Abstract 4140672: Integrated Bioinformatic Analysis of the Shared Molecular Mechanisms Between Osteoporosis and Aortic Stenosis

Background: osteoporosis (OP) and aortic stenosis (AS) are common in the elderly population, conferring a heavy world burden. Several epidemiological studies show a correlation between AS and OP independent of aging. However, the molecular and cellular mechanisms underlying stenotic calcification and OP remain poorly understood. Herein, this study aimed to identify crosstalk genes between OP and AS and potential mechanism. Method: AS and OP datasets were downloaded from the GEO database and were performed Weighted Gene Co-Expression Network Analysis (WGCNA) to get common key genes, and differential expression analysis to get common differentially expressed genes (DEGs). We also analyzed the protein–protein interactions (PPIs) of the common key genes and DEGs using the NetworkAnalyst online tool to find the intersection of correlated genes in OP and AS, the top 20 genes in each algorithm were selected as the candidate genes and the genes were calculated by more than six algorithms at the same time were chosen as candidate genes. We downloaded genes related to AS in the Comparative Toxicogenomics Database (CTD) to get the intersection with candidate genes as hub genes. The hub genes were certified in another OP dataset and AS dataset downloaded from GEO respectively. Result: 665 common Genes from WGCNA, which were enriched in the myeloid leukocyte activation, secretory granule membrane, cell adhesion molecules.13 of them were screened as candidate genes by 12 prediction algorithms in the Cytoscape. 161 common DEGs were differentiated in another AS database and OP database, which were enriched in natural killer cell mediated immunity, collagen-containing extracellular.19 genes were recognized as candidate genes by Cytoscape in the same way. 15 hub genes were selected by intersecting with candidate genes and genes downloaded from CTD as hub genes, including CD226, CD247, CD38, CD4, CD96, FCGR2B, GNG2, GRB2, GAMB, HSD17B6, ITGB2, KSD17B6, ITGB2, KLRB1, NGF, PECAM1, SDC1. The transcription factors hub genes interaction network was constructed using TRRUST. Hub genes were certified in another AS and OP dataset. CD4, GZMB, SDC1 are upregulated remarkably in AS dataset, whereas SDC1 and ITGB2 are downregulated and GZMB is upregulated in OP dataset. Conclusion: GZMB, SDC1may be potential hub genes in AS and OP, through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for AS and OP, providing a theoretical basis for future studies.