Pan-Cancer Metabolic Landscapes: A Multiomics View

Metabolic reprogramming fuels cancer progression, but whether common metabolic patterns exist across diverse malignancies remains incompletely understood. To address this, we integrated large-scale proteomic, transcriptomic [The Cancer Genome Atlas (TCGA)], and spatial transcriptomic (Spatial Meta-Transcriptome Database) data sets comprising 3226 samples across 24 human cancer types. Utilizing a highly controlled, predominantly patient-matched design to minimize background noise, we characterized the pan-cancer metabolic landscape via reaction-level functional task scoring, pathway enrichment, multiomics integration, network modeling, survival analysis, and microenvironmental coupling. Consistently upregulated glycan biosynthesis pathways─particularly fucosylation and sialylation─and broadly enhanced nucleotide metabolism highlighted conserved programs potentially associated with tumor growth, adaptation, and immune evasion. These signatures proved robust against variations in tumor purity across the TCGA cohorts. Furthermore, gene coexpression network modeling prioritized specific hub genes associated with this pan-cancer restructuring. Exploratory high-resolution mapping aligned tumor immunogenicity with specific glycan pathways. Notably, tumor-intrinsic glycan upregulation negatively correlates with their immune score alignment, suggesting that tumor hyperactivation may obscure immune signals in bulk tissue. Together, our results define a robust computational landscape of shared metabolic alterations, providing a data-driven framework to guide future experimental validation of candidate therapeutic targets across diverse oncological contexts.