Predicting favourable prognosis of urothelial carcinoma: gene expression and genome profiling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it