miR-191 promotes radiation resistance of prostate cancer through interaction with RXRA
Bibliographic record
Abstract
Radiation therapy is a common treatment for prostate cancer, however recurrence remains a problem. MicroRNA expression is altered in prostate cancer and may promote therapy resistance. Through bioinformatic analyses of TCGA and CPC-GENE patient cohorts, we identified higher miR-191 expression in tumor versus normal tissue, and increased expression in higher Gleason scores. In vitro and in vivo experiments demonstrated that miR-191 overexpression promotes radiation survival, and contributes to a more aggressive phenotype. Retinoid X receptor alpha, RXRA, was discovered to be a novel target of miR-191, and knockdown recapitulated radioresistance. Furthermore, treatment of prostate cancer cells with the RXRA agonist 9-cis-retinoic acid restored radiosensitivity. Supporting this relationship, patients with high miR-191 and low RXRA abundance experienced quicker biochemical recurrence. Reduced RXRA translated to a higher risk of distant failure after radiotherapy. Notably, this miR-191/RXRA interaction was conserved in a novel primary cell line derived from radiorecurrent prostate cancer. Together, our findings demonstrate that miR-191 promotes prostate cancer survival after radiotherapy, and highlights retinoids as a potential option to improve radiotherapy response.
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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) | 0.002 | 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".