The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
TM4: a free, open-source system for microarray data management and analysis
Saeed, A. I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N., Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., et al. (2003) · BioTechniques · doi:10.2144/03342mt01 · Cited by 4,837
Kind, T., & Fiehn, O. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-105 · Cited by 1,201
Human metabolic phenotype diversity and its association with diet and blood pressure
Holmes, E., Loo, R. L., Stamler, J., Bictash, M., Yap, I. K. S., Chan, Q., Ebbels, T., De Iorio, M., Brown, I. J., Veselkov, K. A., et al. (2008) · Nature · doi:10.1038/nature06882 · Cited by 1,054
Emerging molecular markers of cancer
Sidransky, D. (2002) · Nature Reviews Cancer · doi:10.1038/nrc755 · Cited by 662
Ayers, M., Symmans, W. F., Stec, J., Damokosh, A. I., Clark, E., Hess, K., Lecocke, M., Metivier, J., Booser, D., Ibrahim, N., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.05.166 · Cited by 566
Cited by
Kumagai, S., Koyama, S., Itahashi, K., Tanegashima, T., Lin, Y.-T., Togashi, Y., Kamada, T., Irie, T., Okumura, G., Kono, H., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.01.001 · Cited by 892
Malamud, D. (2011) · Dental Clinics of North America · doi:10.1016/j.cden.2010.08.004 · Cited by 489
Gut commensal Bacteroides acidifaciens prevents obesity and improves insulin sensitivity in mice
Yang, J.-Y., Lee, Y.-S., Kim, Y., Lee, S.-H., Ryu, S., Fukuda, S., Hase, K., Yang, C.-S., Lim, H. S., Kim, M.-S., et al. (2017) · Mucosal Immunology · doi:10.1038/mi.2016.42 · Cited by 432
Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review
Yang, J., Xu, R., Wang, C., Qiu, J., Ren, B., & You, L. (2021) · Cancer Communications · doi:10.1002/cac2.12204 · Cited by 379
Kuehnbaum, N. L., & Britz-McKibbin, P. (2013) · Chemical Reviews · doi:10.1021/cr300484s · Cited by 354
Metadata
- Authors
- Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M.
- Venue
- Metabolomics
- Published
- 2009-09-10
- Volume
- 6
- Issue
- 1
- Pages
- 78-95
- ISSN
- 1573-3882 (ISSN-L)
- 1573-3890
- OpenAlex
- W2018507034
- Cited by
- 993
- References
- 5
- FWCI
- 10.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. https://app.cochat.ai/papers/10.1007/s11306-009-0178-y
- The paper
- Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics. https://doi.org/10.1007/s11306-009-0178-y
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.