基于高通量芯片对强直性脊柱炎的生物信息学分析

Bioinformatic analysis of ankylosing spondylitis based on high-throughput microarray

目的运用生物信息分析工具筛选出强直性脊柱炎(ankylosing spondylitis,AS)相关的差异表达基因(differentially expressed genes,DEGs)并进行文献挖掘。方法从GEO数据库中检索获取AS患者的芯片数据,通过GEO2R进行DEGs的筛选,运用DAVID数据库对筛选获得的DEGs行基因富集和通路分析,通过STRING数据库构建蛋白相互作用(protein-protein interaction,PPI)网络,并采用Cytoscape软件行进一步分析。结果共筛选出190个差异基因,其中上调的基因75个,下调的基因115个。GO分析显示差异表达基因主要涉及炎症反应、白细胞迁移、胞外区、细胞外结构域、细胞外区域、细胞外基质、脂蛋白颗粒结合和碳水化合物结合等功能簇;KEGG分析提示其在包括类风湿性关节炎和细胞因子-细胞因子受体相互作用等通路中富集;运用STRING数据库构建蛋白相互作用网络;运用Cytoscape筛选出CXCR4、SELL、CD79A、MMP3、CD68、ADIPOQ、CCL19、IL-7R、IL-1β、MYH14、APOE和FCGR3A等12个连接度最高基因,并运用iRegulon插件挖掘得到ETS1、GATA3和IRF8等41个作用于上述12个核心基因的转录因子。结论新发现的12个核心基因和41个转录因子可能在AS的致病机制中起重要作用,并可能成为防治AS的新靶点。

Objective To screen the differentially expressed genes (DEGs) of ankylosing spondylitis (AS) by bioinformatic analysis and make literature review for their functions. Methods The data about genome microarray of AS were retrieved from the Gene Expression Omnibus (GEO) database, and then employed to screen out DEGs between synovial biopsies from AS and undifferentiated spondylitis patients and the samples from healthy individuals and osteoarthritis patients by GEO2R. The DEGs were then subjected to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis in DAVID 6.8 database. Finally, protein-protein interaction (PPI) network was constructed by STRING10.5 and then further analyzed by the Cytoscape software. Results A total of 190 differential genes were screened out, of which 75 were up-regulated and 115 were down-regulated. GO analysis showed that the obtained DGEs were mainly involved in inflammatory response, leukocyte migration, extracellular domain, extracellular region, extracellular matrix, lipoprotein particle binding and carbohydrate binding. KEGG analysis suggested that these DGEs were enriched in rheumatoid arthritis and cytokinecytokine receptor interaction, and others. The PPI network was constructed by STRING database. And then, with the aid of Cytoscape, 12 Hub genes with highest degree of ankylosing spondylitis (including CXCR4, SELL, CD79A, MMP3, CD68, ADIPOQ, CCL19, IL-7R, IL-1β, MYH14, APOE and FCGR3A) were screened out. What's more, 41 transcription factors (TFs) interacting with the above 12 Hub genes, including ETS1, GATA3, IRF8 and so on were screened out with the iRegulon, a plugin in Cytoscape. Conclusion The firstly selected 12 Hub genes and 41 TFs may play important roles in pathogenesis of AS, and are regarded as new biomarkers for the diagnosis and treatment for AS.