Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Interventional Institute of Zhengzhou University, Zhengzhou, China.
ESC Heart Fail. 2024 Aug;11(4):2234-2248. doi: 10.1002/ehf2.14762. Epub 2024 Apr 17.
In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression.
Three hundred eighty-five samples from four independent cohorts were systematically retrieved. The weighted gene co-expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P < 0.05), and protein-protein interaction network were sequentially performed to identify HCM-related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune-cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co-expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty-seven co-regulated genes were recognized as HCM-related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n = 385). Considering the tight association between HCM and immune-related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high-risk and low-risk groups.
Our study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.
在诊断方法不断发展的时代，现有的诊断系统还不能充分及时地识别出无症状和无仪器特征的早期肥厚型心肌病（HCM）患者。鉴于 HCM 的猝死风险，开发一种新的诊断模型来明确早期 HCM 患者的免疫特征，可以避免误诊和减缓疾病进展。
系统检索了四个独立队列的 385 个样本。通过加权基因共表达网络分析、差异表达分析（|log2(foldchange)|>0.5 和调整后的 P<0.05）和蛋白质-蛋白质相互作用网络，识别与 HCM 相关的枢纽基因。使用机器学习算法——最小绝对收缩和选择算子回归算法，建立了一个稳定的诊断模型。还探索了 HCM 的免疫细胞浸润和生物学功能，以表征其潜在的发病机制和免疫特征。基于加权基因共表达网络分析筛选出两个关键模块。发现与细胞外基质和免疫途径相关的致病机制。确定了 27 个共调控基因作为 HCM 相关的枢纽基因。基于最小绝对收缩和选择算子算法，构建了一个稳定的 HCM 诊断模型，并在其余三个队列（n=385）中进一步验证。考虑到 HCM 与免疫相关功能之间的紧密关联，我们根据 xCell 算法评估了各种免疫细胞和基质细胞的浸润丰度，高危组和低危组之间某些免疫细胞存在显著差异。
本研究揭示了一些枢纽基因和新的途径，为 HCM 的治疗提供了潜在的靶点。建立了一个稳定的模型，为 HCM 的诊断提供了有效的工具。
