Investigation of ATG16L1 rs2241880 Polymorphism with Cancer Risk: A Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: Previous studies have investigated the impact of the ATG16L1 rs2241880 (Thr300Ala) polymorphism on individual susceptibility to cancer, but the conclusions are still controversial. To get a more precise evaluation of the correlation between ATG16L1 rs2241880 polymorphism and cancer susceptibility, we performed a meta-analysis of the association of all eligible studies. Materials and Methods: Searches were performed in the Web of Science, PubMed, Scopus and Google Scholar databases up to November 2018. A total of 12 case-control studies from 9 articles comprising 2254 cases and 4974 controls were included. Statistical analysis was achieved by STATA 14.1 and Review Manager 5.3 software. The odds ratios (ORs) with 95% confidence intervals (95% CIs) under five genetic models were used to determine the strength of association among rs2241880 polymorphism and cancer susceptibility. Results: The findings did not support an association between the rs2241880 variant in either the overall study population or the subgroups, based on cancer types and ethnicity in any of the genetic models. As far as we know, our study is the first meta-analysis of the association between rs2241880 polymorphism and cancer risk. Conclusions: In conclusion, the findings of this meta-analysis proposes that the ATG16L1 rs2241880 polymorphism may not play a role in cancer development. Further well-designed studies are necessary to clarify the precise role of the ATG16L1 rs2241880 polymorphism on cancer risk.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.050 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".