A Functional Epidermal Growth Factor (EGF) Polymorphism, EGF Serum Levels, and Esophageal Adenocarcinoma Risk and Outcome
Bibliographic record
Abstract
PURPOSE: The epidermal growth factor (EGF) pathway is important in esophageal adenocarcinoma (EAC) tumorigenesis. We hypothesized that the EGF A61G homozygous variant genotype (GG) is (a) both a risk and poor prognostic factor for EAC and (b) associated with higher EGF serum levels in individuals with gastroesophageal reflux disease (GERD). EXPERIMENTAL DESIGN: Using unconditional logistic regression, we compared EGF A61G in 312 EAC cases and 447 GERD-free controls, adjusting for age, gender, smoking history, and healthy adult body mass index. Using the method of Kaplan and Meier, log-rank tests, and Cox proportional hazard models, we correlated EGF A61G with overall and failure-free survival in the EAC cases. Serum EGF levels and EGF genotype (G/G versus others) were correlated in 144 GERD patients using Wilcoxon rank sum tests. RESULTS: The EGF A61G G/G genotype conferred increased EAC risk, with an adjusted odds ratio of 1.81 (95% confidence interval, 1.2-2.7), and was even higher in the subgroup of EAC patients with concurrent Barrett's esophagus (adjusted odds ratio, 2.18; 95% confidence interval, 1.3-3.7). However, EGF A61G was not associated with a more aggressive phenotype or prognosis in EAC patients. Higher serum EGF levels were found in GERD patients carrying G/G compared with A/A or A/G (P = 0.03, Wilcoxon rank sum test). CONCLUSION: The EGF A61G G/G genotype is associated with a near 2-fold greater risk of EAC. The G/G allele was also associated with higher EGF levels in tumor-free patients with GERD. EGF genotyping can potentially identify high-risk patients with GERD and Barrett's metaplasia who might benefit from increased surveillance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".