Background:

Lung 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.

Methods:

In 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%.

Results:

We 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%).

Conclusions:

Plasma 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.

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©2025 The Authors; Published by the American Association for Cancer Research
2025
American Association for Cancer Research
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First page of External Validation of Plasma Glycosaminoglycans as Biomarkers to Improve Lung Cancer Risk Stratification<alt-title alt-title-type="short">Validation of Plasma GAGs to Improve Lung Cancer Risk Models</alt-title>

Supplementary data

Figure S1

Title: Patient Flow. Diagram to report flow of patients through the study.

- pdf file
Figure S2.

Title: GAGome scores in LC vs. controls. GAGome scores (in log-odds) in LC vs. controls by A) group and by subsets of B) TNM stage at diagnosis, C) LLPv3 5-year predicted risk, and D) LC subtype at diagnosis. The black horizontal segment indicates the group median, and the vertical segment indicates the group interquartile range.

- pdf file
Figure S3

Title: GAGome scores in OC vs. controls. GAGome scores (in log-odds) in OC vs. controls(A), by subsets of LLPv3 risk (B), and primary disease at diagnosis (C). The black horizontal segment indicates the group median, and the vertical segment indicates the group interquartile range.

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Table S1

Checklist for the STARD 2015 reporting guideline for diagnostic accuracy studies.

- docx file
Table S2

Baseline characteristics for OC. Continuous variables were summarized as median (interquartile range in brackets). Key: LLPv3 - Liverpool Lung Project risk score version 3; COPD - Chronic obstructive pulmonary disease. *7 missing data were omitted.

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Table S3

Observed AUC of the GAGome score and LLPv3-predicted 5-year risk across subsets in LC (N = 653) versus control patients (N = 653).

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Table S4

Posterior probability of direction, of significance, or of being large for different variables on the GAGome score based on multivariable Bayesian linear regression. Probabilities >90% are highlighted in bold. Key: HDI – Highest Density Interval.

- docx file
Table S5

Observed AUC of the GAGome score and LLPv3 5-year predicted risk across subsets in the OC versus control population. Key: NE – not estimable.

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Supp Materials and Methods

Study Protocol

- pdf file