To investigate the function of age-related genes in different subtypes of asthma based on bioinformatics analysis

Asthma is a heterogeneous airway inflammatory disease that can be classified according to the inflammatory phenotype. The pathogenesis, clinical features, response to hormone therapy, and prognosis of different inflammatory phenotypes differ significantly. This condition also refers to age-related chronic ailments. Here, we intend to identify the function of aging-related genes in different inflammatory phenotypes of asthma using bioinformatic analyses. Initially, the research adopted the GSEA analysis to understand the fundamental mechanisms that govern different inflammatory phenotypes of asthma pathogenesis and use the CIBERSORT algorithm to assess the immune cell composition. The differentially expressed genes (DEGs) of eosinophilic asthma (EA), neutrophilic asthma (NA), and paucigranulocytic asthma (PGA) were identified through the limma R package. Aging-related genes, screened from multiple databases, were intersected with DEGs of asthma to obtain the asthma-aging-related DEGs. Then, the GO and KEGG pathway enrichment analyses showed that the NA- and EA-aging-related DEGs are involved in the various cytokine-mediated signaling pathways. PPI network and correlation analysis were performed to identify and evaluate the correlation of the hub genes. Further, the clinical characteristics of asthma-aging-related DEGs were explored through ROC analysis. 3 and 12 aging-related DEGs in EA and NA patients show high diagnostic accuracy, respectively (AUC >0.7). This study provided valuable insights into aging-related gene therapy for phenotype-specific asthma. Moreover, the study suggests that effective interventions against asthma may operate by disrupting the detrimental cycle of "aging induces metabolic diseases, which exacerbate aging".