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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BMC
2025-02-01
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| Series: | Cardio-Oncology |
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| Online Access: | https://doi.org/10.1186/s40959-025-00309-6 |
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| author | Jessica C. Lal Michelle Z. Fang Muzna Hussain Abel Abraham Reina Tonegawa-Kuji Yuan Hou Mina K. Chung Patrick Collier Feixiong Cheng |
| author_facet | Jessica C. Lal Michelle Z. Fang Muzna Hussain Abel Abraham Reina Tonegawa-Kuji Yuan Hou Mina K. Chung Patrick Collier Feixiong Cheng |
| author_sort | Jessica C. Lal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4097e02a2d254cbfb52700306d6577dd |
| institution | DOAJ |
| issn | 2057-3804 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardio-Oncology |
| spelling | doaj-art-4097e02a2d254cbfb52700306d6577dd2025-08-20T02:48:16ZengBMCCardio-Oncology2057-38042025-02-0111111210.1186/s40959-025-00309-6Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patientsJessica C. Lal0Michelle Z. Fang1Muzna Hussain2Abel Abraham3Reina Tonegawa-Kuji4Yuan Hou5Mina K. Chung6Patrick Collier7Feixiong Cheng8Genomic Medicine Institute, Lerner Research Institute, Cleveland ClinicGenomic Medicine Institute, Lerner Research Institute, Cleveland ClinicDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland ClinicDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland ClinicGenomic Medicine Institute, Lerner Research Institute, Cleveland ClinicGenomic Medicine Institute, Lerner Research Institute, Cleveland ClinicDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland ClinicDepartment of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve UniversityGenomic Medicine Institute, Lerner Research Institute, Cleveland ClinicAbstract 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.https://doi.org/10.1186/s40959-025-00309-6CardiotoxicityMachine learning modelsMulti-omicsPlasma proteomicsPlasma metabolomics |
| spellingShingle | Jessica C. Lal Michelle Z. Fang Muzna Hussain Abel Abraham Reina Tonegawa-Kuji Yuan Hou Mina K. Chung Patrick Collier Feixiong Cheng Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients Cardio-Oncology Cardiotoxicity Machine learning models Multi-omics Plasma proteomics Plasma metabolomics |
| title | Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients |
| title_full | Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients |
| title_fullStr | Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients |
| title_full_unstemmed | Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients |
| title_short | Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients |
| title_sort | discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan cancer patients |
| topic | Cardiotoxicity Machine learning models Multi-omics Plasma proteomics Plasma metabolomics |
| url | https://doi.org/10.1186/s40959-025-00309-6 |
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