Phase II Drug-Metabolizing Polymorphisms and Smoking Predict Recurrence of Non–Muscle-Invasive Bladder Cancer: A Gene–Smoking Interaction
Bibliographic record
Abstract
Cigarette smoking is the most important known risk factor for urinary bladder cancer. Selected arylamines in cigarette smoke are recognized human bladder carcinogens and undergo biotransformation through several detoxification pathways, such as the glutathione S-transferases (GST), and uridine-diphospho-glucuronosyltransferases (UGT) pathways. GSTM1 deletion status and UGT1A1*28 rs8175347 genotypes were assessed in 189 non-muscle-invasive bladder cancers (NMIBC) patients with pTa (77.2%) and pT1 (22.8%) tumors and a mean follow-up of 5.6 years, to investigate whether two common functional polymorphisms in GSTM1 and UGT1A1 genes and smoking history are associated with recurrence-free survival of patients with NMIBC. Most patients were current (48.7%) or previous (35.4%) cigarette smokers and 15.9% never smoked. Tumor recurrence occurred in 65.1% of patients, at a median time of 12.9 months. Upon multivariate analysis, previous and current smokers approximately tripled their risk of recurrences [HR = 2.76; 95% confidence interval (CI), 1.03-7.40 and HR = 2.93; 95% CI, 1.08-7.94, respectively]. When adjusted for age, smoking status, stage, grade, gender, and presence of carcinoma in situ, carriers of GSTM1 (+/- and -/-) and UGT1A1*28/*28 alleles were significantly at risk of NMIBC recurrence (HR = 10.05; 95% CI, 1.35-75.1 and HR = 1.91; 95% CI, 1.01-3.62, respectively). Compared with nonsmokers with UGT1A1*1/*1 and *1/*28 genotypes, previous and current smokers homozygous for the UGT1A1*28 allele demonstrated a risk of recurrence of 4.95 (95% CI, 1.02-24.0) and 5.32 (95% CI, 2.07-13.7), respectively. This study establishes a connection between GSTM1, UGT1A1, and tobacco exposure as prognostic markers of NMIBC recurrence in bladder cancer patients. These findings warrant validation in larger cohorts.
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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 | 0.000 | 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) | 0.003 | 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".