Evidence map›Paper›PMID 39057690›Full record

ArticleMetabolites2024

Optimising Extracellular Vesicle Metabolomic Methodology for Prostate Cancer Biomarker Discovery.

Mahmoud Assem Hamed, Valerie Wasinger, Qi Wang, Joanna Biazik, Peter Graham, David Malouf, Joseph Bucci, Yong Li

Abstract read
In one paragraph

Article in Metabolites, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. 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

8 authors.

Mahmoud Assem HamedSt. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.ORCID 0000-0002-9823-079X
Valerie WasingerBioanalytical Mass Spectrometry Facility, Mark Wainwright Analytical Centre, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.ORCID 0000-0001-5338-0869
Qi WangSt. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.
Joanna BiazikElectron Microscope Unit, Mark Wainwright Analytical Centre, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.
Peter GrahamSt. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.ORCID 0000-0002-4957-4895
David MaloufDepartment of Urology, St. George Hospital, Kogarah, NSW 2217, Australia.
Joseph BucciSt. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.
Yong LiSt. George and Sutherland Clinical Campuses, School of Clinical Medicine, University of New South Wales (UNSW) Sydney, Kensington, NSW 2052, Australia.ORCID 0000-0002-8910-9787

Funding

Cancer Care Center Research Trust Fund N/AThe St George and Sutherland Medical Research Foundation RG220652the University International Postgraduate Award (UIPA) Scholarship at UNSW Sydney, Australia. N/A
6 · The paper itself

Abstract

Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol-microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery.

Indexed as

biomarker discoverychromatography columnsextracellular vesiclesmetabolite extractionmetabolomicsprostate cancer

Identifiers

PMID39057690
PMCPMC11279087

What OpenQuestion holds

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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.