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

Area of Science:

• Genomics and Bioinformatics
• Cardiovascular Research
• Molecular Biology

Background:

• Atrial fibrillation (AF) is a prevalent arrhythmia linked to significant cardiovascular morbidity and mortality.
• Despite advancements, the precise etiology and pathogenesis of AF remain incompletely understood.
• Identifying novel genetic factors and diagnostic markers is crucial for managing AF.

Purpose of the Study:

• To comprehensively analyze gene networks and biological functions associated with AF.
• To elucidate the underlying etiology and pathogenesis of atrial fibrillation.
• To identify potential biomarkers and develop a diagnostic model for AF.

Main Methods:

• Integrated three ChIP-seq and one RNA-seq data sets for network and pathway analysis.
• Performed differential co-expression analysis to identify AF-specific genes.
• Constructed AF-specific protein-protein interaction (PPI) networks and analyzed network topology.
• Developed a diagnostic model using a support vector machine (SVM).

Main Results:

• Identified 360 differentially expressed genes in AF, significantly associated with focal expression and autophagy.
• Constructed AF-specific PPI networks, identifying key hub genes including PLEKHA7, YWHAQ, and AKT1.
• These hub genes are implicated in oocyte meiosis and focal expression pathways, suggesting roles in AF development.
• An SVM-based diagnostic model achieved high predictive performance (AUC>0.9 internally, >0.75 across platforms).

Conclusions:

• The study identified key genes and pathways involved in AF pathogenesis.
• Discovered potential novel biomarkers for atrial fibrillation.
• Developed a highly accurate diagnostic model for early AF identification and prediction.