Integrated metabolite–network modeling of metabolic nodes and oncogenic signaling in gastrointestinal cancers.

Abstract

e15506 Background: Gastrointestinal (GI) cancers are driven by metabolic–signaling feedback loops that stabilize tumorigenic phenotypes. However, the mechanistic characterization of how metabolic fluxes interface with oncogenic network rewiring remains incomplete. A computational framework integrating metabolomics with regulatory network inference can address this gap. Methods: Multi-omics datasets were harmonized using a pipeline combining: Differential metabolite calling (XCMS, MetaboAnalyst), Flux–balance inference (iMAT and COBRApy), Network construction (STRING + Cytoscape), Hub prioritization (degree, betweenness, and MCODE modularity), Ligand–target mapping (SwissTargetPrediction and PharmMapper), Molecular docking (Vina), 200 ns Molecular dynamics simulation (GROMACS), and Binding free energy (MM/PBSA). Results: Flux–balance modeling revealed increased glycolytic flux and glutamine anaplerosis supplying the TCA cycle in GI tumors. Mechanistically, excess lactate reinforced HIF1A stability, while elevated α-ketoglutarate supported chromatin remodeling, promoting Wnt/β-catenin activation. Network topology identified PI3K, KRAS, MYC, and MAPK as flux-sensitive hubs with high information centrality. Molecular docking + Molecular dynamics simulation demonstrated that quercetin, luteolin, and epigallocatechin gallate engage catalytic pockets of KRAS-G12D, LDHA, and AKT1 with stable interactions. MM/PBSA revealed strong binding energies, suggesting inhibition of both metabolic and proliferative signaling nodes. Conclusions: Mechanistic flux-network integration shows that natural compounds can collapse GI tumor viability by simultaneously destabilizing metabolic drivers (LDHA, GLS) and oncogenic nodes (KRAS, AKT). This computational framework provides a mechanistic roadmap for multi-target drug discovery. Keywords: gastrointestinal cancers, metabolomics, network pharmacology, molecular docking, molecular dynamics simulation.