Global School of Media, College of IT, Soongsil University, Seoul 06978, Republic of Korea.
S&E Bio, Inc., Seoul 05855, Republic of Korea.
Int J Mol Sci. 2024 Jun 20;25(12):6761. doi: 10.3390/ijms25126761.
Ischemic stroke is a major cause of mortality worldwide. Proper etiological subtyping of ischemic stroke is crucial for tailoring treatment strategies. This study explored the utility of circulating microRNAs encapsulated in extracellular vesicles (EV-miRNAs) to distinguish the following ischemic stroke subtypes: large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO). Using next-generation sequencing (NGS) and machine-learning techniques, we identified differentially expressed miRNAs (DEMs) associated with each subtype. Through patient selection and diagnostic evaluation, a cohort of 70 patients with acute ischemic stroke was classified: 24 in the LAA group, 24 in the SAO group, and 22 in the CES group. Our findings revealed distinct EV-miRNA profiles among the groups, suggesting their potential as diagnostic markers. Machine-learning models, particularly logistic regression models, exhibited a high diagnostic accuracy of 92% for subtype discrimination. The collective influence of multiple miRNAs was more crucial than that of individual miRNAs. Additionally, bioinformatics analyses have elucidated the functional implications of DEMs in stroke pathophysiology, offering insights into the underlying mechanisms. Despite limitations like sample size constraints and retrospective design, our study underscores the promise of EV-miRNAs coupled with machine learning for ischemic stroke subtype classification. Further investigations are warranted to validate the clinical utility of the identified EV-miRNA biomarkers in stroke patients.
缺血性脑卒中是全球范围内主要的致死病因。对缺血性脑卒中进行恰当的病因亚型分类对于制定治疗策略至关重要。本研究旨在探讨循环微小 RNA（miRNA）在细胞外囊泡（EV-miRNA）中的作用，以区分以下缺血性脑卒中亚型：大动脉粥样硬化（LAA）、心源性栓塞性脑卒中（CES）和小动脉闭塞（SAO）。我们采用下一代测序（NGS）和机器学习技术，确定与每种亚型相关的差异表达 miRNA（DEM）。通过患者选择和诊断评估，我们对 70 名急性缺血性脑卒中患者进行了分类：LAA 组 24 例，SAO 组 24 例，CES 组 22 例。我们的研究结果表明，各组之间存在明显的 EV-miRNA 谱差异，提示其具有作为诊断标志物的潜力。机器学习模型，尤其是逻辑回归模型，对亚型鉴别具有 92%的高诊断准确性。多个 miRNA 的综合影响比单个 miRNA 更为重要。此外，生物信息学分析阐明了 DEM 在脑卒中病理生理学中的功能意义，为潜在机制提供了深入了解。尽管存在样本量限制和回顾性设计等局限性，但本研究强调了 EV-miRNA 与机器学习相结合用于缺血性脑卒中亚型分类的潜力。需要进一步的研究来验证所鉴定的 EV-miRNA 生物标志物在脑卒中患者中的临床应用价值。
