ArticleCommunications medicine2026
Cardiovascular risk assessment characterized by proteomics in cancer survivors.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe classic cardiovascular disease (CVD) risk scores perform poorly in predicting CVD in cancer survivors. This study aimed to identify proteins associated with major CVDs risk and explore their roles in risk prediction for major CVDs in cancer survivors.
methodsWe included 4225 cancer survivors from the UK Biobank with available plasma proteomic data and no major CVDs at recruitment. Associations between proteins and risks of major CVDs (heart failure, atrial fibrillation, myocardial infarction, angina, peripheral vascular disease, and stroke) were estimated using Cox proportional hazards models, followed by enrichment analysis. Candidate protein biomarkers were further identified through random forest model, and classification performance was evaluated using area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement index (IDI).
resultsThis study identified 182 proteins, the majority of which were positively associated with major CVDs risk in cancer survivors (HRs:1.11-1.93), especially NTproBNP, NPC2, TNFRSF12A, LMNB2, and EDA2R, mainly involved in biological processes of immune, inflammatory, and angiogenesis. Further, a panel of 23 proteins derived from a random forest model demonstrated moderate predictive performance for major, 5-year, and 10-year CVDs in the test set (AUCs: 0.646-0.665), outperforming several classic CVD risk scores. Incorporating the protein panel into CVD risk scores significantly improved discrimination (AUCs: 0.647-0.705) and 5-year risk reclassification (NRI: 0.245-0.327; IDI: 0.055-0.060).
conclusionsCertain plasma proteins were associated with major CVD risk and may serve as promising biomarkers for risk prediction in cancer survivors, but these require further investigation.
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Registered trials
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