A data-driven pan-cancer proteogenomic analysis reveals the characteristics of human cancer protein expression

Genomics and epigenomics outline potential cellular changes, while proteomics reflects actual molecular events. To systematically bridge the molecular hierarchies and validate their functional interplay, we established the most comprehensive pan-cancer paired multi-omics resource to date, systematically integrating proteomic, transcriptomic, and genomic data from both tumor and adjacent normal tissues spanning 15 cancer types (2,555 tumor samples), thereby enabling a robust cross-omics exploration. Analysis revealed that tumor tissues exhibit higher correlation between transcriptomic and proteomic expression levels compared to normal tissues. Key tumor development pathways exhibited strong mRNA-protein correlations. Genes with high mRNA-protein correlation and high expression were associated with lower survival rates, highlighting potential therapeutic targets. We developed a comprehensive tool, the CPGTA R package, based on reintegrated datasets that facilitates multi-omics data integration and reanalysis. Our research enhances cancer molecular characterization while providing insights into mechanisms underlying cancer progression and treatment resistance.