Gene-Smoking Interaction Associations for the <i>ERCC1</i> Polymorphisms in the Risk of Lung Cancer
Bibliographic record
Abstract
Cigarette smoking may induce DNA damage. Lower DNA repair capacities have been associated with higher risk of lung cancer. Excision repair cross-complementing group 1 (ERCC1) is the lead enzyme in the nucleotide excision repair process, and low expression of ERCC1 mRNA levels has been associated with higher risk of cancers. We examined the association between two polymorphisms of ERCC1, 8092C > A (rs3212986) and 19007T > C (codon 118, rs11615), which are associated with altered ERCC1 mRNA stability and mRNA levels, in 1,752 Caucasian lung cancer patients and 1,358 controls. The results were analyzed using logistic regression models, adjusting for relevant covariates. The two polymorphisms were in Hardy-Weinberg disequilibrium and in linkage disequilibrium. There was no overall association between ERCC1 polymorphisms and lung cancer risk, with the adjusted odds ratios (AOR) of 1.26 [95% confidence interval (95% CI), 0.81-1.96] for the 8092C > A polymorphism (A/A versus C/C) and 0.93 (95% CI, 0.67-1.30) for the 19007T > C polymorphism (C/C versus T/T). Stratified analyses revealed that the AORs for the 8092C > A polymorphism (A/A versus C/C) decreased significantly as pack-years increased, with the AOR of 2.11 (95% CI, 1.03-4.31) in never smokers and 0.50 (95% CI, 0.25-1.01) in heavy smokers (>/=56 pack-years), respectively. Consistent results were found when gene-smoking interaction was incorporated by joint effects and interactions models that considered both discrete and continuous variables for cumulative smoking exposure. The same direction for the gene-smoking interaction was found for the 19007T > C polymorphism, although the interaction was not statistically significant. In conclusion, ERCC1 8092C > A polymorphism may modify the associations between cumulative cigarette smoking and lung 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".