Evidence map›Paper›PMID 34771740›Full record

ArticleCancers2021

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification.

Ammara Muazzam, Davide Chiasserini, Janet Kelsall, Nophar Geifman, Anthony D Whetton, Paul A Townsend

Abstract read
In one paragraph

Article in Cancers, 2021. 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. Article
  2. Review
  3. Article
  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

6 authors.

Ammara MuazzamManchester Cancer Research Centre, Division of Cancer Sciences, Faculty of Biology, School of Medical Sciences, Medicine and Health, University of Manchester, Wilmslow Road, Manchester M20 4GJ, UK.ORCID 0000-0001-7035-411X
Davide ChiasseriniDepartment of Medicine and Surgery, Section of Physiology and Biochemistry, University of Perugia, 06132 Perugia, Italy.
Janet KelsallStoller Biomarker Discovery Centre, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK.
Nophar GeifmanFaculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK.
Anthony D WhettonManchester Cancer Research Centre, Division of Cancer Sciences, Faculty of Biology, School of Medical Sciences, Medicine and Health, University of Manchester, Wilmslow Road, Manchester M20 4GJ, UK.ORCID 0000-0002-1098-3878
Paul A TownsendManchester Cancer Research Centre, Division of Cancer Sciences, Faculty of Biology, School of Medical Sciences, Medicine and Health, University of Manchester, Wilmslow Road, Manchester M20 4GJ, UK.

Funding

CRUK Manchester Centre award C5759/A25254Medical Research Council MR/M008959Medical Research Council MR/M008959/1Medical Research Council MR/N00583X/1
6 · The paper itself

Abstract

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort's pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651.

Indexed as

blood proteomicsmass spectrometrypeptideprostate cancerproteinspectral librarySWATH-MS

Identifiers

PMID34771740
PMCPMC8582933

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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.