Feature gene mining for prediction of acute exacerbation of childhood asthma
Objective:Analyzing the gene expression microarray of patients experiencing acute asthma by bioinformatics methods,in order to explore the mechanism of acute asthma and to identify early indicator of acute asthma attacks.Methods:The GSE103166 microarray dataset was retrieved from the GEO database,and after cleaning and organizing the data,differential expres-sion analysis was conducted between children experiencing acute asthma attacks and non-asthmatic children.Enrichment analysis was performed using R language to annotate the functions of the differentially expressed genes(DEGs).Using gene interaction informa-tion,a network of interactions among these genes was constructed,and the network structure was deconstructed to identify key genes and regulatory sub-networks.Results:GSE103166 data set contained 87 nasal swab specimens from children(56 cases of acute asth-ma,31 cases of control).Compared with control group,the acute asthma group had 63 differentially expressed genes(39 upregulated and 24 downregulated).The results of enrichment analysis showed that the DEGs were primarily enriched in endocytic vesicle and en-docytic vesicle membrane.Among these,CD163 was the only gene significantly upregulated in both cellular components.The network dismantling analysis demonstrated four different sub-networks.CD163 was involved in sub-networkⅠ,which was composed of CD163,ARG2,CAT,GSTA2,SCNN1G and MT2A.Among them,ARG2 and CAT were at the regulatory center of the network.Conclusion:CD163 and regulatory networks based on CD163,ARG2,CAT,GSTA2,SCNN1G and MT2A are possible the crucial factors contributing to acute asthma.This study provides research ideas for the precise and early identification of biomarkers and thera-peutic targets for acute asthma attacks.
国家科技期刊平台
NSTL
万方数据
