can further analyze the prognosis of ESCC patients using the levels of immune cell markers.

[0093] To determine the prognostic value of each immune cell type, we used Kaplan-Meier curve analysis and Cox multivariate analysis to identify survival differences between analyses. The hazard ratio, 95% confidence interval, and P-value for each cell type were statistically analyzed, and cells were ranked according to their hazard ratio from lowest to highest. Results are as follows: Figure 2 As shown in C, high levels of B cells and NK cells are associated with poor prognosis of esophageal cancer, while higher levels of CD8+ T cells indicate better prognosis for esophageal cancer patients. Furthermore, NK cell levels above 2.02 are a poor prognostic marker for esophageal cancer.

[0094] To clarify whether NK cells were validated across different datasets, we used the same method to analyze immune cell markers from 120 local samples from Example 1 and 90 samples from TCGA data (in the TCGA data processing, NK cell levels were divided into high and low groups using 0.658 as the cutoff value). Then, we used Kaplan-Meier curve analysis to determine their survival predictive value. The results are as follows: Figure 2 As shown in Figure D, the results showed that the prognosis of ESCC patients differentiated by NK cell markers differed significantly in both the local sample set (China) and the TCGA sample set of Example 1.

[0095] To examine the correlation between gene expression-based typing and immunophenotyping, we compared the results of the two typing methods, examining the distribution of differentiation, immunophenotyping, metabolite, and stem cell genotypes within each immunophenotypic subtype, and performed statistical analysis. The conclusion was that the Stemness transcriptome genotype had the highest proportion in the C3 immunophenotyping subtype. Figure 2 E).

[0096] LGR6 is the representative gene of the stem cell type identified in Example 1. We calculated its expression level and plotted a Pearson correlation between its expression level and the expression of NK cell characteristic genes using a fitted linear curve. The results are as follows: Figure 3 As shown in F, in the C3 immune type patient samples, the expression of NK cell marker genes such as XCL1, XCL2, and CD160 and the stem cell representative gene LGR6 showed a significant positive correlation.

[0097] Finally, immunohistochemical analysis was performed on the co-expression of LGR6, XCL1, and CD160 in the tumor specimens using serial sections. The results are as follows: Figure 3 As shown in G, the immunohistochemical results of XCL1 and LGR6 indicate that XCL1 is mostly distributed in tumor cells rather than immune cells, while CD160 is expressed and distributed in both immune cells and tumor cells.

[0098] The results show that the immune subtypes of stem cell ESCC patients are relatively consistent, both belonging to the immune subtype (C3 subtype) with high expression of NK cell markers. The prognostic analysis also shows that both subtypes have poor prognostic results.

[0099] Example 3: Drug sensitivity differences among different ESCC subtypes

[0100] RNA-seq gene expression data of 22 esophageal squamous cell carcinoma cell lines were downloaded from the CCLE database. Differential gene analysis was performed by grouping the XCL1 expression lines according to the median value. Correlation analysis was also conducted on the expression levels of the LGR6 marker and XCL1 in stem-type esophageal squamous cell carcinoma. The results showed that, based on the median XCL1 expression value, esophageal squamous cell carcinoma cell lines could be divided into 11 low-expression groups and 11 high-expression groups. Figure 3 A) A total of 97 genes showed significant differences in expression levels (t-test p-value < 0.05). Differential hierarchical clustering of these 97 differentially expressed genes showed that XCL1 high-expression and low-expression cell lines could be perfectly clustered into two subpopulations based on these 97 differences. Furthermore, Pearson correlation analysis showed a significant positive correlation between XCL1 expression levels and LGR6 (r: 0.59). Figure 3 B).

[0101] Ten cell lines, including KYESE-30, KYSE-140, KYSE-510, and KYSE-520 (all XCL1 high expression), and KYSE-180, KYSE-270, KYSE450, KYSE-70, KYSE-150, and KYSE410 (all XCL1 low expression), were selected from our laboratory for 5-fluorouracil drug killing experiments. The specific experimental procedure is as follows:

[0102] (1) Cell preparation: Logarithmic phase cells were digested with trypsin and resuspended with 10% DMD to a final concentration of 4.4 x 104 cells / ml. Cells were added to a 96-well plate at a concentration of 4000 cells per well. 100 μl of PBS was added to the top, right and bottom wells, and 10% DMEM was added to the left well as a blank control.

