Shared molecular signatures between atrial fibrillation and chronic obstructive pulmonary disease: an integrated bioinformatic analysis with experimental validation

Atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD) are highly prevalent conditions that significantly impact patient outcomes. While mounting evidence suggests a strong epidemiological and clinical association between AF and COPD, the molecular basis underlying their frequent co-occurrence remains incompletely characterized. Rather than inferring direct disease-specific mechanistic links, this study aimed to identify shared systemic inflammatory and immune-related transcriptional signatures that are detectable across different tissues in AF and COPD. A total of 208 DEGs were found to be shared between AF and COPD. Functional enrichment analyses indicated that these genes were predominantly associated with inflammatory, immune, and extracellular matrix–related biological processes, including the NOD-like receptor, Influenza A, Phospholipase D, and ECM-receptor interaction pathways. PPI network analysis and external dataset verification highlighted CASP1, CXCR2, and IFIT5 as reproducibly upregulated genes across independent AF and COPD transcriptomic datasets. Their elevated expression was further observed in AF, COPD, and comorbid samples in exploratory validation experiments. No single TF or miRNA was found to simultaneously regulate all three genes, suggesting heterogeneous and context-dependent regulatory patterns rather than a unified regulatory axis. Drug–gene association analysis identified estradiol and methyl methanesulfonate as compounds computationally linked to the shared gene set, without implying biological efficacy or therapeutic applicability. This study delineates shared inflammatory and immune-related transcriptional signatures observed across AF and COPD datasets, highlighting common pathways and candidate genes that may reflect systemic inflammatory states. These findings are hypothesis-generating and primarily reflect shared systemic inflammatory and immune responses rather than disease-specific causal mechanisms, providing a bioinformatic framework for future mechanistic and clinical investigations.