| Summary: | Abstract Background Different organisms such as bacteria, molds and humans produce and release a relative unknown class of metabolites which are responsible for the individual olfactory pattern. These volatile organic metabolites (VOM) represent a kind of biosignature that reflects the sum of all multifactorial influences, including genetics, environmental factors, nutritional and disease status. As a result of pathological processes the individual body odor can be influenced by newly produced or altered compositions of the VOM. Until now, human VOM have been detected in various body media, such as on the skin, in the exhaled air as well as in body fluids such as saliva, mother’s milk, sweat, blood and urine. Methods In this retrospective case-control study urinary VOM of 53 therapy-naive patients with head and neck squamous cell carcinoma (HNSCC) and 82 healthy controls were semi-quantified by headspace solid-phase micro extraction (SPME) gas chromatography (GC) mass spectrometry (MS). At first, the procedure was optimized in respect to the extraction parameters. By using Student’s t-test significant differences in the VOM pattern with the corresponding p-values were obtained. For multivariate metabolite pattern recognition the hierarchical cluster analysis by Ward was applied, followed by the supervised partial least squares-discriminant analysis (PLS-DA). Results In total 81 VOMs could identified in the urine samples of all study participants, of which 25 were significantly increased and of which were 10 significantly reduced in HNSCC-patients compared to the controls. In addition, the multivariate statistics confirmed that on the basis of the renal excreted pattern of the volatile metabolites a high discrimination can be carried out between patients with a tumor in the head and neck region and controls. The substance group of the saturated, unbranched aldehydes makes a substantial contribution in this context. Conclusions The systematic pattern analysis of urinary VOMs appears to have potential clinical application as a diagnostic tool for cancer, especially HNSCC.
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