A prognostic risk model, tumor immune environment modulation, and drug prediction of ferroptosis and amino acid metabolism-related genes in hepatocellular carcinoma.

Abstract

The prognosis of hepatocellular carcinoma (HCC) is challenging due to its heterogeneity. Ferroptosis and amino acid metabolism have been shown to be closely related to HCC. We obtained HCC-related expression data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We then crossed differentially expressed genes (DEGs), amino acid metabolism genes, and ferroptosis-related genes (FRGs) to obtain amino acid metabolism-ferroptosis-related differentially expressed genes (AAM-FR DEGs). Moreover, we developed a prognostic model using Cox analysis, followed by a correlation analysis of risk scores with clinical characteristics. We also performed an immune microenvironment analysis and drug sensitivity analysis. Finally, the expression levels of model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical assays. We found that the 18 AAM-FR DEGs were mainly enriched to the alpha-amino acid metabolic process and amino acid biosynthesis pathways. Cox analysis identified CBS, GPT2, SUV39H1, and TXNRD1 as prognostic biomarkers for the risk model construction. Our results showed that the risk scores differed between pathology stage, pathology T stage, and HBV, and the number of HCC patients in the two groups. In addition, the expression of PD-L1 and CTLA-4 was high in the high-risk group, and the half-maximal inhibitory concentration (IC50) of sorafenib also differed between the two groups. Finally, the experimental validation demonstrated that the expression of biomarkers was consistent with the study analysis. Therefore, in this study, we constructed and validated a prognostic model (CBS, GPT2, SUV39H1, and TXNRD1) related to ferroptosis and amino acid metabolism and examined their prognostic value for HCC.