Identification of a disease-specific gene expression profile of children with acute asthma by weighted gene co-expression network analysis

Abstract

Asthma is one of the most common diseases, with a high prevalence among children. To date, systemic co-expression analysis for this disease has not been undertaken to explain its pathogenesis. Here we identified differentially expressed genes (DEGs) in 87 samples, and then constructed co-expression modules via weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichment of co-expressed genes in terms of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). Meanwhile, protein–protein interaction (PPI) network and miRNA–transcription factor–target (miRNA–TF–target) regulatory network analyses were performed to screen hub genes. As a result, 3,469 DEGs were identified in this study, of which 1,860 genes were up-regulated and 1,609 genes were down-regulated. Using WGCNA, we identified two key modules, named MEbrown and MEblue, that may play important roles in asthma. Functional enrichment analysis revealed that MEbrown was enriched in 37 KEGG pathways and 472 biological processes (BPs), while MEblue was enriched in 16 KEGG pathways and 449 BPs. From PPI and miRNA–TF–target regulatory network analysis, a total of 31 TFs, seven miRNAs and 28 nodes were identified. Our findings should provide a framework of therapeutic targets for treating children with acute asthma.