<scp><i>TERT</i></scp> gene harbors multiple variants associated with pancreatic cancer susceptibility
Bibliographic record
Abstract
A small number of common susceptibility loci have been identified for pancreatic cancer, one of which is marked by rs401681 in the TERT-CLPTM1L gene region on chromosome 5p15.33. Because this region is characterized by low linkage disequilibrium, we sought to identify whether additional single nucleotide polymorphisms (SNPs) could be related to pancreatic cancer risk, independently of rs401681. We performed an in-depth analysis of genetic variability of the telomerase reverse transcriptase (TERT) and the telomerase RNA component (TERC) genes, in 5,550 subjects with pancreatic cancer and 7,585 controls from the PANcreatic Disease ReseArch (PANDoRA) and the PanScan consortia. We identified a significant association between a variant in TERT and pancreatic cancer risk (rs2853677, odds ratio = 0.85; 95% confidence interval = 0.80-0.90, p = 8.3 × 10(-8)). Additional analysis adjusting rs2853677 for rs401681 indicated that the two SNPs are independently associated with pancreatic cancer risk, as suggested by the low linkage disequilibrium between them (r(2) = 0.07, D' = 0.28). Three additional SNPs in TERT reached statistical significance after correction for multiple testing: rs2736100 (p = 3.0 × 10(-5) ), rs4583925 (p = 4.0 × 10(-5) ) and rs2735948 (p = 5.0 × 10(-5) ). In conclusion, we confirmed that the TERT locus is associated with pancreatic cancer risk, possibly through several independent variants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".