Evidence map›Paper›PMID 40243499›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2025

External Validation of Plasma Glycosaminoglycans as Biomarkers to Improve Lung Cancer Risk Stratification.

Michael P A Davies, John K Field, Francesco Gatto

Abstract readValidation Study
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

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5 · Who and what money

Authors and funding

3 authors.

Michael P A DaviesMolecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0002-7609-4977
John K Field *Molecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0003-3951-6365
Francesco Gatto *Elypta AB, Stockholm, Sweden.ORCID 0000-0002-9031-9562

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer screening excludes individuals not considered at an increased risk for lung cancer, as predicted by risk models like the Liverpool Lung Project version 3 (LLPv3). In this study, we sought to validate whether plasma glycosaminoglycan profiles (GAGomes) could predict lung cancer independent of LLPv3 and other prespecified comorbidities.

methodsIn this retrospective cohort-based case-control study, we included patients who were suspected of having lung cancer at baseline and were either diagnosed with lung cancer (cases) or remained cancer-free for 5 years after baseline (controls). Plasma GAGomes were measured at baseline and used to compute a prespecified GAGome score to discriminate lung cancer from controls. We then applied multivariable Bayesian logistic regression to evaluate the likelihood that 7 LLPv3 predictors or 14 comorbidities had an effect on the GAGome score. We tested the independence of the GAGome score from LLPv3-predicted 5-year risk using the likelihood ratio test and assessed whether it improved lung cancer risk prediction in a set equivalent to an LLPv3-predicted 5-year risk of ≥1.51%.

resultsWe included 653 lung cancer and 653 controls. The AUC of the GAGome score was 0.63 (95% confidence interval, 0.62-63). None of the LLPv3 predictors or comorbidities were compatible with a significant effect on the score. The GAGome score was independent of LLPv3 (P < 0.001) and improved its sensitivity (72% vs. 69%) and specificity (61% vs. 59%).

conclusionsPlasma GAGomes identified additional lung cancer cases beyond those predicted by LLPv3 alone. IMPACT: GAGomes could improve risk-stratified lung cancer if validated in a screening population.

Indexed as

Biomarkers, TumorGlycosaminoglycansLung NeoplasmsAgedCase-Control StudiesEarly Detection of CancerFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsBiomarkers, TumorGlycosaminoglycans

Identifiers

PMID40243499
PMCPMC12209813

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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.