Abstract
BACKGROUND: Asthma is a chronic inflammatory airway disease characterized by variable degrees of inflammation and airway hyperresponsiveness. The current study used a bioinformatic meta-analysis to identify key target genes and miRNA biomarkers for early diagnostics, thereby suggesting possible therapeutic targets that could impact the management and treatment of asthma sufferers. METHODS: This study used microarray bioinformatic analysis to discover potential asthma biomarkers by analyzing four microarray datasets of asthma patients and normal groups, namely GSE64913, GSE41863, GSE41862, and GSE165934. Additionally, pathway analysis, gene ontology (GO), and a protein-protein interaction (PPI) network were performed to investigate crucial pathways related to possible biological processes. A meta-analysis of the datasets to identify differentially expressed genes (DEGs) and their hub genes, with their targeting microRNAs, was implemented using bioinformatics tools. RESULTS: In this regard, the genes CD44, KRT6A, FOSL1, PTGS2, JUN, CXCL8, IL1B, and DUSP1 were identified as the hub genes while considering the results of the present study. GO analysis of the DEGs revealed significant enrichment of genes involved in antigen presentation and recognition by T cells, along with pathways related to inflammation and metabolism. Finally, hsa-let-7a-5p, hsa-miR-27a-3p, hsa-miR-34a-5p, hsa-miR-92a-3p, hsa-miR-18a-5p, hsa-mir-155-5p, hsa-mir-129-2-3p, hsa-miR-101-3p, hsa-miR-191-5p, and hsa-miR-185-5p presented considerable associations with most hub genes. CONCLUSIONS: These genetic factors may serve as valuable biomarkers for understanding the etiology and progression of asthma.