Work

Characterization of lactylation-related genes for predictive model development and molecular subtyping in HBV-associated hepatocellular carcinoma

Year: 2026

Type: article

Abstract: Chronic hepatitis B virus (HBV) infection is the leading risk factor for hepatocellular carcinoma (HCC). Lactylation-related genes (LRGs) participate in HCC progression, yet their roles in HBV-associated HCC (HBV-HCC) remain largely unclear. In this study, we screened differentially expressed LRGs (DE-LRGs) from HBV-HCC transcriptomic data using weighted gene co-expression network analysis (WGCNA) and built a high-precision random forest prediction model. Three hub genes (RACGAP1, STMN1, ILF2) were verified to be overexpressed in HBV-HCC tissues across multiple datasets and quantitative real-time polymerase chain reaction (qPCR), and their high expression correlated with unfavorable overall survival and distinct immune infiltration. A three-gene nomogram was constructed in the GSE47197 cohort, with an area under the curve (AUC) of 0.928 validated by calibration curves, decision curve analysis (DCA) and receiver operating characteristic (ROC) curves. External validation in GSE121248 reached an AUC of 0.965, verifying its robust predictive power. Moreover, consensus clustering divided HBV-HCC into two LRG-based molecular subtypes, where Cluster 2 had worse prognosis. Collectively, this study establishes an LRG-based predictive model and identifies two molecular subtypes for HBV-HCC, providing reliable and promising biomarker candidates for HBV-HCC risk prediction and individualized immunotherapy.

Source: Open Life Sciences

DOI: 10.1515/biol-2025-1394