Integrating Serum Proteomics with Tissue Multi-Omics Data Identifies TOP2A as a Key Molecule in Colorectal Cancer

Background Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with a rising incidence among younger adults. Current diagnostic biomarkers lack sufficient sensitivity and specificity, highlighting an urgent need for novel biomarkers. Methods We integrated serum proteomic data from 16 CRC patients and 8 healthy controls with tissue multi-omics data from three external datasets (GSE117606, GSE110223, and GSE110224). Weighted gene co-expression network analysis (WGCNA) was performed to identify CRC-associated modules, followed by support vector machine (SVM) to screen hub proteins. Hub proteins were validated through cross-dataset intersection analysis of differentially expressed genes. Immunohistochemistry was used to assess protein localization, and Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance. Results WGCNA identified the MEbrown module as significantly correlated with CRC. GO and KEGG analyses revealed that this module was enriched in pathways related to actin filament organization, focal adhesion, and endocytosis. By intersecting differentially expressed genes from three external datasets and constructing a PPI network, TOP2A was determined as the core molecule, which exhibited differential expression at both the tissue genomic and transcriptomic levels. Immunohistochemistry showed that TOP2A was significantly overexpressed in CRC tissues, with positive signals predominantly localized in the nucleus. ROC curve analysis demonstrated that TOP2A exhibited favorable diagnostic performance across all three datasets, with AUC values all exceeding 0.8. Conclusion This multi-dimensional, multi-dataset integrative strategy successfully identified TOP2A as a potential novel biomarker for CRC. TOP2A exhibited consistent differential expression across serum proteomic, tissue genomic, transcriptomic, and proteomic levels, demonstrating favorable diagnostic performance.