Genetic polymorphisms of VEGF, interactions with cigarette smoking exposure and esophageal adenocarcinoma risk
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is a major regulator of angiogenesis in the process of tumor growth and metastasis in esophageal adenocarcinoma (EA). Polymorphisms in the VEGF gene have been associated with altered VEGF expression and plasma VEGF levels. We hypothesized that polymorphisms of VEGF may contribute to EA risk. Functional polymorphisms in the VEGF gene (-460C/T, +405C/G and +936C/T) were determined in 308 patients with EA and 546 healthy controls. Logistic regression analysis was employed to assess the associations between genotypes, haplotypes of VEGF and EA risk, adjusting for multiple confounding factors. Compared with the +936CC genotype, the combined +936CT+TT genotypes were significantly associated with increased risk of developing EA, with adjusted odds ratio (OR) = 1.49 [95% confidence interval (CI), 1.05-2.12; P = 0.027]. The -460CT+CC were associated with increased risk of EA in smokers (adjusted OR = 1.57; 95% CI, 1.07-2.30; P = 0.021), whereas the -460CT/CC were associated with decreased risk of EA (adjusted OR = 0.47; 95% CI, 0.25-0.91; P = 0.025) in non-smokers. Compared with non-smokers with the +460TT, smokers with the +460CT+CC had significantly higher risk of EA (adjusted OR = 3.32; 95% CI, 1.56-7.10; P = 0.002). No overall or interacting association with EA risk was found for the +405C/G polymorphism. Haplotype CGT (-460C/+405G/+936T) was significantly associated with higher risk of EA (adjusted OR = 1.70; 95% CI, 1.04-2.73; P = 0.034). These results suggested that cigarette smoking modifies the association between VEGF polymorphisms and EA risk among Caucasians.
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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 | 0.002 |
| 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".