A microRNA CRISPR screen reveals microRNA-483-3p as an apoptotic regulator in prostate cancer cells
Bibliographic record
Abstract
The development of traditional protein-targeted cancer therapies is a slow and arduous process, often taking years or even decades. In contrast, RNA-based therapies targeting crucial microRNA (miRNA) offer a faster alternative due to the sequence-specific nature of miRNA inhibitor binding. This, combined with the capacity of individual miRNA to influence multiple cellular pathways, makes these small RNA attractive targets for cancer therapy. While miRNA are known to be dysregulated in prostate cancer (PCa), identifying their individual contributions to disease progression and the identification of therapeutically actionable miRNA targets in PCa has been challenging due to limited profiling and lack of screening tools. To address this need, we developed miRKOv2, a miRNA-only CRISPR knockout library enabling systematic, genome-wide loss-of-function screens to identify miRNA essential for PCa cell survival. Our screens uncovered 70 potential essential miRNA candidates, with miR-483 demonstrating the most significant impact on PCa cell viability. Functional characterization revealed that miR-483 disruption potentiated apoptosis in PCa cell lines. Mechanistically, we uncovered a novel regulatory axis wherein miR-483-3p directly modulates a BCLAF1/PUMA/BAK1 apoptotic signaling network, highlighting its critical role in maintaining PCa cell survival. Our findings provide novel insights into the complex regulatory role of miRNA in PCa progression and offer a potential therapeutic strategy for targeting miRNA-mediated pathways in metastatic disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".