Department of Cardiology, Shanghai Putuo District People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Department of Clinical Medicine, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Cardiology. 2023;148(2):150-160. doi: 10.1159/000529043. Epub 2023 Feb 9.
Atrial fibrillation (AF) is the most common cardiac arrhythmia in the general population, and stroke is the most severe complication of AF. Exosomal miRNAs have been reported to be candidates as biomarkers for cardiovascular diseases, including AF and stroke. This study aimed to identify differentially expressed miRNAs (DEMs) in serum exosomes of AF and AF-associated ischemic stroke (AF-IS) patients and evaluate their potential in distinguishing AF and AF-IS patients.
Serum exosomes were isolated from 8 healthy individuals with sinus rhythm (SR controls), 8 AF patients, and 8 AF-IS patients. miRNA-seq was performed to identify DEMs, and qRT-PCR analysis was performed to confirm the sequencing results. A support vector machine (SVM) model was developed using Python to distinguish AF and AF-IS patients.
68 and 86 DEMs were identified in serum exosomes of AF patients compared to AF-IS patients and SR controls, respectively. Levels of miR-641 and miR-30e-5p were found significantly higher in AF-IS patients. The SVM model achieved an accuracy of 100%, with an area under curve of 1.
The results indicated that miRNA expression profiles of serum exosomes in AF patients were distinct from those in AF-IS patients, and based on the distinction, AF and AF-IS patients can be distinguished.
心房颤动(AF)是普通人群中最常见的心律失常,而中风是 AF 最严重的并发症。已有研究报道,外泌体 miRNA 可作为心血管疾病(包括 AF 和 AF 相关缺血性中风(AF-IS))的生物标志物。本研究旨在鉴定 AF 和 AF-IS 患者血清外泌体中差异表达的 miRNA(DEMs),并评估其区分 AF 和 AF-IS 患者的潜力。
从 8 名窦性心律(SR 对照)健康个体、8 名 AF 患者和 8 名 AF-IS 患者中分离血清外泌体。采用 miRNA-seq 鉴定 DEMs,并用 qRT-PCR 分析验证测序结果。使用 Python 开发支持向量机(SVM)模型以区分 AF 和 AF-IS 患者。
与 AF-IS 患者相比,AF 患者血清外泌体中分别鉴定出 68 个和 86 个 DEMs。AF-IS 患者中 miR-641 和 miR-30e-5p 的水平明显升高。SVM 模型的准确率为 100%,曲线下面积为 1。
结果表明,AF 患者血清外泌体的 miRNA 表达谱与 AF-IS 患者不同,基于这种区分,可以区分 AF 和 AF-IS 患者。