MiR-182 Is Associated with Growth, Migration and Invasion in Prostate Cancer via Suppression of FOXO1
Bibliographic record
Abstract
BACKGROUND: MicroRNA (miRNA) have been shown to regulate gene expression in many cancers. MiR-182 has recently been found to be prognostic for patients treated with radical prostatectomy for prostate cancer. We sought to assess miR-182 as a prognostic marker and understand its role in prostate cancer progression and metastasis. METHODS: We analysed miR-182 expression among 147 men treated for prostate cancer using biochemical recurrence and metastasis as the endpoints. We examined miR-182 expression in prostate cancer cells and created cell lines that overexpressed miR-182 for functional assays. Finally, we examined pathways through which miR-182 may function using prediction algorithms and confirmed by Western blotting and knock-down assays. RESULTS: We found that miR-182 was not associated with biochemical recurrence (p=0.1111) or metastasis (p=0.9268) following radical prostatectomy. However, in mechanistic assays, we found that miR-182 expression was higher among aggressive prostate cancer cells and that ectopic miR-182 expression resulted in increased proliferation, migration and invasion in vitro. We identified FOXO1 as regulated by miR-182 in prostate cancer cells, confirmed that ectopic miR-182 expression resulted in diminished FOXO1 levels, and showed that miR-182 inhibition results in increased FOXO1 levels. Expression of FOXO1 (p=0.0014) in tumors from patients who developed biochemical recurrence compared to tumors from patients who were recurrence-free five years after their radical prostatectomy. CONCLUSIONS: Our findings suggest that miR-182 may act to increase prostate cancer proliferation, migration and invasion through suppression of FOXO1. This may be valuable in the development of further therapeutic interventions.
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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 | 0.000 |
| 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) | 0.003 | 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".