Transcriptomic profiling and bioinformatics-driven statistical prioritization of CRC biomarkers: A step toward precision oncology

Abstract

BackgroundColorectal adenocarcinoma (COAD) is among the most common causes of cancer-related death globally. Early detection and targeted therapy depend on identifying key molecular biomarkers that drive tumor progression. The molecular heterogeneity of COAD demands robust computational strategies to improve the accuracy of biomarker discovery. MethodsWe developed and implemented a comprehensive, multi-step bioinformatics and statistical pipeline to systematically prioritize clinically relevant biomarkers in COAD. This pipeline integrated differential gene expression analysis, protein–protein interaction (PPI) network construction, and functional enrichment analysis to identify key hub genes associated with tumor progression. We subsequently applied principal component analysis (PCA) and overall survival modeling to evaluate the diagnostic and prognostic relevance of these candidates. Receiver operating characteristic (ROC) curve analysis was used to assess their sensitivity and specificity. Finally, experimental validation of the prioritized hub genes was conducted via qPCR across three CRC cell lines (LoVo, HCT-116, and HT-29), confirming their upregulation and supporting their clinical potential. ResultsOur integrative pipeline prioritized five key hub genes (CDH3, CXCL1, MMP1, MMP3, and TGFBI) as significantly upregulated in COAD tissues compared to normal controls. Functional enrichment analysis linked these genes to extracellular matrix degradation, epithelial-mesenchymal transition (EMT), inflammatory signaling, and tumor invasion, underscoring their roles in key oncogenic processes. Survival analysis revealed varying degrees of association with patient prognosis, most notably for CXCL1. Diagnostic performance, assessed by ROC analysis, yielded moderate AUC values (0.669–0.692), supporting their potential as biomarkers. Finally, qPCR validation across three CRC cell lines confirmed robust upregulation of all five genes, reinforcing their biological relevance in COAD progression. ConclusionOur study establishes a reproducible, integrative bioinformatics and statistical framework for the systematic identification of clinically actionable biomarkers in CRC. The five hub genes prioritized (CDH3, CXCL1, MMP1, MMP3, and TGFBI) demonstrated consistent diagnostic and prognostic value, offering a solid basis for the development of non-invasive molecular diagnostics and contributing to precision oncology.