Interactions between environmental factors and polymorphisms in angiogenesis pathway genes in esophageal adenocarcinoma risk: A case‐only study
Bibliographic record
Abstract
BACKGROUND: Gastroesophageal reflux disease (GERD), higher body mass index (BMI), smoking, and genetic variants in angiogenic pathway genes have been individually associated with increased risk of esophageal adenocarcinoma. However, how angiogenic gene polymorphisms and environmental factors jointly affect esophageal adenocarcinoma development remains unclear. METHODS: By using a case-only design (n = 335), the authors examined interactions between 141 functional/tagging angiogenic single nucleotide polymorphisms (SNPs) and environmental factors (GERD, BMI, smoking) in modulating esophageal adenocarcinoma risk. Gene-environment interactions were assessed by a 2-step approach. First, the authors applied random forest to screen for important SNPs that had either main or interaction effects. Second, they used case-only logistic regression to assess the effects of gene-environment interactions on esophageal adenocarcinoma risk, adjusting for covariates and false-discovery rate. RESULTS: Random forest analyses identified 3 sets of SNPs (17 SNPs-GERD, 26 SNPs-smoking, and 34 SNPs-BMI) that had the highest importance scores. In subsequent logistic regression analyses, interactions between 2 SNPs (rs2295778 of HIF1AN, rs13337626 of TSC2) and GERD, 2 SNPs (rs2295778 of HIF1AN, rs2296188 of VEGFR1) and smoking, and 7 SNPs (rs2114039 of PDGRFA, rs2296188 of VEGFR1, rs11941492 of VEGFR1, rs17708574 of PDGFRB, rs7324547 of VEGFR1, rs17619601 of VEGFR1, and rs17625898 of VEGFR1) and BMI were significantly associated with esophageal adenocarcinoma development (all false-discovery rates ≤0.10). Moreover, these interactions tended to have SNP dose-response effects for increased esophageal adenocarcinoma risk with increasing number of combined risk genotypes. CONCLUSIONS: These findings suggest that genetic variations in angiogenic genes may modify esophageal adenocarcinoma susceptibility through interactions with environmental factors in an SNP dose-response manner.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".