Steroidogenic Germline Polymorphism Predictors of Prostate Cancer Progression in the Estradiol Pathway
Bibliographic record
Abstract
PURPOSE: Reliable biomarkers that predict prostate cancer outcomes are urgently needed to improve and personalize treatment approaches. With this goal in mind, we individually and collectively appraised common genetic polymorphisms related to estradiol metabolic pathways to find prostate cancer prognostic markers. METHODS: The genetic profiles of 526 men with organ-confined prostate cancer were examined to find common genetic polymorphisms related to estradiol metabolic pathways and these findings were replicated in a cohort of 213 men with more advanced disease (follow-up time for both cohorts, >7.4 years). Specifically, we examined 71 single-nucleotide polymorphisms (SNP) in SULT2A1, SULT2B1, CYP1B1, COMT, CYP3A4, CYP3A5, CYP3A43, NQO1, and NQO2 and assessed the impact of the SNPs alone and in combination on prostate cancer progression and on circulating hormone levels. RESULTS: According to a multivariate analysis, CYP1B1 (rs1800440), COMT (rs16982844), and SULT2B1 (rs12460535, rs2665582, rs10426628) were significantly associated with prostate cancer progression and hormone levels. Remarkably, by combining the SNP information with previously identified HSD17B2 markers, the patients could be stratified into four distinct prognostic subgroups. The most prominent association was observed for the eight-marker combination [CYP1B1 (rs1800440), SULT2B1 (rs12460535, rs2665582, and rs10426628), and HSD17B2 (rs4243229, rs1364287, rs2955162, and rs1119933)]. CONCLUSION: This study identified specific germline variations in estradiol metabolism-related pathways, namely CYP1B1, SULT2B1, and HSD17B2, as novel prognostic markers that are cumulatively associated with increased risk of prostate cancer progression. This panel of markers warrants additional investigation and validation to help stratify patients according to their risk of progression. Clin Cancer Res; 20(11); 2971-83. ©2014 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".