Integrated bioinformatic analysis reveals the underlying mitochondria-associated endoplasmic reticulum membranes-related biomarkers for atrial fibrillation - Wang, Youcheng; Song, Mengyang; Liu, Huanting; Fang, Sini; Lei, Yumeng; Liu, Jiulin; Zhang, Jiayuan; Zhang, Ke; Mao, Ying; Yan, Liqiu (Frontiers in Physiology, 2025)
PurposeTo provide novel insights into the diagnosis of atrial fibrillation (AF), we aimed to identify mitochondria-associated endoplasmic reticulum membranes (MAMs)-related biomarkers for AF.MethodsThe training and validation datasets of AF were sourced from the Gene Expression Omnibus (GEO) database. A comprehensive analysis was conducted to identify MAM-related biomarkers, including support vector machine-recursive feature elimination (SVM-RFE) and differentially expressed analysis. Moreover, causal effects of biomarkers on AF were assessed through the two-sample Mendelian randomization (MR) analysis. Functional enrichment, immune infiltration, and single-cell analyses were conducted to investigate the possible mechanisms of biomarkers regulating AF. Finally, the expression of biomarkers was validated at the mRNA and protein levels by developing an in-vivo canine AF model.ResultsThrough the comprehensive analysis, TP53, HLA-G, and MAPKAPK5 were identified, which were highly expressed in atrial tissues of AF samples. Notably, MAPKAPK5 was a risk factor for occurrence of AF (P = 0.022, OR = 1.065, 95%CI = 1.009–1.125). Enrichment analysis revealed that three biomarkers were associated with immune-related pathways. Immune infiltration further demonstrated that a total of infiltration abundance of 18 immune cells was significantly different between AF and controls, and all biomarkers had marked positive associations with these immune cells. Moreover, at the cellular level, the expression of TP53 and MAPKAPK5 was markedly different in lymphoid cells and neutrophils between AF and controls. At the experimental levels, the expression of three biomarkers was significantly higher in the AF model than that in the control model, consistent with the bioinformatics results.ConclusionWe identified three potential MAMs-related biomarkers (TP53, HLA-G, and MAPKAPK5) for AF, thereby providing novel insights for the prevention and treatment of AF.