Elucidating common biomarkers and pathways of osteoporosis and aortic valve calcification: insights into new therapeutic targets

Abstract

Abstract Background: Osteoporosis and aortic valve calcification, prevalent in the elderly, have unclear common mechanisms. This study aims to uncover them through bioinformatics analysis. Methods: Microarray data from GEO was analyzed for osteoporosis and aortic valve calcification. Differential expression analysis identified co-expressed genes. SVM-RFE and random forest selected key genes. GO and KEGG enrichment analyses were performed. Immunoinfiltration and GSEA analyses were subsequently performed. NetworkAnalyst analyzed microRNAs/TFs. HERB predicted drugs, and molecular docking assessed targeting potential. Results: Thirteen genes linked to osteoporosis and aortic valve calcification were identified. TNFSF11, KYNU, and HLA-DMB emerged as key genes. miRNAs, TFs, and drug predictions offered therapeutic insights. Molecular docking suggested 17-beta-estradiol and vitamin D3 as potential treatments. Conclusion: The study clarifies shared mechanisms of osteoporosis and aortic valve calcification, identifies biomarkers, and highlights TNFSF11, KYNU, and HLA-DMB. It also suggests 17-beta-estradiol and vitamin D3 as potential effective treatments.

Keywords

• Osteoporosis
• Aortic valve calcification
• Bioinformatics analysis
• Machine learning
• Molecular mechanisms