An evaluation of urinary microRNA reveals a high sensitivity for bladder cancer
Bibliographic record
Abstract
BACKGROUND: Urinary biomarkers are needed to improve the care and reduce the cost of managing bladder cancer. Current biomarkers struggle to identify both high and low-grade cancers due to differing molecular pathways. Changes in microRNA (miR) expression are seen in urothelial carcinogenesis in a phenotype-specific manner. We hypothesised that urinary miRs reflecting low- and high-grade pathways could detect bladder cancers and overcome differences in genetic events seen within the disease. METHODS: We investigated urinary samples (n=121) from patients with bladder cancer (n=68) and age-matched controls (n=53). Fifteen miRs were quantified using real-time PCR. RESULTS: We found that miR is stable within urinary cells despite adverse handling and detected differential expression of 10 miRs from patients with cancer and controls (miRs-15a/15b/24-1/27b/100/135b/203/212/328/1224, ANOVA P<0.05). Individually, miR-1224-3p had the best individual performance with specificity, positive and negative predictive values and concordance of 83%, 83%, 75% and 77%, respectively. The combination of miRs-135b/15b/1224-3p detected bladder cancer with a high sensitivity (94.1%), sufficient specificity (51%) and was correct in 86% of patients (concordance). CONCLUSION: The use of this panel in patients with haematuria would have found 94% of urothelial cell carcinoma, while reducing cystoscopy rates by 26%. However, two invasive cancers (3%) would have been missed.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".