Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model...

Area of Science:

• Cardiovascular Research
• Bioinformatics
• Genomics

Background:

• Atrial fibrillation (AF) is a prevalent cardiac arrhythmia with significant healthcare impact.
• Current understanding of AF mechanisms is incomplete, and existing treatments have limitations.
• Developing a detection system for AF risk is crucial for personalized patient management.

Purpose of the Study:

• To identify differentially expressed genes and potential therapeutic compounds for atrial fibrillation.
• To elucidate the biological functions and regulatory networks of key genes in AF pathogenesis.
• To construct a reliable machine learning-based diagnostic model for AF.

Main Methods:

• Integrated six AF microarray datasets using robust rank aggregation.
• Employed Connectivity Map database for potential compound identification.
• Utilized weighted gene co-expression network analysis and protein-protein interaction networks to identify hub genes.
• Constructed a machine learning model for AF diagnosis.

Main Results:

• Identified 156 upregulated and 34 downregulated genes in AF patients.
• Discovered potential therapeutic agents, including MAPK and EGFR inhibitors.
• Identified CXCL12, LTBP1, LOXL1, and IGFBP3 as key hub genes, with CXCL12 implicated in inflammatory responses.
• Developed a machine learning diagnostic model with high efficacy and reliability.

Conclusions:

• Identified key genes and potential therapeutic compounds for AF.
• Highlighted CXCL12 as a potential biomarker for AF subset distinction and a significant factor in AF development.
• Established a robust machine learning model for AF diagnosis.
• Enhanced understanding of AF pathogenesis and identified novel therapeutic strategies.