DNA methylation level in blood and relations to breast cancer, risk factors and environmental exposure in Greenlandic Inuit women
Bibliographic record
Abstract
Several studies have found aberrant DNA methylation levels in breast cancer cases, but factors influencing DNA methylation patterns and the mechanisms are not well understood. This case-control study evaluated blood methylation level of two repetitive elements and selected breast cancer-related genes in relation to breast cancer risk, and the associations with serum level of persistent organic pollutants (POPs) and breast cancer risk factors in Greenlandic Inuit. DNA methylation was determined using bisulphite pyrosequencing in blood from 74 breast cancer cases and 80 controls. Using first tertile as reference, the following was observed. Positive associations for ATM in second tertile (OR: 2.33, 95% CI: 1.04; 5.23) and ESR2 in third tertile (OR: 2.22, 95% CI: 0.97; 5.05) suggest an increased breast cancer risk with high DNA methylation. LINE-1 methylation was lower in cases than controls. In third tertile (OR: 0.42, 95% CI: 0.18; 0.98), associations suggest in accordance with the literature an increased risk of breast cancer with LINE-1 hypomethylation. Among controls, significant associations between methylation levels and serum level of POPs and breast cancer risk factors (age, body mass index, cotinine level) were found. Thus, breast cancer risk factors and POPs may alter the risk through changes in methylation levels; further studies are needed to elucidate the mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".