Yang Lingzhi, Chen Yunwei, Huang Wei
Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Cardiovasc Med. 2022 Jul 14;9:920399. doi: 10.3389/fcvm.2022.920399. eCollection 2022.
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.
In this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein-protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.
A total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. 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, including , and , were identified as hub genes. was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.
Key genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of in AF were investigated using integrative bioinformatics tools. The results suggested that might be a biomarker that could be used for distinguishing subsets of AF, and indicated that might be an important intermediate in the development of AF. 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.
心房颤动（AF）是最常见的持续性心律失常，给全球医疗保健带来了重大负担。AF的潜在机制仍有待揭示，目前AF的治疗选择存在局限性。此外，一个检测系统有助于识别有发生AF风险的人群，并实现个性化管理。
在本研究中，我们利用稳健秩聚合方法整合了来自基因表达综合数据库的六个AF微阵列数据集，并鉴定出一组AF患者与对照组之间的差异表达基因。通过挖掘连接图谱数据库来鉴定潜在化合物。分别使用加权基因共表达网络分析和蛋白质-蛋白质相互作用网络来鉴定功能模块和紧密相互作用的簇。对重叠的枢纽基因进行进一步筛选。随后进行分析以分析其功能、生物学特征和调控网络。此外，构建并可视化了一个基于机器学习的可靠诊断模型，以阐明这些基因的诊断特征。
共鉴定出156个上调基因和34个下调基因，其中一些基因此前未被研究过。基于连接图谱分析，我们表明丝裂原活化蛋白激酶和表皮生长因子受体抑制剂可能减轻AF。包括[具体基因名称缺失]在内的四个基因被鉴定为枢纽基因。[具体基因名称缺失]被证明在局部炎症反应和免疫细胞浸润的调节中起重要作用。通过构建转录因子- miRNA - mRNA网络分析了AF中[具体基因名称缺失]表达的调控。本研究中生成的基于机器学习的诊断模型显示出良好的疗效和可靠性。
鉴定出了涉及AF发病机制的关键基因和AF的潜在治疗化合物。使用综合生物信息学工具研究了AF中[具体基因名称缺失]的生物学特征。结果表明[具体基因名称缺失]可能是可用于区分AF亚组的生物标志物，并表明[具体基因名称缺失]可能是AF发展中的重要中间体。构建了一个基于机器学习的可靠诊断模型。我们的工作增进了对AF易感性和进展机制的理解，并确定了AF治疗的潜在途径。
