Heat-map visualization of gas chromatography-mass spectrometry based quantitative signatures on steroid metabolism

Title
Heat-map visualization of gas chromatography-mass spectrometry based quantitative signatures on steroid metabolism
Authors
문주연정현진문명희정봉철최만호
Keywords
GC-MS; steroid; metabolite profiling; enzyme activity
Issue Date
2009-09
Publisher
Journal of the American Society for Mass Spectrometry
Citation
VOL 20, 1626-1637
Abstract
Abnormalities in steroid hormones are responsible for the development and prevention of endocrine diseases. Due to their biochemical roles in endocrine system, the quantitative evaluation of steroid hormones is needed to elucidate altered expression of steroids. Gas chromatographic-mass spectrometric (GC-MS) profiling of 70 urinary steroids, containing 22 androgens, 18 estrogens, 15 corticoids, 13 progestins, and 2 sterols, were validated and its quantitative data were visualized using hierarchically clustered heat maps to allow “steroid signatures”. The devised method provided a good linearity (r2 0.994) with the exception of cholesterol (r2 0.983). Precisions (% CV) and accuracies (% bias) ranged from 0.9% to 11.2% and from 92% to 119%, respectively, for most steroids tested. To evaluate metabolic changes, this method was applied to urine samples obtained from 59 patients with benign prostatic hyperplasia (BPH) versus 41 healthy male subjects. Altered concentrations of urinary steroids found and heat maps produced during this 70-compound study showed also differences between the ratios of steroid precursors and their metabolites (representing enzyme activity). Heat maps showed that oxidoreductases clustered (5 -reductase, 3 -HSD, 3 -HSD, and 17 -HSD, except for 20 -HSD). These results support that data transformation is valid, since 5 -reductase is a marker of BPH and 17 -HSD is positively expressed in prostate cells. Multitargeted profiling analysis of steroids generated quantitative results that help to explain correlations between enzyme activities. The data transformation and visualization described may to be found in the integration with the mining biomarkers of hormone-dependent diseases. (J Am Soc Mass Spectrom 2009, 20, 1626–1637) © 2009 American Society for Mass Spectrometry
URI
http://pubs.kist.re.kr/handle/201004/35807
ISSN
1044-0305
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KIST Publication > Article
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