Molecular Markers in Key Steroidogenic Pathways, Circulating Steroid Levels, and Prostate Cancer Progression
Bibliographic record
Abstract
PURPOSE: Prostate cancer is a heterogeneous genetic disease, and molecular methods for predicting prognosis in patients with aggressive form of the disease are urgently needed to better personalize treatment approaches. The objective was to identify host genetic variations in candidate steroidogenic genes affecting hormone levels and prostate cancer progression. EXPERIMENTAL DESIGN: The study examined two independent cohorts composed of 526 Caucasian men with organ-confined prostate cancer and 601 Taiwanese men on androgen-deprivation therapy. Caucasians were genotyped for 109 haplotype-tagging single-nucleotide polymorphisms (SNP) in CYP17A1, ESR1, CYP19A1, and HSD3B1, and their prognostic significance on disease progression was assessed using Kaplan-Meier survival curves and Cox regression models. Positive findings, including previously identified SRD5A1, SRD5A2, HSD17B2, HSD17B3, and HSD17B12 polymorphisms, were then explored in Taiwanese men (n = 32 SNPs). The influence of positive markers on the circulating hormonal levels was then appraised in Caucasians using specific and sensitive mass spectrometry-based methods. RESULTS: After adjusting for known risk factors, variants of CYP17A1 (rs6162), HSD17B2 (rs4243229 and rs7201637), and ESR1 (rs1062577) were associated with progressive disease in both cohorts. Indeed, the presence of these variations was significantly associated with progression in Caucasians (HR, 2.29-4.10; P = 0.0014-2 × 10(-7)) and survival in Taiwanese patients [HR = 3.74; 95% confidence interval (CI): 1.71-8.19, P = 0.009]. Remarkably, the CYP17A1 rs6162 polymorphism was linked to plasma dehydroepiandrosterone-sulfate (DHEA-S) levels (P = 0.03), HSD17B2 rs7201637 with levels of dihydrotestosterone (P = 0.03), and ESR1 rs1062577 with levels of estrone-S and androsterone-glucuronide (P ≤ 0.05). CONCLUSION: This study identifies, in different ethnic groups and at different disease stages, CYP17A1, HSD17B2, and ESR1 as attractive prognostic molecular markers of prostate cancer progression.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".