Bioinformatics analysis and preliminary validation of autophagy-related genes in asthma disease

Many studies have suggested that Autophagy may be involved in the development of asthma disease. However, the mechanisms involved have not been fully elucidated. We aimed to identify and validate potential autophagy-related genes in asthma through bioinformatics analysis and experimental verification. Autophagy-related differentially expressed genes were analyzed by protein-protein interaction (PPI) network analysis, subject operating characteristic curve (ROC) analysis, construction of relevant MicroRNAs (miRNAs), transcription factors (TFs), and drug interaction networks and immune infiltration analysis. Finally, validation was performed by western blotting (WB) and quantitative real-time polymerase chain reaction (qRT-PCR). Five hub genes were identified by PPI network analysis and key module construction. These genes showed good diagnostic value for asthma. We also predicted 34 associated miRNAs and 8 associated TFs as well as 10 predictive drugs. The abundance of immune cells, such as memory B cells, naïve CD4 + T cells, follicular helper T cells and gamma delta T cells, was higher compared with the control group. WB and qRT-PCR results showed that the expression levels of TP53, SQSTM1/p62 and ATG5 in the asthma group and healthy control group were consistent with the bioinformatics analysis of the mRNA microarrays, and the dexamethasone (Dex) treatment group was able to inhibit Autophagy of cells and affect the expression levels of TP53, SQSTM1/p62 and ATG5 in the lung tissue of asthmatic mice. The present study provides a new insight that Autophagy dysregulation exists in asthma and may be involved in the etiology of asthma by participating in multiple pathways and biological functions. Autophagy-related genes in asthma may be valuable biomarkers for diagnosis and prognosis, and they may be developed as clinical therapeutic targets in the future.

Asthma; Autophagy; Bioinformatics analysis; Comprehensive gene expression dataset.