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<p>Atrial Fibrillation (AF) is a common arrhythmia with high recurrence after Radiofrequency Ablation, and the absence of universal predictive tools impedes clinical prognosis evaluation and treatment optimization. To address this gap, we developed a retrospective microRNA (miRNA)-based recurrence risk stratification model for post-RFA AF patients using an integrated bioinformatics and clinical validation approach. First, we performed differential expression analysis of two Gene Expression Omnibus (GEO) datasets (GSE144384, GSE71963) via GEO2R and R software (version 4.2.1, Limma package) with strict thresholds (|logFC| &gt; 1.5, <i>P</i> &lt; 0.05) identified upregulated miR-483-5p and downregulated miR-150 as core biomarkers. This finding was cross-validated across both datasets (<i>P</i> &lt; 0.001). Subsequently, we enrolled 122 AF patients after RFA and 60 patients with sinus rhythm as controls, and we measured plasma miRNA levels by Quantitative real-time Polymerase Chain Reaction and left atrial functional/structural parameters via color Doppler ultrasound. Receiver Operating Characteristic curve analysis determined optimal cut-off values (miR-483-5p: 1.025; miR-150: 0.805) for constructing a three-tier recurrence risk stratification model. Validation during 12-month follow-up confirmed the model, showing gradient recurrence rates of 69.05%, 24.14% and 4.55% across high/medium/low-risk groups (<i>P</i> &lt; 0.001). The combined Area Under the Curve for predictive performance was 0.888 (superior to that of single biomarkers). We propose a reproducible miRNA-driven predictive framework integrating public omics mining and clinical validation, providing an engineering-based tool for early identification of high-risk post-RFA patients and guidance for individualized treatment. The framework is extendable to other arrhythmias, laying a foundation for translational research of miRNA-based predictive models.</p>

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A risk model based on miR-483-5p and miR-150 for atrial fibrillation recurrence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Wenwen Lai,
  • Hong Chen,
  • Mingwei Huang,
  • Zhendong Cheng,
  • Naping Lin,
  • Huiyao Lu

摘要

Atrial Fibrillation (AF) is a common arrhythmia with high recurrence after Radiofrequency Ablation, and the absence of universal predictive tools impedes clinical prognosis evaluation and treatment optimization. To address this gap, we developed a retrospective microRNA (miRNA)-based recurrence risk stratification model for post-RFA AF patients using an integrated bioinformatics and clinical validation approach. First, we performed differential expression analysis of two Gene Expression Omnibus (GEO) datasets (GSE144384, GSE71963) via GEO2R and R software (version 4.2.1, Limma package) with strict thresholds (|logFC| > 1.5, P < 0.05) identified upregulated miR-483-5p and downregulated miR-150 as core biomarkers. This finding was cross-validated across both datasets (P < 0.001). Subsequently, we enrolled 122 AF patients after RFA and 60 patients with sinus rhythm as controls, and we measured plasma miRNA levels by Quantitative real-time Polymerase Chain Reaction and left atrial functional/structural parameters via color Doppler ultrasound. Receiver Operating Characteristic curve analysis determined optimal cut-off values (miR-483-5p: 1.025; miR-150: 0.805) for constructing a three-tier recurrence risk stratification model. Validation during 12-month follow-up confirmed the model, showing gradient recurrence rates of 69.05%, 24.14% and 4.55% across high/medium/low-risk groups (P < 0.001). The combined Area Under the Curve for predictive performance was 0.888 (superior to that of single biomarkers). We propose a reproducible miRNA-driven predictive framework integrating public omics mining and clinical validation, providing an engineering-based tool for early identification of high-risk post-RFA patients and guidance for individualized treatment. The framework is extendable to other arrhythmias, laying a foundation for translational research of miRNA-based predictive models.