A pilot study of urinary microRNA as a biomarker for urothelial cancer
Bibliographic record
Abstract
OBJECTIVE: MicroRNAs (miRNAs) are part of a class of small ribonucleic acid (RNAs). They are important regulatory molecules, involved in several cell processes, such as developmental timing, stem cell division and apoptosis. Dysregulated miRNAs have been identified in several human malignancies, including bladder cancer tissue samples, and may confer a "tumour signature" that can be exploited for diagnostic purposes. We report on a prospective pilot study investigating the diagnostic capability of miRNAs in the urine of patients with urothelial cancer. METHODS: Voided urine samples were collected from patients with urothelial carcinoma just prior to bladder tumour resection, as well as age-matched healthy control patients. Pathology demonstrated both low- and high-grade cancer. Total RNA was isolated and quantitative reverse transcriptase-polymerase chain reaction was performed on the RNA extracts using primers for 4 miRNAs shown previously to be dysregulated in solid urothelial carcinomas with RNU6B as the endogenous control. Standard urine cytology was performed on all samples in a blinded fashion. RESULTS: Two miRNAs of interest were dysregulated in the urine from cancer patients with miR-125b showing an average 10.42-fold decrease (p < 0.01) and miR-126 showing an average 2.70-fold increase (p = 0.30) in the cancer samples compared to the normal controls. The sensitivity and specificity of the cytology on the same urine samples were 50% and 80%, respectively. Using these 2 miRNAs only, a decision-tree prediction model was generated for a validation cohort of patients yielding a specificity of 100% and a sensitivity of 80%. DISCUSSION: This preliminary study of candidate urinary miRNA in patients with low- and high-grade urothelial cancer demonstrated a significantly improved diagnostic accuracy over cytology. These results provide rationale for further studies on discovery and validation of candidate miRNAs in voided urine and may potentially lead to the development of a non-invasive and sensitive test for bladder cancer diagnosis and prognosis.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".