Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. There is an interplay between ferroptosis and lipid metabolism in various types of cancer. The objective of this research is to examine the functions of genes related to ferroptosis and lipid metabolism (FLMRGs) in HCC, develop prognostic models, and analyze their significance for immunotherapy and treatment response. METHODS: RNA-seq data from TCGA and GEO databases were analyzed. Differential expression analysis was used to identify FLMRGs in HCC. Consensus clustering categorized patients into distinct clusters. Prognostic models were built using Cox regression and machine learning. Immune infiltration, mutation, and drug response analyses were conducted to assess the tumor microenvironment and therapeutic potential. RESULTS: We identified 14 differentially expressed FLMRGs between HCC and normal samples, of which 12 genes were correlated with the prognosis of HCC patients. HCC patients were categorized into two FLMRG clusters. These clusters were associated with HCC patient survival and immune cell infiltration. We identified differentially expressed genes between the two FLMRG clusters, and prognosis-related DEGs were utilized to construct a FLMRG prognostic signature (risk score). HCC patients with a high-risk score exhibited poor prognosis. Furthermore, the risk score model was correlated with immune cell infiltration, responses to immunotherapy, and drug sensitivity in HCC patients. High-risk patients demonstrated increased expression of immune checkpoints and higher tumor mutational burden (TMB). The signature genes displayed differential expression and prognostic value in HCC. CONCLUSION: Our study highlights the significance of FLMRGs in HCC prognosis and may provide insights for personalized immunotherapy and chemotherapy strategies, contributing to the understanding of HCC pathogenesis and treatment.