State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin, 301617, People's Republic of China.
Department of Nephropathy, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, 215600, Jiangsu, People's Republic of China.
J Transl Med. 2023 Oct 27;21(1):761. doi: 10.1186/s12967-023-04573-x.
Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results.
A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML.
Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils.
AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.
急性心肌梗死（AMI）具有两个临床特征：高漏诊率和白细胞功能障碍。白细胞的转录 RNA 与 AMI 患者的病程演变密切相关。我们假设白细胞中的转录 RNA 可能为 AMI 提供潜在的诊断价值。首次应用集成机器学习（IML）探索 AMI 鉴别基因。随后进行了临床验证研究。
共纳入 4 个 AMI 微阵列（源自基因表达综合数据库）进行生物分析（样本量 220）。然后，用 20 例 AMI 和 20 例稳定型冠状动脉疾病（SCAD）患者进行临床验证。以 5:2 的比例，将 GSE59867 纳入训练集，GSE60993、GSE62646 和 GSE48060 纳入测试集。本研究明确提出了 IML，它由 6 种机器学习算法组成，包括支持向量机（SVM）、神经网络（NN）、随机森林（RF）、梯度提升机（GBM）、决策树（DT）和最小绝对收缩和选择算子（LASSO）。IML 在本研究中有两个功能：过滤优化变量和预测分类值。最后，分析了招募患者的 RNA，以验证 IML 的结果。
从训练集中识别出对照组和 AMI 个体之间的 39 个差异表达基因（DEGs）。在这 39 个 DEGs 中，采用 IML 处理预测分类模型，并识别出整体归一化权重 > 1 的潜在候选基因。最后，在训练集和测试集中，两个基因（AQP9 和 SOCS3）的 AUC > 0.9，显示出诊断价值。临床研究验证了 AQP9 和 SOCS3 的意义。值得注意的是，更多的狭窄冠状动脉或更严重的 Killip 分级表明这两个基因水平更高，尤其是 SOCS3。这两个基因与两种免疫细胞类型（单核细胞和中性粒细胞）相关。
白细胞中的 AQP9 和 SOCS3 可能有助于识别 AMI 患者与 SCAD 患者。AQP9 和 SOCS3 与单核细胞和中性粒细胞密切相关，可能有助于推进 AMI 诊断，并为新的遗传标志物提供启示。还需要更多的临床特征、多中心和大样本相关试验来确认其临床价值。
