Ma Yazhe, Wang Youcheng, Ke Yuanjia, Zhao Qingyan, Fan Jie, Chen Yang
Yunnan Arrhythmia Research Center, The First People's Hospital of Yunnan Province & The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China.
Department of Cardiology, The Affiliated Dongguan Songshan Lake Central Hospital, Dongguan Key Laboratory of Cardiovascular Aging and Myocardial Regeneration, Dongguan Cardiovascular Research Institute, Dongguan, China.
Front Cardiovasc Med. 2025 Apr 22;12:1567310. doi: 10.3389/fcvm.2025.1567310. eCollection 2025.
Atrial fibrillation (AF) is a common arrhythmia associated with an increased risk of stroke, heart failure, and mortality. Immune infiltration plays a crucial role in AF pathogenesis, yet its mechanisms remain unclear. Lactylation, a novel post-translational modification, has been implicated in immune regulation, but its association with AF remains unexplored. This study aims to elucidate the relationship between lactylation and immune infiltration in AF and identify potential diagnostic biomarkers.
Gene expression data from left atrial tissue samples of AF and sinus rhythm (SR) patients were obtained from the Gene Expression Omnibus (GEO) database (GSE41177, GSE79768, GSE115574, GSE2240, GSE14975, and GSE128188). Differentially expressed genes (DEGs) between AF and SR samples were identified, followed by pathway enrichment and immune infiltration analysis. Correlation analysis and WGCNA were performed to assess interactions between lactylation-related genes and immune-associated DEGs. Machine learning models, including Random Forest and Support Vector Machine (SVM), were applied to select potential AF-related diagnostic biomarkers, and validated in the animal model (beagles; = 6).
A total of 5,648 DEGs were identified, including six lactylation-related genes (DDX39A, ARID3A, TKT, NUP50, G6PD, and VCAN). Co-expression and WGCNA analyses identified lactylation- and immune-associated gene modules in AF. Functional enrichment analysis highlighted immune-related pathways such as T cell activation and neutrophil degranulation. A five-gene diagnostic model (FOXK1, JAM3, LOC100288798, MCM4, and RCAN1) achieved high predictive accuracy (AUC = 0.969 in training, 0.907 in self-test, and 0.950, 0.760, 0.890 in independent datasets). Experimental validation confirmed the upregulated expression of these biomarkers in AF.
This study reveals a strong association between lactylation-related genes and immune infiltration in AF, suggesting their involvement in immune remodeling. The identified five-gene signature serves as a potential diagnostic biomarker set, offering novel insights into AF pathogenesis and contributing to improved diagnosis and targeted therapeutic strategies. Future studies integrating proteomic and single-cell analyses will further clarify the role of lactylation in AF.
心房颤动（AF）是一种常见的心律失常，与中风、心力衰竭和死亡风险增加相关。免疫浸润在AF发病机制中起关键作用，但其机制仍不清楚。乳酰化是一种新的翻译后修饰，已被证明与免疫调节有关，但其与AF的关系尚未得到探索。本研究旨在阐明AF中乳酰化与免疫浸润之间的关系，并确定潜在的诊断生物标志物。
从基因表达综合数据库（GEO）（GSE41177、GSE79768、GSE115574、GSE2240、GSE14975和GSE128188）获取AF和窦性心律（SR）患者左心房组织样本的基因表达数据。识别AF和SR样本之间的差异表达基因（DEG），随后进行通路富集和免疫浸润分析。进行相关性分析和加权基因共表达网络分析（WGCNA）以评估乳酰化相关基因与免疫相关DEG之间的相互作用。应用包括随机森林和支持向量机（SVM）在内的机器学习模型选择潜在的AF相关诊断生物标志物，并在动物模型（比格犬；n = 6）中进行验证。
共鉴定出5648个DEG，包括6个乳酰化相关基因（DDX39A、ARID3A、TKT、NUP50、G6PD和VCAN）。共表达和WGCNA分析确定了AF中乳酰化和免疫相关的基因模块。功能富集分析突出了免疫相关通路，如T细胞活化和中性粒细胞脱颗粒。一个五基因诊断模型（FOXK1、JAM3、LOC100288798、MCM4和RCAN1）具有较高的预测准确性（训练集AUC = 0.969，自检集AUC = 0.907，独立数据集AUC分别为0.950、0.760、0.890）。实验验证证实了这些生物标志物在AF中的表达上调。
本研究揭示了AF中乳酰化相关基因与免疫浸润之间的密切关联，表明它们参与了免疫重塑。所鉴定的五基因特征作为一组潜在的诊断生物标志物，为AF发病机制提供了新的见解，并有助于改善诊断和制定靶向治疗策略。未来整合蛋白质组学和单细胞分析的研究将进一步阐明乳酰化在AF中的作用。
