Integrating machine learning and multi-omics analysis to explore Treg-associated programmed cell death features in clear cell renal cell carcinoma

Treg infiltration and programmed cell death are important factors influencing cancer progression, and they interact with each other. However, the significance of Treg-related programmed cell death (PCD) characteristics in clear cell renal cell carcinoma remains unclear.

Through Mendelian randomization, we identified PCD genes and Treg markers that are highly associated with ccRCC outcomes. Subsequently, based on Treg-related PCD genes, we constructed a diagnostic model utilizing a multi-layer perceptron (MLP) and integrated 10 machine learning algorithms to construct a prognostic model, which was then explained by the SHAP method. After exploring functional differences and chemotherapy sensitivity differences between high- and low-risk groups in the prognostic model, we validated the core gene of the model through in vitro cell experiments. Finally, we screened molecular drugs targeting the core genes using the DSigDB database and performed molecular docking and molecular dynamics validation.