Differential Expression of the Androgen-Conjugating UGT2B15 and UGT2B17 Enzymes in Prostate Tumor Cells during Cancer Progression
Bibliographic record
Abstract
CONTEXT: Androgens play major roles in prostate cancer initiation and development. In prostate cells, the human uridine diphosphate-glucuronosyltransferase (UGT)2B15 and UGT2B17 enzymes inactivate androgens. OBJECTIVE: We investigated in vivo how UGT2B15 and UGT2B17 expressions are affected during prostate cancer development. DESIGN: We conducted an observational study of the UGT2B15 and UGT2B17 mRNA and protein levels. SETTING: The study was conducted at Laval University (Québec, Canada) and at the University of British Columbia (Vancouver, Canada). PATIENTS/PARTICIPANTS: Participants were from a cohort of prostate cancer patients from the Hôtel-Dieu de Québec hospital (Québec; mRNA analyses) and from the Vancouver Prostate Centre tissue bank (Vancouver; tissue microarray experiments). MAIN OUTCOME MEASURES: UGT mRNA and protein levels were determined using real-time PCR and immunohistochemical analyses, respectively. RESULTS: Both UGT2B15 and UGT2B17 mRNA and protein levels were not significantly associated with Gleason score stratification. However, when protein levels were compared to benign prostatic hyperplasia, UGT2B17 was significantly more abundant in all Gleason-scored tumors. By contrast, UGT2B15 levels were significantly reduced in naive and castration-resistant tumors and undetectable in lymph node metastases. Finally, UGT2B17 proteins were 5-fold more abundant in metastases than in benign samples. CONCLUSIONS: The current study reveals that UGT2B15 and UGT2B17 are differentially regulated during prostate cancer progression. Furthermore, this study also identifies the UGT2B15 gene as a negatively regulated target gene in castration-resistant prostate cancer and lymph node metastases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".