Metabolic classification between benign prostatic hyperplasia and prostate cancer evaluated by GC-MS based steroid signatures
- Metabolic classification between benign prostatic hyperplasia and prostate cancer evaluated by GC-MS based steroid signatures
- 문주연; 최만호; 정현진; 문명희; 정봉철
- Prostate; GC-MS; steroid signature; androgen; biomarker
- Issue Date
- ENDO 2011
- Abnormalities in steroid hormones synthesized from cholesterol in the adrenal cortex, ovaries, and testes are responsible for development and prevention of many diseases including cancer. Due to their biochemical roles in endocrine system, the quantitative evaluation of steroid hormones is needed to elucidate altered expression of steroids.
The present method was used for GC-MS profiling of 84 urinary steroids as their trimethylsilyl derivatives, containing 25 androgens, 17 estrogens, 23 corticoids, 14 progestins, and 5 sterols, obtained from 59 patients with benign prostatic hyperplasia (BPH) and 19 prostate cancer (PCa) to evaluate changes in metabolic patterns versus 41 healthy male subjects. After obtaining quantitative data for each steroid, data were z-score transformed for visualization in the heat-map generated by hierarchical-clustering analysis to allow steroid signatures.
Urinary concentrations of 11β-hydroandrosterone, epiandrosterone, 17α-OH-pregnenolone, cortisol, corticosterone, 11-deoxycortisol, 11-deoxycorticosterone, cortisone, 11-dehydrocorticosterone, tetrahydrocortisol, tetrahydrocorticosterone, α-cortolone, β-cortolone, α-cortol, 20α-dihydrocortisone were significantly higher (P < 0.001) in patients with PCa than BPH patients. In multi-substrate enzyme assays, 5α-reductase activity (DHT/T ; P < 1.4 × 10–5, allo-DHB/B ; P < 9.8 × 10–8) known as a marker of BPH were present in much higher levels in both PC and healthy cases, while oxidoreductase activity (11β-HSD, 17β-HSD, 20α-HSD; P < 0.001) were significantly higher in patients with PCa than BPH patients.
Multi-targeted profiling analysis of steroid hormones generates quantitative results and the metabolic signature of steroids manipulated by multivariate data analysis may be a useful tool for clinical diagnosis as well as mining biomarker in prostate diseases.
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