Germline variation in inflammation-related pathways and risk of Barrett's oesophagus and oesophageal adenocarcinoma
Bibliographic record
Abstract
Objective Oesophageal adenocarcinoma (OA) incidence has risen sharply in Western countries over recent decades. Local and systemic inflammation is considered an important contributor to OA pathogenesis. Established risk factors for OA and its precursor, Barrett's oesophagus (BE), include symptomatic reflux, obesity and smoking. The role of inherited genetic susceptibility remains an area of active investigation. Here, we explore whether germline variation related to inflammatory processes influences susceptibility to BE/OA. Design We used data from a genomewide association study of 2515 OA cases, 3295 BE cases and 3207 controls. Our analysis included 7863 single-nucleotide polymorphisms (SNPs) in 449 genes assigned to five pathways: cyclooxygenase (COX), cytokine signalling, oxidative stress, human leucocyte antigen and nuclear factor-κB. A principal components-based analytic framework was employed to evaluate pathway-level and gene-level associations with disease risk. Results We identified a significant signal for the COX pathway in relation to BE risk (p=0.0059, false discovery rate q=0.03), and in gene-level analyses found an association with microsomal glutathione-S-transferase 1 (MGST1); (p=0.0005, q=0.005). Assessment of 36 MGST1 SNPs identified 14 variants associated with elevated BE risk (q<0.05). Four of these were subsequently confirmed (p<5.5×10−5) in a meta-analysis encompassing an independent set of 1851 BE cases and 3496 controls, and are known strong expression quantitative trait loci for MGST1. Three such variants were associated with similar elevations in OA risk. Conclusions This study provides the most comprehensive evaluation of inflammation-related germline variation in relation to risk of BE/OA and suggests that variants in MGST1 influence disease susceptibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".