Data_Sheet_1_Analyses of m6A regulatory genes and subtype classification in atrial fibrillation.zip

ObjectiveTo explore the role of m6A regulatory genes in atrial fibrillation (AF), we classified atrial fibrillation patients into subtypes by two genotyping methods associated with m6A regulatory genes and explored their clinical significance.
MethodsWe downloaded datasets from the Gene Expression Omnibus (GEO) database. The m6A regulatory gene expression levels were extracted. We constructed and compared random forest (RF) and support vector machine (SVM) models. Feature genes were selected to develop a nomogram model with the superior model. We identified m6A subtypes based on significantly differentially expressed m6A regulatory genes and identified m6A gene subtypes based on m6A-related differentially expressed genes (DEGs). Comprehensive evaluation of the two m6A modification patterns was performed.
ResultsThe data of 107 samples from three datasets, GSE115574, GSE14975 and GSE41177, were acquired from the GEO database for training models, comprising 65 AF samples and 42 sinus rhythm (SR) samples. The data of 26 samples from dataset GSE79768 comprising 14 AF samples and 12 SR samples were acquired from the GEO database for external validation. The expression levels of 23 regulatory genes of m6A were extracted. There were correlations among the m6A readers, erasers, and writers. Five feature m6A regulatory genes, ZC3H13, YTHDF1, HNRNPA2B1, IGFBP2, and IGFBP3, were determined (p < 0.05) to establish a nomogram model that can predict the incidence of atrial fibrillation with the RF model. We identified two m6A subtypes based on the five significant m6A regulatory genes (p < 0.05). Cluster B had a lower immune infiltration of immature dendritic cells than cluster A (p < 0.05). On the basis of six m6A-related DEGs between m6A subtypes (p < 0.05), two m6A gene subtypes were identified. Both cluster A and gene cluster A scored higher than the other clusters in terms of m6A score computed by principal component analysis (PCA) algorithms (p < 0.05). The m6A subtypes and m6A gene subtypes were highly consistent.
ConclusionThe m6A regulatory genes play non-negligible roles in atrial fibrillation. A nomogram model developed by five feature m6A regulatory genes could be used to predict the incidence of atrial fibrillation. Two m6A modification patterns were identified and evaluated comprehensively, which may provide insights into the classification of atrial fibrillation patients and guide treatment.

目的 为探讨m6A调控基因在心房颤动（atrial fibrillation, AF）中的作用，本研究通过两种与m6A调控基因相关的基因分型方法对心房颤动患者进行亚型分类，并探讨其临床意义。
方法 本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）下载数据集，提取m6A调控基因的表达水平。构建并比较随机森林（random forest, RF）与支持向量机（support vector machine, SVM）模型，选取特征基因利用性能更优的模型构建列线图模型。基于显著差异表达的m6A调控基因鉴定m6A亚型，并通过m6A相关差异表达基因（differentially expressed genes, DEGs）鉴定m6A基因亚型。对两种m6A修饰模式进行综合评估。
结果 本研究从GEO数据库获取3个数据集（GSE115574、GSE14975及GSE41177）共107例样本数据用于模型训练，其中包括65例心房颤动样本与42例窦性心律（sinus rhythm, SR）样本；同时获取数据集GSE79768的26例样本数据作为外部验证集，包含14例心房颤动样本与12例窦性心律样本。本研究共提取23个m6A调控基因的表达水平，m6A阅读蛋白、清除蛋白与书写蛋白之间存在表达相关性。最终筛选出5个特征性m6A调控基因（ZC3H13、YTHDF1、HNRNPA2B1、IGFBP2及IGFBP3，p<0.05），通过随机森林模型构建列线图以预测心房颤动的发生风险。基于上述5个显著m6A调控基因，本研究鉴定出2个m6A亚型，其中B簇未成熟树突状细胞的免疫浸润水平低于A簇（p<0.05）。基于m6A亚型间的6个m6A相关差异表达基因（p<0.05），本研究进一步鉴定出2个m6A基因亚型；通过主成分分析（principal component analysis, PCA）算法计算m6A评分，A簇与基因A簇的m6A评分均显著高于其余簇（p<0.05）。m6A亚型与m6A基因亚型具有高度一致性。
结论 m6A调控基因在心房颤动中发挥不可忽视的作用。基于5个特征性m6A调控基因构建的列线图模型可用于预测心房颤动的发生风险。本研究鉴定并综合评估了2种m6A修饰模式，可为心房颤动患者的分型及临床治疗指导提供新思路。