Machine learning-based programmed cell death-related index to predict prognosis and immunotherapy response in skin cutaneous melanoma.

ABSTRACT: Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy with heterogeneous outcomes and a variable response to immunotherapy. Programmed cell death (PCD) serves a key role in tumor progression and immune regulation, but its prognostic and therapeutic relevance in SKCM remains to be elucidated. An integrative machine learning strategy encompassing 77 algorithm combinations was employed to construct a PCD-related index (PCDI). Associations between the PCDI and immune microenvironment, immunotherapy response and drug sensitivity were also investigated. Subsequently, a 13-gene risk signature was identified via the Lasso algorithm and used to calculate a PCDI-based risk score. Functional enrichment and experimental validation were conducted to explore potential biological functions of

SUBMITTER: Wang X 

PROVIDER: S-EPMC12536388 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature