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Harnessing the Power of MicroRNA Cargoes in Small Extracellular Vesicles Released from Fresh-Frozen Human Brain Sections
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Machine Learning-Based Etiologic Subtyping of Ischemic Stroke Using Circulating Exosomal microRNAs.

Ji Hoon Bang1, Eun Hee Kim2, Hyung Jun Kim3

  • 1Global School of Media, College of IT, Soongsil University, Seoul 06978, Republic of Korea.

International Journal of Molecular Sciences
|June 27, 2024
PubMed
Summary

Extracellular vesicle microRNAs (EV-miRNAs) show promise for classifying ischemic stroke subtypes. Machine learning models accurately distinguished large artery atherosclerosis, cardioembolic stroke, and small artery occlusion using these EV-miRNA profiles.

Keywords:
etiologyextracellular vesicleischemic strokemachine learningmicroRNAssubtype

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Area of Science:

  • Biochemistry and Molecular Biology
  • Neurology
  • Genomics

Background:

  • Ischemic stroke is a leading global cause of death, necessitating precise etiological subtyping for effective treatment.
  • Current methods for stroke subtyping can be challenging, highlighting the need for novel diagnostic biomarkers.
  • Extracellular vesicles (EVs) contain microRNAs (miRNAs) that reflect cellular states and may serve as disease indicators.

Purpose of the Study:

  • To investigate the potential of circulating extracellular vesicle microRNAs (EV-miRNAs) as biomarkers for distinguishing ischemic stroke subtypes.
  • To differentiate between large artery atherosclerosis (LAA), cardioembolic stroke (CES), and small artery occlusion (SAO) using EV-miRNA profiles.
  • To develop and evaluate machine-learning models for accurate ischemic stroke subtyping based on EV-miRNA signatures.

Main Methods:

  • Collected plasma samples from 70 acute ischemic stroke patients, classified into LAA (n=24), SAO (n=24), and CES (n=22) groups.
  • Utilized next-generation sequencing (NGS) to profile EV-miRNAs and identified differentially expressed miRNAs (DEMs) for each subtype.
  • Applied machine-learning algorithms, including logistic regression, to build predictive models for stroke subtype classification.

Main Results:

  • Distinct EV-miRNA profiles were identified across the LAA, SAO, and CES stroke subtypes.
  • Machine-learning models achieved a high diagnostic accuracy of 92% in discriminating between stroke subtypes.
  • Bioinformatics analysis revealed the functional roles of DEMs in stroke pathophysiology, with collective miRNA influence being more significant than individual markers.

Conclusions:

  • Circulating EV-miRNAs represent a promising, non-invasive biomarker panel for accurate ischemic stroke etiological subtyping.
  • Machine learning integration significantly enhances the diagnostic capability of EV-miRNA profiles for clinical application.
  • Further research is required to validate these EV-miRNA biomarkers in larger, diverse patient cohorts for clinical implementation.