Evidence map›Paper›PMID 42371508›Full record

ArticleInternational journal of nanomedicine2026

Identification of Bone Marrow and Peripheral Blood Plasma Extracellular Vesicle Protein Biomarker Signatures for Multiple Myeloma Diagnosis and Staging.

Angelique Cheryl, Rebecca Sheridan, Kieran Brennan, Despina Bazou, David Matallanas, Peter O'Gorman, Luis F Iglesias-Martinez, Margaret M Mc Gee

Abstract read
In one paragraph

Article in International journal of nanomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Angelique Cheryl *School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.
Rebecca Sheridan *School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.
Kieran BrennanSchool of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.
Despina BazouSchool of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.
David MatallanasSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
Peter O'GormanDepartment of Haematology, Mater Misericordiae University Hospital, Dublin, Ireland.
Luis F Iglesias-MartinezSystems Biology Ireland, School of Medicine, University College Dublin, Dublin, Ireland.
Margaret M Mc GeeSchool of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of abnormal plasma cells within the bone marrow (BM). Despite advances in treatment that have improved survival, the disease remains incurable. MM diagnosis requires invasive bone marrow biopsy to quantify the percentage of malignant plasma cells. In this study, the potential of extracellular vesicles (EVs) as a non-invasive liquid biopsy for MM diagnosis and staging was investigated, highlighting the diagnostic value of their proteomic biomarker cargo. Patients and Methods: Plasma-derived EVs from peripheral blood and bone marrow of 33 MM patients and 12 healthy donors were isolated, and their proteomic content was profiled via mass spectrometry. Biomarker signatures were identified using supervised machine learning to predict monoclonal gammopathy of undetermined significance (MGUS), progression to symptomatic MM, and relapse. Their discriminatory power was further evaluated through receiver operating characteristic curve analysis, and complementary performance metrics, including accuracy, sensitivity, specificity, predictive values, and F1 score. Significantly altered proteins were additionally assessed for functional enrichment in relevant biological pathways. Results: The analysis identified a six-protein biomarker signature, forming four optimal logistic regression diagnostic MM peripheral blood models with predictive accuracies of >85% and areas under the curve of >0.91. The signature was characterized by increased abundance of APOC1 and LGALS1 and decreased abundance of S100A7, CD226, ALAD, and KRT78, reflecting immune modulation, impaired immune surveillance, and disrupted proteostatic pathways. Conclusion: The performance of the identified proteins supports their potential as a minimally invasive EV-based liquid biopsy in MM diagnosis and monitoring, warranting future validation.

Indexed as

Biomarkers, TumorBone MarrowExtracellular VesiclesMultiple MyelomaAgedBlood ProteinsFemaleHumansMachine LearningMaleMiddle AgedNeoplasm StagingProteomicsSensitivity and SpecificityBiomarkers, TumorBlood ProteinscancerEVsliquid biopsymachine learningproteomics

Identifiers

PMID42371508
PMCPMC13310408

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Registered trials

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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.