The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer
Abstract
A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.
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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification
Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006) · Journal of Chemometrics · doi:10.1002/cem.1006 · Cited by 1,475
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Understanding 'Global' Systems Biology: Metabonomics and the Continuum of Metabolism
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Discovery of meaningful associations in genomic data using partial correlation coefficients
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Cited by
Novikov, O., Wang, Z., Stanford, E. A., Parks, A. J., Ramirez-Cardenas, A., Landesman, E., Laklouk, I., Sarita-Reyes, C., Gusenleitner, D., Li, A., et al. (2016) · Molecular Pharmacology · doi:10.1124/mol.116.105361 · Cited by 167
Metadata
- Authors
- Chen, Y., Zhang, R., Song, Y., He, J., Sun, J., Bai, J., An, Z., Dong, L., Zhan, Q., & Abliz, Z.
- Venue
- The Analyst
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2009
- Volume
- 134
- Issue
- 10
- Pages
- 2003
- ISSN
- 0003-2654 (ISSN-L)
- 1364-5528
- OpenAlex
- W2013946586
- Cited by
- 261
- References
- 33
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, Y., Zhang, R., Song, Y., He, J., Sun, J., Bai, J., An, Z., Dong, L., Zhan, Q., & Abliz, Z. (2009). RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer. The Analyst. https://doi.org/10.1039/b907243h
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