Integrating Bibliometrics and Bioinformatics to Map Knowledge Structure, Trends, and Genetic Insights in Polycystic Ovary Syndrome and Tumors (2015-2024).

Objective: This study aims to construct a knowledge map of polycystic ovary syndrome (PCOS)-cancer research through bibliometric analysis to elucidate its developmental trajectory and global research landscape, and further employ bioinformatics approaches to investigate the underlying molecular mechanisms linking PCOS and related cancer. Methods: Utilizing the Web of Science Core Collection as the data source, English-language publications from 2015 to 2024 were retrieved. CiteSpace and VOSviewer were employed for co-occurrence analysis, co-citation network construction, cluster identification, and keyword burst detection. PCOS and endometrial cancer-related genes were extracted from the Genecards database, followed by screening of overlapping genes for protein-protein interaction (PPI) network analysis to identify key targets. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to pinpoint critical signaling pathways. Results: Publications on PCOS and cancer exhibited a significant and steady growth over the past decade, with the United States and China demonstrating prominent contributions in both output volume and collaborative networks. Conclusion: By integrating bibliometric analysis with bioinformatics, this study systematically maps the knowledge structure, emerging trends, and molecular mechanisms linking PCOS and cancer. Our findings specifically highlight the association between PCOS and endometrial cancer, may driven by dysregulation of the TP53 and PI3K/AKT signaling pathways. This work provides valuable insights for researchers to understand the foundational knowledge framework, identify emerging trends, potential collaborators, and mechanistic targets for future studies.