Estrogen Bioactivation, Genetic Polymorphisms, and Ovarian Cancer
Bibliographic record
Abstract
Recent experimental evidence has shown that catechol estrogens can be activated through metabolism to form depurinating DNA adducts and thereby initiate cancer. Limited data are available regarding this pathway in epithelial ovarian cancer. We conducted a case-control study of 503 incident epithelial ovarian cancer cases at the Mayo Clinic in Rochester, MN, and Jacksonville, FL, and a 48-county region in North Carolina. Six hundred nine cancer-free controls were frequency matched to the cases on age, race, and residence. After an interview to obtain data on risk factors, a sample of blood was collected for DNA isolation. Subjects were genotyped for seven common single nucleotide polymorphisms in four genes involved in catechol estrogen formation (CYP1A1 and CYP1B1) or conjugation (COMT and SULT1A1). Data were analyzed using logistic regression, stratified by race, and with adjustment for design factors and potential confounders. None of the individual genotypes were significantly associated with ovarian cancer risk. However, an oligogenic model that considered the joint effects of the four candidate genes provided evidence for an association between combinations of these genes and ovarian cancer status (P = 0.015). Although preliminary, this study provides some support for the hypothesis that low-penetrance susceptibility alleles may influence risk of epithelial ovarian cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".