The DOI matches its Crossref record.
Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers
Open accessAbstract
BACKGROUND The metabolomic approaches for mining biomarkers of women's cancers based on gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry combined with partial least squares-discriminant analysis are described. METHODS To identify urinary potential biomarkers, the qualitative and quantitative analyses were introduced with 10 breast, 9 ovarian and 12 cervical cancer patients as well as 22 normal controls, which were considered with their ages and menopausal state. RESULTS For comprehensive metabolomic approaches, the non-targeted qualitative profiling was first achieved to get metabolic patterns of collected samples and the targeted quantitative analysis focused on hormonal metabolism was also conducted. Two known biomarkers, i.e., 5-hydroxymethyl-2-deoxyuridine and 8-hydroxy-2-deoxyguanosine, in breast cancer were also confirmed using the present methods. In addition, 3 potential biomarkers for ovarian cancer i.e. 1-methyladenosine, 3-methyluridine, and 4-androstene-3,17-dione, which were categorized in significantly increased level using one way of variance analysis (p<0.05), were identified as quantitatively targeted metabolites with pattern analysis. The cancer markers identified in this study are highly related to metabolites which are responsible for oxidative DNA damage and DNA methylation process. CONCLUSION The present metabolomic approaches are not only useful for diagnostic tools and patient stratification, but may be mapped on metabolic network to reflect disease states.
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
Cited by
Bridgewater BR, E. A. (2014) · Journal of Postgenomics Drug & Biomarker Development · doi:10.4172/2153-0769.1000132 · Cited by 389
Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma
Chen, T., Xie, G., Wang, X., Fan, J., Qiu, Y., Zheng, X., Qi, X., Cao, Y., Su, M., Wang, X., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.004945 · Cited by 357
Mass spectrometric based approaches in urine metabolomics and biomarker discovery
Khamis, M. M., Adamko, D. J., & El‐Aneed, A. (2017) · Mass Spectrometry Reviews · doi:10.1002/mas.21455 · Cited by 342
The Metabolic Theory of Pulmonary Arterial Hypertension
Paulin, R., & Michelakis, E. D. (2014) · Circulation Research · doi:10.1161/circresaha.115.301130 · Cited by 314
Triple‐negative breast cancer: Present challenges and new perspectives
Podo, F., Buydens, L. M. C., Degani, H., Hilhorst, R., Klipp, E., Gribbestad, I. S., Van Huffel, S., W.M. van Laarhoven, H., Luts, J., Monleon, D., et al. (2010) · Molecular Oncology · doi:10.1016/j.molonc.2010.04.006 · Cited by 299
Metadata
- Authors
- Woo, H. M., Kim, K. M., Choi, M. H., Jung, B. H., Lee, J., Kong, G., Nam, S. J., Kim, S., Bai, S. W., & Chung, B. C.
- Venue
- Clinica Chimica Acta
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 400
- Issue
- 1-2
- Pages
- 63-69
- ISSN
- 0009-8981 (ISSN-L)
- OpenAlex
- W2052812447
- Cited by
- 182
- References
- 0
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- green
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: Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers. https://app.cochat.ai/papers/10.1016/j.cca.2008.10.014
- The paper
- Woo, H. M., Kim, K. M., Choi, M. H., Jung, B. H., Lee, J., Kong, G., Nam, S. J., Kim, S., Bai, S. W., & Chung, B. C. (2009). Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers. Clinica Chimica Acta. https://doi.org/10.1016/j.cca.2008.10.014
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.