数据独立获取蛋白质组学鉴定血浆前列腺素- hdd异构酶作为STEMI和NSTEMI的早期诊断生物标志物。

IF 5.5 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Molecular & Cellular Proteomics Pub Date : 2025-07-01 Epub Date: 2025-05-23 DOI:10.1016/j.mcpro.2025.100996
Hong-Mei Xue, Hai-Tao Hou, Yu Song, Huan-Xin Chen, Yun-Qiang Zhang, Wen-Tao Sun, Jie Zhou, Xiao-Lin Zhou, Na Sun, Qin Yang, Guo-Wei He
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引用次数: 0

摘要

心肌梗死(MI)包括st段升高的MI (STEMI)和非st段升高的MI (NSTEMI),仍然是世界范围内死亡的主要原因。本研究旨在确定STEMI和NSTEMI的早期诊断生物标志物。将386例患者的血浆样本分为4组:对照组(CON) (n=62)、不稳定型心绞痛(UA) (n=62)、STEMI (n=182)和NSTEMI (n=80)。采用基于数据独立采集(DIA)的蛋白质组学方法对蛋白质谱进行分析,鉴定差异丰富蛋白(DAPs),然后进行生物信息学分析和ELISA验证。STEMI检测到93个dap。在新队列患者中进一步验证的选择的DAPs中,前列腺素- h2 d -异构酶(PTGDS)在STEMI最早发病时间(T1, 1.45h (95%CI: 1.16-1.73))或NSTEMI (T1, 1.48h (95%CI: 0.97-1.98))升高,而当前的生物标志物(hs-TnI, Myo, CKMB和BNP)保持在正常范围内。血浆PTGDS诊断指标分析显示,STEMI的敏感性为63.95%,特异性为65.38%,NSTEMI的敏感性为70%,特异性为71.15%。此外,STEMI的AUC为0.61 (95%CI: 0.53-0.69), NSTEMI的AUC为0.78 (95%CI: 0.70-0.86)。本研究表明,在心肌梗死患者中,血浆PTGDS比目前的生物标志物在发病时间更早的阶段增加,但具有相似的敏感性和特异性。因此,PTGDS作为一种早期诊断生物标志物具有很大的发展潜力。特别是PTGDS对于疑似NSTEMI的患者可能具有更大的临床意义,其生物标志物在早期识别心肌梗死高危患者方面可能更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Independent Acquisition Proteomics Identifies Plasma Prostaglandin-H2 D-Isomerase as an Early Diagnostic Biomarker for STEMI and NSTEMI.

Myocardial infarction (MI), including ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), remains a leading cause of death worldwide. This study aimed to identify the early diagnostic biomarkers for STEMI and NSTEMI. Plasma samples from 386 patients were classified into four groups: control (CON) (n = 62), unstable angina (UA) (n = 62), STEMI (n = 182), and NSTEMI (n = 80). The protein profiles were analyzed using data-independent acquisition (DIA)-based proteomics to identify differentially abundant proteins (DAPs) followed by bioinformatics analysis and ELISA validation. In STEMI, 93 DAPs were detected. Among the selected DAPs that were further validated in a new cohort of patients, prostaglandin-H2 D-isomerase (PTGDS) was elevated at the earliest onset time of STEMI (T1, 1.45 h (95% CI: 1.16-1.73)) or NSTEMI (T1, 1.48 h (95% CI: 0.97-1.98)) while the current biomarkers (hs-TnI, Myo, CK-MB, and BNP) remained within normal ranges. The analysis of diagnostic indices for plasma PTGDS demonstrated a sensitivity of 63.95% and specificity of 65.38% in STEMI, 70% and 71.15% in NSTEMI. Moreover, AUC was 0.61 (95% CI: 0.53-0.69) in STEMI and 0.78 (95% CI: 0.70-0.86) in NSTEMI. The present study demonstrates that in patients with MI, plasma PTGDS increases at an earlier stage of onset time than the current biomarkers, with similar sensitivity and specificity. Therefore, PTGDS has high potential to be developed as an early diagnostic biomarker. In particular, PTGDS might be of greater clinical significance for patients suspected of NSTEMI, for which the biomarker could be more effective in identifying high-risk patients suffering from MI at an early stage.

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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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