Objective <p>Ovarian cancer (OC) ranks as the seventh most prevalent malignancy diagnosed in women. This work sought to delineate the hub and core genes, as well as the probable pathways implicated in the molecular pathogenesis of ovarian cancer (OC).</p> Methods <p>This study included the analysis of six microarray and single-cell datasets from the Gene Expression Omnibus (GEO) database, using the GEO2R program to identify differentially expressed genes (DEGs) in ovarian cancer cells and SINE-resistant ovarian cancer cells. We performed Gene Ontology (GO) and KEGG pathway enrichment analyses for the functional annotation of the differentially expressed genes (DEGs) using the DAVID system. Protein–protein interaction (PPI) networks were established using the STRING database, and Cytoscape software facilitated visualization.</p> Results <p>This research identified 24 key genes (KGs) associated with cytoskeletal protein function by constructing and analyzing a protein-protein interaction (PPI) network derived from DEGs in ovarian cancer. Several genes associated with tight junctions, such as CLDN3, CLDN4, and CLDN7, were dramatically downregulated, suggesting their possible involvement in impairing cell-cell adhesion and facilitating tumor growth. Conversely, genes like BMP2, FGF13, and GIPC2 were increased, underscoring their role in growth factor signaling and extracellular matrix remodeling, both of which are essential for cancer spread. Utilizing topological metrics, we established the significance of these KGs, with SPON1, CDH6, and SPP1 identified as very crucial regulators. The results indicate that the deregulation of cytoskeleton-associated genes may propel ovarian cancer growth by affecting cell adhesion, signaling pathways, and the tumor microenvironment.</p> Conclusion <p>This work elucidates the molecular pathophysiology of ovarian cancer and aims to identify possible molecular biomarkers that may enhance therapy and clinical molecular diagnosis of the disease.</p>