Association of Enhancer of Zeste 2 (EZH2) Genotypes with Bladder Cancer Risk in Taiwan
Bibliographic record
Abstract
AIM: Bladder cancer is the sixth most common cancer worldwide and its incidence is particularly high in many developed regions including southwestern Taiwan. However, the genetic contribution to the etiology of bladder cancer is not well-understood. The aim of this study was to evaluate the association of the enhancer of zeste homolog 2 (EZH2) genotypes with Taiwan bladder cancer risk. MATERIALS AND METHODS: Three polymorphic variants of EZH2 were analyzed regarding their association with bladder cancer risk, and three hundred and seventy-five patients with bladder cancer and same number of age- and gender-matched healthy controls recruited were genotyped by the PCR-RFLP method. RESULTS: Among the three polymorphic sites examined, the genotypes of EZH2 rs887569 (C to T), but not rs41277434 (A to C) or rs3757441 (T to C), were positively associated with bladder cancer risk (p for trend =0.0146). Individuals with the EZH2 rs887569 TT genotypes were associated with decreased cancer risk than those with wild-type CC genotype. The stratified analyses showed that EZH2 rs887569 TT genotypes had protective effects on non-smokers but obviously not on smokers. CONCLUSION: Our findings provide evidence that the T allele of EZH2 rs887569 may be associated with the lower risk of bladder cancer development, especially among non-smokers.
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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 | 0.001 |
| 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) | 0.001 | 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".