Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

ABSTRACT: Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognostic biomarkers and therapeutic targets to enhance patient outcomes. Data from the GEO, TCGA, and ICGC databases were analyzed to identify differentially expressed genes associated with PCD in HCC. A cell death signature (CDS) model was constructed based on seven key PCD genes using machine learning techniques, including Random Survival Forest and Cox regression models. The model was validated across multiple cohorts to evaluate its predictive accuracy for clinical outcomes, immune infiltration, and therapeutic response, and further validation of

SUBMITTER: Wei F 

PROVIDER: S-EPMC11876361 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature