Cytochrome P450 2A6 whole-gene deletion (<i>CYP2A6*4</i> ) polymorphism reduces risk of lung cancer: A meta-analysis
Bibliographic record
Abstract
INTRODUCTIONLung cancer is the most commonly diagnosed cancer worldwide and is the leading cause of cancer death.Smoking is a major contributor to the pathogenesis of lung cancer.Cytochrome P450 2A6 (CYP2A6) is responsible for the metabolic activation of most tobacco carcinogens.CYP2A6 genetic polymorphism can cause variations in the human metabolism of xenobiotics.We performed this meta-analysis to determine the association between whole-gene CYP2A6 deletion polymorphism (CYP2A6*4) and lung cancer risk.METHODS The PubMed, SAGE, Science Direct, the Cochrane Library and Ovid databases were searched for observational studies before October 2018.Methodological quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS).RESULTS Nine case-control studies involving 4385 lung cancer cases and 4142 controls were included in the analysis.The random-effects model was used to combine results from individual studies.The pooled odds ratio was 0.39 (95% CI: 0.27-0.56).There was no heterogeneity across studies (χ 2 =2.49, p=0.96,I 2 =0%).CONCLUSIONS Current evidence from the case-control studies suggests that the CYP2A6 whole-gene deletion polymorphism decreases the risk of lung cancer.Further research is needed to identify any potential confounding factors that may impact this association.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.003 | 0.005 |
| 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".