Exploring the relationship between osteoporosis and polycystic ovary syndrome based on bioinformatics

BACKGROUND: In recent years, clinical studies have found that there is a close relationship between osteoporosis and polycystic ovary syndrome. However, there are few literature on the pathogenesis of osteoporosis and polycystic ovary syndrome. In order to clarify their common pathogenic mechanism and provide potential targets for drugs to regulate them at the same time, bioinformatics methods are used to explore, so as to provide a new direction for the study of the relationship between diseases in the future. METHODS: To screen the targets of osteoporosis and polycystic ovary syndrome by Genecards, Online Mendelian Inheritance in Man databases and Therapeutic Target Database to take the intersection of the two mappings and upload the intersection targets to the STRING database to construct protein-protein interaction network; to screen the core targets by degree value and import them to Metascape database for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis; and finally, to construct the visualization network of core targets and pathways by Cytoscape software. Ethical approval and informed consent of patients are not required because the data used in this study is publicly available and does not involve individual patient data or privacy. RESULTS: The core targets of polycystic ovary syndrome and osteoporosis were insulin gene, insulin-like growth factor 1, CTNNB1, serine/threonine kinase 1, signal transducer and activator of transcription 3, LEP, etc. The biological processes involved include the regulation of protein phosphorylation, cell proliferation and differentiation, hormone endocrine, reproductive system and skeletal system. The related pathways were concentrated in Foxo signaling pathway, HTLV-I infection, PI3K-AKT signaling pathway, MAPK signaling pathway and AGE-RAGE signaling pathway in diabetic complications. CONCLUSIONS: There is a close relationship between osteoporosis and polycystic ovary syndrome in terms of target and molecular mechanism. This study used bioinformatics to clarify their targets and mechanisms, providing potential targets for drugs to regulate both diseases simultaneously and providing new directions to explore the relationship between the diseases.