MicroRNA-194 promotes lineage plasticity in advanced prostate cancer
Bibliographic record
Abstract
ABSTRACT MicroRNA-194 (miR-194) promotes prostate cancer metastasis, but the precise molecular mechanisms by which it achieves this are unknown. Here, by integrating Argonaute high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (Ago-HITS-CLIP) with RNA sequencing and exon-intron split analysis, we defined a 163-gene miR-194 “targetome” in prostate cancer. These target genes were predominantly down-regulated through canonical 3’UTR recognition sites and were enriched within pathways involved in cytoskeletal organisation and cell movement. In clinical prostate cancer samples, miR-194 activity was inversely correlated with the androgen receptor (AR) signalling axis. At a mechanistic level, this inverse correlation was explained by down-regulation of miR-194 expression by AR. Accordingly, miR-194 expression and activity was significantly elevated in neuroendocrine prostate cancer (NEPC), an aggressive AR-independent disease subtype. MiR-194 enhanced the transdifferentiation of prostate adenocarcinoma cells to a neuroendocrine-like state, at least in part by targeting FOXA1, a transcription factor with a key role in maintaining the prostate epithelial lineage. Importantly, a miR-194 inhibitor effectively inhibited the growth of cell lines and patient-derived organoids with neuroendocrine features. Overall, our study reveals a novel post-transcriptional mechanism regulating the plasticity of prostate cancer cells and provides a rationale for targeting miR-194 in this NEPC.
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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) | 0.001 | 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".