Dual contribution of the mTOR pathway and of the metabolism of amino acids in prostate cancer
Bibliographic record
Abstract
BACKGROUND: Prostate cancer is the leading cause of cancer in men, and its incidence increases with age. Among other risk factors, pre-existing metabolic diseases have been recently linked with prostate cancer, and our current knowledge recognizes prostate cancer as a condition with important metabolic anomalies as well. In malignancies, metabolic disorders are commonly associated with aberrations in mTOR, which is the master regulator of protein synthesis and energetic homeostasis. Although there are reports demonstrating the high dependency of prostate cancer cells for lipid derivatives and even for carbohydrates, the understanding regarding amino acids, and the relationship with the mTOR pathway ultimately resulting in metabolic aberrations, is still scarce. CONCLUSIONS AND PERSPECTIVES: In this review, we briefly provide evidence supporting prostate cancer as a metabolic disease, and discuss what is known about mTOR signaling and prostate cancer. Next, we emphasized on the amino acids glutamine, leucine, serine, glycine, sarcosine, proline and arginine, commonly related to prostate cancer, to explore the alterations in their regulatory pathways and to link them with the associated metabolic reprogramming events seen in prostate cancer. Finally, we display potential therapeutic strategies for targeting mTOR and the referred amino acids, as experimental approaches to selectively attack prostate cancer cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".