MicroRNA Theranostics in Prostate Cancer Precision Medicine
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is the second most frequently diagnosed cancer in men worldwide. Theranostics, a combination of diagnostics and therapeutics, is an emerging concept in the field of precision medicine, and microRNAs (miRNAs) are predictive pioneers in this area. CONTENT: miRNAs are small endogenous noncoding RNA molecules that regulate gene expression posttranscriptionally by targeting messenger RNAs. More than 60% of all protein coding genes are controlled by miRNAs, which makes them powerful regulators of the different cellular processes involved in the pathogenesis of various types of cancer, including prostate cancer. Growing evidence indicates the differential expression of miRNAs in tumor tissues. In addition, miRNAs in body fluids, known as circulating miRNAs, are present in remarkably stable forms and their alteration in prostate cancer has been well documented. Circulating miRNAs are known to originate from tumor tissues, thereby enabling intercellular communication via carriers to promote tumorigenesis and malignancy. In addition, fueled by recent advances, the use of miRNA-based anticancer therapies has been proposed with the onset of early phase clinical trials to assess the therapeutic efficacy of miRNAs. SUMMARY: In this review, we summarize the theranostic utility of miRNAs and outline their diagnostic and prognostic potential in prostate cancer. In addition, we discuss the current detection methodologies and emerging innovative strategies for the detection of miRNAs in body fluids and tumor tissues in the clinical setting. We also provide insight into the current and future therapeutic potential of miRNAs in prostate cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".