A novel programmed cell death signature predicts clinical outcomes in clear cell renal cell carcinoma and identifies PLK1 as a therapeutic target

Abstract

Clear-cell renal cell carcinoma (ccRCC) remains therapeutically challenging despite recent treatment advances. Here, we analyzed 18 distinct programmed cell death (PCD) patterns across multiple cohorts and developed a novel prognostic scoring system (PCDscore) based on eight PCD-related genes. We established an eight-gene signature that demonstrated robust predictive capability and, when integrated with clinical staging, yielded a nomogram with strong performance across independent cohorts. High PCDscore groups exhibited enhanced immunosuppressive features, while low PCDscore groups showed better immunotherapy responses. Single-cell analysis of 54,166 cells revealed activation of multiple oncogenic pathways in high PCDscore tumor cells, along with extensive intercellular communication networks. To further investigate the role of PLK1, we identified 282 co-expressed genes and conducted functional enrichment analyses, revealing its significant association with pathways such as the cell cycle and NF-κB signaling. A protein-protein interaction (PPI) network and Bayesian network analysis highlighted PLK1 as a key regulator of PKMYT1, with CDC20 and CCNB2 acting upstream. Functional validation confirmed PLK1, the highest weighted gene in our signature, significantly influences tumor progression in ccRCC. This study establishes a reliable prognostic scoring system and identifies PLK1 as a potential therapeutic target, providing valuable clinical guidance for treatment decision-making in ccRCC patients.

Keywords: Bioinformatics analysis; Clear-cell renal cell carcinoma; Programmed cell death; Therapeutic target.