Relationship between osteoporosis and Cushing syndrome based on bioinformatics

Abstract

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.