Promoter Hypermethylation Identifies Progression Risk in Bladder Cancer
Bibliographic record
Abstract
PURPOSE: New methods to accurately predict an individual tumor behavior are urgently required to improve the treatment of cancer. We previously found that promoter hypermethylation can be an accurate predictor of bladder cancer progression, but it is not cancer specific. Here, we investigate a panel of methylated loci in a prospectively collected cohort of bladder tumors to determine whether hypermethylation has a useful role in the management of patients with bladder cancer. EXPERIMENTAL DESIGN: Quantitative methylation-specific PCR was done at 17 gene promoters, suspected to be associated with tumor progression, in 96 malignant and 30 normal urothelial samples. Statistical analysis and artificial intelligence techniques were used to interrogate the results. RESULTS: Using log-rank analysis, five loci were associated with progression to more advanced disease (RASSF1a, E-cadherin, TNFSR25, EDNRB, and APC; P < 0.05). Multivariate analysis revealed that the overall degree of methylation was more significantly associated with subsequent progression and death (Cox, P = 0.002) than tumor stage (Cox, P = 0.008). Neuro-fuzzy modeling confirmed that these five loci were those most associated with tumor progression. Epigenetic predictive models developed using artificial intelligence techniques identified the presence and timing of tumor progression with 97% specificity and 75% sensitivity. CONCLUSION: Promoter hypermethylation seems a reliable predictor of tumor progression in bladder cancer. It is associated with aggressive tumors and could be used to identify patients with either superficial disease requiring radical treatment or a low progression risk suitable for less intensive surveillance. Multicenter studies are warranted to validate this marker.
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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 | 0.001 | 0.000 |
| 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".