Urinary Volatilomic Signatures for Non-Invasive Detection of Lung Cancer: A HS-SPME/GC-MS Proof-of-Concept Study

TL;DR

This study explores using urine samples to detect lung cancer non-invasively by analyzing volatile organic compounds with a new method.

Contribution

The study introduces a novel non-invasive approach for lung cancer detection using urinary volatilomic profiling.

Findings

LC patients showed higher levels of terpenoids and aldehydes, indicating oxidative stress and metabolic changes.

Octanal, dehydro-p-cymene, and other compounds were identified as potential biomarkers for lung cancer.

Multivariate analysis confirmed strong separation between lung cancer patients and healthy controls.

Abstract

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.

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