| Objective: Esophageal cancer(EC)is a kind of tumor with extremely high mortality in the world.In the past decades,the incidence rate of western countries has been rising.The prognosis of esophageal cancer patients is poor.Despite the rapid progress of clinical treatment,the 5-year survival rate is still less than 15%.Chemotherapy is an optional treatment for resectable esophageal cancer,and it can preserve the esophagus for patients who cannot tolerate surgery.In addition,the combination of radiotherapy and chemotherapy and conservative surgery can prolong the survival period of patients.Esophageal cancer can be divided into two types:adenocarcinoma and squamous cell carcinoma.In 2020,four clinical trials of Check Mate-649,ATTRACTION-4,KEYNOTE-590 and Check Mate 577 confirmed that anti-PD-1 therapy is the first-line treatment for ESCC patients.The latest research shows that the resistance of PD-1 in esophageal adenocarcinoma is higher than that in esophageal squamous cell carcinoma,so its application prospect is more optimistic.Immune infiltrating cells have been shown to be important in response to immunotherapy.Previous studies have established a prognosis model of non non-small cell lung cancer,ovarian cancer,breast cancer,colorectal cancer,osteosarcoma and bladder cancer based on immune related genes(IRGs).In this study,we established a prognosis model of immune-related genes prognostic index(IRGPI)and verified its role in different molecular characteristics and prognosis of EC.Methods: Download RNA-seq data of esophageal cancer samples from the Cancer Genome Atlas(TCGA)database,including cancer samples and para-cancer samples,as well as matching clinical information.RNA-seq data and clinical information are downloaded from Gene Expression Omnibus(GEO).GEO cohort data(GSE53625)is a validation sample of esophageal cancer.The list of immune-related genes is downloaded from and Innat DB database.The regulatory relationship between m RNAs,transcription factors(TF)and mi RNAs is downloaded from the v Bio Portal database.The immune score was calculated using the TIDE tool.Differently expressed genes(DEGs)in cancer tissues compared with normal tissues were identified by R package lima,and the error detection rate was<0.05,and the log was 2 times changed>1.In functional enrichment analysis,the gene was selected from differentially expressed genes and immune-related genes.Gene ontology(GO)and Kyoto Genome Encyclopedia(KEGG)enrichment analysis are run using the "clusterprofile" R package.WGCNA was performed to identify the central genes significantly related to EC.The immune-related central genes significantly related to survival were obtained,and then these genes were selected for further analysis(p<0.05,log-rank test).The IRGPI model is based on multivariate Cox analysis.According to the results of Kaplan-Meier survival analysis,the constructed model can predict the prognosis of esophageal cancer patients.Kaplan-Meier survival analysis was carried out using R package "survival" and "surviviner".Univariate and multivariate Cox regression analysis was performed to determine independent risk factors for prognosis.Finally,the model of immune related gene prognostic indicators of esophageal cancer was established to verify the prognostic model of TCGA and GEO groups.Through the calculation of multiple Cox regression analysis,the model formula of training group and test group can be obtained.By sorting out the clinical data set of the GES53625 data set,two key information were obtained:survival time and survival status.Next,the expression of the model gene was extracted and the risk score of the test group was obtained.Then the test group is divided into high risk group and low risk group according to the median value of risk score.Kaplan-Meier(KM)survival analysis was used to evaluate the prognostic ability of IRGPI in TCGA and GEO cohorts.Results: In this study,363 up-regulated IRGs and 83 down-regulated IRGs were identified.Next,through multiple Cox regression analysis and WGCNA,we found a prognostic model constructed by eight IRGs(OSM,CEACAM8,HSPA6,HSP90AB1,PCSK2,PLXNA1,TRIB2,HMGB3).According to the results of Kaplan-Meier survival analysis,the constructed model can predict the prognosis of esophageal cancer patients.The results will be verified by the gene expression comprehensive database(GEO).The patients were divided into two groups,and the two groups showed different results.The overall survival rate of patients with low IRGPI was higher than that of patients with high IRGPI. |
