The development of low- or non-invasive screening tests for cancer is crucial for early detection. Saliva is an ideal biofluid containing informative components for monitoring oral and systemic diseases. Metabolomics has frequently been used to identify and quantify numerous metabolites in saliva samples, serving as novel biomarkers associated with various conditions, including cancers. This review summarizes the recent applications of salivary metabolomics in biomarker discovery in oral cancers. We discussed the prevalence, epidemiologic characteristics, and risk factors of oral cancers, as well as the currently available screening programs, in India and Japan. These data imply that the development of biomarkers by itself is inadequate in cancer detection. The use of current diagnostic methods and new technologies is necessary for efficient salivary metabolomics analysis. We also discuss the gap between biomarker discovery and nationwide screening for the early detection of oral cancer and its prevention.

Background: Saliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non‐invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL).Materials and Methods: Unstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.Results: The profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516–0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back‐selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598–0.861, p = 0.003).Conclusion: The four salivary metabolites can serve as potential non‐invasive biomarkers for distinguishing LC from BLL.

Background/aim: The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS). Patients and Methods: Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers. Results: A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference. Conclusion: The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening.

Background: Several studies have surveyed the relationship between the presence of ≥ 20 natural teeth and mortality. However, very few have evaluated this association over a long-term follow-up of more than ten years within a large population in Japan. This study aimed to prospectively confirm the associations between mortality and the presence of ≥ 20 natural teeth within a community-based population in Japan.Methods: A prospective observational study including 2208 participants aged ≥ 40 years was conducted in Takahata Town, Japan, between May 2005 and December 2016. All participants answered a self-administered questionnaire to provide their background characteristics, including their number of teeth. The participants were classified into two categories based on their self-reported number of teeth (< 20 and ≥ 20 teeth). Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox proportional-hazards regression model to assess risk factors for all-cause, cancer-, and cardiovascular disease-related mortality.Results: The total follow-up period was 131.4 ± 24.1 months (mean ± SD). After adjusting for covariates, the risk of all-cause mortality was significantly higher in the group with < 20 teeth than in those with ≥ 20 teeth (HR = 1.604, 95% CI 1.007–2.555,p = 0.047). However, the risk of cancer- and cardiovascular disease-related mortalities was not statistically significant between the two groups.Conclusion: In this study, participants with < 20 teeth had a significantly higher risk of all-cause mortality, although the difference was borderline significant. These results emphasize the importance of having ≥ 20 natural teeth for a healthy life expectancy.