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Zhu J, Djukovic D, Deng L, Gu H, Himmati F, Abu Zaid M, Chiorean EG, Raftery D. Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring. Anal Bioanal Chem 2015; 407:7857-63. [PMID: 26342311 DOI: 10.1007/s00216-015-8984-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 08/03/2015] [Accepted: 08/14/2015] [Indexed: 11/28/2022]
Abstract
Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.
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Affiliation(s)
- Jiangjiang Zhu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA
| | - Danijel Djukovic
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA
| | - Lingli Deng
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA.,Departments of Electronic Science and Communication Engineering, State Key Laboratory for the Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, 361005, China
| | - Haiwei Gu
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA
| | - Farhan Himmati
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA
| | - Mohammad Abu Zaid
- Simon Cancer Center, Indiana University, 535 Barnhill Dr., Indianapolis, IN, 46202, USA
| | - Elena Gabriela Chiorean
- Simon Cancer Center, Indiana University, 535 Barnhill Dr., Indianapolis, IN, 46202, USA.,University of Washington, 825 Eastlake Ave. East, Seattle, WA, 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA, 98109, USA. .,Public Health Sciences Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, WA, 98109, USA.
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