Germline variation in inflammation-related pathways and risk of Barrett's oesophagus and oesophageal adenocarcinoma
Bibliographic record
Abstract
Esophageal adenocarcinoma (EA) incidence has risen sharply in Western countries over recent decades. Local and systemic inflammation, operating downstream of disease-associated exposures, is considered an important contributor to EA pathogenesis. Several risk factors have been identified for EA and its precursor, Barrett’s esophagus (BE), including symptomatic reflux, obesity, and smoking. The role of inherited genetic susceptibility remains an area of active investigation. To explore whether germline variation related to inflammatory processes influences susceptibility to BE/EA, we used data from a genome-wide association study (GWAS) of 2,515 EA cases, 3,295 BE cases, and 3,207 controls. Our analysis included 7,863 single nucleotide polymorphisms (SNPs) in 449 genes assigned to five pathways: cyclooxygenase (COX), cytokine signaling, oxidative stress, human leukocyte antigen, and NFκB. A principal components-based analytic framework was employed to evaluate pathway-level and gene-level associations with disease risk. We identified a significant signal for the COX pathway in relation to BE risk (P=0.0059, FDR q=0.03), and in gene-level analyses found an association with MGST1 (microsomal glutathione-S-transferase 1; P=0.0005, q=0.005). Assessment of 36 MGST1 SNPs identified 14 variants associated with elevated BE risk (q<0.05). Of these, four were subsequently confirmed (P<5.5 × 10−5) in a meta-analysis encompassing an independent set of 1,851 BE cases and 3,496 controls. Three of these SNPs (rs3852575, rs73112090, rs4149204) were associated with similar elevations in EA risk. This study provides the most comprehensive evaluation of inflammation-related germline variation in relation to risk of BE/EA, and suggests that variants in MGST1 influence disease susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".