Prostate cancer resistance leads to a global deregulation of translation factors and unconventional translation of long non-coding RNAs
Bibliographic record
Abstract
ABSTRACT Emerging evidence associates translation factors and regulators to tumorigenesis. Recent advances in our ability to perform global translatome analyses indicate that our understanding of translational changes in cancer resistance is still limited. Here, we generated an enzalutamide-resistant prostate cancer (PCa) model, which recapitulated key features of clinical enzalutamide-resistant PCa. Using this model and polysome profiling, we investigated global translation changes that occur during the acquisition of PCa resistance. We found that enzalutamide-resistant cells exhibit a discordance in biological pathways affected in their translatome relative to their transcriptome, a deregulation of proteins involved in translation, and an overall decrease in translational efficiency. We also show that genomic alterations in proteins with high translational efficiency in enzalutamide-resistant cells are good predictors of poor patient prognosis. Additionally, long non-coding RNAs in enzalutamide-resistant cells show increased association with ribosomes, higher translation efficiency, and an even stronger correlation with poor patient prognosis. Taken together, this suggests that aberrant translation of coding and non-coding genes are strong indicators of PCa enzalutamide-resistance. Our findings thus point towards novel therapeutic avenues that may target enzalutamide resistant PCa.
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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".