Evidence map›Paper›PMID 42133214›Full record

ReviewDiscover oncology2026

The role of extracellular vesicles-associated proteins markers in prostate cancer: a review.

Diana Suhaiza Said, Muhammad Nazrul Hakim Abdullah, Armania Nurdin

Abstract readReview
In one paragraph

Review in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

3 authors.

Diana Suhaiza SaidLaboratory of UPM-MAKNA Cancer Research (CANRES), Institute of Bioscience, Universiti Putra Malaysia, UPM, Serdang, 43400, Selangor, Malaysia.
Muhammad Nazrul Hakim AbdullahDepartment of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, UPM, Serdang, 43400, Selangor, Malaysia.
Armania NurdinLaboratory of UPM-MAKNA Cancer Research (CANRES), Institute of Bioscience, Universiti Putra Malaysia, UPM, Serdang, 43400, Selangor, Malaysia. armania@upm.edu.my.

Funding

Minister of Higher Education's FRGS/1/2019/SKK06/UPM/02/7
6 · The paper itself

Abstract

Prostate cancer (PCa) is in the top three most common cancers among the world's male population, and its incidence has been increasing over the years. However, the primary screening and diagnostic tools still rely on serum prostate-specific antigen (PSA) and digital rectal examination (DRE), both of which have been inconclusive. Raised serum PSA and abnormal DRE findings require an invasive prostate biopsy, which has inherent surgical risk. This review aims to provide a comprehensive understanding of the biological production and properties of extracellular vesicles (EVs), which are released by all cell types, including cancer cells, and carry proteins and other biomolecules from their parent cells. EVs are becoming increasingly popular as possible biomarker candidates to improve the accuracy of diagnosing many diseases. Growing data suggest that EVs are crucial for cellular communication and tumour progression via multiple signalling pathways, including epithelial-to-mesenchymal transition (EMT), migration, and invasion, making them suitable for tracking PCa growth and metastasis. Furthermore, choosing the appropriate EV isolation method is essential to ensure accurate diagnosis. More clinically relevant, this review also identifies potential EV protein biomarkers derived from urine, serum, and tissue samples from PCa patients.

Indexed as

Extracellular vesiclesIsolation techniquesMetastasisProstate cancerProtein biomarkers

Identifiers

PMID42133214
PMCPMC13342012

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.