Relationship between osteoporosis and Cushing syndrome based on bioinformatics.

BACKGROUND: Many clinical studies have reported a relatively high incidence of osteoporosis and fragility fractures in patients with Cushing syndrome (CS). However, few papers have investigated osteoporosis and CS in terms of pathogenesis, so this study explores the association between the 2 and predicts upstream micro-ribonucleic acids (miRNAs) through bioinformatics, which provides potential targets for simultaneous pharmacological interventions in both diseases and also provides a basis for pathological screening. METHODS: We used Genecards, Online Mendelian Inheritance in Man and Therapeutic Target Database databases to screen the targets of osteoporosis and Cushing syndrome; import target genes to Database for Annotation, Visualization and Integrated Discovery for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis; the intersecting genes were uploaded to Search Tool for the Retrieval of Genes and Genomes database to construct protein-protein interaction network; Cytoscape software was used to screen core genes, and Molecular Complex Detection module was used to analyze cluster modules; finally, the NetworkAnalyst data platform was used to predict the miRNAs that interact with core genes. RESULTS: The core genes of osteoporosis and Cushing syndrome were insulin, tumor necrosis factor, signal transducer and activator of transcription 3 (STAT3), interleukin-6, insulin-like growth factor 1, etc. A total of 340 upstream miRNAs including hsa-let-7a-5p, hsa-mir-30a-5p and hsa-mir-125b-5p were predicted. The biological processes involved include regulating the transcription of ribonucleic acid polymerase II promoter and participating in the transduction of cytokine signaling pathways, which focus on the binding of nerve system ligand, JAK-STAT signaling pathway, Rap1 signaling pathway, PI3K-Akt signaling pathway, etc. CONCLUSION: Osteoporosis and Cushing syndrome are closely related in terms of targets and molecular mechanisms. In this study, bioinformatics methods were used to identify their targets and mechanisms, providing potential targets for drug simultaneous regulation of the 2 diseases, and providing a new direction for exploring the relationship between diseases.

Network pharmacological analysis and molecular docking of Huangqin-Baizhu herb pair in the treatment of threatened abortion.

BACKGROUND: The incidence of threatened abortion (TA) is increasing due to poor diet and living habits, which brings great pressure to pregnant women and their families. Huangqin-Baizhu herb pair recorded in ancient books of traditional Chinese medicine has been widely used in the treatment of TA with remarkable effect. In this study, we will use the network pharmacology method to predict the target and mechanism of Huangqin-Baizhu herb pair. METHODS: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database was used to screen the active components of Huangqin-Baizhu herb pair. Pubchem and Swiss Target Prediction databases were used to predict the action targets. Genecards, OMIM, and Drugbank databases were used to predict the related targets of TA. The intersection of drug target and disease target was selected and the intersection genes were uploaded to STRING database to construct protein-protein interaction network and conduct module analysis. Metascape database was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, which was imported into Cytoscape software to construct component-pathway-gene network and finally verified by molecular docking. 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 main active components of the herb pair are baicalein, flavanone, and norwogonin, etc. The main targets are AKT1, VEGFA, STAT3, MAPK1, SRC, etc. Cluster module analysis shows that the targets are related to cell metabolism, immune regulation and hormone level regulation. There were 2073, 3169, and 161 KEGG pathways involved in the biological processes, cell components, and molecular functions of Gene Ontology analysis, respectively. The main KEGG pathways involved in the intervention were HIF1 signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway. Molecular docking showed that the main active components of the herb pair were well combined with the key targets. CONCLUSIONS: In this study, 42 active components, 152 potential targets and 11 key targets of Huangqin-Baizhu herb pair for the treatment of TA were revealed, participating in multiple signaling pathways such as PI3K-Akt, providing a theoretical basis for further experimental research.

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.