Evidence map›Paper›PMID 27426280›Full record

SynthesisEBioMedicine2016

Building the Evidence Base of Blood-Based Biomarkers for Early Detection of Cancer: A Rapid Systematic Mapping Review.

Lesley Uttley, Becky L Whiteman, Helen Buckley Woods, Susan Harnan, Sian Taylor Philips, Ian A Cree, Early Cancer Detection Consortium

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in EBioMedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 7 pooled it
6.0field-weighted citation impact, top 4% of its field
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

22 citing papers in PubMed, 7 syntheses or guidelines pooled it, 54 citations in OpenAlex.

  1. Pooled it
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  3. Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
    Pooled it
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  8. Review
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  11. Article
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  13. Platelets as messengers of early-stage cancer.Cancer metastasis reviews · 2021
    Review
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  16. Review
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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

7 authors at 3 institutions in 1 country.

Lesley UttleyThe University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK.
Becky L WhitemanCentre for Technology Enabled Health Research, Faculty of Health and Life Sciences, Coventry University, Coventry CV1 5FB, UK; Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
Helen Buckley WoodsThe University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK.
Susan HarnanThe University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK.
Sian Taylor PhilipsWarwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
Ian A CreeCentre for Technology Enabled Health Research, Faculty of Health and Life Sciences, Coventry University, Coventry CV1 5FB, UK; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry CV2 2DX, UK. Electronic address: ian.cree@uhcw.nhs.uk.
Early Cancer Detection Consortium
University of Sheffield · GBUniversity of Warwick · GBUniversity Hospital Coventry · GB

Funding

Cancer Research UK 18554Department of Health WMCLAHRC-2014-1
6 · The paper itself

Abstract

backgroundThe Early Cancer Detection Consortium is developing a blood-test to screen the general population for early identification of cancer, and has therefore conducted a systematic mapping review to identify blood-based biomarkers that could be used for early identification of cancer.

methodsA mapping review with a systematic approach was performed to identify biomarkers and establish their state of development. Comprehensive searches of electronic databases Medline, Embase, CINAHL, the Cochrane library and Biosis were conducted in May 2014 to obtain relevant literature on blood-based biomarkers for cancer detection in humans. Screening of retrieved titles and abstracts was performed using an iterative sifting process known as "data mining". All blood based biomarkers, their relevant properties and characteristics, and their corresponding references were entered into an inclusive database for further scrutiny by the Consortium, and subsequent selection of biomarkers for rapid review. This systematic review is registered with PROSPERO (no. CRD42014010827).

findingsThe searches retrieved 19,724 records after duplicate removal. The data mining approach retrieved 3990 records (i.e. 20% of the original 19,724), which were considered for inclusion. A list of 814 potential blood-based biomarkers was generated from included studies. Clinical experts scrutinised the list to identify miss-classified and duplicate markers, also volunteering the names of biomarkers that may have been missed: no new markers were identified as a result. This resulted in a final list of 788 biomarkers.

interpretationThis study is the first to systematically and comprehensively map blood biomarkers for early detection of cancer. Use of this rapid systematic mapping approach found a broad range of relevant biomarkers allowing an evidence-based approach to identification of promising biomarkers for development of a blood-based cancer screening test in the general population.

Indexed as

Biomarkers, TumorEarly Detection of CancerComputational BiologyData MiningEvidence-Based MedicineHumansNeoplasmsWorkflowBiomarkers, TumorAssayBiomarkerBloodCancerDiagnosisEarly detectionSystematic review

Identifiers

PMID27426280
PMCPMC5006664
OpenAlexW2462240398

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.