Interactions between genetic polymorphisms in the apoptotic pathway and environmental factors on esophageal adenocarcinoma risk
Bibliographic record
Abstract
How genetic variations in apoptosis pathway interact with environmental factors to contribute to esophageal adenocarcinoma (EA) risk has not been comprehensively investigated. We conducted a case-only analysis in 335 Caucasian EA patients that were genotyped for 242 single nucleotide polymorphisms (SNPs) in 43 apoptotic genes. Gene-environment interactions were assessed using a two-step approach. First, random forest algorithm was used to screen for the potential interacting markers. Next, we used case-only logistic regression model to estimate the effects of gene-environment interactions on EA risk. Four SNPs (PERP rs648802; PIK3CA rs4855094, rs7644468 and TNFRSF1A rs4149579) had significant interaction with gastroesophageal reflux disease (GERD). The presence of variant alleles in TP53BP1 rs560191, CASP7 rs7907519 or BCL2 rs12454712 enhanced the risk of smoking by 2.08-2.58 times [interaction odds ratio (ORi)=2.08-2.58, adjusted P-value (Padj)=0.02-0.04]. Compared with patients carrying ≤1 risk genotype, the risk of GERD on EA was increased in persons with two (ORi=1.89, Padj=0.016) or ≥3 (ORi=4.30, Padj<0.0001) risk genotypes. Compared with cases with ≤1 risk genotype, smoking-associated EA risk increased by 3.15 times when ≥2 risk genotypes were present (ORi=3.15, Padj<0.0001). In conclusion, interactions among apoptotic SNPs and GERD or smoking play an important role in EA development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".