[0103] (2) After culturing for 24 hours, the drug was diluted to 10× concentration with 10% DMEM. 10 μl of the drug was added to the 96-well plate in descending order of drug concentration. The rightmost cell well was the negative control and no drug was added.

[0104] (3) Incubate at 37℃ in a 5% CO2 environment for 72 hours. Under light-protected conditions, mix MTS and PMS at a ratio of 20:1, then add 20 μl to all wells except PBS, and continue incubating at 37℃ in a CO2 incubator for 3 hours.

[0105] (4) During the reaction time, MTS and PMS form a stable solution, and the OD value of each well is read at a wavelength of 490 nm using an enzyme-linked immunosorbent assay reader.

[0106] (5) The half-inhibitory concentration (IC50) was calculated using Graphpad Prism 5 based on cell viability.

[0107] Furthermore, we constructed an XCL1 overexpressing cell line using the XCL1 low-expression cell line KYSE-150 and then performed the same drug-killing experiment. The process for constructing the overexpression cell line is as follows:

[0108] (1) Human XCL1 cDNA was synthesized using GENEWIZ and cloned into a lentiviral vector;

[0109] (2) Take 1x10 5 Cells were added to 24-well plates and infected with either cloned XCL1 lentivirus or uncloned XCL1 lentivirus at an MOI (multiple of infection) of 50. After culturing for 72 hours, cells were selected using puromycin. The expression level of XCL1 was quantified by qRT-PCR, and the primers were designed as follows:

[0110] Upstream primer: 5'-TGCTCTCTCACTGCATACATTG-3'

[0111] Downstream primer: 5'-TGGTGTAGGTCTTGATTCTGCT-3'

[0112] After obtaining the KYSE-150 cell line overexpressing XCL1, the above killing experiment was repeated.

[0113] The results showed that the 5-fluorouracil drug sensitivity test indicated that the XCL1 high-expressing cell line was insensitive to 5-fluorouracil. Figure 3 C), the same conclusion was also verified in the KYSE-150 cell line overexpressing XCL1. ​ D).

[0114] Finally, we further compared the XCL1 high-expression and low-expression groups of 367 drugs in the GDSC (Genomics of Drug Sensitivity in Cancer, https: / / www.cancerrxgene.org) cytotoxicity sensitivities. The activity screening results for some drugs with significant activity differences are as follows: ​ As shown in E, esophageal squamous cell carcinoma with high XCL1 expression is more sensitive to LCL-161 (structure shown below).

[0115]

[0116] Finally, it should be noted that the above embodiments are only for those skilled in the art to understand the essence of the present invention, and are not intended to limit the scope of protection of the present invention. SEQUENCE LISTING <110> Zhengzhou University <120> Molecular markers of human esophageal squamous cell carcinoma and their applications <160> 10 <210> 1 <211> twenty one <212> DNA <213> Artificial sequence <400> 1 ctgcccaatg ataaggaggg t 21 <210> 2 <211> 20 <212> DNA <213> Artificial sequence <400> 2 ttgcggcagc atttcatctg 20 <210> 3 <211> twenty one <212> DNA <213> Artificial sequence <400> 3 ctgcacactg tcccttctac a 21 <210> 4 <211> 20 <212> DNA <213> Artificial sequence <400> 4 ggtagccgtc cagtccttct 20 <210> 5 <211> 20 <212> DNA <213> Artificial sequence <400> 5 tggcccgaga tgcttaagtg 20 <210> 6 <211> 20 <212> DNA <213> Artificial sequence <400> 6 cctcagtgca aactcgctgg 20 <210> 7 <211> twenty one <212> DNA <213> Artificial sequence <400> 7 tgggaagacc aaggttgaca c 21 <210> 8 <211> twenty one <212> DNA <213> Artificial sequence <400> 8 agagagacgc agctcctcca a 21 <210> 9 <211> twenty two <212> DNA <213> Artificial sequence <400> 9 tgctctctca ctgcatacat tg 22 <210> 10 <211> twenty two <212> DNA <213> Artificial sequence <400> 10 tggtgtaggt cttgattctg ct 22

Claims

1. The application of a reagent for detecting the molecular marker XCL1 in the preparation of a kit for identifying esophageal squamous cell carcinoma sensitive to 5-fluorouracil, characterized in that, The identification is defined as: detecting high expression of the aforementioned molecular marker.

Images