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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.

No MeSH data available.


Related in: MedlinePlus

Representative dot plots for the relative area of detected metabolites in samples from all groups. The colored dots denote healthy controls (blue), oral (red), breast (pink), pancreatic cancer (green), and periodontal disease (purple). The Y- and X-axes denote the relative peak area (no units) and the group name, respectively. The horizontal, center long bars and the short top/bottom bars indicate the means and standard deviations, respectively. The stars indicates * P < 0.05, ** P < 0.01, and *** P < 0.001 (Steel–Dwass test). Only metabolites showing a significant difference between oral cancer and controls at P < 0.001 and matched with standard library are displayed. The dot plots of the other metabolites are shown in Supplementary Fig. S1
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Fig2: Representative dot plots for the relative area of detected metabolites in samples from all groups. The colored dots denote healthy controls (blue), oral (red), breast (pink), pancreatic cancer (green), and periodontal disease (purple). The Y- and X-axes denote the relative peak area (no units) and the group name, respectively. The horizontal, center long bars and the short top/bottom bars indicate the means and standard deviations, respectively. The stars indicates * P < 0.05, ** P < 0.01, and *** P < 0.001 (Steel–Dwass test). Only metabolites showing a significant difference between oral cancer and controls at P < 0.001 and matched with standard library are displayed. The dot plots of the other metabolites are shown in Supplementary Fig. S1

Mentions: The marker pool used to discriminate between individuals with oral cancer and healthy controls revealed 28 metabolites; namely pyrroline hydroxycarboxylic acid, leucine plus isoleucine, choline, tryptophan, valine, threonine, histidine, pipecolic acid, glutamic acid, carnitine, alanine, piperidine, taurine, and two other metabolites with a significance of P < 0.001 (Steel–Dwass test); piperidine, alpha-aminobutyric acid, phenylalanine and an additional metabolite with a significance of P < 0.01 (Steel–Dwass test); and betaine, serine, tyrosine, glutamine, beta-alanine, cadaverine, and two other metabolite with a significance of P < 0.05 (Steel–Dwass test). The overlaid electropherograms of these CE-TOF-MS peaks with a 2-dimensional map (migration time and m/z) visualizing the difference in intensity between the averaged control and oral cancer samples are shown in Fig. 1. The vertical smear lines in the first few minutes (5–7 min) and those at a later time (at 19 min) were derived from salt ions and neutral molecules, respectively, and most of the peaks derived from charged metabolites were distributed between these times. Using a similar strategy, we identified 28 metabolites for breast cancer, 48 for pancreatic cancer and 27 for periodontal disease (P < 0.05; Steel–Dwass test) as biomarker candidates. The detected markers and the statistical results are listed in Table 2; dot plots of the quantified peak areas are shown in Fig. 2 and Supplementary Fig. S1. Although, several metabolites in the dot plots achieved a statistically significant difference, individual metabolites could not separate any two groups with high sensitivity and specificity. The score plots of the PCA analyses for all individuals are shown in Fig. 3 and in Supplementary Fig. S2. Although the PCA developed using the metabolite profiles of all subjects showed no unequivocal group-specific clusters, PCAs developed individually for the control and each disease group showed partial discriminative separation of the subjects, which might be attributed to the reduced complexity of the given datasets, or the extinction in the overlap between the distribution of the score plots for all disease groups.Fig. 1


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)

Representative dot plots for the relative area of detected metabolites in samples from all groups. The colored dots denote healthy controls (blue), oral (red), breast (pink), pancreatic cancer (green), and periodontal disease (purple). The Y- and X-axes denote the relative peak area (no units) and the group name, respectively. The horizontal, center long bars and the short top/bottom bars indicate the means and standard deviations, respectively. The stars indicates * P < 0.05, ** P < 0.01, and *** P < 0.001 (Steel–Dwass test). Only metabolites showing a significant difference between oral cancer and controls at P < 0.001 and matched with standard library are displayed. The dot plots of the other metabolites are shown in Supplementary Fig. S1
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Related In: Results  -  Collection

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Fig2: Representative dot plots for the relative area of detected metabolites in samples from all groups. The colored dots denote healthy controls (blue), oral (red), breast (pink), pancreatic cancer (green), and periodontal disease (purple). The Y- and X-axes denote the relative peak area (no units) and the group name, respectively. The horizontal, center long bars and the short top/bottom bars indicate the means and standard deviations, respectively. The stars indicates * P < 0.05, ** P < 0.01, and *** P < 0.001 (Steel–Dwass test). Only metabolites showing a significant difference between oral cancer and controls at P < 0.001 and matched with standard library are displayed. The dot plots of the other metabolites are shown in Supplementary Fig. S1
Mentions: The marker pool used to discriminate between individuals with oral cancer and healthy controls revealed 28 metabolites; namely pyrroline hydroxycarboxylic acid, leucine plus isoleucine, choline, tryptophan, valine, threonine, histidine, pipecolic acid, glutamic acid, carnitine, alanine, piperidine, taurine, and two other metabolites with a significance of P < 0.001 (Steel–Dwass test); piperidine, alpha-aminobutyric acid, phenylalanine and an additional metabolite with a significance of P < 0.01 (Steel–Dwass test); and betaine, serine, tyrosine, glutamine, beta-alanine, cadaverine, and two other metabolite with a significance of P < 0.05 (Steel–Dwass test). The overlaid electropherograms of these CE-TOF-MS peaks with a 2-dimensional map (migration time and m/z) visualizing the difference in intensity between the averaged control and oral cancer samples are shown in Fig. 1. The vertical smear lines in the first few minutes (5–7 min) and those at a later time (at 19 min) were derived from salt ions and neutral molecules, respectively, and most of the peaks derived from charged metabolites were distributed between these times. Using a similar strategy, we identified 28 metabolites for breast cancer, 48 for pancreatic cancer and 27 for periodontal disease (P < 0.05; Steel–Dwass test) as biomarker candidates. The detected markers and the statistical results are listed in Table 2; dot plots of the quantified peak areas are shown in Fig. 2 and Supplementary Fig. S1. Although, several metabolites in the dot plots achieved a statistically significant difference, individual metabolites could not separate any two groups with high sensitivity and specificity. The score plots of the PCA analyses for all individuals are shown in Fig. 3 and in Supplementary Fig. S2. Although the PCA developed using the metabolite profiles of all subjects showed no unequivocal group-specific clusters, PCAs developed individually for the control and each disease group showed partial discriminative separation of the subjects, which might be attributed to the reduced complexity of the given datasets, or the extinction in the overlap between the distribution of the score plots for all disease groups.Fig. 1

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