Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral,breast and pancreatic cancer-specific profiles |
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Authors: | Masahiro Sugimoto David T Wong Akiyoshi Hirayama Tomoyoshi Soga Masaru Tomita |
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Institution: | (1) Institute for Advanced Biosciences, Keio University, Tsuruoka Yamagata, 997-0052, Japan;(2) Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa Kanagawa, 252-8520, Japan;(3) School of Dentistry and Dental Research Institute, University of California, Los Angeles, CA 90095-1668, USA; |
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Abstract: | Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases
including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based
diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing
the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis
of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease
patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified
57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease.
Although small but significant correlations were found between the known patient characteristics and the quantified metabolites,
the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison
with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded
in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves
(AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993
for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation
AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations
enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. |
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