Role of METTL3 Protein in Asthma: Insights from Transcriptomic Profiling and Molecular Docking Analysis

Asthma is a chronic inflammatory disease characterized byimmune dysregulation. This study aimed to perform unbiased analysis of transcriptomic data to identify differentially expressed m6A-related genes in asthma, with a focus on exploring their potential as biomarkers and therapeutic targets. Gene Expression Omnibus (GEO) (GSE134544) dataset was analyzed to identify differentially expressed m6A-related genes. Functional enrichment analysis was performed clusterProfiler, immune infiltration profiling was conducted with CIBERSORT, and a competing endogenous RNA (ceRNA, including microRNA [miR] and lncRNA) network was constructed. Drug enrichment analysis was carried out using DSigDB, and molecular docking was utilized to assess the interaction between dabigatran and the METTL3 protein. From 192 differentially expressed genes, four m6A-related genes (METTL3, HNRNPC, IGFBP2, and RBMX) were identified as the intersecting genes between the m6A-related gene set and differentially expressed genes (DEGs) from the GSE134544 dataset. Gene Ontology (GO) analysis revealed significant enrichment in biological processes related to RNA metabolic processes and post-transcriptional regulation, while Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified important pathways such as spliceosome and p53 signaling pathways. METTL3 and HNRNPC were central in the ceRNA network, interacting with miRs such as hsa-miR-93-3p and lncRNAs like LINC01529. Drug enrichment analysis identified dabigatran as a potential METTL3 inhibitor, with molecular docking confirming a stable binding affinity (-5.9 kcal/mol). This study emphasizes the critical role of m6A-related genes, particularly METTL3 and HNRNPC, as macromolecules in asthma pathophysiology, and provides insights into their potential as biomarkers and therapeutic targets for asthma treatment.