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利用生物信息学分析鉴定类风湿关节炎中组织特异性表达的枢纽基因和潜在药物

Identification of Tissue-Specific Expressed Hub Genes and Potential Drugs in Rheumatoid Arthritis Using Bioinformatics Analysis.

作者信息

Xing Xuewu, Xia Qun, Gong Baoqi, Shen Zhongyang, Zhang Yingze

机构信息

Department of Orthopaedics, Tianjin First Central Hospital, Tianjin, China.

School of Medicine, Nankai University, Tianjin, China.

出版信息

Front Genet. 2022 Mar 18;13:855557. doi: 10.3389/fgene.2022.855557. eCollection 2022.

Abstract

Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted drugs. Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM-DEG pairs and ceRNA network containing key DEMs were established by Cytoscape. We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-κB pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3 tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA-miRNA-mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB. This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for RA.

摘要

类风湿关节炎(RA)是一种常见的自身免疫性疾病,其特征为进行性、破坏性多关节炎。然而,RA的病因及潜在分子事件尚不清楚。在此,我们应用综合生物信息学方法来识别参与RA的组织特异性表达的枢纽基因,并揭示潜在的靶向药物。从基因表达综合数据库(GEO)下载了涉及成纤维样滑膜细胞(FLS)的人类微阵列数据集的三个表达谱,使用GEO2R工具筛选正常与RA滑膜样本之间差异表达的mRNA(DEG)、miRNA(DEM)和lncRNA(DEL)。利用BioGPS识别组织特异性表达基因。使用DAVID数据库对常见DEG进行功能和通路富集分析,并通过STRING数据库构建常见DEG的蛋白质-蛋白质相互作用(PPI)网络以识别枢纽基因。基于受试者工作特征(ROC)曲线,我们进一步研究了组织特异性表达的枢纽基因在RA患者中的预后价值。运行连通性图谱(CMap)以识别新型抗RA潜在药物。通过Cytoscape建立DEM-DEG对以及包含关键DEM的ceRNA网络。我们共获得418个DEG、23个DEM和49个DEL。64个DEG被验证为组织特异性表达基因,其中大多数来自血液/免疫系统(20/64,31.25%)。基因本体(GO)术语和京都基因与基因组百科全书(KEGG)通路富集分析表明,DEG主要集中在免疫相关生物学过程和NF-κB通路。通过使用MCODE插件生成了10个枢纽基因。其中,精子相关抗原5(SPAG5)、CUX2和自身免疫调节因子2(THEMIS2)被鉴定为组织特异性表达的枢纽基因,与骨关节炎(OA)样本相比,这3个组织特异性表达的枢纽基因在RA样本中具有更高的诊断价值。5种化合物(醋竹桃霉素、左旋多巴、曲古抑菌素A、LY-294002和左旋咪唑)在连通性得分中排名前五。此外,鉴定出5个miRNA为关键DEM,形成了具有5个关键DEM的lncRNA-miRNA-mRNA网络。包含组织特异性表达枢纽基因的网络如下:ARAP1-AS2/miR-20b-3p/TRIM3、ARAP1-AS2/miR-30c-3p/FRZB。本研究表明,使用综合生物信息学分析在RA中筛选鉴定组织特异性表达的枢纽基因和ceRNA网络有助于我们了解RA的发病机制。此外,SPAG5和THEMIS2可能是RA诊断的候选生物标志物。LY-294002、曲古抑菌素A和醋竹桃霉素可能是RA的潜在药物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1016/8971206/26465e14ac97/fgene-13-855557-g001.jpg

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