Abstract
Introduction: An improved understanding of the pathogenesis of AF and atrial substrate remodeling is necessary for development of novel therapeutic approaches and new management strategies. Hypothesis: CXCL12 could be involved in the regulation of AF inflammatory microenvironment and have the potential to be a marker for AF subset classification. Methods: In this study, we utilized the Robust Rank Aggregation method to integrate AF microarray datasets. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using WGCNA analysis and PPI network, respectively. Biological functions were further analysed. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes. Results: Differentially expressed genes were identified. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Conclusions: Key genes involving in the pathogenesis of AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF by increasing the infiltration of mast cells, neutrophils, and γδ T cells, and reducing infiltration of regulatory T cells. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.
Published Version
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