Integrative analysis of immune infiltration and microenvironment characteristics in renal clear cell carcinoma induced by cell senescence

Abstract

Abstract Background Our study aims to investigate the characteristics of the tumor microenvironment as well as to study the immunological infiltration in renal clear cell carcinoma that results from cell senescence. Methods Firstly, based on information from the Cancer Genome Atlas (TCGA) database, we collected ccRCC's mRNA, clinical data, and mutation data. From the comprehensive gene expression database (GEO), we acquired individuals gene expression profiles and relevant clinical data with ccRCC. We obtained senescence genes from the Aging Atlas database, extracted the expression of senescence genes from TCGA and GEO databases, and subsequently analyzed the differences. After which, the Kaplan Meier (KM) survival rate was utilised to determine survival-related prognostic genes; Cross genes were obtained from the intersection of differential genes and prognostic genes. By utilising the least absolute shrinkage and selection operator (lasso) regression and cross-validation, the genes included in the construction of the prognostic model were identified. The risk score was detected based on the signature, and the sample was then categorized into high-risk and low-risk groups. GSEA enrichment analysis, immune checkpoint analysis and the expression degree analysis of each model gene in immune cells were conducted among high-risk group and low-risk group respectively. The model we built was validated using the IMvigor210 database. Finally, we screened drugs that can inhibit the expression of high-risk genes from the Connectivity Map (CMAP) database by using risk differential genes. Results We obtained 37 cross genes and identified 17 genes that could be used to construct prediction model. We found that the tumor mutation load was higher in the high-risk groups. Even though high-risk patients were more likely to evade immunotherapy, there was no significant difference between the two groups when treated with PD-1, CTLA-4, or PD-1, combined with CTLA-4 immunotherapy. The verification results of IMvigor210 database were compatible with the study outcomes. Finally, we screened 6 drugs that can inhibit the expression of high-risk genes from the CMAP database by using risk differential genes. Conclusion The tumor microenvironment of ccRCC induced by cell senescence may have an immune escape or resistance when receiving immunotherapy. These findings may have some guiding significance for clinical individualized immunotherapy.