Association of <i><scp>MDM</scp>2 <scp>T</scp>309<scp>G</scp></i> and <i>p53 <scp>A</scp>rg72<scp>P</scp>ro</i> polymorphisms and gastroesophageal reflux disease with survival in esophageal adenocarcinoma
Bibliographic record
Abstract
BACKGROUND AND AIM: Although gastroesophageal reflux disease (GERD) is a risk factor for esophageal adenocarcinoma (EAC), some patients develop EAC in the absence of GERD. A putative mechanism of reflux-induced tumorigenesis involves disruptions in the p53 pathway. We assessed the interaction of GERD and p53 pathway polymorphisms on EAC prognosis. METHODS: In a prospective cohort of 358 EAC patients, clinical data (including GERD history and survival) were collected. Germline DNA was genotyped for MDM2 T309G and p53 Arg72Pro. Cox proportional hazards models were used to determine adjusted hazard ratios (AHR) for associations between genotype, GERD, and genotype-GERD interactions with survival. RESULTS: Compared with other genotypes, MDM2 G/G (median overall survival 21 vs 30 months; P < 0.001) and p53 Pro/Pro (12 vs 30 months; P = 0.004) were associated with shorter survival. When analyzed by GERD, MDM2 G/G was associated with shorter survival in patients without GERD (AHR 3.4, 95% CI 2.0-6.0), but not in patients with GERD (AHR 1.1 [0.7-1.8]); the MDM2-GERD interaction was significant (P = 0.003). A similar trend was seen for p53 Pro/Pro (AHRs 2.5 without GERD vs 1.4 with GERD). Combined analysis of at-risk variants (MDM2 G or p53 Pro), revealed each additional at-risk variant was associated with shorter survival in patients without GERD (AHR 1.6) but not with GERD (AHR 1.0). CONCLUSIONS: MDM2 G/G and the combination of MDM2 G and p53 Pro were negative prognostic factors for EAC patients without GERD but not for those with GERD. There may be biological differences between GERD positive and GERD negative EAC.
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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 | 0.001 |
| 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".