miR‐30a inhibits androgen‐independent growth of prostate cancer via targeting MYBL2, FOXD1, and SOX4
Bibliographic record
Abstract
BACKGROUND: Castrate-resistant prostate cancer (CRPC) is an aggressive and lethal disease. The pathogenesis of CRPC is not fully understood and novel therapeutic targets need to be identified to improve the patients' prognosis. MicroRNA-30a (miR-30a) has been demonstrated to be a tumor suppressor in many types of solid malignancies. However, its role in androgen-independent (AI) growth of prostate cancer (PCa) received limited attention as yet. METHODS: The clinical association of miR-30a and its potential targets with AI growth was characterized by bioinformatics analyses. Regulation of cell proliferation and colony formation rates by miR-30a were tested using PCa cell models. Xenograft models were used to measure the regulation of prostate tumor growth by miR-30a. The real-time quantitative polymerase chain reaction was used to validate whether miR-30a and its targets regulate cell cycle control genes and androgen receptor (AR)-dependent transcription. Bioinformatics tools, Western blot, and luciferase reporter assays were utilized to identify miR-30a targets. RESULTS: Bioinformatic analysis showed that low expression of miR-30a is associated with castration resistance of PCa patients and poor outcomes. Transfection of miR-30a mimics inhibited the AI growth of PCa cells in vitro and in vivo. Upregulation of miR-30a in 22RV1 cells altered the expression of cell cycle control genes and AR-mediated transcription, while downregulation of miR-30a in LNCaP cells had the opposite effects to AR-mediated transcription. MYBL2, FOXD1, and SOX4 were identified as miR-30a targets. Downregulation of MYBL2, FOXD1, and SOX4 affected the expression of cell cycle control genes and AR-mediated transcription and suppressed the AI growth of 22RV1 cells. CONCLUSIONS: Our results suggest that miR-30a inhibits AI growth of PCa by targeting MYBL2, FOXD1, and SOX4. They provide novel insights into developing new treatment strategies for CRPC.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".