Índice de senescencia derivado del aprendizaje automático para predecir el pronóstico y la sensibilidad a los...

Cellular senescence, an inevitable phase in the cellular lifecycle, is increasingly implicated in cancer development. Clear cell renal cell carcinoma (ccRCC), a lethal malignancy of the urinary system, underscores the need for senescence-based risk models. Through single-cell analysis, we identified senescent cells within ccRCC tumors and delineated their distinct biological features. We then integrated ten machine learning algorithms-plsRcox, Ridge, Enet, CoxBoost, Lasso, StepCox, RSF, SuperPC, GBM, and survivalSVM-generating 101 combinatorial models via pairwise integration. The optimal Lasso-StepCox model was selected based on the highest mean concordance index (C-index), yielding a minimized senescence-related gene signature of only 9 genes (significantly below the typical 15-30-gene range). This signature formed the basis of a senescence-related scoring model (SRSM) for ccRCC patient survival assessment. Patients with high SRSM exhibited significantly poorer survival (P < 0.001), enhanced oxidative phosphorylation, and an immunosuppressive tumor microenvironment (TME) characterized by elevated regulatory T cell (Treg) infiltration. In vitro validation confirmed that NME2 knockdown suppressed ccRCC proliferation and invasion. Collectively, the SRSM framework provides a precise tool for prognostic stratification and therapeutic targeting in ccRCC. Therefore, to bridge the gap between the intricate landscape of cellular senescence revealed by scRNA-seq and the pressing clinical need for robust prognostic tools, we aimed to develop a quantifiable senescence-related scoring model (SRSM). By integrating multiple machine learning algorithms, we sought not only to achieve superior predictive accuracy but also to derive a minimized gene signature for enhanced clinical applicability, ultimately translating the biological insights of senescence into a precise prognostic framework for ccRCC.