Nuclear mTOR acts as a transcriptional integrator of the androgen signaling pathway in prostate cancer
Bibliographic record
Abstract
Androgen receptor (AR) signaling reprograms cellular metabolism to support prostate cancer (PCa) growth and survival. Another key regulator of cellular metabolism is mTOR, a kinase found in diverse protein complexes and cellular localizations, including the nucleus. However, whether nuclear mTOR plays a role in PCa progression and participates in direct transcriptional cross-talk with the AR is unknown. Here, via the intersection of gene expression, genomic, and metabolic studies, we reveal the existence of a nuclear mTOR-AR transcriptional axis integral to the metabolic rewiring of PCa cells. Androgens reprogram mTOR-chromatin associations in an AR-dependent manner in which activation of mTOR-dependent metabolic gene networks is essential for androgen-induced aerobic glycolysis and mitochondrial respiration. In models of castration-resistant PCa cells, mTOR was capable of transcriptionally regulating metabolic gene programs in the absence of androgens, highlighting a potential novel castration resistance mechanism to sustain cell metabolism even without a functional AR. Remarkably, we demonstrate that increased mTOR nuclear localization is indicative of poor prognosis in patients, with the highest levels detected in castration-resistant PCa tumors and metastases. Identification of a functional mTOR targeted multigene signature robustly discriminates between normal prostate tissues, primary tumors, and hormone refractory metastatic samples but is also predictive of cancer recurrence. This study thus underscores a paradigm shift from AR to nuclear mTOR as being the master transcriptional regulator of metabolism in PCa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".