A glycolysis-associated four-gene risk model for prognosis and immune microenvironment characterization in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has a dismal prognosis and profound metabolic reprogramming, yet the role of glycolysis-associated genes (GRGs) remains incompletely defined. This study aimed to develop a glycolysis‑associated prognostic model and explore its association with the tumor immune microenvironment (TME). We curated 760 GRGs from MSigDB and performed consensus clustering to identify molecular subtypes. Using WGCNA integrated with three machine learning algorithms (LASSO, Random Forest, and SVM-RFE), we constructed a GRGs risk score model and validated it across multiple independent cohorts. Its correlation with prognosis, genomic alterations, immune infiltration, and in silico drug sensitivity was comprehensively evaluated. Experimental validation was conducted in clinical specimens and HCC cell lines. Two distinct GRGs-based subtypes were identified, with cluster B exhibiting worse prognosis and significant enrichment of cell cycle, glycolysis, EMT, and PI3K‑AKT‑mTOR pathways. A four-gene risk model ( CBX1 , DNMT1 , GAS2L3 , KPNA2 ) was constructed. The risk score demonstrated moderate prognostic performance as an independent prognostic factor and correlated with advanced AJCC stage, higher tumor mutational burden, and an immunosuppressive TME characterized by increased Tregs and M0 macrophages. A nomogram incorporating the risk score showed moderate predictive accuracy and clinical net benefit. Experimental validation supported upregulation of all four genes in HCC tissues, and knockdown of CBX1 or GAS2L3 markedly suppressed cell proliferation, migration, and invasion in vitro. The GRGs risk model demonstrates clinical applicability and net benefit in terms of prognostic stratification and immunophenotype characterization in HCC patients, offering a potentially useful tool for risk assessment and personalized management.