Unraveling the Role of EV-Derived miR-150-5p in Prostate Cancer Metastasis and Its Association with High-Grade Gleason Scores: Implications for Diagnosis
Bibliographic record
Abstract
Metastasis remains the leading cause of mortality in prostate cancer patients. The presence of tumor cells in lymph nodes is an established prognostic indicator for several cancer types, such as melanoma, breast, oral, pancreatic, and cervical cancers. Emerging evidence highlights the role of microRNAs enclosed within extracellular vesicles as facilitators of molecular communication between tumors and metastatic sites in the lymph nodes. This study aims to investigate the potential diagnostic utility of EV-derived microRNAs in liquid biopsies for prostate cancer. By employing microarrays on paraffin-embedded samples, we characterized the microRNA expression profiles in metastatic lymph nodes, non-metastatic lymph nodes, and primary tumor tissues of prostate cancer. Differential expression of microRNAs was observed in metastatic lymph nodes compared to prostate tumors and non-metastatic lymph node tissues. Three microRNAs (miR-140-3p, miR-150-5p, and miR-23b-3p) were identified as differentially expressed between tissue and plasma samples. Furthermore, we evaluated the expression of these microRNAs in exosomes derived from prostate cancer cells and plasma samples. Intriguingly, high Gleason score samples exhibited the lowest expression of miR-150-5p compared to control samples. Pathway analysis suggested a potential regulatory role for miR-150-5p in the Wnt pathway and bone metastasis. Our findings suggest EV-derived miR-150-5p as a promising diagnostic marker for identifying patients with high-grade Gleason scores and detecting metastasis at an early stage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".