Evidence map›Paper›PMID 39020428›Full record

ArticleJournal of ovarian research2024

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer.

Tyler T Cooper, Dylan Z Dieters-Castator, Jiahui Liu, Gabrielle M Siegers, Desmond Pink, Lorena Veliz, John D Lewis, François Lagugné-Labarthet, Yangxin Fu, Helen Steed and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of ovarian research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. 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

12 authors.

Tyler T CooperDepartment of Biochemistry, Western University, London, ON, Canada.
Dylan Z Dieters-CastatorDepartment of Anatomy and Cell Biology, Western University, London, ON, Canada.
Jiahui LiuDepartment of Oncology, University of Alberta, Edmonton, AB, Canada.
Gabrielle M SiegersDepartment of Oncology, University of Alberta, Edmonton, AB, Canada.
Desmond PinkDepartment of Oncology, University of Alberta, Edmonton, AB, Canada.
Lorena VelizDepartment of Chemistry, Western University, London, ON, Canada.
John D LewisDepartment of Oncology, University of Alberta, Edmonton, AB, Canada.
François Lagugné-LabarthetDepartment of Chemistry, Western University, London, ON, Canada.
Yangxin FuDepartment of Oncology, University of Alberta, Edmonton, AB, Canada.
Helen SteedDepartment of Obstetrics and Gynecology, University of Alberta, Edmonton, AB, Canada.
Gilles A LajoieDepartment of Biochemistry, Western University, London, ON, Canada. glajoie@uwo.ca.
Lynne-Marie PostovitDepartment of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada. l.postovit@queensu.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.

resultsWe conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC.

conclusionsThis study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Indexed as

Biomarkers, TumorEarly Detection of CancerExtracellular VesiclesMucin-1Ovarian NeoplasmsProteomicsAdultAgedCystadenocarcinoma, SerousFemaleHumansMiddle AgedNeoplasm GradingBiomarkers, TumorMUC1 protein, humanMucin-1BiomarkersExtracellular vesiclesHigh-grade serous carcinomaOvarian cancerProteomics

Identifiers

PMID39020428
PMCPMC11253408

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.