XRCC1 Gene Polymorphisms and Breast Cancer Risk: A Systematic Review and Meta-analysis Study
Bibliographic record
Abstract
Breast cancer risk assessment has developed during years and evaluation of genetic factor affecting risk of breast cancer is an important component of this risk assessment. The aim of this meta-analysis was to investigate the role of XRCC1 polymorphisms (Arg194Trp, Arg280His and Arg399Gln) for risk of breast cancer among different population and categories of menopausal status. PubMed, Medline, Web of Science, and PubMed Central were systematically searched to identify studies evaluating association between breast cancer and XRCC1 gene polymorphisms (Arg194Trp, Arg280His and Arg399Gln). Two authors independently extracted required information. Odds Ratios were pooled for four genetic inheritance models using both fixed and the DerSimonian and Laird random-effect models. Egger's test and contour-enhanced funnel plot were used to evaluate publication bias and small study effect. Additional subgroup analysis was performed for menopausal status, ethnicity, and source of controls. After evaluation and applying inclusion criteria on extracted studies, fifty three studies were included in this meta-analysis. For polymorphisms of Arg194Trp and Arg280His, no significant association was observed in all genetic models. Arg194Trp had a protective effect in post-menopausal status only in homozygote model (OR=0.57 [0.37-0.88]). Arg399Gln showed significant association with breast cancer in homozygote (OR=1.21 [1.10-1.34]), dominant (OR=1.09 [1.03-1.15]) and recessive (OR=1.21 [1.09-1.35]) models. Arg399Gln was associated with higher risk in post-menopausal status for homozygote and heterozygote models. Our findings suggest that XRCC1 gene polymorphisms modify breast cancer risk in different populations and different categories of menopausal status.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".