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Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.

Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M - Metabolomics (2009)

Bottom Line: 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.These metabolites are promising biomarkers for medical screening.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

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Related in: MedlinePlus

Heat map of 57 peaks showing significantly different levels (P < 0.05; Steel–Dwass test) between control samples (n = 87) and samples from patients with at least one disease (n = 128). Each row shows data for a specific metabolite and each column shows an individual. The colors correspond to the relative metabolite areas that were converted to Z-scores
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Fig5: Heat map of 57 peaks showing significantly different levels (P < 0.05; Steel–Dwass test) between control samples (n = 87) and samples from patients with at least one disease (n = 128). Each row shows data for a specific metabolite and each column shows an individual. The colors correspond to the relative metabolite areas that were converted to Z-scores

Mentions: The MLR model developed for oral cancer yielded a high AUC (0.865), and the trained models also showed high separation ability in the CV (AUC = 0.810). The receiver operating characteristic (ROC) curves and selected parameters of the MLR models for each disease are shown in Fig. 4 and Supplementary Table S1, respectively. The MLR models for pancreatic cancer and periodontal disease yielded high AUCs in the CV test (0.944 and 0.954, respectively), using only five and two metabolic markers, respectively; while oral and breast cancers (0.810 and 0.881, respectively) used 9 and 14 metabolites, respectively, with lower AUCs. On the metabolite heat map (Fig. 5), the control group and the periodontal disease group were relatively lower and the pancreatic cancer group tended to be homologically higher, while the oral and breast cancers exhibited more diverse profiles compared with the other groups. This suggests that our MLR models for oral and breast cancer require additional parameters for accurate classification. The heterogeneous nature of oral cancers, including oral squamous cell carcinoma (OSCC), oropharyngeal, tongue and neck cancer, may produce different profiles; this diminishes the discriminative capability of a single classification model. The diverse profiles associated with breast cancer may result in a similar situation because breast cancer comprises structurally differing types according to the expression of hormone receptors such as estrogen and progesterone, and is affected by clinical parameters, such as the patient’s age or menopause status. Three metabolites, taurine, piperidine, and a peak at 120.0801 m/z, were oral cancer-specific markers (different from all of the other groups at P < 0.05; Steel–Dwass test) and eight metabolites (leucine with isoleucine, tryptophan, valine, glutamic acid, phenylalanine, glutamine, and aspartic acid) were pancreatic cancer-specific markers. Although several metabolites in breast cancer patients yielded a statistically significant difference between breast cancer and healthy controls, including taurine and lysine (P < 0.001 for both; Steel–Dwass test), there were no differences in metabolites between breast cancer and other cancer, and they were not unique for breast cancer.Fig. 4


Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.

Sugimoto M, Wong DT, Hirayama A, Soga T, Tomita M - Metabolomics (2009)

Heat map of 57 peaks showing significantly different levels (P < 0.05; Steel–Dwass test) between control samples (n = 87) and samples from patients with at least one disease (n = 128). Each row shows data for a specific metabolite and each column shows an individual. The colors correspond to the relative metabolite areas that were converted to Z-scores
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2818837&req=5

Fig5: Heat map of 57 peaks showing significantly different levels (P < 0.05; Steel–Dwass test) between control samples (n = 87) and samples from patients with at least one disease (n = 128). Each row shows data for a specific metabolite and each column shows an individual. The colors correspond to the relative metabolite areas that were converted to Z-scores
Mentions: The MLR model developed for oral cancer yielded a high AUC (0.865), and the trained models also showed high separation ability in the CV (AUC = 0.810). The receiver operating characteristic (ROC) curves and selected parameters of the MLR models for each disease are shown in Fig. 4 and Supplementary Table S1, respectively. The MLR models for pancreatic cancer and periodontal disease yielded high AUCs in the CV test (0.944 and 0.954, respectively), using only five and two metabolic markers, respectively; while oral and breast cancers (0.810 and 0.881, respectively) used 9 and 14 metabolites, respectively, with lower AUCs. On the metabolite heat map (Fig. 5), the control group and the periodontal disease group were relatively lower and the pancreatic cancer group tended to be homologically higher, while the oral and breast cancers exhibited more diverse profiles compared with the other groups. This suggests that our MLR models for oral and breast cancer require additional parameters for accurate classification. The heterogeneous nature of oral cancers, including oral squamous cell carcinoma (OSCC), oropharyngeal, tongue and neck cancer, may produce different profiles; this diminishes the discriminative capability of a single classification model. The diverse profiles associated with breast cancer may result in a similar situation because breast cancer comprises structurally differing types according to the expression of hormone receptors such as estrogen and progesterone, and is affected by clinical parameters, such as the patient’s age or menopause status. Three metabolites, taurine, piperidine, and a peak at 120.0801 m/z, were oral cancer-specific markers (different from all of the other groups at P < 0.05; Steel–Dwass test) and eight metabolites (leucine with isoleucine, tryptophan, valine, glutamic acid, phenylalanine, glutamine, and aspartic acid) were pancreatic cancer-specific markers. Although several metabolites in breast cancer patients yielded a statistically significant difference between breast cancer and healthy controls, including taurine and lysine (P < 0.001 for both; Steel–Dwass test), there were no differences in metabolites between breast cancer and other cancer, and they were not unique for breast cancer.Fig. 4

Bottom Line: 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.These metabolites are promising biomarkers for medical screening.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus