Abstract

Background Osteoporosis (OP) and type 2 diabetes (T2D) are commonly encountered metabolic disorders in clinical practice, but the comorbidity mechanism has not been clarified. This study explored the underlying mechanisms for utilizing bioinformatics methods. Furthermore, it predicted traditional Chinese medicines (TCMs) with preventive and therapeutic effects. Materials and Methods GSE35958 and GSE43950 were retrieved and downloaded from the GEO database, and differential expression analysis was performed to identify differentially expressed genes (DEGs) with similar expression patterns in OP and T2D. Then, the common DEGs were uploaded to the STRING database to construct a protein interaction network. Enrichment analysis of the screened genes was conducted using R language packages. Relevant TCMs were searched and screened based on gene targets using the Encyclopedia of traditional Chinese medicine (ETCM) database. Molecular docking of active ingredients of the TCMs and related gene targets was performed using AutoDock Vina software. Results By analyzing the gene expression microarrays, GSE35958 and GSE43950, 34 genes with the same expression pattern shared by OP and T2D were identified. Among these genes, 32 were upregulated and two were downregulated. Protein interaction network analysis revealed that tumor necrosis factor, vascular endothelial growth factor A, and CD44 might play key roles in the co-pathogenesis of T2D and OP. TCMs, including Wolfberry (枸杞), Ginseng (人参), and Yam (山药), were screened based on key genes. Molecular docking results demonstrated binding activity between all active ingredients and the related gene targets. Conclusion This study explored the potential molecular co-pathogenesis of OP and T2D through bioinformatic analysis and preliminarily predicted traditional herbal medicines that may have preventive and therapeutic effects.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.