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

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 differentially expressed genes (ACMRDEGs). Importantly, LASSO regression analysis identified six hub genes (EPHX2, FABP5, SQLE, ADH4, HMGCS2, and CYP7A1) as critical prognostic biomarkers, demonstrating significant correlation with overall survival (OS). Furthermore, immune cell infiltration analysis indicated significant differences in 12 immune cell types within LIHC microenvironment, underscoring the immune system's involvement in disease progression. cholesterol and alcohol metabolism pathways were significantly enriched among hub gene modules, as quantified by multiple gene enrichment analyses. Single-cell analysis identified six major cell types, providing a deeper understanding of the cellular heterogeneity within LIHC. In summarize, this study presents the first integrated apoptosis-cholesterol metabolic pathway-based six-gene prognostic model for LIHC, validated for robustness across multiple cohorts, which may facilitate personalized therapeutic strategies and refined risk assessment in clinical practice.