Expression Data Analysis to Identify Biomarkers Associated with Asthma in Children

Abstract

Asthma is characterized by recurrent episodes of wheezing, shortness of breath, chest tightness, and coughing. It is usually caused by a combination of complex and incompletely understood environmental and genetic interactions. We obtained gene expression data with high-throughput screening and identified biomarkers of children's asthma using bioinformatics tools. Next, we explained the pathogenesis of children's asthma from the perspective of gene regulatory networks: DAVID was applied to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enriching analysis for the top 3000 pairs of relationships in differentially regulatory network. Finally, we found that HAND1, PTK1, NFKB1, ZIC3, STAT6, E2F1, PELP1, USF2, and CBFB may play important roles in children's asthma initiation. On account of regulatory impact factor (RIF) score, HAND1, PTK7, and ZIC3 were the potential asthma-related factors. Our study provided some foundations of a strategy for biomarker discovery despite a poor understanding of the mechanisms underlying children's asthma.