Quantitative assessment of the association between DNMT3B-579G>T polymorphism and cancer risk
Bibliographic record
Abstract
BACKGROUND: DNA methyltransferase 3B (DNMT3B) has been discovered to play an important role in tumorigenesis. However, the association between DNMT3B-579G>T and the cancer risk has not been demonstrated. OBJECTIVE: The aim of this study is to provide a precise quantification for the association between DNMT3B-579G>T and the cancer susceptibility. METHODS: We performed a systematic literature review and assessed the methodological quality of included case-control designed studies based on Newcastle-Ottawa Scale (NOS). Pooled odds ratios (ORs) and corresponding 95% confidence intervals (95%CIs) were calculated to assess the strengths of the association. RESULTS: We identified 18 studies for pooled analyses. Overall, the results demonstrated that the DNMT3B-579G>T polymorphism was significantly associated with a subtly decreased cancer risk (GT vs TT: OR = 0.78, 95%CI: 0.70-0.87, P< 0.01; GT + GG vs TT: OR = 0.81, 95%CI: 0.68-0.97, P= 0.02), especially in the Asian population and in colorectal cancer subgroup. In addition, when stratified for source of controls, the results of population-based subgroup showed the GT genotype might have a significantly decreased cancer risk, but not hospital-based subgroups. CONCLUSIONS: DNMT3B-579G>T polymorphism might contribute to the susceptibility of cancers especially in the Asian population and for colorectal cancer.
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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 | 0.051 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".