Wu Yanze, Chen Hui, Li Lei, Zhang Liuping, Dai Kai, Wen Tong, Peng Jingtian, Peng Xiaoping, Zheng Zeqi, Jiang Ting, Xiong Wenjun
Department of Cardiology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Jiangxi Medical College, Nanchang University, Nanchang, China.
Front Cardiovasc Med. 2022 May 25;9:876543. doi: 10.3389/fcvm.2022.876543. eCollection 2022.
Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value constructing the receiver operating characteristic (ROC).
We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls).
A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00).
Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.
急性心肌梗死（AMI）是全球最常见的死亡原因之一。AMI的早期诊断有助于改善预后。在我们的研究中，我们旨在使用人工神经网络（ANN）构建一种用于诊断AMI的新型预测模型，并通过构建受试者工作特征（ROC）曲线来验证其诊断价值。
我们从基因表达综合数据库（GEO）下载了三个公开可用的数据集（训练集GSE48060、GSE60993和GSE66360），并在87例AMI样本和78例对照样本之间鉴定了差异表达基因（DEG）。我们应用随机森林（RF）和ANN算法进一步鉴定新的基因特征，并构建一个模型来预测AMI的可能性。此外，我们的模型在验证集GSE61144（7例AMI患者和10例对照）、GSE34198（49例AMI患者和48例对照）和GSE97320（3例AMI患者和3例对照）中进一步验证了诊断价值。
共鉴定出71个DEG，其中68个上调，3个下调。首先，使用RF分类器从71个DEG中筛选出11个关键基因，用于AMI样本和对照样本的分类。然后，我们使用ANN计算每个关键基因的权重。此外，构建了诊断模型并将其命名为neuralAMI，具有显著的预测能力（曲线下面积[AUC]=0.980）。最后，我们的模型在独立数据集GSE61144（AUC=0.900）、GSE34198（AUC=0.882）和GSE97320（AUC=1.00）中得到验证。
利用机器学习开发了一种可靠的用于诊断AMI 的预测模型。我们的研究结果为早期疾病筛查提供了潜在的基因生物标志物。
