Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients.

IF 3.2 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Cardio-oncology Pub Date : 2025-02-13 DOI:10.1186/s40959-025-00309-6
Jessica C Lal, Michelle Z Fang, Muzna Hussain, Abel Abraham, Reina Tonegawa-Kuji, Yuan Hou, Mina K Chung, Patrick Collier, Feixiong Cheng
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Abstract

Background: Cancer-therapy related cardiac dysfunction (CTRCD) remains a significant cause of morbidity and mortality in cancer survivors. In this study, we aimed to identify differential plasma proteins and metabolites associated with left ventricular dysfunction (LVD) in cancer patients.

Methods: We analyzed data from 50 patients referred to the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment, integrating electronic health records, proteomic, and metabolomic profiles. LVD was defined as an ejection fraction ≤ 55% based on echocardiographic evaluation. Classification-based machine learning models were used to predict LVD using plasma metabolites and proteins as input features.

Results: We identified 13 plasma proteins (P < 0.05) and 14 plasma metabolites (P < 0.05) associated with LVD. Key proteins included markers of inflammation (ST2, TNFRSF14, OPN, and AXL) and chemotaxis (RARRES2, MMP-2, MEPE, and OPN). Notably, sex-specific associations were observed, such as uridine (P = 0.003) in males. Furthermore, metabolomic features significantly associated with LVD included 1-Methyl-4-imidazoleacetic acid (P = 0.015), COL1A1 (P = 0.009), and MMP-2 (P = 0.016), and pointing to metabolic shifts and heightened inflammation in patients with LVD.

Conclusion: Our findings suggest that circulating metabolites may non-invasively detect clinical and molecular differences in patients with LVD, providing insights into underlying disease pathways and potential therapeutic targets.

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泛癌患者左心室心功能障碍相关血浆蛋白和代谢物的发现。
背景:癌症治疗相关心功能障碍(CTRCD)仍然是癌症幸存者发病和死亡的一个重要原因。在这项研究中,我们旨在确定与癌症患者左心室功能障碍(LVD)相关的不同血浆蛋白和代谢物:我们分析了转诊到克利夫兰诊所心肿瘤中心接受超声心动图评估的 50 名患者的数据,并整合了电子健康记录、蛋白质组和代谢组图谱。根据超声心动图评估,射血分数≤55%即为低密度心血管病。以血浆代谢物和蛋白质为输入特征,使用基于分类的机器学习模型预测 LVD:结果:我们确定了 13 种血浆蛋白(P 结论:血浆中的代谢物和蛋白质可预测 LVD:我们的研究结果表明,循环代谢物可以无创检测 LVD 患者的临床和分子差异,为了解潜在的疾病通路和潜在的治疗靶点提供见解。
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来源期刊
Cardio-oncology
Cardio-oncology Medicine-Cardiology and Cardiovascular Medicine
CiteScore
5.00
自引率
3.00%
发文量
17
审稿时长
7 weeks
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