Germline variation in the insulin-like growth factor pathway and risk of Barrett’s esophagus and esophageal adenocarcinoma
Bibliographic record
Abstract
Genome-wide association studies (GWAS) of esophageal adenocarcinoma (EAC) and its precursor, Barrett's esophagus (BE), have uncovered significant genetic components of risk, but most heritability remains unexplained. Targeted assessment of genetic variation in biologically relevant pathways using novel analytical approaches may identify missed susceptibility signals. Central obesity, a key BE/EAC risk factor, is linked to systemic inflammation, altered hormonal signaling and insulin-like growth factor (IGF) axis dysfunction. Here, we assessed IGF-related genetic variation and risk of BE and EAC. Principal component analysis was employed to evaluate pathway-level and gene-level associations with BE/EAC, using genotypes for 270 single-nucleotide polymorphisms (SNPs) in or near 12 IGF-related genes, ascertained from 3295 BE cases, 2515 EAC cases and 3207 controls in the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) GWAS. Gene-level signals were assessed using Multi-marker Analysis of GenoMic Annotation (MAGMA) and SNP summary statistics from BEACON and an expanded GWAS meta-analysis (6167 BE cases, 4112 EAC cases, 17 159 controls). Global variation in the IGF pathway was associated with risk of BE (P = 0.0015). Gene-level associations with BE were observed for GHR (growth hormone receptor; P = 0.00046, false discovery rate q = 0.0056) and IGF1R (IGF1 receptor; P = 0.0090, q = 0.0542). These gene-level signals remained significant at q < 0.1 when assessed using data from the largest available BE/EAC GWAS meta-analysis. No significant associations were observed for EAC. This study represents the most comprehensive evaluation to date of inherited genetic variation in the IGF pathway and BE/EAC risk, providing novel evidence that variation in two genes encoding cell-surface receptors, GHR and IGF1R, may influence risk of BE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".