Predicting favourable prognosis of urothelial carcinoma: gene expression and genome profiling
Bibliographic record
Abstract
PURPOSE OF REVIEW: During the past few years, information on (epi)genetic and expression profiling of urothelial carcinomas has expanded, allowing a better appreciation of their correlation with clinicopathological features of bladder cancer. RECENT FINDINGS: The two-pathway model of bladder carcinogenesis separating a favourable pathway characterized by mutations in the fibroblast growth factor 3 gene (FGFR3) and a clinically unfavourable pathway characterized by genetic instability and mutations in the p53 gene is now well established. Noninvasive (pTa), superficially invasive (pT1) and muscle invasive (pT2) bladder cancers can be separated statistically on the basis of extent of genomic instability. Expression (cDNA) array analyses are able to define mRNA signatures specifically associated with the two pathways of bladder carcinogenesis. Currently, attention is shifting to the role of epigenetic alterations in bladder carcinogenesis, including promoter hypermethylation of specific genes and aberrant expression of microRNAs. The level of promoter hypermethylation gradually increases from morphologically normal urothelium to invasive carcinoma. Aberrant expression of specific microRNAs is specifically related to the FGFR3 mutant defined bladder carcinogenesis pathway. SUMMARY: Quantitative genomic (DNA) alterations are associated with the two major molecular pathways of bladder carcinogenesis, defined by FGFR3 and p53 mutations. Chromosomal alterations, cancer specific mRNA expression signature and promoter hypermethylation may precede clinically and histopathologically detectable bladder cancer. As gene expression signature, promoter hypermethylation of selected genes and aberrant expression of some microRNAs are promising as bladder cancer biomarkers, future studies should explore their potential clinical significance taking into account their robustness and cost-effectiveness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".