ArticleNPJ precision oncology2026
Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer.
Article in NPJ precision oncology, 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.
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.
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Who cites it
1 citing paper in PubMed.
- Exosome-Associated Proteins as Mediators and Biomarkers of Ovarian Cancer Dissemination.Biomolecules · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
This study utilized a novel Proximity Barcoding Assay to perform high-resolution proteomic profiling of individual plasma extracellular vesicles from 85 patients with advanced high-grade serous ovarian carcinoma (OC) and 95 healthy controls (HC). Single-EV analysis identified 119 differentially expressed proteins and 17 distinct EV subpopulations. Cluster 7 (enriched in integrins ITGB3, ITGB1, and ITGA6) was significantly elevated in OC plasma (4.47% in HC vs. 14.79-15.82% in OC). Machine learning (SVM-RFE, LASSO, Random Forest) identified a diagnostic panel (ITGA6, ITGB2, ILK) achieving exceptional accuracy in distinguishing OC from HC (AUC = 0.999 training; 1.000 validation). Furthermore, risk models incorporating specific protein signatures effectively stratified patients by platinum sensitivity/resistance (9-protein panel: ILK, CDCP1, CD86, CLDN4, CLEC1B, CDHR5, CLDN11, JAM2, FOLH1), lymph node metastasis status (7-protein panel: APOE, CD28, CLDN4, FOLH1, ITGAL, JAML, ULBP3), and post-surgical residual disease burden (4-protein panel: CD44, CLMP, ITGA4, AMIGO1), with Cluster 13 (ITGB1-high) also significantly associated with residual disease. This work demonstrates the power of single-EV proteomics combined with machine learning for non-invasive diagnosis and clinical outcome assessment in advanced ovarian cancer, though the absence of early-stage patients limits its applicability for early detection.
Identifiers
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Registered trials
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