Association of a Deletion of GSTT2B with an Altered Risk of Oesophageal Squamous Cell Carcinoma in a South African Population: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Polymorphisms in the Glutathione S-transferase genes are associated with altered risks in many cancers, but their role in oesophageal cancer is unclear. Recently a 37-kb deletion polymorphism of GSTT2B that reduces expression of GSTT2 has been described. We evaluated the influence of the GSTT1 and GSTT2B deletion polymorphisms, and the GSTP1 Ile105Val polymorphism (rs1695) on susceptibility to oesophageal squamous cell carcinoma (OSCC) in the Black and Mixed Ancestry populations of South Africa. METHODS AND RESULTS: The GSTT1, GSTT2B and GSTP1 variants were genotyped in 562 OSCC cases and 907 controls, and tested for association with OSCC and for interaction with smoking and alcohol consumption. Linkage disequilibrium (LD) between the deletions at GSTT1 and GSTT2B was determined, and the haplotypes tested for association with OSCC. Neither the GSTT1 deletion nor the GSTP1 Ile105Val polymorphism was associated with OSCC risk in the Black or Mixed Ancestry populations. The GSTT2B deletion was not associated with OSCC risk in the Black population, but was associated with reduced risk of OSCC in the Mixed Ancestry population (OR=0.71; 95% CI 0.57-0.90, p=0.004). Case-only analysis showed no interaction between the GST polymorphisms and smoking or alcohol consumption. LD between the neighboring GSTT1 and GSTT2B deletions was low in both populations (r(2)(Black)=0.04; r(2)(MxA)=0.07), thus these deletions should be assessed independently for effects on disease risk. CONCLUSIONS: Although there was no association between the GSTT1 deletion polymorphism or the GSTP1 Ile105Val polymorphism with OSCC, our results suggest that the presence of the recently described GSTT2B deletion may have a protective effect on the risk of OSCC in the Mixed Ancestry South African population. This is the first report of the contribution of the GSTT2B deletion to 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".