A gene signature associated with alpha-linolenic acid metabolism predicts clinical prognosis in hepatocellular carcinoma.

[OBJECTIVE] Alpha-linolenic acid (ALA) has been implicated in the initiation and progression of multiple cancer types. Nevertheless, the molecular mechanism by which ALA metabolism influences hepatocellular carcinoma (HCC), as well as its effects on the HCC immune microenvironment, is still largely unclear.

[METHODS] Data from HCC patients were collected from the TCGA-LIHC project, HCCDB18, and GEO database. ssGSEA, consensus clustering, and COX regression were employed to identify differentially expressed genes (DEGs) linked to ALA metabolism. Based on these findings, a novel prognostic model was developed and validated. Subsequently, functional pathways, immune infiltration levels, potential response to immunotherapy, and drug sensitivity associated with the identified risk genes were explored. Additionally, RT-qPCR was performed to assess the expression levels of key genes in THLE2 and Huh7 cell lines.

[RESULTS] Three distinct molecular subtypes were classified based on ALA metabolism-related gene expression patterns, and DEGs across these subtypes were identified. A six-gene prognostic signature, termed the RiskScore model, was constructed and shown to effectively stratify patients according to clinicopathological features, immune infiltration levels, immunotherapy responsiveness, and drug sensitivity. Multivariate Cox regression incorporating both the RiskScore and clinicopathological features confirmed the RiskScore as the most prominent independent predictor of survival, demonstrating superior prognostic accuracy. Moreover, the risk-related genes TBL1X exhibited significantly higher expression in Huh7 cells compared to THLE2 cells.

[CONCLUSION] This study provides a comprehensive analysis of ALA metabolism-associated genes in HCC and proposes a novel risk-based prognostic framework. The developed model demonstrates strong predictive performance for patient survival outcomes, representing the first such approach with robust validation in forecasting prognosis in HCC.