Cuproptosis‑related gene signature links immune-metabolic crosstalk to poor prognosis in hepatocellular carcinoma

Abstract Background Cuproptosis, a copper‑dependent cell death pathway, has emerged as a novel regulatory mechanism in cancer biology. However, the global expression landscape and prognostic value of cuproptosis‑related genes in hepatocellular carcinoma (HCC) remain poorly characterized. Methods We systematically analyzed the expression profiles of 18 cuproptosis‑related genes using The Cancer Genome Atlas (TCGA) HCC dataset. A prognostic risk model was constructed via LASSO and multivariate Cox regression analyses. Comprehensive bioinformatics analyses were performed to evaluate genomic alterations, functional enrichment, immune infiltration, methylation patterns, and drug sensitivity. Virtual knockout simulations were conducted using scTenifoldKnk to assess gene functions. External validation were performed in the ICGC-LIRI-JP cohort. Results We identified a four‑gene prognostic signature (CDKN2A, DLAT, MTF1, and PDHA1) that effectively stratifies HCC patients into high‑ and low‑risk groups, with the risk score serving as an independent prognostic factor. The model genes were significantly enriched in mitochondrial metabolic pathways, including the TCA cycle and pyruvate metabolism, and showed strong associations with immune cell infiltration and immune checkpoint expression. Distinct promoter methylation patterns and differential drug sensitivity profiles were observed across the four genes. Virtual knockout experiments indicated that the four-gene signature acts in concert to modulate both metabolic homeostasis and immune-related pathways in HCC, corroborating the enrichment and immune infiltration findings. External validation indicate that the four-gene signature reproduces its prognostic stratification in an independent RNA-sequencing cohort of HCC. Conclusion Collectively, this study provides the systematic integration of cuproptosis‑related genes into a prognostic framework for HCC, offering a novel biomarker panel that may inform individualized risk stratification and precision therapeutic decision‑making.