MicroRNA signature predicts post-operative atrial fibrillation after coronary artery bypass grafting

Abstract Background Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and lifestyle factors often fail as predictors of AFib, particularly in studies with small samples. This study investigated pre-operative, circulating microRNAs (miRNAs) as potential biomarkers for post-operative AFib (POAF) in patients undergoing coronary artery bypass grafting (CABG). Methods We used an array polymerase chain reaction method to detect pre-operative, circulating miRNAs in seven patients who subsequently developed POAF after CABG (cases) and eight patients who did not develop POAF after CABG (controls). The top 10 miRNAs from 84 candidates were selected and assessed for their performance in predicting POAF using machine learning models, including Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Support Vector Machine (SVM). Results The Random Forest and XGBoost models showed superior predictive performance, with test sensitivities of 0.76 and 0.83, respectively. Differential expression analysis revealed four upregulated miRNAs—hsa-miR-96-5p, hsa-miR-184, hsa-miR-17-3p, and hsa-miR-200-3p—that overlapped with the AFib-miRNA signature. The AFib-miRNA signature was significantly associated with various cardiovascular diseases, including acute myocardial infarction, hypertrophic cardiomyopathy, and heart failure. Biological pathway analysis indicated these miRNAs target key signaling pathways involved in cardiovascular pathology, such as the MAPK, PI3K-Akt, and TGF-beta signaling pathways. Conclusion The identified miRNAs demonstrate significant potential as predictive biomarkers for AFib post-CABG, implicating critical cardiovascular pathways and highlighting their role in AFib development and progression. These findings suggest that miRNA signatures could enhance predictive accuracy for AFib, offering a novel, noninvasive approach to early detection and personalized management of this condition.