Exploring Cuproptosis-Related Prognostic Signature for Hepatocellular Carcinoma: Bioinformatics and In Vitro Analyses.

BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor with a poor prognosis. Cuproptosis, a newly identified form of programmed cell death, holds potential prognostic value in cancers, but its significance in HCC remains limited.

METHODS

Differentially expressed cuproptosis-related genes (CRGs) were identified. Univariate and multivariate Cox analyses were used to construct a prognostic model. The model's performance and independence were evaluated using Kaplan-Meier curves, ROC, and Cox regression analyses. Immune infiltration was assessed via Cibersort, and underlying mechanisms were explored using Gene Set Enrichment Analysis (GSEA). Additionally, in vitro experiments were applied to assess the functional impact of key genes on the proliferation, invasion, and migration of HCC cells.

RESULTS

A prognostic CRG model consisting of AACS, ABCB6, and CKAP2 was developed. Kaplan-Meier analysis showed significantly lower survival rates in high-risk groups. ROC analysis indicated superior predictive accuracy of the signature compared to individual genes. Cox regression analysis confirmed its independence as a prognostic factor. Moreover, the CRG model was significantly correlated with stromal score, regulatory T cells, and M0 macrophages. High-risk patients exhibited enrichment in pathways including cell cycle, meiosis, mitotic nuclear division, and DNA replication. In vitro experiments confirmed significantly upregulated AACS expression in HCC cells (P < 0.05), and its knockdown markedly suppressed HCC cell proliferation, invasion, and migration.

CONCLUSION

This study established and validated a prognostic model for HCC by using CRGs and demonstrated high predictive effectiveness. These findings enhance the understanding of CRGs and offer theoretical support for prognostic prediction in HCC.