Analysis of variants in DNA damage signalling genes in bladder cancer
Bibliographic record
Abstract
BACKGROUND: Chemicals from occupational exposure and components of cigarette smoke can cause DNA damage in bladder urothelium. Failure to repair DNA damage by DNA repair proteins may result in mutations leading to genetic instability and the development of bladder cancer. Immunohistochemistry studies have shown DNA damage signal activation in precancerous bladder lesions which is lost on progression, suggesting that the damage signalling mechanism acts as a brake to further tumorigenesis. Single nucleotide polymorphisms (SNPs) in DSB signalling genes may alter protein function. We hypothesized that SNPs in DSB signalling genes may modulate predisposition to bladder cancer and influence the effects of environmental exposures. METHODS: We recruited 771 cases and 800 controls (573 hospital-based and 227 population-based from a previous case-control study) and interviewed them regarding their smoking habits and occupational history. DNA was extracted from a peripheral blood sample and genotyping of 24 SNPs in MRE11, NBS1, RAD50, H2AX and ATM was undertaken using an allelic discrimination method (Taqman). RESULTS: Smoking and occupational dye exposure were strongly associated with bladder cancer risk. Using logistic regression adjusting for age, sex, smoking and occupational dye exposure, there was a marginal increase in risk of bladder cancer for an MRE11 3'UTR SNP (rs2155209, adjusted odds ratio 1.54 95% CI (1.13-2.08, p = 0.01) for individuals homozygous for the rare allele compared to those carrying the common homozygous or heterozygous genotype). However, in the hospital-based controls, the genotype distribution for this SNP deviated from Hardy-Weinberg equilibrium. None of the other SNPs showed an association with bladder cancer and we did not find any significant interaction between any of these polymorphisms and exposure to smoking or dye exposure. CONCLUSION: Apart from a possible effect for one MRE11 3'UTR SNP, our study does not support the hypothesis that SNPs in DSB signaling genes modulate predisposition to bladder cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".