Novel biomarkers for lung cancer diagnosis in chronic obstructive pulmonary disease: a systematic review and metanalysis

Chronic Obstructive Pulmonary Disease (COPD) significantly increases the risk of developing lung cancer, yet early detection remains challenging due to overlapping clinical features. Identifying reliable, non-invasive biomarkers for lung cancer diagnosis in this high-risk population could enhance screening strategies and outcomes. We conducted a systematic review and meta-analysis of studies evaluating the diagnostic accuracy of biomarkers for lung cancer in patients with COPD. Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science for relevant studies up to April 2025. Data were synthesized using random-effects models to estimate pooled area under the curve (AUC) and diagnostic odds ratio (DOR). Subgroup analyses were conducted according to biomarker class. Risk of bias was assessed with the QUADAS-2 tool, and publication bias was evaluated via funnel plots and Egger’s test. Seventeen studies were included, encompassing diverse biomarker categories: proteomics, volatile organic compounds (VOCs), telomere length, oxidative stress, genomics, inflammatory markers, and clinical models. The pooled AUC was 0.82 and the pooled logDOR was 2.76, indicating good overall diagnostic performance. VOCs and telomere length showed the highest pooled accuracy. Despite substantial heterogeneity, sensitivity analyses confirmed the robustness of the findings. Publication bias was present for AUC estimates but not for DOR. This review highlights promising biomarker classes, particularly VOCs, telomere length, and clinical models, for the early detection of lung cancer in COPD patients. Further multicentre, prospective studies with standardized methodologies are needed to validate these biomarkers and support their integration into clinical practice. Trial registration: PROSPERO IDENTIFIER CRD420251066505.