MSH2 −118T>C and MSH6 −159C>T promoter polymorphisms and the risk of colorectal cancer
Bibliographic record
Abstract
The most important indicator of colorectal cancer (CRC) risk is the presence of family history of the disease. Inherited genetic changes, such as single nucleotide polymorphisms, in key candidate genes may contribute to CRC risk. We investigated whether promoter polymorphisms in DNA mismatch repair (MMR) genes MSH2 and MSH6 are associated with the risk of CRC. We genotyped 929 CRC patients and 1098 control subjects from Ontario, and 467 patients and 344 controls from Newfoundland and Labrador, for two promoter polymorphisms in the MMR genes MSH2 and MSH6 using the fluorogenic 5' nuclease assay. We used unconditional logistic regression to evaluate the association between each polymorphism and CRC after adjusting for age and sex. The associations between polymorphisms and tumor clinicopathological features were evaluated with a Pearson's chi-squared test or Fisher's exact test. All statistical tests were two sided. We observed strong associations between the MSH2 -118T>C polymorphism and family history of CRC based on the Amsterdam criteria I (P = 0.005) and Amsterdam criteria I and II (P = 0.036) among cases from Ontario. This association was especially evident among female CRC patients in Ontario (for Amsterdam criteria I, and I and II combined, P = 0.003 and P = 0.0001, respectively). The MSH2 -118T>C polymorphism was associated with strong family history of CRC in Ontario patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".