Cai Yu-Lun, Hao Ben-Chuan, Chen Jian-Qiao, Li Yue-Rui, Liu Hong-Bin
Department of Cardiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Medical School of Chinese PLA, Beijing, China.
Front Cardiovasc Med. 2022 Apr 19;9:867646. doi: 10.3389/fcvm.2022.867646. eCollection 2022.
Chronic coronary syndrome (CCS) is a newly proposed concept and is hallmarked by more long-term major adverse cardiovascular events (MACEs), calling for accurate prognostic biomarkers for initial risk stratification.
Data-independent acquisition liquid chromatography tandem mass spectrometry (DIA LC-MS/MS) quantitative proteomics was performed on 38 patients with CCS; 19 in the CCS events group and 19 in the non-events group as the controls. We also developed a machine-learning-based pipeline to identify proteins as potential biomarkers and validated the target proteins by enzyme-linked immunosorbent assay in an independent prospective cohort.
Fifty-seven differentially expressed proteins were identified by quantitative proteomics and three final biomarkers were preliminarily selected from the machine-learning-based pipeline. Further validation with the prospective cohort showed that endothelial protein C receptor (EPCR) and cholesteryl ester transfer protein (CETP) levels at admission were significantly higher in the CCS events group than they were in the non-events group, whereas the carboxypeptidase B2 (CPB2) level was similar in the two groups. In the Cox survival analysis, EPCR and CETP were independent risk factors for MACEs. We constructed a new prognostic model by combining the Framingham coronary heart disease (CHD) risk model with EPCR and CETP levels. This new model significantly improved the C-statistics for MACE prediction compared with that of the Framingham CHD risk model alone.
Plasma proteomics was used to find biomarkers of predicting MACEs in patients with CCS. EPCR and CETP were identified as promising prognostic biomarkers for CCS.
慢性冠状动脉综合征（CCS）是一个新提出的概念，其特点是有更多的长期主要不良心血管事件（MACE），因此需要准确的预后生物标志物用于初始风险分层。
对38例CCS患者进行了数据非依赖采集液相色谱串联质谱（DIA LC-MS/MS）定量蛋白质组学分析；其中19例为CCS事件组，19例为非事件组作为对照。我们还开发了一种基于机器学习的流程来识别潜在的生物标志物蛋白质，并在一个独立的前瞻性队列中通过酶联免疫吸附测定对目标蛋白质进行验证。
通过定量蛋白质组学鉴定出57种差异表达蛋白质，并从基于机器学习的流程中初步筛选出三种最终生物标志物。在前瞻性队列中的进一步验证表明，CCS事件组入院时内皮蛋白C受体（EPCR）和胆固醇酯转运蛋白（CETP）水平显著高于非事件组，而两组间羧肽酶B2（CPB2）水平相似。在Cox生存分析中，EPCR和CETP是MACE的独立危险因素。我们通过将弗雷明汉姆冠心病（CHD）风险模型与EPCR和CETP水平相结合构建了一个新的预后模型。与单独的弗雷明汉姆CHD风险模型相比，这个新模型显著提高了MACE预测的C统计量。
血浆蛋白质组学被用于寻找CCS患者中预测MACE的生物标志物。EPCR和CETP被确定为CCS有前景的预后生物标志物。
