Investigation of sweat VOC profiles in assessment of cancer biomarkers using HS-GC-MS

Volatile organic compounds (VOCs) have been studied in biological samples in order to be related to the presence of diseases. Sweat can represent substances existing in blood, has less complex composition (compared with other biological matrices) and can be obtained in a non-invasive way. In this work, sweat patches were collected from healthy controls and volunteers with cancer. Static Headspace was used for VOCs extraction, analysis was performed by gas chromatography coupled with mass spectrometry. Principal Components Analysis was used to investigate data distribution. Random Forest was employed to develop classificatory models. Controls and positive cases could be distinguished with maximum sensitivity and specificity (100% of accuracy) in a model based on the incidence of 2-ethyl-1-hexanol, hexanal and octanal. Discrimination between controls, primary tumors and metastasis was achieved using a panel with 11 VOCs. Balanced accuracy of more than 70% was obtained for the classification of a neoplasm site. Total n-aldehydes presented to be strongly correlated with staging of adenocarcinomas, while phenol and 2,6-dimethyl-7-octen-2-ol were correlated with Gleason score. These findings corroborate with the development of accessible screening tools based on VOC analysis and highlight sweat as a promising matrix to be studied in a clinical context for cancer diagnosis.