Circulating blood miRNAs for prostate cancer risk stratification: miRroring the underlying tumor biology with liquid biopsies
Bibliographic record
Abstract
Christianne Hoey,1,2 Stanley K Liu1–3 1Biological Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, Canada; 2Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; 3Department of Radiation Oncology, University of Toronto, Toronto, ON M5S 3E2, Canada Abstract: Current risk stratification methods for prostate cancer – although they have seen marked improvements over the past decades – are far from perfect. Despite the significant utility of prostate-specific antigen as a biomarker to monitor for disease recurrence, it cannot predict which tumors will recur or recommend the best treatment for patients. Similarly, although biopsies are imperative for diagnosis and staging, they are saddled with limitations and risks. We must move toward a noninvasive biomarker that has predictive and prognostic efficacy. We therefore review the current literature on circulating miRNA biomarkers, apply their use to two significant clinical problems (ie, how limitations of prostate biopsies can impact diagnosis and treatment management, and the need to tailor treatment for a clinically heterogeneous disease), and evaluate how circulating miRNAs have inherent properties that make them ideal liquid biomarkers. We also outline current gaps in knowledge that must be addressed before they can be implemented into routine clinical practice. With further research on their function and validation of their biomarker utility in large prospective cohorts, circulating miRNAs will likely prove to be the liquid biopsies of tomorrow. Keywords: miRNA, biomarker, prostate cancer, risk stratification, serum, plasma
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".