Genetic polymorphisms of <i>MDM2</i>, cumulative cigarette smoking and nonsmall cell lung cancer risk
Bibliographic record
Abstract
Abnormalities of the tumor suppressor TP53 pathway are critical in the development of many cancers since it regulates cell cycle components and apoptosis. Murine double minute-2 (MDM2) protein is a central node in the p53 pathway and a direct negative regulator of p53. The MDM2 SNP309 (rs2279744) polymorphism increases MDM2 RNA and protein levels, attenuating the p53 pathway. The MDM2 SNP309 polymorphism was investigated in 1,787 Caucasian nonsmall cell lung cancer (NSCLC) patients and 1,360 healthy controls. Cases and controls were analyzed for associations with genotype and adjusted for age, gender, histology and smoking history. There were no overall associations between the MDM2 genotypes and risk of lung cancer (adjusted odds ratios [AORs] = 0.82 [95% confidence interval [CI] = 0.6-1.1] for the T/G genotype and AOR = 1.32 [95% CI = 0.9-2.0] for the G/G genotype). A statistically significant interaction (p = 0.01) was found between smoking and MDM2 genotypes. Consistent with this interaction, stratified analysis by pack-years of smoking demonstrated that the AORs of G/G vs. T/T were 1.56 (1.0-2.7), 1.46 (1.0-2.2), 0.80 (0.5-1.3) and 0.63 (0.4-1.1), respectively, for never, mild (<30 pack-years), moderate (30-57 pack-years) and heavy smokers (>or=58 pack-years). In conclusion, a strong gene-smoking interaction was observed between the MDM2 SNP309 and NSCLC 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".