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山东大学学报 (医学版) ›› 2021, Vol. 59 ›› Issue (3): 10-17.doi: 10.6040/j.issn.1671-7554.0.2020.1075

• 基础医学 • 上一篇    下一篇

生物信息学分析骨关节炎滑膜炎症相关基因和分子途径

华芳1,2,张薇薇1,2,吕波1,2,辛玮1,2   

  1. 山东大学附属省立医院 1.检验科;2.中心实验室, 山东 济南 250021
  • 发布日期:2021-04-06
  • 通讯作者: 辛玮. E-mail:weixin@mail.sdu.edu.cn
  • 基金资助:
    国家自然科学基金(81471007)

Bioinformatic analysis of genes and molecular pathways associated with osteoarthritis synovitis

HUA Fang1,2, ZHANG Weiwei1,2, LYU Bo1,2, XIN Wei1,2   

  1. 1. Department of Clinical Laboratory;
    2. Department of Central Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250021, Shandong, China
  • Published:2021-04-06

摘要: 目的 旨在利用生物信息学分析鉴定与骨关节炎滑膜炎症进展相关的差异表达基因(DEGs)。 方法 从基因表达总览(GEO)数据库下载GSE55457、GSE55235和GSE12021基因表达谱,筛选骨关节炎发生发展过程中的相关基因。对筛选出的差异表达基因(DEGs)进行热图绘制、基因本体论(GO)和京都基因与基因组百科全书(KEGG)分析。用STRING和Cytoscape构建蛋白互作网络(PPI),并用MCODE插件筛选核心模块,cytoHubba插件进行关键基因筛选。 结果 GSE55457、GSE55235和GSE12021基因表达谱中均上调表达基因72个,均下调表达基因151个。GO分析发现,DEGs主要参与白细胞迁移、对糖皮质激素的反应、糖胺聚糖结合、内质网腔和细胞外基质等。KEGG分析发现,DEGs主要参与的信号通路有MAPK信号通路、破骨细胞分化和TNF信号通路等。用cytoHubba插件筛选出来10个关键基因:IL-6、TLR7、SELE、VEGFA、LDLR、JUN、MYC、CD44、SNAI1、hnRNA1。 结论 生物信息学网络分析有助于发现骨关节炎患者滑膜炎症的分子机制和关键基因。

关键词: 骨关节炎, 生物信息学, 差异表达基因

Abstract: Objective To identify the differentially expressed genes(DEGs)associated with the progression of osteoarthritis synovitis by bioinformatic analysis. Methods The gene expression profiles of GSE55457, GSE55235 and GSE12021 were downloaded from the Gene Expression Omnibus(GEO)to screen related genes in the pathogenesis of osteoarthritis. After the DEGs were identified, heatmaps were drawn, and functional enrichment of GO and KEGG was analyzed. The protein-protein interaction network(PPI)was constructed with STRING and Cytoscape, top module was screened with MCODE plug-in unit, and hub genes were screened with cytoHubba plug-in unit. Results There were 72 upregulated genes and 151 downregulated genes in the GSE55457, GSE55235 and GSE12021 gene expression profiles. GO analysis showed DEGs were involved in leukocyte migration, response to glucocorticoid, glycosaminoglycan binding, endoplasmic reticulum lumen, and nuclear outer membrane. KEGG analysis revealed DEGs were involved in MAPK signaling pathway, osteoclast differentiation and TNF signaling pathway. The cytoHubba screened out 10 key genes, including IL6, TLR7, SELE, VEGFA, LDLR, JUN, MYC, CD44, SNAI1 and hnRNA1. Conclusion Bioinformatic analysis can help to discover the molecular mechanism and key genes of synovitis in patients with osteoarthritis.

Key words: Osteoarthritis, Bioinformatics, Differentially expressed genes

中图分类号: 

  • R684.3
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