CAG repeat polymorphisms in the androgen receptor and breast cancer risk in women: a meta-analysis of 17 studies
Bibliographic record
Abstract
The association between polymorphic CAG repeats in the androgen receptor gene in women and breast cancer susceptibility has been studied extensively. However, the conclusions regarding this relationship remain conflicting. The purpose of this meta-analysis was to identify whether androgen receptor CAG repeat lengths were related to breast cancer susceptibility. The MEDLINE, PubMed, and EMBASE databases were searched through to December 2014 to identify eligible studies. Data and study quality were rigorously assessed by two investigators according to the Newcastle-Ottawa Quality Assessment Scale. The publication bias was assessed by the Begg's test. Seventeen eligible studies were included in this meta-analysis. The overall analysis suggested no association between CAG polymorphisms and breast cancer risk (odds ratio [OR] 1.031, 95% confidence interval [CI] 0.855-1.245). However, in the subgroup analysis, we observed that long CAG repeats significantly increased the risk of breast cancer in the Caucasian population (OR 1.447, 95% CI 1.089-1.992). Additionally, the risk was significantly increased in Caucasian women carrying two alleles with CAG repeats ≥22 units compared with those with two shorter alleles (OR 1.315, 95% CI 1.014-1.707). These findings suggest that long CAG repeats increase the risk of breast cancer in Caucasian women. However, larger scale case-control studies are needed to validate our results.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".