Association Between miR-146a rs2910164 Polymorphism and Breast CancerSusceptibility: An Updated Meta-Analysis of 9545 Cases and 10030Controls
Bibliographic record
Abstract
BACKGROUND: Several studies have reported a possible association of miR-146a rs2910164 polymorphism with Breast Cancer (BC) development. However, the correlation between this polymorphism and susceptibility to BC is under debate. The current meta-analysis was designed and performed to more conclusively evaluate the miR-146a rs2910164 polymorphism and its potential link to BC. METHODS: Our team has selected eligible studies (published up to October 2, 2020) from several electronic databases, including Web of Science, PubMed, Scopus and Google Scholar. A total number of 9,545 BC cases and 10,030 controls extracted from 26 eligible articles were included in this study. We utilized pooled Odds Ratios (ORs) as well as 95% confidence intervals (95% CIs) under five genetic models for quantitative estimation of any possible association between miR-146a rs2910164 polymorphism and BC. RESULTS: Based on this meta-analysis, our findings suggest that there is no significant association between miR-146a rs2910164 polymorphism and BC risk. However, stratified analysis revealed that the rs2910164 polymorphism significantly increased the risk of BC in hospital-based studies using the homozygous genetic model (OR=1.37, 95%CI=1.01-1.86, p=0.043, CC vs. GG). Neither Asian nor Caucasian populations showed any significant association between rs2910164 polymorphism and BC susceptibility. CONCLUSION: In summary, our findings suggest that BC development is not associated with miR-146a rs2910164 polymorphism. However, larger ingenious future investigations might be needed for a more precise estimation of any association between miR-146a rs2910164 polymorphism and BC.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".