Heat-Map Visualization of Gas Chromatography-Mass Spectrometry Based Quantitative Signatures on Steroid Metabolism

• Published in: Journal of the American Society for Mass Spectrometry 2009-09, Vol.20 (9), p.1626-1637
• Main Authors: Moon, Ju-Yeon, Jung, Hyun-Jin, Moon, Myeong Hee, Chung, Bong Chul, Choi, Man Ho
• Format: Article
• Language: English
• Subjects: Abnormalities; Algorithms; Analytical Chemistry; Bioinformatics; Biological and medical sciences; Biomarkers; Biotechnology; Chemistry; Chemistry and Materials Science; Cholesterol; Computer Graphics; Enzyme activity; Enzymes; Estrogens; Gas chromatography; Gas Chromatography-Mass Spectrometry - methods; Graphical representations; Hormones; Host-tumor relations. Immunology. Biological markers; Hot Temperature; Humans; Investigative techniques, diagnostic techniques (general aspects); Linearity; Mass spectrometry; Medical sciences; Metabolism; Metabolites; Miscellaneous. Technology; Organic Chemistry; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Prostate; Proteomics; Quantitative analysis; Scientific visualization; Steroids; Steroids - urine; Sterols; Transformations; Tumors; Urinalysis - methods; Urine; User-Computer Interface; Visualization
• ISSN: 1044-0305; 1879-1123
• DOI: 10.1016/j.jasms.2009.04.020

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 (5α-reductase, 3α-HSD, 3β-HSD, and 17β-HSD, except for 20α-HSD). These results support that data transformation is valid, since 5α-reductase is a marker of BPH and 17β-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. A hierarchically clustered heat map of the urinary steroid levels of 59 BPH patients and 41 healthy subjects reveals altered steroid metabolism.