Evidence map›Paper›PMID 38475692›Full record

ArticleClinical proteomics2024

Proteomics of prostate cancer serum and plasma using low and high throughput approaches.

Ghaith M Hamza, Rekha Raghunathan, Stephanie Ashenden, Bairu Zhang, Eric Miele, Andrew F Jarnuczak

Abstract read
In one paragraph

Article in Clinical proteomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. AI-Driven Prediction of Cardio-Oncology Biomarkers Through Protein Corona Analysis.Chemical engineering journal (Lausanne, Switzerland : 1996) · 2025
    Article
  3. Article
  4. Article
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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

6 authors.

Ghaith M HamzaDiscovery Sciences, R&D, AstraZeneca, Cambridge, UK.
Rekha Raghunathan *Bioanalytical and Biomarker, Prevail Therapeutics, Wholly Owned Subsidiary of Eli Lilly and Company, New York, NY, 10016, USA.
Stephanie AshendenDiscovery Sciences, R&D, AstraZeneca, Cambridge, UK.
Bairu ZhangDiscovery Sciences, R&D, AstraZeneca, Cambridge, UK.
Eric MieleDiscovery Sciences, R&D, AstraZeneca, Cambridge, UK. Eric.Miele1@astrazeneca.com.
Andrew F JarnuczakDiscovery Sciences, R&D, AstraZeneca, Cambridge, UK. andrew.jarnuczak@astrazeneca.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

Indexed as

BiofluidsBiomarkersBOXCARDiagnosticFAIMSMass spectrometryPlasmaProstate cancerProteomicsSerumTMT

Identifiers

PMID38475692
PMCPMC10929178

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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.