The UGT1 locus is a determinant of prostate cancer recurrence after prostatectomy
Bibliographic record
Abstract
The prognostic significance of common deletions in uridine diphospho-glucuronosyltransferase 2B (UGT2B) genes encoding sex steroid metabolic enzymes has been recently recognized in localized prostate cancer (PCa) after radical prostatectomy (RP). However, the role of germline variations at the UGT1 locus, encoding half of all human UGTs and primarily involved in estrogen metabolism, remains unexplored. We investigated whether variants of UGT1 are potential prognostic markers. We studied 526 Caucasian men who underwent RP for clinically localized PCa. Genotypes of patients for 34 haplotype-tagged single-nucleotide polymorphisms (htSNPs) and 11 additional SNPs across the UGT1 locus previously reported to mark common variants including functional polymorphisms were determined. The risk of biochemical recurrence (BCR) was estimated using adjusted Cox proportional hazards regression and Kaplan-Meier analysis. We further investigated whether variants are associated with plasma hormone levels by mass spectrometry. In multivariable models, seven htSNPs were found to be significantly associated with BCR. A greater risk was revealed for four UGT1 intronic variants with hazard ratios (HRs) of 1.59-1.88 (P<0.002) for htSNPs in UGT1A10, UGT1A9, and UGT1A6. Conversely, decreased BCR was associated with three htSNPs in introns of UGT1A10 and UGT1A9 (HR=0.56-058; P≤0.01). An unfavorable UGT1 haplotype comprising all risk alleles, with a frequency of 14%, had a HR of 1.68 (95% CI=1.13-2.50; P=0.011). Significant alteration in circulating androsterone levels was associated with this haplotype, consistent with changes in hormonal exposure. This study provides the first evidence, to our knowledge, that germline polymorphisms of UGT1 are potential predictors of recurrence of PCa after prostatectomy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".