Methods Volatomic analysis of urine samples collected from HNC patients (n = 29) and healthy controls (n = 31) was performed using headspace solid phase microextraction coupled to gas chromatography mass spectrometry (GC-MS). Both univariate and multivariate statistical approaches were used to investigate HNC specific volatomic alterations. Results Statistical analysis revealed a total of 28 metabolites with highest contribution towards discrimination of HNC patients from healthy controls (VIP >1, p < 0.05, Log 2 FC ≥0.58/≤−0.57). The discrimination efficiency and accuracy of urinary VOCs was ascertained by ROC curve analysis that allowed the identification of four metabolites viz. 2,6-dimethyl-7-octen-2-ol, 1-butanol, p-xylene and 4-methyl-2-heptanone with highest sensitivity and specificity to discriminate HNC patients from healthy controls. Further, the metabolic pathway analysis identified several dysregulated pathways in HNC patients and their detailed investigations could unravel novel mechanistic insights into the disease pathophysiology. Conclusion Overall, this study provides valuable fingerprint of the volatile profile of HNC patients, which in turn, might help in improving the current understanding of this form of cancer and lead to the development of non-invasive approaches for HNC diagnosis.

A non-invasive urinary volatilomics approach for exploring the IDC type breast cancer.

Head and neck cancer (HNC) is a heterogeneous malignant disease with distinct global distribution. Metabolic adaptations of HNC are significantly gaining clinical interests nowadays. Here, we investigated effects of HNC on differential expression of volatile metabolites in human saliva. We applied headspace solid phase microextraction coupled with gas chromatography-mass spectrometry analysis of saliva samples collected from 59 human subjects (HNC − 32, Control − 27). We identified and quantified 48 volatile organic metabolites (VOMs) and observed profound effects of HNC on these metabolites. These effects were VOM specific and significantly differed in the biologically comparable healthy controls. HNC induced changes in salivary VOM composition were well attributed to in vivo metabolic effects. A panel of 15 VOMs with variable importance in projection (VIP) score >1, false discovery rate (FDR) corrected p-value < 0.05 and log2 fold change (log2 FC) value of ≥0.58/≤−0.58 were regarded as discriminatory metabolites of pathophysiological importance. Afterwards, receiver operator characteristic curve (ROC) projected certain VOMs viz., 1,4-dichlorobenzene, 1,2-decanediol, 2,5-bis1,1-dimethylethylphenol and E-3-decen-2-ol with profound metabolic effects of HNC and highest class segregation potential. Moreover, metabolic pathways analysis portrayed several dysregulated pathways in HNC, which enhanced our basic understanding on salivary VOM changes. Our observations could redefine several known/already investigated systemic phenomenons (e.g. biochemical pathways). These findings will inspire further research in this direction and may open unconventional avenues for non-invasive monitoring of HNC and its therapy in the future.