Polymorphisms in genes in the androgen pathway and risk of Barrett's esophagus and esophageal adenocarcinoma
Bibliographic record
Abstract
The strong male predominance in Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) remains inadequately explained, but sex hormones might be involved. We hypothesized that single nucleotide polymorphisms (SNPs) in the androgen pathway influence risk of developing BE and EAC. This genetic-epidemiological analysis included 14 studies from Australia, Europe and North America. Polymorphisms in 16 genes coding for the androgen pathway were analyzed using a gene-based approach: versatile gene-based test association study. This method evaluates associations between a trait and all SNPs within a specific gene rather than each SNP marker individually as in a conventional GWAS. The data were stratified for sex, body-mass index, waist-to-hip ratio, tobacco smoking and gastroesophageal reflux status. Included were data from 1,508 EAC patients, 2,383 BE patients and 2,170 control participants. SNPs within the gene CYP17A1 were associated with risk of BE in the sexes combined (p = 0.002) and in males (p = 0.003), but not in females separately (p = 0.3). This association was found in tobacco smokers (p = 0.003) and in BE patients without reflux (p = 0.004), but not in nonsmokers (p = 0.2) or those with reflux (p = 0.036). SNPs within JMJD1C were associated with risk of EAC in females (p = 0.001). However, none of these associations replicated in a subsequent sample. Fourteen other genes studied did not reach statistically significant levels of association with BE, EAC or the combination of BE and EAC, after correcting for the number of genes included in the analysis. In conclusion, genetic variants in the androgen-related genes CYP17A1 and JMJD1C might be associated with risk of BE and EAC, respectively, but replication data with larger sample sizes are needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".