Decoding the cholesterol-apoptosis axis in HCC: a machine learning-based multi-omics integration and single-cell transcriptomic analysis.

ABSTRACT: Liver hepatocellular carcinoma (LIHC), a predominant form of primary hepatic malignancy, demonstrates a progressively escalating global incidence, imposing substantial health and economic burdens on patients and society. Early diagnosis remains challenging, often resulting in late-stage detection, which limits the efficacy of current therapeutic strategies. This study systematically examines the transcriptional signatures of apoptosis-associated and cholesterol metabolic pathways in LIHC, providing insights into its underlying mechanisms and identifying potential prognostic markers. We employed multi-omics and machine learning to evaluate gene expression variations and construct a prognostic risk scoring model. This study identified apoptosis- and cholesterol metabolism-related differentia

SUBMITTER: Li Y 

PROVIDER: S-EPMC12647489 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature