Genetic Polymorphisms of Selected DNA Repair Genes, Estrogen and Progesterone Receptor Status, and Breast Cancer Risk
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Genetic polymorphisms of DNA repair genes seem to determine the DNA repair capacity, which in turn may affect the risk of breast cancer. To evaluate the role of genetic polymorphisms of DNA repair genes in breast cancer, we conducted a hospital-based case-control study of Korean women. EXPERIMENTAL DESIGN: We included 872 incident breast cancer cases and 671 controls recruited from several teaching hospitals in Seoul from 1995 to 2002. Twelve loci of selected DNA repair genes were genotyped by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (XRCC2 Arg188His, XRCC4 921G > T, XRCC6 1796G > T, LIG4 1977T/C, RAD51 135G > C, 172G > T, RAD52 2259C > T, LIG1 551A > C, ERCC1 8092A > C, 354C > T, hMLH1 -93G > A, and Ile219Val). RESULTS: We found that the RAD52 2259 CT or TT, hMLH1 -93 GG, and ERCC1 8092 AA genotypes were associated with breast cancer risk after adjustment for known risk factors [odds ratio (OR), 1.33; 95% confidence interval (95% CI), 1.02-1.75; OR, 1.31; 95% CI, 0.99-1.74; and OR, 0.58; 95% CI, 0.38-0.89, respectively]. When Bonferroni's method was used to correct for multiple comparisons for nine polymorphisms with P = 0.005, all of these associations were not significant. However, the effects of RAD52 2259 CT or TT and ERCC1 354 CT or TT genotypes were more evident for the estrogen/progesterone receptor-negative cases (OR, 2.03; 95% CI, 1.24-3.34 and OR, 1.99; 95% CI, 1.35-2.94, respectively). CONCLUSION: Our findings suggest that genetic polymorphisms of RAD52, ERCC1, and hMLH1 may be associated with breast cancer risk in Korean women.
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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 | 0.001 |
| 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 it