儿童克罗恩病差异表达基因的生物信息学分析

Bioinformatics Analysis of Differentially Expressed Genes in Children with Crohn's Disease

关键词:  生物信息学  儿童克罗恩病  差异表达基因

Key words:  bioinformatics  Crohn's disease in children  differentially expressed genes

摘要:

目的 应用生物信息学方法探讨儿童克罗恩病的差异表达基因，为阐明儿童克罗恩病的发病机制及干预靶点提供新思路。方法 从公共基因表达数据库（Gene Expression Omnibus，GEO）下载儿童克罗恩病相关的基因芯片数据集GSE9686，使用在线分析工具GEO2R筛选出儿童克罗恩病结肠组织与正常结肠组织的差异表达基因。使用数据库DAVID对差异表达基因进行GO功能注释和KEGG通路分析，使用STRING数据库进行蛋白互作网络（PPI）分析，使用Cytoscape筛选出儿童克罗恩病的关键基因。结果 筛选出85个儿童克罗恩病差异表达基因，包括63个上调基因和22个下调基因。GO分析揭示差异表达基因主要富集于趋化因子活性、CXCR趋化因子受体结合、细胞外空间、细胞外区域、趋化因子介导的信号通路及炎症反应等过程，KEGG分析揭示差异表达基因主要富集于细胞因子-细胞因子受体相互作用、趋化因子信号通路、TNF信号通路等。Cytoscape筛选出了15个关键基因，所有关键基因在儿童克罗恩病结肠组织中表达均上调。结论 采用生物信息学对儿童克罗恩病结肠组织与正常结肠组织的差异表达基因进行综合分析，可为儿童克罗恩病的早期诊断及靶向治疗提供新的理论依据。

Abstract:

OBJECTIVE To investigate the differentially expressed genes of children's Crohn's disease(CD) by the bioinformatics method, providing a new idea for elucidating the pathogenesis and intervention targets of CD in children. METHODS The gene expression dataset GSE9686 related to CD was downloaded from the Gene Expression Omnibus(GEO). The online analysis tool GEO2R was used to screen the DEGs of CD colon tissue and normal colon tissue in children. The GO function annotation and KEGG pathway analysis were performed on the DEGs using the database DAVID. The protein-protein interaction network(PPI) analysis was performed using the STRING database, and the key genes of CD in children were screened using Cytoscape. RESULTS Eighty-five DEGs were obtained in Crohn's disease, including 63 up-regulated genes and 22 down-regulated genes. GO analysis revealed that DEGs were mainly enriched in chemo`kine activity, chemokine receptor binding, extracellular space, extracellular region, chemokine-mediated signaling pathway and inflammatory response, and KEGG analysis revealed DEGs were mainly enriched in Cytokine-cytokine receptor interaction, Chemokine signaling pathway, TNF signaling pathway. Fifteen key genes were obtained by Cytoscape, and all of them were up-regulated in CD children. CONCLUSION The comprehensive analysis of DEGs between CD colonic tissue and normal colon tissue in children using bioinformatics can provide a new theoretical basis for early diagnosis and targeted therapy of CD in children.