Determination of Untargeted Metabolomic Profile in Breath Condensate Using LC-QTOF/MS and Investigation of Potential Lung Cancer-Specific Biomarkers.

introductionLung cancer remains the leading cause of cancer-related mortality worldwide, primarily due to late diagnosis and the lack of sensitive, non-invasive biomarkers for early detection. Conventional diagnostic methods such as imaging and biopsy are either invasive or incapable of identifying disease at its earliest stages. Therefore, there is a growing demand for analytical strategies that enable non-invasive, rapid, and reliable diagnosis of lung cancer. MATERIAL AND

methodsEBC samples were collected in the morning after overnight fasting. Participants inhaled filtered air through their noses and exhaled through their mouths into a thermostat-controlled condenser system maintained at +4 °C for 15 minutes. Saliva contamination was prevented using a U-shaped glass trap. The collected condensate (approximately 2-2.5 mL) was transferred into Teflon tubes and stored at -80 °C until analysis. Metabolites were separated by LC-QTOF/MS, a high-resolution analytical platform suitable for untargeted metabolomics due to its excellent mass accuracy and sensitivity. Raw data were processed using Signpost MS software for peak alignment, normalization, and statistical comparison. Principal component analysis (PCA) and hierarchical clustering were applied to visualize group separation and identify metabolite patterns associated with lung cancer. The workflow of the method is shown in Figure 1.

resultsThe PCA score plot revealed a clear separation between lung cancer patients (Group A) and healthy controls (Group B) along the PC2 axis, accounting for 8.5% of total variance, while PC1 explained 21.7%. The partial overlap between groups indicated some biological heterogeneity within the cancer cohort, which is expected due to tumor stage and individual metabolic variability. Nevertheless, the distinct clustering pattern confirmed that EBC contains disease-specific metabolic information. A heatmap visualization demonstrated differential metabolite expression profiles between the two groups. Notably, several metabolites exhibited increased intensity in the cancer group, suggesting potential involvement in tumor metabolism. Subsequent statistical evaluation (

conclusionThe results of this study demonstrated that untargeted metabolomic profiling of EBC can successfully distinguish lung cancer patients from healthy individuals. Identified biomarkers such as L-alanine and creatine show potential for early disease detection and could serve as the foundation for developing non-invasive screening tools. Future studies should validate these findings in larger cohorts and explore longitudinal sampling to assess biomarker stability and diagnostic accuracy. The integration of high-resolution mass spectrometry with non-invasive sampling techniques represents a powerful direction in personalized oncology. Such approaches may complement existing diagnostic modalities and contribute to earlier detection, better prognosis, and improved patient outcomes.