基于GEO数据库筛选川崎病的差异表达基因及通路的研究

Screening differentially expressed genes and pathways of Kawasaki disease based on GEO database

目的 应用生物信息学方法分析与川崎病发病可能相关的基因及蛋白信号通路。方法 从综合基因表达数据库中下载川崎病基因表达数据集GSE18606和GSE68004。利用GEO2R工具对数据集进行预处理,筛选差异基因,将两者进行交集,得到差异表达基因(DEGs)。利用R软件富集分析DEGs的生物学功能和信号通路,利用STRING数据库和cytoscape软件构建并改进蛋白-蛋白相互作用(PPI)网络,在mirdip数据库中预测核心基因对应的靶miRNAs。结果 在GSE18606和GSE68004数据集中共筛选出269个DEGs;DEGs的生物学功能主要包括免疫反应、中性粒细胞活化、细胞因子产生的正向调控、免疫反应T细胞活化、分泌颗粒膜和葡萄糖结合免疫受体活性等;信号通路主要包括造血细胞系、白细胞内皮细胞迁移、Toll样受体信号通路、胰岛素信号通路和T细胞受体信号通路;PPI网络包含218个DEGs和674对互相作用关系,其中STAT3、FGR、IL7R、HOOK3、MAPK14、LILRB2、CD2、CSF3R、SOCS3、GZMA、H2AC20、GZMK、H2BC5、ITK共14个为网络中的核心基因,预计受445个miRNA调控。结论 本研究应用生物信息学方法对公共GEO数据库中川崎病差异表达基因及其生物学功能和相关信号通路进行了筛查及预测,为今后阐明KD的病因及发病机制提供了新的研究方向。

Objective To analyze the gene and protein signal pathways that may be related to the pathogenesis of Kawasaki disease by bioinformatics methods.Methods The gene expression data sets GSE18606 and GSE68004 of Kawasaki disease were downloaded from the comprehensive gene expression database.GEO2R tool was used to preprocess the data set and screen the differential genes,and the two was intersected to obtain the differentially expressed genes(DEGs).The biological functions and signal pathways of DEGs were enriched and analyzed by R software,and the protein-protein interaction(PPI)network was constructed and improved by STRING database and cytoscape software,and the target miRNAs corresponding to core genes were predicted in the mirdip database.Results A total of 269 DEGs were screened from GSE18606 and GSE68004 data sets.The biological functions of DEGs mainly included immune response,neutrophil activation,positive regulation of cytokine production,immune response T cell activation,secretory granule membrane and glucose-binding immune receptor activity,etc.The signal pathways mainly included hematopoietic cell line,leukocyte endothelial cell migration,Toll-like receptor signal pathway,insulin signaling pathway and T cell receptor signaling pathway.The PPI network contained 218 DEGs and 674 pairs of interaction relationships,of which there were 14 core genes in the network,including STAT3,FGR,IL7R,HOOK3,MAPK14,LILRB2,CD2,CSF3R,SOCS3,GZMA,H2AC20,GZMK,H2BC5 and ITK,and they were predicted to be regulated by 445 miRNAs.Conclusion In this study,bioinformatics methods are applied to screen and predict DEGs,and their biological functions and related pathways of KD in public GEO database,which providing a new research direction for elucidating the etiology and pathogenesis of KD in the future.