Yu Wei, Li Lingjiao, Tan Xingling, Liu Xiaozhu, Yin Chengliang, Cao Junyi
Chongqing Medical University, Chongqing, China.
Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Front Med (Lausanne). 2023 Oct 5;10:1239056. doi: 10.3389/fmed.2023.1239056. eCollection 2023.
Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.
The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.
A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.
The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.
扩张型心肌病（DCM）是一种进行性心脏疾病，其特征为心室扩张和心肌收缩功能受损，死亡率很高。DCM的分子特征尚未确定。因此，发现DCM的潜在生物标志物和治疗选择至关重要。
使用加权基因共表达网络分析（WGCNA）和Cytoscape中的三种不同算法筛选DCM的核心基因。然后在阿霉素（DOX）诱导的DCM小鼠模型中验证这些基因。基于经过验证的核心基因，构建了预测模型和神经网络模型，并在单独的数据集中进行了验证。最后，我们评估了核心基因的诊断效率及其与免疫细胞的关系。
共鉴定出8个核心基因。通过RT-qPCR，我们验证了与正常小鼠相比，DCM小鼠中五个关键基因（ASPN、MFAP4、PODN、HTRA1和FAP）的表达水平显著更高，这与微阵列结果一致。此外，由这些基因构建的风险预测模型和神经网络模型在合并数据集和验证数据集中均显示出良好的准确性和敏感性。这些基因还具有更好的诊断能力，在合并数据集和验证数据集中的AUC均大于0.7。免疫细胞浸润分析显示，DCM样本和正常样本中大多数免疫细胞的丰度存在差异。
目前的研究结果表明DCM与这些关键基因之间存在潜在关联，这些基因可作为诊断和治疗DCM的潜在生物标志物。
