Systems-Level in Silico Bioinformatic Profiling Identifies Key Hub Genes and Potential Therapeutic Targets in Atrial Fibrillation Without Overt Comorbidity

Fadhlan Abdur  Rahman; Suryono  Suryono; Aditha Satria  Maulana; Pipiet  Wulandari

doi:10.18502/jthc.v21i1.21282

Fadhlan Abdur Rahman Citra Husada Hospital, Jember, Indonesia.
Suryono Suryono Department of Cardiology and Vascular Medicine, Faculty of Medicine, Jember University, Jember, Indonesia.
Aditha Satria Maulana Dr. Soebandi General Hospital, Jember, Indonesia.
Pipiet Wulandari Department of Cardiology and Vascular Medicine, Faculty of Medicine, Jember University, Jember, Indonesia.

DOI: https://doi.org/10.18502/jthc.v21i1.21282

Keywords: Bioinformatics; In Silico; Atrial Fibrillation Without Overt Comorbidity; Hub Genes; Protein–Protein Interaction

Abstract

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with substantial morbidity and mortality. AF occurring in individuals without structural heart disease or conventional risk factors, currently referred to as AF without overt comorbidity, remains poorly understood. Genetic susceptibility is thought to contribute, but the underlying molecular mechanisms are incompletely defined. This study aimed to identify key genes and biological processes associated with AF without overt comorbidity using an in-silico bioinformatics approach.

Methods: Genes associated with AF without overt comorbidity were retrieved from the GeneCards database using a knowledge-based, database-driven strategy. Functional enrichment analysis of Gene Ontology biological processes was performed using WebGestalt. Protein-protein interaction (PPI) analysis was conducted using STRING and visualized in Cytoscape. Hub genes were identified exclusively using the Density of Maximum Neighborhood Component (DMNC) algorithm via the CytoHubba plugin. Three-dimensional protein structures of selected hub genes were modeled using SWISS-MODEL and evaluated using PROCHECK for exploratory structural characterization.

Results: Eighty-one genes associated with AF without overt comorbidity were identified. PPI analysis demonstrated significant interaction enrichment (P<1.0×10⁻¹⁶), indicating a nonrandom and biologically coherent network. Functional enrichment analysis revealed cardiac muscle cell action potential and cardiac muscle contraction as the most significantly enriched biological processes. Ten hub genes were identified based on DMNC ranking. Among these, GPD1L, SCN1B, SCN4B, and KCNE2 showed central network positions and acceptable stereochemical quality in exploratory structural evaluation.

Conclusion: This in silico study identifies candidate genes and biological processes potentially involved in AF without overt comorbidity. The findings are hypothesis generating and warrant further functional and clinical validation.