Role of Metastasis-Related microRNAs in Prostate Cancer Progression and Treatment
Bibliographic record
Abstract
Prostate cancer (PCa) is one of the most prevalent cancer types in males and the consequences of its distant metastatic deposits are the leading cause of PCa mortality. Therefore, identifying the causes and molecular mechanisms of hematogenous metastasis formation is of considerable clinical importance for the future development of improved therapeutic approaches. MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level by targeting messenger RNAs. Numerous studies have identified miRNAs as promotors or inhibitors of metastasis and revealed, in part, their targeting pathways in PCa. Because miRNAs are remarkably stable and can be detected in both tissue and body fluid, its potential as specific biomarkers for metastasis and therapeutic response is also currently under preclinical evaluation. In the present review, we focus on miRNAs that are supposed to initiate or suppress metastasis by targeting several key mRNAs in PCa. Metastasis-suppressing miRNAs include miR-33a-5p, miR-34, miR-132 and miR-212, miR-145, the miR-200 family (incl. miR-141-3p), miR-204-5p, miR-532-3p, miR-335, miR-543, miR-505-3p, miR 19a 3p, miR-802, miR-940, and miR-3622a. Metastasis-promoting RNAs, such as miR-9, miR-181a, miR-210-3, miR-454, miR-671-5p, have been shown to increase the metastatic potential of PCa cells. Other metastasis-related miRNAs with conflicting reports in the literature are also discussed (miR-21 and miR-186). Finally, we summarize the recent developments of miRNA-based therapeutic approaches, as well as current limitations in PCa. Taken together, the metastasis-controlling miRNAs provide the potential to be integrated in the strategy of diagnosis, prognosis, and treatment of metastatic PCa. Nevertheless, there is still a lack of consistency between certain miRNA signatures and reproducibility, which impedes clinical implementation.
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 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) | 0.001 | 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 | 0.000 | 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".