Polymorphisms in the <i><scp>AKT</scp>1</i> and <i><scp>AKT</scp>2</i> genes and oesophageal squamous cell carcinoma risk in an Eastern Chinese population
Bibliographic record
Abstract
Ethnic Han Chinese are at high risk of developing oesophageal squamous cell carcinoma (ESCC). Aberrant activation of the AKT signalling pathway is involved in many cancers, including ESCC. Some single nucleotide polymorphisms (SNPs) in genes involved in this pathway may contribute to ESCC susceptibility. We selected five potentially functional SNPs in AKT1 (rs2494750, rs2494752 and rs10138277) and AKT2 (rs7254617 and rs2304186) genes and investigated their associations with ESCC risk in 1117 ESCC cases and 1096 controls in an Eastern Chinese population. None of individual SNPs exhibited an association with ESCC risk. However, the combined analysis of three AKT1 SNPs suggested that individuals carrying one of AKT1 variant genotypes had a decreased ESCC risk [adjusted odds ratio (OR) = 0.60, 95% CI = 0.42-0.87]. Further stratified analysis found that AKT1 rs2294750 SNP was associated with significantly decreased ESCC risk among women (adjusted OR = 0.63, 95% CI = 0.43-0.94) and non-drinkers (OR = 0.79, 95% CI = 0.64-0.99). Similar protective effects on women (adjusted OR = 0.56, 95% CI = 0.37-0.83) and non-drinker (adjusted OR = 0.75, 95% CI = 0.60-0.94) were also observed for the combined genotypes of AKT1 SNPs. Consistently, logistic regression analysis indicated significant gene-gene interactions among three AKT1 SNPs (P < 0.015). A three-AKT1 SNP haplotype (C-A-C) showed a significant association with a decreased ESCC risk (adjusted OR = 0.70, 95% CI = 0.52-0.94). Multifactor dimensionality reduction analysis confirmed a high-order gene-environment interaction in ESCC risk. Overall, we found that three AKT1 SNPs might confer protection against ESCC risk; nevertheless, these effects may be dependent on other risk factors. Our results provided evidence of important gene-environment interplay in ESCC carcinogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".