Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma.

ABSTRACT

BACKGROUND: Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI). METHODS: The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology. RESULTS: GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients. CONCLUSION: This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.