Li Guanmou, Lin Dongqun, Fan Xiaoping, Peng Bo
Zhujiang Hospital of Southern Medical University, Guangzhou 510120, Guangdong, China.
Department of Cardiovascular Surgery Guangdong Provincial Hospital of Chinese Medicine The Second Affiliated Hospital of Guangzhou University of Chinese Medicine The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou 510120, Guangdong, China.
Cardiol Res Pract. 2024 Jul 4;2024:4639334. doi: 10.1155/2024/4639334. eCollection 2024.
HCM is a heterogeneous monogenic cardiac disease that can lead to arrhythmia, heart failure, and atrial fibrillation. This study aims to identify biomarkers that have a positive impact on the treatment, diagnosis, and prediction of HCM through bioinformatics analysis. We selected the GSE36961 and GSE180313 datasets from the Gene Expression Omnibus (GEO) database for differential analysis. GSE36961 generated 6 modules through weighted gene co-expression network analysis (WGCNA), with the green and grey modules showing the highest positive correlation with HCM (green module: cor = 0.88, = 2 - 48; grey module: cor = 0.78, = 4 - 31). GSE180313 generated 17 modules through WGCNA, with the turquoise module exhibiting the highest positive correlation with HCM (turquoise module: cor = 0.92, = 6 - 09). We conducted GO and KEGG pathway analysis on the intersection genes of the selected modules from GSE36961 and GSE180313 and intersected their GO enriched pathways with the GO enriched pathways of endothelial cell subtypes calculated after clustering single-cell data GSE181764, resulting in 383 genes on the enriched pathways. Subsequently, we used LASSO prediction on these 383 genes and identified RTN4, COL4A1, and IER3 as key genes involved in the occurrence and development of HCM. The expression levels of these genes were validated in the GSE68316 and GSE32453 datasets. In conclusion, RTN4, COL4A1, and IER3 are potential biomarkers of HCM, and protein degradation, mechanical stress, and hypoxia may be associated with the occurrence and development of HCM.
肥厚型心肌病（HCM）是一种异质性单基因心脏病，可导致心律失常、心力衰竭和心房颤动。本研究旨在通过生物信息学分析鉴定对HCM的治疗、诊断和预测有积极影响的生物标志物。我们从基因表达综合数据库（GEO）中选择了GSE36961和GSE180313数据集进行差异分析。GSE36961通过加权基因共表达网络分析（WGCNA）生成了6个模块，其中绿色和灰色模块与HCM的正相关性最高（绿色模块：cor = 0.88，P = 2 - 48；灰色模块：cor = 0.78，P = 4 - 31）。GSE180313通过WGCNA生成了17个模块，其中绿松石色模块与HCM的正相关性最高（绿松石色模块：cor = 0.92，P = 6 - 09）。我们对GSE36961和GSE180313中选定模块的交集基因进行了GO和KEGG通路分析，并将其GO富集通路与单细胞数据GSE181764聚类后计算的内皮细胞亚型的GO富集通路进行交集，在富集通路上得到383个基因。随后，我们对这383个基因进行LASSO预测，确定RTN4、COL4A1和IER3为参与HCM发生发展的关键基因。这些基因的表达水平在GSE68316和GSE32453数据集中得到验证。总之，RTN4、COL4A1和IER3是HCM的潜在生物标志物，蛋白质降解、机械应激和缺氧可能与HCM的发生发展有关。
