Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis.

Chaona Song, Shixiong Wei, Yunlong Fan, Shengli Jiang
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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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基于生物信息学的主动脉瓣狭窄相关基因鉴定。
背景:主动脉瓣狭窄(AS)是发达国家最常见的瓣膜疾病。AS的病理复杂,其主要过程包括瓣膜间质钙化,涉及遗传因素、脂蛋白沉积氧化、慢性炎症、心脏瓣膜间质细胞成骨转化、活动性瓣膜钙化等。本研究的目的是确定与AS相关的潜在基因。方法从gene expression Omnibus (GEO)数据库中下载三个原始基因表达谱(GSE153555、GSE12644和GSE51472),使用GEO2R工具或R中的“limma”进行分析,鉴定差异表达基因(deg)。使用R Bioconductor中的ClusterProfiler包分析功能富集。利用STRING构建蛋白-蛋白相互作用(Protein-Protein Interaction, PPI)网络,利用BioGPS数据库鉴定组织特异性基因表达。利用Cytoscape软件筛选中心基因。在Targetscan、miWalk、miRDB、Hoctar和TarBase中预测了相关mirna。结果共筛选出58个上调基因和20个下调基因,这些基因主要富集在基质重塑和免疫系统过程中。因此,通过PPI网络分析聚类成一个模块,主要涉及Fc γ r介导的吞噬作用、破骨细胞分化。随后,通过CytoHubba插件鉴定出10个程度得分最高的基因(IBSP、NCAM1、MMP9、FCGR3B、COL4A3、FCGR1A、THY1、RUNX2、ITGA4和COL10A1)为as的枢纽基因。最终确定hsa-miR-1276为潜在miRNA,并利用NetworkAnalyst构建miRNA-基因调控网络。结论通过分析发现,IBSP、NCAM1、MMP9、FCGR3B、COL4A3、FCGR1A、THY1、RUNX2、ITGA4、COL10A1可能是与AS相关的枢纽基因,hsa-miR-1276可能是潜在的miRNA。该结果可为今后AS的病理和治疗提供新的思路。
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