Cai Songyan, Jin Tianying, Liu Mintong, Dai Qingyuan
Department of Cardiology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Department of Physical Examination for Cadres, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Front Cardiovasc Med. 2025 Apr 9;12:1546865. doi: 10.3389/fcvm.2025.1546865. eCollection 2025.
In hypertrophic cardiomyopathy (HCM), limited reports exist regarding its association with energy metabolism. Here, biomarkers related to energy metabolism in HCM were identified through bioinformatics analysis.
HCM transcriptome data were acquired from the GEO (GSE36961) database for comparative analysis in order to identify differentially expressed genes (DEGs). Subsequently, the identified DEGs were intersected with key module genes in Weighted gene co-expression network analysis (WGCNA) and energy metabolism related genes (EMRGs) to identify DE-EMRGs. Then, feature biomarkers were screened using the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) methods, and the intersection of the feature biomarkers obtained from both methods was used for subsequent analysis. Furthermore, biomarkers defined as biomarkers with consistent expression trends across both GSE36961 and GSE89714 datasets and significant inter-cohort differences were selected for subsequent analysis. Subsequently, an immune analysis was conducted. Additionally, the transcription factors (TFs), and drugs regulating the biomarkers were predicted based on online databases.
The co-selection of seven potential biomarkers based on machine learning identified IGFBP3 and JAK2 as biomarkers in HCM. Upregulation of IGFBP3 and JAK2 in the HCM cohort was observed in the GSE36961 and GSE89714 datasets. Utilizing ssGSEA, it was unveiled that the HCM cohort exhibited elevated ratings of effector memory CD4T cells while displaying diminished scores across 22 other immune cell categories. Notably, JAK2 expression exhibited a strong negative correlation with myeloid-derived suppressor cells (MDSCs) infiltration, while IGFBP3 showed no significant associations with immune cell infiltration. Utilizing NetworkAnalyst, miRNAs and TFs regulating biomarkers expression in HCM were predicted, with hsa-mir-16-5p, hsa-mir-147a, hsa-mir-210b-3p, hsa-let-7b-5p, and hsa-mir-34a-5p identified as regulators of both IGFBP3 and JAK2. GATA2 was also found to be a TF regulating the expression of both biomarkers. Furthermore, the potential therapeutic targets of JAK2 and IGFBP3 in HCM were ruxolitinib and celecoxib, respectively.
In conclusion, the identification of IGFBP3 and JAK2 as biomarkers in HCM, highlight promising avenues for further research and treatment development in HCM.
在肥厚型心肌病（HCM）中，关于其与能量代谢关联的报道有限。在此，通过生物信息学分析鉴定了HCM中与能量代谢相关的生物标志物。
从GEO（GSE36961）数据库获取HCM转录组数据进行比较分析，以鉴定差异表达基因（DEGs）。随后，将鉴定出的DEGs与加权基因共表达网络分析（WGCNA）中的关键模块基因以及能量代谢相关基因（EMRGs）进行交集分析，以鉴定差异表达的能量代谢相关基因（DE-EMRGs）。然后，使用最小绝对收缩和选择算子（LASSO）回归和支持向量机递归特征消除（SVM-RFE）方法筛选特征生物标志物，并将两种方法获得的特征生物标志物的交集用于后续分析。此外，选择在GSE36961和GSE89714数据集中表达趋势一致且队列间差异显著的生物标志物进行后续分析。随后进行免疫分析。此外，基于在线数据库预测调节生物标志物的转录因子（TFs）和药物。
基于机器学习共同选择的7种潜在生物标志物将IGFBP3和JAK2鉴定为HCM中的生物标志物。在GSE36961和GSE89714数据集中观察到HCM队列中IGFBP3和JAK2上调。利用单样本基因集富集分析（ssGSEA）发现，HCM队列中效应记忆CD4T细胞评分升高，而在其他22种免疫细胞类别中评分降低。值得注意的是，JAK2表达与髓系来源的抑制细胞（MDSCs）浸润呈强烈负相关，而IGFBP3与免疫细胞浸润无显著关联。利用NetworkAnalyst预测了调节HCM中生物标志物表达的miRNA和TFs，已确定hsa-mir-16-5p、hsa-mir-147a、hsa-mir-210b-3p、hsa-let-7b-5p和hsa-mir-34a-5p为IGFBP3和JAK2的调节因子。还发现GATA2是调节这两种生物标志物表达的TF。此外，JAK2和IGFBP3在HCM中的潜在治疗靶点分别为鲁索替尼和塞来昔布。
总之，将IGFBP3和JAK2鉴定为HCM中的生物标志物，为HCM的进一步研究和治疗开发指明了有前景的途径。
