| Objective:In this study,multiple bioinformatics methods were used to screen immunorelated prognostic genes(PPARGC1A,TNFRSF11A and VIPR1)for colorectal adenocarcinoma,and a immunorelated risk score model was constructed to evaluate the prognosis of patients with colorectal adenocarcinoma.Methods:In this study,weighted gene co-expression network analysis(WGCNA)was used to screen the modules with the most negative correlation with adenocarcinoma in GSE41657 and GSE50115,and the modules with the most negative correlation with adenocarcinoma in TCGA data were screened with the same method Nnatedb database download immune related genes,immune related gene take three intersections with colonic carcinoma is the most negative correlation immune related genes,using the single factor COX regression analysis and multi-factors COX regression analysis method,further screening out is closely related to the prognosis of important genes,and thus build colonic carcinoma immune related risk score model,Receiver operating characteristic curve(ROC)analysis and area under curve(AUC)results were used to test the efficacy of the immunoprognostic risk scoring model.Multivariate COX regression analysis showed that age,TNM stage and immune risk score were independent prognostic factors for colon cancer patients,and nomogram function of R language was used to better evaluate the prognosis of these patients.Functions involved in the high and low risk groups of the immunoprognostic risk score model were analyzed by GSEA.CIBERSORT of R language was used to analyze the differences of 22 kinds of immune cell infiltration in patients with high or low risk of immunoprognostic risk scoring model.Survdiff function of R language was used to analyze the influence of different expression of various immune cells on prognosis in patients with high or low risk of immunoprognostic risk scoring model.Single sample gene set enrichment analysis(SSGSEA)was used to analyze the differences in immune function between the high and low risk groups of the immunoprognostic risk scoring model,and TIDE website was used to evaluate the differences in immune escape between the high and low risk groups of the immunoprognostic risk scoring model.Results:WGCNA analysis was performed on TCGA dataset and GEO dataset respectively to screen the modules with the most negative correlation with adenocarcinoma,that is,the possible tumor suppressor gene modules.The modules were intermingled with immune-related genes obtained from IMMport and InnatEDB database,and 21 immune-related genes were obtained.Then,univariate and multivariate COX regression analysis was conducted to obtain the immunoprognostic risk scoring model composed of PPARGC1A,TNFRSF11A and VIPR1.Survival analysis indicated that there was a difference in the prognosis between the high and low risk groups.ROC analysis showed that AUC=0.725,indicating that the model had good evaluation efficiency.Patients’ prognosis was personalized by drawing a line graph.Correlation analysis of immune cell infiltration indicated that there were differences in the types of immune cell infiltration and immune-related functions in the high and low risk groups,and macrophages,neutrophils,dendritic cells and T cells were found to have an impact on prognosis.By immune escape analysis,the prognosis of high-risk group was lower than that of low-risk group.Conclusions:In this study,an immune risk assessment model composed of PPARGC1A,TNFRSF11A and VIPR1 was constructed to predict the prognosis of colon cancer patients by using bioinformatics analysis methods,univariate COX regression analysis and multivariate COX regression analysis.At the same time,the correlation analysis of immune infiltration suggested that the difference in prognosis between the high and low risk groups of the immune risk assessment model might be related to the difference in expression of tumor-associated macrophages(TAMs),M2-type macrophages,plasmacytoid dendritic cells,INF-y and immune escape. |
PDF Full Text Request