AUTHOR=Zheng Peng-Fei , Chen Lu-Zhu , Liu Peng , Liu Zheng-Yu , Pan Hong Wei 

TITLE=Integrative identification of immune-related key genes in atrial fibrillation using weighted gene coexpression network analysis and machine learning

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.922523

DOI=10.3389/fcvm.2022.922523

ISSN=2297-055X

ABSTRACT=<sec><title>Background</title><p>The immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.</p></sec><sec><title>Methods</title><p>CIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.</p></sec><sec><title>Results</title><p>In comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (<italic>GALNT16, HTR2B, BEX2</italic>, and <italic>RAB8A</italic>) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT–PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced <italic>BEX2</italic> and <italic>GALNT16</italic> expression, as well as dramatically elevated <italic>HTR2B</italic> expression. The AUC measurement showed that the diagnostic efficiency of <italic>BEX2, HTR2B</italic>, and <italic>GALNT16</italic> in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.</p></sec><sec><title>Conclusions</title><p>Three novel genes, <italic>BEX2, HTR2B</italic>, and <italic>GALNT16</italic>, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.</p></sec>