Innate immunity gene polymorphisms and the risk of colorectal neoplasia
Bibliographic record
Abstract
Inherited variation in genes that regulate innate immunity and inflammation may contribute to colorectal neoplasia risk. To evaluate this association, we conducted a nested case-control study of 451 colorectal cancer cases, 694 colorectal advanced adenoma cases and 696 controls of European descent within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. A total of 935 tag single-nucleotide polymorphisms (SNPs) in 98 genes were evaluated. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association with colorectal neoplasia. Sixteen SNPs were associated with colorectal neoplasia risk at P < 0.01, but after adjustment for multiple testing, only rs2838732 (ITGB2) remained suggestively associated with colorectal neoplasia (OR(per T allele) = 0.68, 95% CI: 0.57-0.83, P = 7.7 × 10(-5), adjusted P = 0.07). ITGB2 codes for the CD18 protein in the integrin beta chain family. The ITGB2 association was stronger for colorectal cancer (OR(per T allele) = 0.41, 95% CI: 0.30-0.55, P = 2.4 × 10(-) (9)) than for adenoma (OR(per T allele) = 0.84, 95%CI: 0.69-1.03, P = 0.08), but it did not replicate in the validation study. The ITGB2 rs2838732 association was significantly modified by smoking status (P value for interaction = 0.003). Among never and former smokers, it was inversely associated with colorectal neoplasia (OR(per T allele) = 0.5, 95% CI: 0.37-0.69 and OR(per T allele) = 0.72, 95% CI: 0.54-0.95, respectively), but no association was seen among current smokers. Other notable findings were observed for SNPs in BPI/LBP and MYD88. Although the results need to be replicated, our findings suggest that genetic variation in inflammation-related genes may be related to the risk of colorectal neoplasia.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".