A prominent hallmark of tumors is aberrant lipid metabolism, and various peroxisome-related genes (PRGs) are associated with aberrant tumoral metabolic signaling. However, the influence of PRGs on the prognosis of hepatocellular carcinoma (HCC) patients remains debatable. Thus, the current study was designed to evaluate the effect of PRGs on HCC and construct a prognostic model for predicting survival.

We initially acquired HCC-related gene expression profiles from the Cancer Genome Atlas and International Cancer Genome Consortium databases. We then utilized Cox analysis and Lasso regression to identify suitable PRGs for the risk model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of PRGs. Single-sample gene set enrichment analysis (ssGSEA) was conducted to confirm the relationship between PRGs and immunity.

Four PRGs were correlated with HCC patient survival: 2 risk genes (MPV17, and ABCD1) and 2 protective genes (ACSL1 and ACSL6). We derived risk scores based on PRGs to construct a predictive model that could accurately predict overall survival (OS) among HCC patients. Furthermore, GO and KEGG analyses revealed that these PRGs were potentially involved in lipid metabolism and ferroptosis in HCC. Moreover, ssGSEA results demonstrated that high PRG scores were associated with immune suppressor activation, which caused the suppression of immune effectors (CD8⁺ T-cells, B cells, and NK cells) and the attenuation of the immune-mediated antitumor effect.

PRGs act as key regulators in tumorigenesis and tumor progression by affecting lipid synthesis and utilization, which we used to predict the outcome of HCC patients. Moreover, PRGs have been shown to promote tumoral immune resistance by serving as a vital bridge between metabolism and immunity. Thus, a personalized treatment approach targeting PRGs would clinically benefit patients by blocking the interaction between tumor metabolism and immunity.