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

Abstract

Background Hepatocellular carcinoma (HCC) continues to impose a heavy global health burden, with high incidence and mortality. The disease is highly heterogeneous and is commonly detected at late stages, which compromises treatment outcomes. Ferroptosis and metabolic reprogramming are increasingly recognized as key processes in HCC development; however, their roles in disease progression and therapeutic response remain incompletely understood. This research aimed to identify genes related to ferroptosis and metabolic reprogramming (FPMRRGs) that may serve as putative biomarkers and therapeutic targets in HCC. Methods The Cancer Genome Atlas (TCGA), including 369 HCC specimens and 50 normal controls, along with two Gene Expression Omnibus (GEO) datasets (GSE10143 and GSE76427), were analyzed using R (v4.3.3). From a curated list of 451 FPMRRGs, differentially expressed genes (DEGs) between tumor and normal tissues were identified. Univariate Cox regression analysis was then conducted to explore their prognostic relevance and to define molecular subtypes of HCC. Specimens were categorized into 2 subtypes using ConsensusClusterPlus, and overall survival differences were evaluated via survival analysis. Functional and pathway enrichment analyses were conducted to investigate the functional roles of these genes. Immune-related features were evaluated using the Mann-Whitney U test. A prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) regression followed by multivariate Cox analysis. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. Immune cell infiltration was estimated by single-sample GSEA, and pathway activity differences were examined using gene set variation analysis (GSVA). Results HCC specimens were divided into 2 molecular subtypes, which demonstrated obvious differences in overall survival and immune-related features, including immune checkpoint gene expression and tumor immune dysfunction and exclusion (TIDE) scores. A prognostic model based on 12 key FPMRRGs demonstrated good predictive performance for 1- and 3-year overall survival, with moderate performance for 5-year survival. The prognostic value and expression patterns of these genes were further validated across independent datasets. In addition, these genes were mainly enriched in pathways linked to fatty acid metabolism and HIF-1 signaling, and were closely associated with patterns of immune cell infiltration. Conclusions This study identified numerous key genes linked to ferroptosis and metabolic reprogramming in HCC and developed a robust prognostic risk model. Our results offer new insight into the molecular basis of HCC and highlight potential biomarkers for more individualized treatment approaches. Further studies, particularly those combining clinical validation with functional experiments, are required to verify these findings and examine the therapeutic potential of targeting these pathways.