Davies Michael P A, Field John K, Gatto Francesco
Molecular and Clinical Cancer Medicine, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom.
Elypta AB, Stockholm, Sweden.
Cancer Epidemiol Biomarkers Prev. 2025 Jul 1;34(7):1219-1225. doi: 10.1158/1055-9965.EPI-24-1537.
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.
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%.
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%).
Plasma GAGomes identified additional lung cancer cases beyond those predicted by LLPv3 alone.
GAGomes could improve risk-stratified lung cancer if validated in a screening population.
肺癌筛查会排除那些根据利物浦肺癌项目第3版（LLPv3）等风险模型预测，患肺癌风险未增加的个体。在本研究中，我们试图验证血浆糖胺聚糖谱（GAGomes）能否独立于LLPv3和其他预先指定的合并症来预测肺癌。
在这项基于回顾性队列的病例对照研究中，我们纳入了基线时疑似患有肺癌且最终被诊断为肺癌（病例组）或在基线后5年仍无癌症（对照组）的患者。在基线时测量血浆GAGomes，并用于计算预先指定的GAGome评分，以区分肺癌患者与对照组。然后，我们应用多变量贝叶斯逻辑回归来评估7个LLPv3预测因子或14种合并症对GAGome评分产生影响的可能性。我们使用似然比检验来检验GAGome评分相对于LLPv3预测的5年风险的独立性，并评估其是否能在一组与LLPv3预测的5年风险≥1.51%相当的人群中改善肺癌风险预测。
我们纳入了653例肺癌患者和653例对照。GAGome评分的曲线下面积为0.63（95%置信区间，0.62 - 63）。没有一个LLPv3预测因子或合并症与对该评分有显著影响相符。GAGome评分独立于LLPv3（P < 0.001），并提高了其敏感性（72%对69%）和特异性（61%对59%）。
血浆GAGomes识别出了超出仅由LLPv3预测的肺癌病例。
如果在筛查人群中得到验证，GAGomes可能会改善肺癌风险分层。
