Identification of a Novel 4-gene Diagnostic Model for Atrial Fibrillation Risk Based on Integrated Analysis Across Independent Data Sets

Abstract

Background: Atrial fibrillation (AF) is the most common persistent arrhythmia and an important factor leading to cardiovascular morbidity and mortality. Several key genes and diagnostic markers have been discovered with the development of advanced modern molecular biology techniques, but the etiology and pathogenesis of AF remained unknown.

Methods: In this study, three-chip-seq data sets and an RNA-seq data set were integrated as a comprehensive network for pathway analysis of the biological functions of related genes in AF, hoping to provide a better understanding of the etiology and pathogenesis of AF.

Results: Differential co-expression analysis identified 360 genes with specific expression in AF, and functional enrichment analysis further revealed that these genes were significantly correlated with focal expression (p <0.01), autophagy (p <0.01), and thyroid cancer. In addition, Af-specific proteinprotein interaction (PPI) networks were constructed based on AF-specific expression genes. Network topology analysis identified PLEKHA7, YWHAQ, PPP1CB, WDR1, AKT1, IGF1R, CANX, MAPK1, SRPK2 and SRSF10 genes as hub genes of the networks, and they were considered as potential biomarkers of AF because they were found to participate in the development of AF through Oocyte meiosis and focal expression. Finally, a diagnostic model for AF established with a support vector machine (SVM) demonstrated excellent predictive performance in internal and external data sets (AUC>0.9) and different platform data sets (mean AUC>0.75).

Conclusion: Finally, a diagnostic model for AF was established, thus showing its potential in the early identification and prediction of AF.

Keywords: Biomarker; atrial fibrillation; bioinformatics; diagnostic model; risk factors; support vector machine.