Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

Area of Science:

• Genomics
• Cardiovascular Research
• Bioinformatics

Background:

• Atrial fibrillation (AF) affects millions globally, with complex and incompletely understood biological mechanisms.
• Existing research necessitates more sensitive and reliable methods to explore AF's underlying pathophysiology.

Purpose of the Study:

• To conduct a meta-analysis of transcriptome data to explore the biological mechanisms of atrial fibrillation.
• To identify key genes and pathways involved in AF pathophysiology for potential therapeutic development.

Main Methods:

• Meta-analysis of ten public transcriptomic datasets.
• Differential gene expression analysis and aggregation using the rth ordered p value method.
• Network analysis (topological, modularity) and functional enrichment analysis of differentially expressed genes.

Main Results:

• Identified 1197 differentially expressed genes in AF.
• Discovered 39 hub-bottleneck genes and four functional modules enriched in AF-related pathways.
• Highlighted endoplasmic reticulum stress, protein catabolism, oxidative stress, and inflammation as key processes. Identified HSPA5, ANK2, CTNNB1, and MAPK1 as critical hub genes.

Conclusions:

• Transcriptome meta-analysis revealed key genes with consistent expression changes in AF.
• Findings provide insights into AF pathophysiology and suggest potential novel therapeutic targets.
• Further experimental validation is recommended to explore identified genes and pathways.