Comprehensive bioinformatics analysis of omics data to reveal molecular mechanisms and biomarkers in multiple cancers.

Breast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (DEGs) that are common across five cancer types. Gene ontology and KEGG pathway analyses of common DEGs were performed using the DAVID database. The cell cycle processes were the most enriched functions, and oocyte meiosis, oocyte maturation, the p53 signaling pathway, cancer pathways, and cellular senescence were the most important pathways identified. Protein-protein interaction (PPI) networks for the DEGs were constructed using the STRING database, and the resulting networks were visualized in Cytoscape. Through PPI network analysis, ten hub genes were identified, and subsequent survival analysis confirmed that CHEK1, DLGAP5, CCNB2, and CCNA2 are significantly associated with poor patient survivability, establishing them as common biomarkers across multiple cancer types. Subsequently, ten transcription factors (TFs) and ten post-transcriptional regulators were identified through the assessment of regulatory networks involving TFs-DEGs and miRNAs-DEGs. Finally, drug-gene association analysis from the GSCA library was used to anticipate drug-like compounds using the drug repurposing approach. Overall, this comprehensive investigation holds promise for future in vitro and in vivo studies, offering a molecular foundation for the diagnosis, prognosis, and treatment of malignant cancers. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-025-00440-3.