T cell proliferation-related genes: Predicting prognosis, identifying the cold and hot tumors, and guiding treatment in clear cell renal cell carcinoma - Huang, Haoran; Cai, Yanmin; Hong, Xitao; Gao, Wenzong; Tang, Jun; Zhang, Shujuan; Xu, Zhe (Frontiers in Genetics, 2022)
Background: Immunotherapy has become a new direction of current research because the effect of traditional radiotherapy and chemotherapy on clear cell renal cell carcinoma (ccRCC) is not satisfactory. T cell proliferation-related genes (TRGs) play a pivotal role in tumor progression by regulating the proliferation, activity, and function of immune cells. The purpose of our study is to construct and verify a prognostic model based on TRGs and to identify tumor subtypes that may guide treatment through comprehensive bioinformatics analyses. Methods: RNA sequencing data, clinical information, and somatic mutation data of ccRCC are obtained from The Cancer Genome Atlas (TCGA) database. The PPI and enrichments of function and pathway were studied after extracting differentially expressed TRGs. Identifying TRGs related to prognosis, we performed the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct a risk-stratified model. Its prediction performance is verified by various methods. Then, Gene Set Enrichment Analysis (GSEA), principal component analysis (PCA), tumor microenvironment (TME) analysis, and the half-maximal inhibitory concentration (IC50) prediction were performed between the different groups of patients. To further discuss the immunotherapy between hot and cold tumors, we divided all patients into two clusters based on TRGs. Similar to the risk groups, survival data and TME between clusters were studied. Finally, we analyzed the gene mutation and calculated the tumor mutation burden (TMB). Results: Risk-stratified model and nomogram predict the prognosis of ccRCC patients accurately. Functional enrichment analyses suggested that TRGs mainly focused on the biological pathways related to tumor progression and immune response. Different TME, drug resistance, and TMB can be distinguished clearly according to both risk stratification and tumor subtype clustering. Conclusions: In this study, a new stratification model of ccRCC based on TRGs was established, which can accurately predict the prognosis of patients. The distinction between hot and cold tumors provides a reference for clinical immunotherapy.