Ye Ling-Fang, Weng Jia-Yi, Wu Li-Da
Changzhi People's Hospital, Changzhi, Shanxi, China.
Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China.
Front Genet. 2023 Feb 7;14:1050696. doi: 10.3389/fgene.2023.1050696. eCollection 2023.
As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including , , , , , and . The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.
作为最常见的心肌病，扩张型心肌病（DCM）常导致进行性心力衰竭和心源性猝死。本研究旨在探究DCM的分子亚组。从GEO数据库（GSE17800、GSE79962和GSE3585）下载了三个DCM数据集。在使用R软件中的“”包进行log2转换和背景校正后，将这三个数据集合并为一个元数据队列。通过“”包进行一致性聚类以揭示DCM的分子亚组。此外，还详细比较了不同分子亚组的临床特征。我们还基于基因表达谱的亚组特异性特征采用加权基因共表达网络分析（WGCNA）来进一步探索每个分子亚组的特定基因模块及其生物学功能。使用LASSO回归算法和SVM - RFE算法这两种机器学习方法筛选出遗传生物标志物，其中通过受试者工作特征（ROC）曲线评估分子亚组的判别能力。基于基因表达谱，DCM患者的心脏组织样本被聚类为三个分子亚组。三个分子亚组在年龄、体重指数（BMI）和舒张末期左心室内径（LVIDD）方面未发现统计学差异。然而，左心室射血分数（LVEF）统计结果显示，亚组2的患者病情比其他组更差。我们发现WGCNA分析中的一些基因模块（粉色、黑色和灰色）与心脏功能显著相关，并且每个分子亚组在调节DCM的发生和发展方面具有其特定的基因模块功能。使用LASSO回归算法和SVM - RFE算法进一步筛选出亚组2的遗传生物标志物，包括 、 、 、 、 、 和 。ROC曲线结果显示，所有遗传生物标志物均具有良好的判别效能。不同分子亚组的患者具有独特的基因表达模式和不同的临床特征。应在基因表达模式的指导下实现更个性化的治疗。
