Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery

Abstract Background Bimodal protein expression, defined as the distribution of protein expression with two modes, is linked to phenotypic variation across various biological systems. To advance the identification of cancer biomarkers and targets for precision oncology beyond RNA-based studies, we developed a proteomics-specific bimodality model. Results By analyzing proteomics data from various cancer types, 2401 tumor-associated bimodal proteins significantly linked to critical cancer pathways were identified, including amino acid metabolism, extracellular matrix-receptor interaction, and central carbon metabolism. Utilizing an AI-enhanced knowledge graph, we further delineated common patterns among pan-cancer tumor-associated bimodal proteins. A case study on TROP2 in colon adenocarcinoma highlights up-regulation of MYC and WNT /β-catenin pathways and down-regulation of inflammatory pathways in TROP2 -high groups. Conclusion This research highlights the biological differences impacting cancer heterogeneity and vulnerability, ultimately aiding treatment decisions. Our findings illustrate the value of proteomics in uncovering novel biomarkers and advancing precision medicine, paving the way for multi-omics integration and clinical validation.