LAUSR

Objectives Endogenous substances have been analyzed in biological samples in order to be related with metabolic dysfunctions and diseases. The study aimed to investigate profiles of volatile organic compounds (VOCs) from fresh and incubated saliva donated by healthy controls, individuals with oral tissue lesions and with oral cancer, in order to assess case-specific biomarkers of oxidative stress. Materials and methods VOCs were pre-concentrated using headspace-solid phase microextraction and analyzed using gas chromatography-mass spectrometry. Then, VOCs positively modulated by incubation process were subtracted, yielding profiles with selected features. Principal component analysis and hierarchical cluster analysis were used to inspect data distribution, while univariate statistics was applied to indicate potential markers of oral cancer. Machine learning algorithm was implemented, aiming multiclass prediction. Results The removal of bacterial contribution to VOC profiles allowed the obtaining of more specific case-related patterns. Artificial neural network model included 9 most relevant compounds (1-octen-3-ol, hexanoic acid, E-2-octenal, heptanoic acid, octanoic acid, E-2-nonenal, nonanoic acid, 2,4-decadienal and 9-undecenoic acid). Model performance was assessed using 10-fold cross validation and receiver operating characteristic curves. Obtained overall accuracy was 90%. Oral cancer cases were predicted with 100% of sensitivity and specificity. Conclusions The selected VOCs were ascribed to lipid oxidation mechanism and presented potential to differentiate oral cancer from other inflammatory conditions. Clinical relevance These results highlight the importance of interpretation of saliva composition and the clinical value of salivary VOCs. Elucidated metabolic alterations have the potential to aid the early detection of oral cancer and the monitoring of oral lesions.