Abstract
BackgroundSevere asthma is a heterogeneous inflammatory disease. The increase in precise immunotherapy for severe asthmatics requires a greater understanding of molecular mechanisms and biomarkers. In this study, we aimed to identify the underlying mechanisms and hub genes that determine asthma severity.MethodsDifferentially expressed genes (DEGs) were identified based on bronchial epithelial brushings from mild and severe asthmatics. Then, weighted gene coexpression network analysis (WGCNA) was used to identify gene networks and the module most significantly associated with asthma severity. Furthermore, hub gene screening and functional enrichment analysis were performed. Replication with another dataset was conducted to validate the hub genes.ResultsDEGs from 14 mild and 11 severe asthmatics were subjected to WGCNA. Six modules associated with asthma severity were identified. Three modules were positively correlated (P < 0.001) with asthma severity and contained genes that were upregulated in severe asthmatics. Functional enrichment analysis showed that genes in the most significant module were mainly enriched in neutrophil activation and degranulation, and cytokine receptor interaction. Hub genes included CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK; CXCR1, CXCR2, and TLR2 were significantly related to asthma severity in the validation dataset. The combination of ten hub genes exhibited a moderate ability to distinguish between severe and mild-moderate asthmatics.ConclusionOur results identified biomarkers and characterized potential pathogenesis of severe asthma, providing insight into treatment targets and prognostic markers.
Highlights
Asthma is a chronic, heterogeneous inflammatory disease with complex pathological mechanisms and diverse clinical phenotypes
As bronchial epithelial cells are thought to be highly informative for describing changes in gene expression in asthma [11, 12], data of epithelial brushings from 14 mild and 11 severe asthmatics were extracted for Weighted gene coexpression network analysis (WGCNA)
Dataset selection and differentially expressed genes (DEGs) identification The microarray gene expression dataset GSE89809 was used in this study
Summary
Heterogeneous inflammatory disease with complex pathological mechanisms and diverse clinical phenotypes. Weighted gene coexpression network analysis (WGCNA) is a bioinformatics method for exploring the complex relationships between gene expression profiles and phenotypes. WGCNA is widely used in studies of multigene diseases to identify potential biomarkers and provide molecular targets for treatment. Differentially expressed genes (DEGs) between healthy controls and asthma patients or genes from all asthmatics, not DEGs between mild and severe asthmatics, were considered to construct a coexpression network in the studies mentioned above. Analysis of DEGs from mild-severe asthmatics could identify genes that especially contribute to disease progression. In this study, such genes were considered for WGCNA and further biologically functional analysis to define hidden mechanisms and key genes in severe asthmatics. We aimed to identify the underlying mechanisms and hub genes that determine asthma severity
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