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

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

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. M...

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Main Authors: Jessica C. Lal, Michelle Z. Fang, Muzna Hussain, Abel Abraham, Reina Tonegawa-Kuji, Yuan Hou, Mina K. Chung, Patrick Collier, Feixiong Cheng
Format: Article
Language:English
Published: BMC 2025-02-01
Series:Cardio-Oncology
Subjects:
Cardiotoxicity
Machine learning models
Multi-omics
Plasma proteomics
Plasma metabolomics
Online Access:https://doi.org/10.1186/s40959-025-00309-6
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Summary: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.
ISSN:2057-3804

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