Glutathione S-Transferase M1 (GSTM1) Polymorphisms and Lung Cancer: A Literature-based Systematic HuGE Review and Meta-Analysis
Bibliographic record
Abstract
Multiple genes have been studied for potential associations with lung cancer. The gene most frequently associated with increased risk has been glutathione S-transferase M1 (GSTM1). The glutathione S-transferase enzyme family is known to catalyze detoxification of electrophilic compounds, including carcinogens, therapeutic drugs, environmental toxins, and products of oxidative stress. In this review, the authors summarize the available evidence associating lung cancer with the GSTM1 gene. They describe results from an updated meta-analysis of 98 published genetic association studies investigating the relation between the GSTM1 null variant and lung cancer risk including 19,638 lung cancer cases and 25,266 controls (counting cases and controls in each study only once). All studies considered, the GSTM1 null variant was associated with an increased risk of lung cancer (odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.14, 1.30), but no increase in risk was seen (OR = 1.01, 95% CI: 0.91, 1.12) when only the five largest studies (>500 cases each) were considered. Furthermore, while GSTM1 null status conferred a significantly increased risk of lung cancer to East Asians (OR = 1.38, 95% CI: 1.24, 1.55), such a genotype did not confer increased risk to Caucasians. More data regarding the predictive value of GSTM1 genetic testing are needed before population-based testing may be reasonably considered.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".