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Abstract：
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.
摘要：
缺血性中风是世界范围内死亡的主要原因。缺血性卒中的正确病因分型对于定制治疗策略至关重要。这项研究探索了包封在细胞外囊泡中的循环microRNAs（EV-miRNAs）用于区分以下缺血性卒中亚型：大动脉粥样硬化（LAA），心源性卒中(CES),和小动脉闭塞（SAO）。使用下一代测序（NGS）和机器学习技术，我们鉴定了与每个亚型相关的差异表达miRNA(DEM)。通过患者选择和诊断评估，对70例急性缺血性卒中患者进行了分类：LAA组24例，在SAO组中有24人，和22在CES组。我们的发现揭示了各组之间不同的EV-miRNA谱，表明它们作为诊断标记的潜力。机器学习模型，特别是逻辑回归模型，亚型鉴别的诊断准确率高达92%。多个miRNA的集体影响比单个miRNA的集体影响更重要。此外,生物信息学分析已经阐明了DEM在中风病理生理学中的功能意义，提供对潜在机制的见解。尽管有样本量限制和回顾性设计等限制，我们的研究强调了EV-miRNA与机器学习相结合用于缺血性卒中亚型分类的前景.需要进一步的研究来验证所鉴定的EV-miRNA生物标志物在中风患者中的临床实用性。
