Zhou Tao, Zhang Yudong, Wu Peng, Sun Qiang, Guo Yanan, Yang Yanfei
The Second Hospital of Shandong University, Jinan, Shandong Province, China.
Int J Rheum Dis. 2014 Jul;17(6):671-8. doi: 10.1111/1756-185X.12391. Epub 2014 May 27.
We aimed in this study to identify the significant latent pathways and precise molecular mechanisms underlying the syndrome of vasculitis.
Agilent dual-channel data of peripheral blood mononuclear cells (PBMCs) from healthy controls and vasculitis patients were downloaded from EBI Array Express database. Differentially expressed genes (DEGs) between normal and vasculitis PBMCs samples were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out to identify significant biological processes and pathways. DEGs were matched to NetBox software database to obtain LINKER genes with statistical significance. Protein-protein interaction (PPI) network was constructed with LINKER genes and DEGs according to STRING database. Latent pathway identification analysis (LPIA) was used to identify the most significant interactions among different pathways involved by DEGs.
A total of 266 DEGs were selected. GO and KEGG pathway analysis showed that the up-regulated genes were significantly enriched in defense and wounding response; the down-regulated genes were enriched in immune response. The modules analysis of PPI network suggested that ISG15 and IFIT3 were the potential biomarkers for vasculitis. The results of LPIA showed that NOD-like receptor signaling pathway and shigellosis related pathway were the two most significant latent pathway interactions for vasculitis. ISG15 and IFIT3 were the potential biomarkers for vasculitis identification.
NOD-like receptor signaling pathway and shigellosis related pathway were the most significant latent pathway interactions for vasculitis. Moreover, LPIA was a useful method for revealing systemic biological pathways and cellular mechanisms of diseases.
本研究旨在确定血管炎综合征潜在的重要途径和精确分子机制。
从欧洲生物信息研究所阵列表达数据库下载健康对照者和血管炎患者外周血单个核细胞(PBMC)的安捷伦双通道数据。选取正常和血管炎PBMC样本之间的差异表达基因(DEG)。进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路分析,以确定重要的生物学过程和通路。将DEG与NetBox软件数据库匹配,以获得具有统计学意义的连接基因。根据STRING数据库,用连接基因和DEG构建蛋白质-蛋白质相互作用(PPI)网络。使用潜在途径识别分析(LPIA)来识别DEG所涉及的不同途径之间最显著的相互作用。
共选取266个DEG。GO和KEGG通路分析表明,上调基因在防御和伤口反应中显著富集;下调基因在免疫反应中富集。PPI网络的模块分析表明,ISG15和IFIT3是血管炎的潜在生物标志物。LPIA结果显示,NOD样受体信号通路和志贺菌病相关通路是血管炎两个最显著的潜在途径相互作用。ISG15和IFIT3是用于识别血管炎的潜在生物标志物。
NOD样受体信号通路和志贺菌病相关通路是血管炎最显著的潜在途径相互作用。此外,LPIA是揭示疾病系统生物学途径和细胞机制的有用方法。