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Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model

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Published on: June 20, 2025

Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis.

Yingyuan Li¹, Wulin Tan¹, Fang Ye¹

¹Department of Anesthesiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

The Journal of International Medical Research

|May 6, 2020

Summary

Bioinformatics analysis identified key genes and microRNAs linked to stroke risk in atrial fibrillation (AF) patients. Dysregulated inflammatory pathways highlight potential biomarkers for stroke prediction and prevention in AF.

Keywords:

CAND1 MEF2A MicroRNAs atrial fibrillation computational biology microarray analysis stroke

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Area of Science:

Biomedical research
Bioinformatics
Genomics

Background:

Atrial fibrillation (AF) is a significant risk factor for stroke.
Identifying genetic and molecular factors influencing stroke risk in AF is crucial for prevention and treatment.

Purpose of the Study:

To discover key genes and microRNAs associated with stroke risk in patients with atrial fibrillation (AF).
To utilize bioinformatics analysis for identifying potential stroke biomarkers in AF.

Main Methods:

Downloaded and analyzed microarray data (GSE66724) from AF patients with and without stroke.
Identified differentially expressed genes (DEGs) and constructed protein-protein interaction (PPI) and microRNA-DEG networks.
Performed functional enrichment analysis to understand biological pathways involved.

Main Results:

Identified 165 DEGs (141 upregulated, 24 downregulated) between AF patients with and without stroke.
Functional enrichment analysis indicated involvement of inflammatory processes.
Key genes (e.g., MEF2A, CAND1) and microRNAs (e.g., miR-1, miR-21, miR-155) were identified through the miR-DEG network.

Conclusions:

Dysregulation of specific genes and microRNAs in inflammatory pathways may increase stroke risk in AF patients.
These identified biomarkers hold potential for improving stroke prediction, prevention, and treatment strategies in AF.
Further evaluation of these molecular markers is warranted for clinical application.