The objective of this study was to explore salivary metabolite biomarkers by profiling both saliva and tumor tissue samples for oral cancer screening. Paired tumor and control tissues were obtained from oral cancer patients and whole unstimulated saliva samples were collected from patients and healthy controls. The comprehensive metabolomic analysis for profiling hydrophilic metabolites was conducted using capillary electrophoresis time-of-flight mass spectrometry. In total, 85 and 45 metabolites showed significant differences between tumor and matched control samples, and between salivary samples from oral cancer and controls, respectively (P < 0.05 correlated by false discovery rate); 17 metabolites showed consistent differences in both saliva and tissue-based comparisons. Of these, a combination of only two biomarkers yielded a high area under receiver operating characteristic curves (0.827; 95% confidence interval, 0.726–0.928, P < 0.0001) for discriminating oral cancers from controls. Various validation tests confirmed its high generalization ability. The demonstrated approach, integrating both saliva and tumor tissue metabolomics, helps eliminate pseudo-molecules that are coincidentally different between oral cancers and controls. These combined salivary metabolites could be the basis of a clinically feasible method of non-invasive oral cancer screening.

The aim of this study is to evaluate the effect of duration after meals for saliva collections for oral cancer detection using metabolomics. Saliva samples were collected from oral cancer patients (n = 22) and controls (n = 44). Saliva from cancer patients was collected 12 h after dinner, and 1.5 and 3.5 h after breakfast. Control subjects fasted >1.5 h prior to saliva collection. Hydrophilic metabolites were analyzed using capillary electrophoresis mass spectrometry. Levels of 51 metabolites differed significantly in controls vs. oral cancer patients at the 12-h fasting time point (P < 0.05). Fifteen and ten metabolites differed significantly at the 1.5- and 3.5-h time points, respectively. The area of under receiver operating characteristic curve for discriminating oral cancer patients from controls was greatest at the 12-h fasting time point. The collection time after meals affects levels of salivary metabolites for oral cancer screening. The 12-h fasting after dinner time point is optimal. This study contributes to design of saliva collection protocols for metabolomics-based biomarker discovery.

ObjectiveThis study was conducted to distinguish salivary metabolites in oral squamous cell carcinoma (OSCC) from those in oral lichen planus (OLP) to identify practical biomarkers for the discrimination of OSCC from OLP.Subjects and MethodsWhole unstimulated saliva samples were collected from patients with OSCC (n = 34) and OLP (n = 26). Hydrophilic metabolites in the saliva samples were comprehensively analysed by capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate OSCC from OLP.ResultsFourteen metabolites were found to be significantly different between the OSCC and OLP groups. Among them, indole‐3‐acetate and ethanolamine phosphate were used to develop the MLR model. The combination of these two metabolites showed a high area under the receiver operating characteristic curve (0.856, 95% confidential interval: 0.762–0.950; p < .001) for discriminating OSCC from OLP.ConclusionsWe identified salivary metabolites for discerning between OSCC and OLP, which is clinically important for detecting the malignant transformation of OLP by both dentists and oral surgery specialists. Our candidate salivary metabolites show potential for non‐invasive screening of OSCC versus OLP.

Poor oral health habits and low educational status are the independent risk factors for having less than 20 teeth.

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