Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R2 = 0.961; Q2 = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks.
Sousa et al. (2026) studied this question.