Identification of key candidate genes for ovarian cancer using integrated statistical and machine learning approaches.

Ovarian cancer (OC) is a highly lethal malignancy worldwide, necessitating the identification of key genes to uncover its molecular mechanisms and improve diagnostic and therapeutic strategies. This study utilized statistical and machine learning approaches to identify key candidate genes for OC. Three microarray datasets were obtained from the gene expression omnibus database, and analysis began with normalization and differential gene expression analysis using the Limma package. Highly discriminative differentially expressed genes (HDDEGs) were identified through a support vector machine-based approach, yielding 84 overlapping HDDEGs across the datasets. Enrichment analysis of HDDEGs was conducted using DAVID. A protein-protein interaction network constructed via STRING pinpointed central hub genes using CytoHubba metrics. Significant modules were analyzed with molecular complex detection, identifying 18 central hub genes, 11 hub module genes, and 54 meta-hub genes. The intersection of these three gene sets revealed eight shared key genes (FANCD2, BUB1B, BUB1, KIF4A, DTL, NCAPG, KIF20A, and UBE2C). Weighted gene co-expression network analysis identified key modules linked to clinical traits and confirmed grouping eight key candidate genes into a single cluster. These genes were validated using two independent datasets (GSE38666 and TCGA-OC), with area under the curve and survival analyses underscoring their predictive and prognostic significance in OC. This integrative approach advances understanding of OC's molecular basis, identifies potential biomarkers, and emphasizes the clinical relevance of the eight key candidate genes for OC diagnosis, prognosis, and treatment.