Zheng Peng-Fei, Chen Lu-Zhu, Liu Peng, Liu Zheng-Yu, Pan Hong Wei
Department of Cardiology, Hunan Provincial People's Hospital, Changsha, China.
Clinical Research Center for Heart Failure in Hunan Province, Changsha, China.
Front Cardiovasc Med. 2022 Jul 27;9:922523. doi: 10.3389/fcvm.2022.922523. eCollection 2022.
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.
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.
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 (, and ) 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 and expression, as well as dramatically elevated expression. The AUC measurement showed that the diagnostic efficiency of , and 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.
Three novel genes, , and , were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.
免疫系统在心房颤动（AF）的病理过程中发挥着重要作用。然而，这种参与的分子机制尚未完全阐明。目前的研究旨在识别参与AF病理过程的关键基因和免疫细胞。
利用CIBERSORT揭示AF患者的免疫细胞浸润模式。同时，采用加权基因共表达网络分析（WGCNA）识别与AF显著相关的有意义模块。通过最小绝对收缩和选择算子（LASSO）逻辑回归和支持向量机递归特征消除（SVM-RFE）算法识别与AF相关的特征基因。
与窦性心律（SR）个体相比，我们观察到AF患者中活化肥大细胞和调节性T细胞（Tregs）较少，而γδT细胞、静息肥大细胞和M2巨噬细胞较多。确定了三个与AF显著相关的重要模块（粉色、红色和品红色）。基因富集分析表明，所有717个基因均与免疫或炎症相关途径及生物学过程有关。通过SVM-RFE算法和LASSO逻辑回归揭示了四个枢纽基因（、和）与AF显著相关。qRT-PCR结果表明，与SR受试者相比，AF患者的和表达显著降低，而表达显著升高。AUC测量显示，在训练集中，、和的诊断效率分别为0.836、0.883和0.893，在验证集中分别为0.858、0.861和0.915。
通过WGCNA结合机器学习确定了三个新基因、和，为AF的早期诊断和预防提供了潜在的新治疗靶点。
