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Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers

Authors
Woo, Han MinKim, Kyung MiChoi, Man HoJung, Byung HwaLee, JeongaeKong, GuNam, Seok JinKim, SunghoonBai, Sang WookChung, Bong Chul
Issue Date
Feb-2009
Publisher
ELSEVIER
Keywords
Metabolomics; Breast cancer; Ovarian cancer; Cervical cancer; PLS-DA
Citation
CLINICA CHIMICA ACTA, v.400, no.1-2, pp.63 - 69
Indexed
SCIE
SCOPUS
Journal Title
CLINICA CHIMICA ACTA
Volume
400
Number
1-2
Start Page
63
End Page
69
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/177319
DOI
10.1016/j.cca.2008.10.014
ISSN
0009-8981
Abstract
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.
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