Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zhengzhou Key Laboratory of Children's Infection and Immunity, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.
Atherosclerosis. 2019 Dec;291:34-43. doi: 10.1016/j.atherosclerosis.2019.10.001. Epub 2019 Oct 9.
We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction, Support Vector Machine (SVM) algorithm, and multi-cohort validation.
In the discovery phase, a public blood-based microarray dataset of 110 patients with known CAD was analyzed by weighted gene coexpression network analysis and protein-protein interaction network analysis to identify candidate hub genes. In the training set with 151 CAD patients, bioinformatically identified hub genes were experimentally verified by real-time polymerase chain reaction, and statistically filtered with the SVM algorithm to develop a GES. Internal and external validation of GES was performed in patients with suspected CAD from two validation cohorts (n = 209 and 206).
The discovery phase screened 15 network-centric hub genes significantly correlated with the Duke CAD Severity Index. In the training cohort, 12 of 15 hub genes were filtered to construct a blood-based GES12, which showed good discrimination for higher modified Gensini scores (AUC: 0.798 and 0.812), higher Sullivan Extent scores (AUC: 0.776 and 0.778), and the presence of obstructive CAD (AUC: 0.834 and 0.792) in two validation cohorts. A nomogram comprising GES12, smoking status, hypertension status, low density lipoprotein cholesterol level, and body mass index further improved performance, with respect to discrimination, risk classification, and clinical utility, for prediction of coronary stenosis severity.
GES12 is useful in predicting the severity of coronary artery stenosis in patients with known or suspected CAD.
本研究旨在通过综合应用基因网络构建、支持向量机（Support Vector Machine，SVM）算法和多队列验证，利用基因表达谱识别一个血液基因表达评分（GES），以预测已知或疑似冠心病（CAD）患者冠状动脉狭窄的严重程度。
在发现阶段，对 110 例已知 CAD 患者的公共血液基因芯片数据集进行加权基因共表达网络分析和蛋白质-蛋白质相互作用网络分析，以鉴定候选枢纽基因。在包含 151 例 CAD 患者的训练集中，通过实时聚合酶链反应对生物信息学鉴定的枢纽基因进行实验验证，并通过 SVM 算法进行统计学筛选，以建立 GES。在来自两个验证队列的疑似 CAD 患者中（n=209 和 206），对 GES 进行内部和外部验证。
发现阶段筛选出与 Duke CAD 严重程度指数显著相关的 15 个网络中心枢纽基因。在训练队列中，对 15 个枢纽基因中的 12 个进行筛选，构建一个基于血液的 GES12，该评分在两个验证队列中对更高的改良 Gensini 评分（AUC：0.798 和 0.812）、更高的 Sullivan 范围评分（AUC：0.776 和 0.778）和阻塞性 CAD 的存在（AUC：0.834 和 0.792）具有良好的区分能力。包含 GES12、吸烟状况、高血压状况、低密度脂蛋白胆固醇水平和体重指数的列线图进一步提高了对冠状动脉狭窄严重程度预测的区分度、风险分类和临床实用性。
GES12 可用于预测已知或疑似 CAD 患者冠状动脉狭窄的严重程度。
