Proteomic Analyses of Clots Identify Stroke Etiologies in Patients Undergoing Endovascular Therapy

IF 4.8 1区医学 Q1 NEUROSCIENCES

CNS Neuroscience & Therapeutics Pub Date : 2025-03-13 DOI:10.1111/cns.70340

Tae Jung Kim, Jin Woo Jung, Young-Ju Kim, Byung-Woo Yoon, Dohyun Han, Sang-Bae Ko

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Abstract

Aims

This study aimed to investigate the correlation between clot composition and stroke mechanisms in patients undergoing endovascular therapy (EVT), using proteomic analysis.

Methods

This study included 35 patients with ischemic stroke (cardioembolism [CE], n = 17; large artery atherosclerosis [LAA], n = 6; cancer-related [CR], n = 4; and undetermined (UD) cause, n = 8) who underwent EVT. Retrieved clots were proteomically analyzed to identify differentially expressed proteins associated with the three stroke mechanisms and to develop the machine learning model.

Results

In the discover stage, 3838 proteins were identified using clot samples from 27 patients with CE, LAA, and CR mechanisms. Through functional enrichment and network analysis, 149 proteins were identified as potential candidates for verification studies. After verification experiments, 34 proteins were selected as the final candidates to predict stroke mechanisms. Furthermore, the machine learning-based model identified three proteins associated with each mechanism (Pleckstrin in CE; CD59 glycoprotein in LAA; and Immunoglobulin Heavy Constant Gamma 1 in CR) in the UD group.

Conclusions

This study identified specific protein markers of clots that could differentiate stroke mechanisms in patients undergoing EVT. Therefore, our results could offer valuable insights into elucidating the mechanisms of ischemic stroke, which could provide information on more effective secondary prevention strategies.

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在接受血管内治疗的患者中，凝块的蛋白质组学分析确定中风的病因

目的本研究旨在通过蛋白质组学分析，探讨血管内治疗（EVT）患者血凝块组成与卒中机制之间的相关性。方法本研究纳入35例缺血性脑卒中(心脏栓塞[CE]， n = 17；大动脉粥样硬化[LAA]， n = 6；cancer-related [CR], n = 4；和不明原因（UD）， n = 8)行EVT。对检索到的凝块进行蛋白质组学分析，以确定与三种中风机制相关的差异表达蛋白，并开发机器学习模型。结果在发现阶段，从27例CE、LAA和CR机制患者的血块样本中鉴定出3838个蛋白。通过功能富集和网络分析，确定了149个蛋白作为验证研究的潜在候选蛋白。经过验证实验，选择34种蛋白作为预测脑卒中机制的最终候选蛋白。此外，基于机器学习的模型确定了与每种机制相关的三种蛋白质(CE中的Pleckstrin；LAA中CD59糖蛋白；和免疫球蛋白重常数γ 1 （CR）。结论：本研究确定了血栓的特异性蛋白标记物，可以区分EVT患者的脑卒中机制。因此，我们的研究结果可以为阐明缺血性卒中的机制提供有价值的见解，从而为更有效的二级预防策略提供信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

CNS Neuroscience & Therapeutics 医学-神经科学

CiteScore

7.30

自引率

12.70%

发文量

240

审稿时长

2 months

期刊介绍： CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews and reports of novel findings of therapeutic relevance to the central nervous system, as well as papers related to clinical pharmacology, drug development and novel methodologies for drug evaluation. The journal focuses on neurological and psychiatric diseases such as stroke, Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, epilepsy, and drug abuse.