Identification of Signal Pathways and Hub Genes of Pulmonary Arterial Hypertension by Bioinformatic Analysis.

IF 2.1 4区 医学 Q3 RESPIRATORY SYSTEM Canadian respiratory journal Pub Date : 2022-08-29 eCollection Date: 2022-01-01 DOI:10.1155/2022/1394088
Rui-Qi Wei, Wen-Mei Zhang, Zhe Liang, Chunmei Piao, Guangfa Zhu
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引用次数: 3

Abstract

Pulmonary arterial hypertension (PAH) is a progressive and complex pulmonary vascular disease with poor prognosis. The aim of this study was to provide a new understanding of the pathogenesis of disease and potential treatment targets for patients with PAH based on multiple-microarray analysis.Two microarray datasets (GSE53408 and GSE113439) downloaded from the Gene Expression Omnibus (GEO) database were analysed. All the raw data were processed by R, and differentially expressed genes (DEGs) were screened out by the "limma" package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed and visualized by R and Cytoscape software. Protein-protein interactions (PPI) of DEGs were analysed based on the NetworkAnalyst online tool. A total of 442 upregulated DEGs and 84 downregulated DEGs were identified. GO enrichment analysis showed that these DEGs were mainly enriched in mitotic nuclear division, organelle fission, chromosome segregation, nuclear division, and sister chromatid segregation. Significant KEGG pathway enrichment included ribosome biogenesis in eukaryotes, RNA transport, proteoglycans in cancer, dilated cardiomyopathy, rheumatoid arthritis, vascular smooth muscle contraction, focal adhesion, regulation of the actin cytoskeleton, and hypertrophic cardiomyopathy. The PPI network identified 10 hub genes including HSP90AA1, CDC5L, MDM2, LRRK2, CFTR, IQGAP1, CAND1, TOP2A, DDX21, and HIF1A. We elucidated potential biomarkers and therapeutic targets for PAH by bioinformatic analysis, which provides a theoretical basis for future study.

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肺动脉高压信号通路及枢纽基因的生物信息学分析。
肺动脉高压(PAH)是一种进行性复杂的肺血管疾病,预后较差。本研究的目的是基于多芯片分析为PAH患者的发病机制和潜在治疗靶点提供新的认识。分析了从Gene Expression Omnibus (GEO)数据库下载的两个微阵列数据集(GSE53408和GSE113439)。所有原始数据均经过R处理,并通过“limma”包筛选出差异表达基因(differential expression genes, deg)。基因本体(GO)和京都基因与基因组百科全书(KEGG)途径富集分析通过R和Cytoscape软件进行并可视化。基于NetworkAnalyst在线工具分析deg的蛋白质-蛋白质相互作用(PPI)。共鉴定出442个上调的deg和84个下调的deg。氧化石墨烯富集分析表明,这些deg主要富集于有丝分裂核分裂、细胞器分裂、染色体分离、核分裂和姐妹染色单体分离。显著的KEGG途径富集包括真核生物中的核糖体生物发生、RNA转运、癌症中的蛋白聚糖、扩张型心肌病、类风湿关节炎、血管平滑肌收缩、局灶粘连、肌动蛋白细胞骨架调控和肥厚性心肌病。PPI网络共鉴定出HSP90AA1、CDC5L、MDM2、LRRK2、CFTR、IQGAP1、CAND1、TOP2A、DDX21、HIF1A等10个枢纽基因。通过生物信息学分析,阐明了多环芳烃潜在的生物标志物和治疗靶点,为今后的研究提供了理论基础。
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来源期刊
Canadian respiratory journal
Canadian respiratory journal 医学-呼吸系统
CiteScore
4.20
自引率
0.00%
发文量
61
审稿时长
6-12 weeks
期刊介绍: Canadian Respiratory Journal is a peer-reviewed, Open Access journal that aims to provide a multidisciplinary forum for research in all areas of respiratory medicine. The journal publishes original research articles, review articles, and clinical studies related to asthma, allergy, COPD, non-invasive ventilation, therapeutic intervention, lung cancer, airway and lung infections, as well as any other respiratory diseases.
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