Gene expression profiling of ovarian cancer reveals candidate diagnostic and prognostic biomarkers: An integrated bioinformatics approach

Background Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to delayed diagnosis and limited effectiveness of current biomarkers. Identifying candidate biomarkers through integrated gene expression analysis may enhance understanding of OC biology and support future diagnostic and prognostic investigations. Methods Four microarray datasets were retrieved from the Gene Expression Omnibus and analyzed using the limma package in R to identify differentially expressed genes (DEGs) through a bidirectional filter (|log 2 FC| ≥ 1.2). Overlapping DEGs across all four datasets were subjected to Gene Ontology and KEGG pathway enrichment analyses. Protein-protein interaction networks were constructed using the STRING database, and hub genes were identified by consensus across three centrality algorithms in Cytoscape. Prognostic significance was evaluated using Kaplan-Meier survival analysis via GEPIA2; diagnostic performance was assessed using Youden Index-based ROC analysis; and external validation was performed using an independent dataset. Results A total of 209 overlapping DEGs were identified, yielding 35 hub genes. Functional enrichment analysis implicated cell cycle dysregulation, metabolic reprogramming, and extracellular matrix remodeling as central biological processes. TRIP13, FGF13, and LYVE1 were significantly associated with overall survival, while ALDH1A1, GATA6, and WNT5A were associated with disease-free survival. KDR demonstrated the highest diagnostic accuracy, and external validation confirmed the generalizability of the diagnostic findings. Conclusion This study identifies a panel of candidate diagnostic and prognostic biomarkers in ovarian cancer, providing a molecular framework for future experimental validation and translational investigation.