Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis

Authors

  • Chao Song Medical School of Chinese PLA, Beijing, 100853, China
  • Shixiong Wei Medical School of Chinese PLA, Beijing, 100853, China
  • Yunlong Fan Medical School of Chinese PLA, Beijing, 100853, China
  • Shengli Jiang Department of Cardiovascular Surgery, the First Medical Centre of Chinese PLA General Hospital, Beijing 100853, China

DOI:

https://doi.org/10.1532/hsf.4263

Keywords:

Aortic valve stenosis, Integrated bioinformatics analyses, Differentially expressed genes, Functional enrichment analysis

Abstract

Background: Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS.

Methods: Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or ‘limma’ in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein–Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase.

Results: A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst.

Conclusions: Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

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Published

2022-01-24

How to Cite

Song, C., Wei, S., Fan, Y., & Jiang, S. (2022). Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis. The Heart Surgery Forum, 25(1), E069-E078. https://doi.org/10.1532/hsf.4263

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