Genetic Polymorphisms of Glutathione<i>S</i>-Transferases and the Risk of Adult Brain Tumors: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Studies investigating the association between genetic polymorphisms of glutathione S-transferases (GST) and risk of adult brain tumors have reported conflicting results. The rationale of this meta-analysis was to determine whether GST variants increase the susceptibility of adult brain tumors by pooling data. METHODS: Two investigators independently searched the HuGENet database, MEDLINE, EMBASE, conference articles, and manually reviewed bibliographies of retrieved articles. Papers were included if they were observational studies investigating the influence of GSTM1, GSTT1, GSTP1 I105V, or GSTP1 A114V on the development of adult brain cancers. Potential sources of heterogeneity between studies were explored in a meta-regression. RESULTS: We identified eight eligible studies, which included 1,630 cases of glioma, 245 cases of meningioma, and 7,151 controls. Using the random effects model, there was no association between any of the GST variants and the risk of glioma [overall odds ratio (OR), 1.08; 95% confidence interval (95% CI), 0.95-1.22]. Subgroup analyses also showed no relationship between GST variants and histopathologic groups; the overall ORs were 1.13 (95% CI, 0.88-1.43) for high-grade glioma and 1.08 (95% CI, 0.76-1.55) for low-grade glioma. A random effects meta-regression suggested that the use of in-hospital controls produced larger effect estimates in glioma than the use of population controls (overall OR, 1.30; 95% CI, 1.03-1.65). The T1 null genotype was significantly associated with a risk of meningioma (OR, 1.95; 95% CI, 1.02-3.76), but the M1 variant was not. CONCLUSION: This study did not suggest any relationship between GST variants and risks of glioma; the T1 null genotype may influence the susceptibility of meningioma, but larger studies are needed to substantiate this relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.068 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".