Multi-omics unravel heterogeneity of glucose metabolism reprogramming in gastric cancer
Liang Liu, Jianming Cao, Qi Xiaoan, Lu Jinxi
Abstract
Gastric cancer (GC) presents striking survival disparities: 85–100% for early-stage versus only 5–20% for advanced disease. Glucose metabolic reprogramming (GMS)—a cancer hallmark linked to the Warburg effect—fuels tumor progression and immune evasion via lactate. This study uses multi-omics data to delineate GMS heterogeneity and its clinical relevance in GC. Single-cell, spatial, and bulk transcriptomic data were integrated. BayesPrism deconvoluted bulk data, CytoTRACE2, CellChat, and NicheNet analyzed cell trajectories, communication, and ligand–receptor regulation, respectively. MOVICS performed multi-omics (mRNA, methylation, mutation, and lncRNA) clustering of TCGA-STAD. Mime1 integrated machine learning to build a prognostic model based on GMS-related genes and CS2/TOP2A features. Differential expression and functional enrichment explored mechanisms. Verification of expression differences in key genes using qPCR. In gastric cancer research, GMS scores exhibit significant enrichment. Single-cell analysis identified a TOP2A + epithelial subtype characterized by high GMS scores, strong stemness, elevated proliferative activity, and poor prognosis. Further analysis suggests this subtype may be regulated by the EFNB2-EPHB2 signaling pathway originating from GABRP⁺ cells, activating cell cycle pathways via ligands such as CKLF. Multi-omics clustering defined the CS2 subtype, exhibiting enrichment in GMS score, cell cycle, and glucose metabolism pathways and correlating with poor prognosis. A prognostic model constructed using eight genes demonstrated robust predictive performance across TCGA and multiple independent cohorts, with high-risk patients potentially exhibiting ‘cold tumor’ characteristics. Among these, the core gene SH3BP1 was identified as a potential tumor suppressor (HR = 0.87), whose overexpression correlated with lower tumor stage and enhanced CD8⁺ T cell killing and infiltration. This study is the first to systematically characterize GMS heterogeneity in GC via integrated multi-omics. It identifies the aggressive TOP2A⁺ subtype, establishes the clinically relevant CS2 classification, and develops a robust 8-gene prognostic model—useful for stratifying patients with immunologically “cold” tumors. Critically, tumor suppressor SH3BP1 (a key regulator) correlates with reduced tumor progression and enhanced CD8 + T cell anti-tumor immunity when highly expressed. These findings underscore that SH3BP1 may represent a promising therapeutic target for precise intervention in GMS-immune interactions in GC.