MiRNA-Related SNPs and Risk of Esophageal Adenocarcinoma and Barrett’s Esophagus: Post Genome-Wide Association Analysis in the BEACON Consortium
Bibliographic record
Abstract
Incidence of esophageal adenocarcinoma (EA) has increased substantially in recent decades. Multiple risk factors have been identified for EA and its precursor, Barrett's esophagus (BE), such as reflux, European ancestry, male sex, obesity, and tobacco smoking, and several germline genetic variants were recently associated with disease risk. Using data from the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON) genome-wide association study (GWAS) of 2,515 EA cases, 3,295 BE cases, and 3,207 controls, we examined single nucleotide polymorphisms (SNPs) that potentially affect the biogenesis or biological activity of microRNAs (miRNAs), small non-coding RNAs implicated in post-transcriptional gene regulation, and deregulated in many cancers, including EA. Polymorphisms in three classes of genes were examined for association with risk of EA or BE: miRNA biogenesis genes (157 SNPs, 21 genes); miRNA gene loci (234 SNPs, 210 genes); and miRNA-targeted mRNAs (177 SNPs, 158 genes). Nominal associations (P<0.05) of 29 SNPs with EA risk, and 25 SNPs with BE risk, were observed. None remained significant after correction for multiple comparisons (FDR q>0.50), and we did not find evidence for interactions between variants analyzed and two risk factors for EA/BE (smoking and obesity). This analysis provides the most extensive assessment to date of miRNA-related SNPs in relation to risk of EA and BE. While common genetic variants within components of the miRNA biogenesis core pathway appear unlikely to modulate susceptibility to EA or BE, further studies may be warranted to examine potential associations between unassessed variants in miRNA genes and targets with disease risk.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".