Identification of Key Signaling Pathways and Genes in Eosinophilic Asthma and Neutrophilic Asthma by Weighted Gene Co-Expression Network Analysis - Chen, Gongqi; Chen, Dian; Feng, Yuchen; Wu, Wenliang; Gao, Jiali; Chang, Chenli; Chen, Shengchong; Zhen, Guohua (Frontiers in Molecular Biosciences, 2022)
Background: Asthma is a heterogeneous disease with different subtypes including eosinophilic asthma (EA) and neutrophilic asthma (NA). However, the mechanisms underlying the difference between the two subtypes are not fully understood. Methods: Microarray datasets (GSE45111 and GSE137268) were acquired from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in induced sputum between EA (n = 24) and NA (n = 15) were identified by “Limma” package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and Gene set enrichment analysis (GSEA) were used to explore potential signaling pathways. Weighted gene co-expression network analysis (WGCNA) were performed to identify the key genes that were strongly associated with EA and NA. Results: A total of 282 DEGs were identified in induced sputum of NA patients compared with EA patients. In GO and KEGG pathway analyses, DEGs were enriched in positive regulation of cytokine production, and cytokine-cytokine receptor interaction. The results of GSEA showed that ribosome, Parkinson’s disease, and oxidative phosphorylation were positively correlated with EA while toll-like receptor signaling pathway, primary immunodeficiency, and NOD-like receptor signaling pathway were positively correlated with NA. Using WGCNA analysis, we identified a set of genes significantly associated NA including IRFG, IRF1, STAT1, IFIH1, IFIT3, GBP1, GBP5, IFIT2, CXCL9, and CXCL11. Conclusion: We identified potential signaling pathways and key genes involved in the pathogenesis of the asthma subsets, especially in neutrophilic asthma.