Integrated m⁶A and transcriptome analysis in a smoking-induced lung injury model reveals key methylated genes and computationally predicts potential therapeutic candidates

The pathogenesis of chronic obstructive pulmonary disease (COPD) involves epigenetic regulation, yet the role of transcriptome-wide N6-methyladenosine (m⁶A) RNA methylation remained unclear. This study profiled the m⁶A epitranscriptome in smoking-induced lung injury. Lung tissues from the model and control rats were examined by Hematoxylin-Eosin (HE) staining. Total RNA was subjected to m⁶A RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq analyses. Differentially methylated peaks were identified using MACS2, and differentially expressed genes (DEGs) were analyzed with DESeq2. Key genes were selected based on hypermethylation-low expression or hypomethylation-high expression correlation. Protein–protein interaction (PPI) and ceRNA networks were constructed using STRING, miRWalk, and miRanda. Methylation profiles were visualized in the Integrative Genomics Viewer (IGV). Immune infiltration was assessed via MCP-counter. Drug–gene interactions from the Comparative Toxicogenomics Database (CTD) were evaluated by molecular docking using AutoDockTools. MeRIP-seq identified 1447 differential m⁶A peaks (761 genes). RNA-seq revealed 1548 DEGs. Integrated analysis yielded 11 key genes ( Tekt1 , Tal1 , RGD1563917 , Hsd17b2 , Idnk , Dntt , Fcrl5 , Gtpbp2 , Gngt2 , Serpinb8 , and Siglec5 ). In the PPI network, interactions were observed among Siglec5 , Serpinb8 and Gngt2 , with Siglec5 and Serpinb8 showing the strongest binding. IGV visualization confirmed significantly different methylation states for Idnk , RGD1563917 , and Tekt1, all of which were hypermethylated in the model group. A ceRNA network was established, comprising 7 key genes, 60 miRNAs, and 10 lncRNAs, for example, pvt1 was predicted to regulate Serpinb8 via rno-miR-18a-3p, and both Gtpbp2 and Serpinb8 via rno-miR-1956–5p. Computational screening identified 99 candidate drugs, and molecular docking predicted Bexarotene, Dexamethasone, and Resveratrol as potential binders of Hsd17b2 . This study delineated the m⁶A landscape in smoking-induced lung injury, revealed key regulatory networks, and computationally predicted drug candidates, thus offering a valuable resource for further mechanistic and therapeutic exploration.