A Prognostic Risk Model for Hepatocellular Carcinoma Integrating Ferroptosis and Metabolic Reprogramming Signatures

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Hepatocellular carcinoma (HCC) presents a significant global health challenge due to high incidence and mortality.
• Late-stage diagnosis and disease heterogeneity complicate treatment outcomes for HCC.
• Ferroptosis and metabolic reprogramming are critical in HCC, but their precise roles require further elucidation.

Purpose of the Study:

• To identify genes associated with ferroptosis and metabolic reprogramming (FPMRRGs) in HCC.
• To explore FPMRRGs as potential biomarkers and therapeutic targets for HCC.
• To develop a prognostic model for HCC based on FPMRRGs.

Main Methods:

• Analysis of TCGA and GEO datasets for differentially expressed genes (DEGs) related to ferroptosis and metabolic reprogramming.
• Univariate Cox regression and ConsensusClusterPlus for identifying prognostic relevance and molecular subtypes.
• LASSO and multivariate Cox regression for constructing a prognostic risk model.
• Functional enrichment, immune feature analysis, and pathway activity assessment (GSVA).

Main Results:

• Two distinct molecular subtypes of HCC were identified with significant differences in overall survival and immune features.
• A 12-gene prognostic model demonstrated good predictive performance for 1- and 3-year survival in HCC.
• Key FPMRRGs were enriched in fatty acid metabolism and HIF-1 signaling pathways, correlating with immune cell infiltration patterns.

Conclusions:

• Identification of key FPMRRGs and development of a robust prognostic model for HCC.
• The findings provide insights into HCC's molecular basis and suggest potential biomarkers for personalized treatment.
• Further clinical validation and functional studies are necessary to confirm findings and explore therapeutic potential.