Discovery of Plasma Proteins and Metabolites for Left Ventricular Cardiac Dysfunction in Pan-Cancer Patients DOI
Jessica C. Lal, Michelle Fang,

Muzna Hussein

et al.

Published: Jan. 1, 2024

Cancer-therapy related cardiac dysfunction remains a significant cause of morbidity and mortality in cancer survivors. Here, we integrated electronic health record data, proteomic, metabolomic profiles from 50 patients referred to the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment. Left ventricular (LVD) was defined by ejection fraction £ 55% echocardiograph. We identified 12 differential plasma proteins (P<0.05) 14 metabolites associated with LVD, including markers inflammation (ST2, TNFRSF14, OPN, AXL) chemotaxis (RARRES2, MMP-2, MEPE, OPN). also observed sex-specific LVD (male- Uridine, P=0.003; female- Kyn/Trp, P=0.175 respectively) suggesting distinct disease etiologies. Furthermore, utilized classification machine learning models predict using differentially expressed proteins. Features significantly included uridine (P=0.0002), 1-Methyl-4-imidazoleacetic acid (P=0.015), COL1A1 (P=0.009), MMP-2 (P=0.016). Our findings suggest that circulating may non-invasively detect clinical molecular differences LVD.

Language: Английский

Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients DOI Creative Commons
Jessica C. Lal, Michelle Fang, Muzna Hussain

et al.

Cardio-Oncology, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 13, 2025

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 metabolites associated with left ventricular (LVD) patients. Methods We analyzed data from 50 patients referred the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment, integrating electronic health records, proteomic, metabolomic profiles. LVD was defined as an ejection fraction ≤ 55% based on echocardiographic evaluation. Classification-based machine learning models were used predict using input features. Results identified 13 ( P < 0.05) 14 LVD. Key included markers inflammation (ST2, TNFRSF14, OPN, AXL) chemotaxis (RARRES2, MMP-2, MEPE, OPN). Notably, sex-specific associations observed, such uridine = 0.003) males. Furthermore, features significantly 1-Methyl-4-imidazoleacetic acid 0.015), COL1A1 0.009), MMP-2 0.016), pointing metabolic shifts heightened Conclusion Our findings suggest that circulating may non-invasively detect clinical molecular differences LVD, providing insights into underlying disease pathways potential therapeutic targets.

Language: Английский

Citations

0
Identification of noval diagnostic biomarker for HFpEF based on proteomics and machine learning DOI Creative Commons

Muyashaer Abudurexiti,

Salamaiti Aimaier,

Nuerdun Wupuer

et al.

Proteome Science, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 8, 2025

Language: Английский

Citations

0
Cardio-oncological dialogue: Understanding the mechanistic correlation between heart failure and cancer DOI

Faisal Ashraf Bhat,

Shujhat Khan,

Aiysha Siddiq Khan

et al.

Life Sciences, Journal Year: 2024, Volume and Issue: 358, P. 123170 - 123170

Published: Oct. 28, 2024

Language: Английский

Citations

2
Discovery of Plasma Proteins and Metabolites for Left Ventricular Cardiac Dysfunction in Pan-Cancer Patients DOI
Jessica C. Lal, Michelle Fang,

Muzna Hussein

et al.

Published: Jan. 1, 2024

Cancer-therapy related cardiac dysfunction remains a significant cause of morbidity and mortality in cancer survivors. Here, we integrated electronic health record data, proteomic, metabolomic profiles from 50 patients referred to the Cleveland Clinic Cardio-Oncology Center for echocardiograph assessment. Left ventricular (LVD) was defined by ejection fraction £ 55% echocardiograph. We identified 12 differential plasma proteins (P<0.05) 14 metabolites associated with LVD, including markers inflammation (ST2, TNFRSF14, OPN, AXL) chemotaxis (RARRES2, MMP-2, MEPE, OPN). also observed sex-specific LVD (male- Uridine, P=0.003; female- Kyn/Trp, P=0.175 respectively) suggesting distinct disease etiologies. Furthermore, utilized classification machine learning models predict using differentially expressed proteins. Features significantly included uridine (P=0.0002), 1-Methyl-4-imidazoleacetic acid (P=0.015), COL1A1 (P=0.009), MMP-2 (P=0.016). Our findings suggest that circulating may non-invasively detect clinical molecular differences LVD.

Language: Английский

Citations

0