T Cell Exhaustion-Related Gene Signatures Predict Immunotherapy and Chemotherapy Response in Kidney Renal Clear Cell...

Background: Understanding T cell exhaustion (TEX)-related molecular characteristics can provide novel insights into treatment response prediction. This study developed a TEX-based prognostic model to predict survival outcomes and therapy responses in kidney renal clear cell carcinoma (KIRC) patients. Methods: The authors analyzed 518 KIRC patients from The cancer genome atlas (TCGA), identifying TEX-related genes via gene set variation analysis and weighted correlation network analysis. Survival random forest and Least Absolute Shrinkage and Selection Operator-Cox analyses selected eight key genes to construct a TEX risk model. Functional analyses explored TEX-related pathways and immune infiltration. The IMvigor210 dataset assessed immunotherapy response, whereas the Genomics of Drug Sensitivity in Cancer (GDSC) database predicted chemotherapy sensitivity. Single-cell RNA sequencing and quantitative real-time polymerase chain reaction validated a key TEX gene. Results: The TEX risk model demonstrated strong prognostic performance, effectively stratifying KIRC patients into high-risk (HR) and low-risk (LR) groups with significant differences in overall survival. Gene set enrichment analysis results revealed that TEX-related pathways were enriched in tumor proliferation, migration, and immune regulation. Immune cell infiltration analysis indicated that the TEX HR group exhibited distinct immune microenvironment characteristics, including increased expression of specific immune checkpoints. The model effectively predicted clinical responses to immunotherapy, with patients in the TEX HR group showing poorer immunotherapy efficacy. In addition, drug sensitivity analysis based on the GDSC database suggested that TEX features could influence chemotherapy response, highlighting potential therapeutic vulnerabilities. Experimental validation confirmed the expression pattern of a key TEX gene in KIRC samples. Conclusion: Their TEX risk model could effectively predict patient outcomes and responses to immunotherapy and chemotherapy, supporting its potential clinical utility in personalized treatment strategies.