Temporal Stability and Prognostic Biomarker Potential of the Prostate Cancer Urine miRNA Transcriptome
Bibliographic record
Abstract
BACKGROUND: The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. METHODS: We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). RESULTS: We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). CONCLUSIONS: These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.
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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".