A scoring model based on ferroptosis genes for prognosis and immunotherapy response prediction and tumor microenvironment evaluation in liver hepatocellular carcinoma

Ferroptosis is a type of iron-dependent programmed cell death. Ferroptosis inducers have been shown to have a great potential for cancer therapy. We aimed to generate a risk scoring model based on ferroptosis-related genes (FRGs) and validate its predictive performances in overall survival (OS) prediction and immunotherapy efficacy evaluation in liver hepatocellular carcinoma (LIHC). Differential and Univariate Cox regression analyses were applied to analyze RNA-seq data of LIHC samples from TCGA and GEO databases to identify prognosis-related ferroptosis genes. Patients were assigned to three clusters (Ferrclusters A, B, and C) based on the cluster analysis of prognostic ferroptosis genes. The principal component analysis (PCA) was performed to build a risk scoring model based on differentially expressed FRGs. Survival analysis revealed that Ferrcluster B LIHC patients had a lower OS rate alongside more severe immune cell infiltration versus Ferrcluster A and C patients; moreover, the LIHC patients in high-ferrscore group had significantly lower survival than the low-ferrscore group. Compared to low-ferrscore patients, Programmed cell death 1 (PD-1) mRNA expression significantly increased, and either PD-1 or PD-1 plus CTLA4 (cytotoxic T-lymphocyte associated protein 4) inhibitors showed unsatisfactory efficacy in high-ferrscore patients. Our study demonstrates the implication of FRGs in prognosis prediction and evaluation of immunotherapy efficacy in LIHC patients.