Evidence map›Paper›PMID 39948601›Full record

ArticleCardio-oncology (London, England)2025

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

Jessica C Lal, Michelle Z Fang, Muzna Hussain, Abel Abraham, Reina Tonegawa-Kuji, Yuan Hou, Mina K Chung, Patrick Collier, Feixiong Cheng

Abstract read
In one paragraph

Article in Cardio-oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. The cholesterol-dependent cytolysin promotesInfection and immunity · 2026
    Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jessica C LalGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA. jclal@mdanderson.org.
Michelle Z FangGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Muzna HussainDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
Abel AbrahamDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
Reina Tonegawa-KujiGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Yuan HouGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
Mina K ChungDepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA.
Patrick CollierDepartment of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA. colliep@ccf.org.
Feixiong ChengGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA. chengf@ccf.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer-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.

methodsWe 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.

resultsWe 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.

conclusionOur 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.

Indexed as

CardiotoxicityMachine learning modelsMulti-omicsPlasma metabolomicsPlasma proteomics

Identifiers

PMID39948601
PMCPMC11823021

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.