Abstract
BACKGROUND: Excessive iron accumulation and lipid peroxidation are primary characteristics of ferroptosis in hepatocellular carcinoma (HCC). Ferroptosis inducer combined with immunotherapy has become a new hope for HCC patients. Therefore, the construction and validation of subtype-specific sensitivity to ferroptosis inducer will be helpful for hierarchical management and precise individual therapy. METHODS: RNA-seq transcriptome and clinical data of HCC patients were extracted from International Cancer Genome Consortium (ICGC) dataset and The Cancer Genome Atlas (TCGA) dataset. Consistency matrix and data clustering of the both cohorts were constructed by 'ConsensusClusterPlus' package. Single-sample gene set enrichment analysis (ssGSEA) analysis was performed to evaluate immune infiltration. Cox analysis was utilized to construct a ferroptosis phenotype-related prognostic model (FRPM) in HCC. The predictive efficiency of the constructed FRPM was evaluated through Kaplan Meier (K-M) survival analyses and Receiver Operating Characteristic (ROC) curves. The expression levels of candidate genes were detected and validated by Real-Time PCR between liver cancer tissues and adjacent non-tumor liver tissues. RESULTS: 45 differentially expressed ferroptosis-related genes (FRGs) were identified between HCC tissues and non-tumor liver tissues. Furthermore, four ferroptosis-associated clusters (FACs) of HCC were established via consensus clustering. Subsequently, we established a FRPM, consisting of four prognostic genes (SLC7A11, SLC1A5, GCLM and SAT1), to evaluate the survival of HCC patients, based on which, patients were divided into high-risk group and low-risk group. The high-risk group exhibited worse survival compared to low-risk group (p < 0.0001 both in TCGA and ICGC cohorts). Patients belong to different FACs or different risk scores showed distinct clinical characteristics. Moreover, in the validation experiment, the transcriptional expression levels of the four prognostic genes were consistent with the results drew from datasets. CONCLUSION: We revealed a novel FRGs signature, which may provide the molecular characteristic data for effectively prognostic evaluation and potential personalized therapy for HCC patients.