TY - JOUR
T1 - Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles
AU - Sugimoto, Masahiro
AU - Wong, David T.
AU - Hirayama, Akiyoshi
AU - Soga, Tomoyoshi
AU - Tomita, Masaru
N1 - Funding Information:
Acknowledgments This work was supported by NIH grant DE017170 (to D.T.W.), a grant from the Global COE Program entitled ‘‘Human Metabolomic Systems Biology,’’ and ‘‘Grant-in-Aid’’ research grants from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan for Scientific Research on Priority Areas ‘‘Systems Genomes’’, as well as research funds from Yamagata prefectural government and the city of Tsuruoka (to M.S., A.H., T.S., and M.T.). D.T.W. acknowledges the colleagues who provided the clinical samples for this study: Elliot Abemayor, Mari-lene Wang, Mai Brooks, James Ferrell, and Perry Klokkevoid and members of his laboratory at UCLA: Bradley Henson, Jianghua Wang, Xinmin Yang and David Akin. M.S., A.H., T.S., and M.T. thank Shinobu Abe and Kenjiro Kami of IAB for technical support and valuable discussions, and Dr. Ursula Petralia for editing the manuscript.
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Breast cancer
KW - Capillary electrophoresis-mass spectrometry
KW - Oral cancer
KW - Pancreatic cancer
KW - Salivary metabolome
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U2 - 10.1007/s11306-009-0178-y
DO - 10.1007/s11306-009-0178-y
M3 - Article
AN - SCOPUS:77949297718
SN - 1573-3882
VL - 6
SP - 78
EP - 95
JO - Metabolomics
JF - Metabolomics
IS - 1
ER -