Leveraging diverse cell-death patterns to predict the prognosis, immunotherapy and drug sensitivity of clear cell renal cell carcinoma

Abstract

Abstract Clear cell renal cell carcinoma (ccRCC) poses clinical challenges due to its varied prognosis, tumor microenvironment attributes, and responses to immunotherapy. We established a novel Programmed Cell Death-related Signature (PRS) for ccRCC assessment, derived through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. We validated PRS using the E-MTAB-1980 dataset and created PCD-related clusters via non-negative matrix factorization (NMF). Our investigation included an in-depth analysis of immune infiltration scores using various algorithms. Additionally, we integrated data from the Cancer Immunome Atlas (TCIA) for ccRCC immunotherapy insights and leveraged the Genomics of Drug Sensitivity in Cancer (GDSC) database to assess drug sensitivity models. We complemented our findings with single-cell sequencing data and employed the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and qRT-PCR to compare gene expression profiles between cancerous and paracancerous tissues. PRS serves as a valuable tool for prognostication, immune characterization, tumor mutation burden estimation, immunotherapy response prediction, and drug sensitivity assessment in ccRCC. We identify five genes with significant roles in cancer promotion and three genes with cancer-suppressive properties, further validated by qRT-PCR and CPTAC analyses, showcasing gene expression differences in ccRCC tissues. Our study introduces an innovative PCD model that amalgamates diverse cell death patterns to provide accurate predictions for clinical outcomes, mutational profiles, and immune characteristics in ccRCC. Our findings hold promise for advancing personalized treatment strategies in ccRCC patients.