Combined Polymorphism Analysis of Glutathione S-transferase M1/G1 and Interleukin-1B (IL-1B)/Interleukin 1-Receptor Antagonist (IL-1RN) and Gastric Cancer Risk in an Omani Arab Population
Bibliographic record
Abstract
BACKGROUND: Host genetics have been implicated in gastric cancer carcinogenesis. Polymorphisms of glutathione S-transferase (GST) M1 and G1 and of interleukin-1B (IL-1B) and interleukin-1 receptor antagonist (IL-1RN) were shown to increase gastric cancer predisposition in several studies. To our knowledge, this is the first report on the combined analysis of polymorphisms GSTM1/G1 and IL-1B/IL-1RN genes in gastric adenocarcinoma. METHODS: Genomic DNA was extracted from peripheral blood of 107 control subjects and 107 gastric cancer patients. Analysis for the GSTM1 and GSTT1 gene polymorphisms was performed by multiplex polymerase chain reaction. The DNA samples were analyzed using the TaqMan allelic discrimination test for the polymorphism of IL-1B at positions-31. The variable number of tandem repeats of IL-1RN was genotyped using polymerase chain reaction followed by agarose gel electrophoresis. RESULTS: There were no statistically significant associations between the GSTM1/G1 or IL-1B-31 genes and gastric cancer risk. There was a statistical association between the presence of the IL-1RN*2 allele and gastric cancer (odds ratio 2.2, 95% confidence interval=1.2-3.7, P=0.01). Combined analysis showed that a combination of the null GSTM1 genotype and carriers of IL-1RN*2 was associated with a statistically significant correlation with gastric cancer (odds ratio=3.6, 95% confidence interval=1.4-9.4, P=0.008). CONCLUSIONS: The current study suggests that the individual variation in both the cellular inflammatory modulator IL-1RN and the antioxidative property of GSTM1 may predispose individuals to an increased risk of gastric cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".