Genetic Variations, Exposure to Persistent Organic Pollutants and Breast Cancer Risk – A Greenlandic Case–Control Study
Bibliographic record
Abstract
This study investigated the effects of single nucleotide polymorphisms (SNPs) in xenobiotic and steroid hormone-metabolizing genes in relation to breast cancer risk and explored possible effect modifications on persistent organic pollutants (POPs) and breast cancer associations. The study also assessed effects of Greenlandic BRCA1 founder mutations. Greenlandic Inuit women (77 cases and 84 controls) were included. We determined two founder mutations in BRCA1: Cys39Gly (rs80357164) and 4684delCC, and five SNPs in xenobiotic and oestrogen-metabolizing genes: CYP17A1 -34T>C (rs743572), CYP19A1 *19C>T (rs10046), CYP1A1 Ile462Val (rs1048943), CYP1B Leu432Val (rs1056836) and COMT Val158Met (rs4680). We used chi-square test for comparison of categorical variables between groups. Odds ratio (OR) estimates with 95% confidence interval (95%CI) were obtained using logistic regression models. The variant allele of BRCA1 Cys39Gly increased breast cancer risk (Gly/Cys versus Cys/Cys, OR: 12.2, 95%CI: 1.53; 98.1), and carriers of the variant allele of CYP17A1 -34T>C had reduced risk (CT+CC versus TT, OR: 0.44, 95%CI: 0.21; 0.93). CYP17A1 -34T>C was an effect modifier on the association between perfluoroalkyl acids (PFAAs) and breast cancer risk (∑PFAA, ratio of OR: 0.18, 95%CI: 0.03; 0.97). Non-significant modifying tendencies were seen for the other SNPs on the effect of polychlorinated biphenyls, organochlorine pesticides and PFAAs. In summary, the BRCA1 Cys39Gly and CYP17A1 -34T>C genetic variations were associated with breast cancer risk. Our results indicate that the evaluated genetic variants modify the effects of POP exposure on breast cancer risk; however, further studies are needed to document the data from the relatively small sample size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".