SREBF1 Activity Is Regulated by an AR/mTOR Nuclear Axis in Prostate Cancer
Bibliographic record
Abstract
Abstract Reprogramming of cellular metabolism is an important feature of prostate cancer, including altered lipid metabolism. Recently, it was observed that the nuclear fraction of mTOR is essential for the androgen-mediated metabolic reprogramming of prostate cancer cells. Herein, it is demonstrated that the androgen receptor (AR) and mTOR bind to regulatory regions of sterol regulatory element-binding transcription factor 1 (SREBF1) to control its expression, whereas dual activation of these signaling pathways also promotes SREBF1 cleavage and its translocation to the nucleus. Consequently, SREBF1 recruitment to regulatory regions of its target genes is induced upon treatment with the synthetic androgen R1881, an effect abrogated upon inhibition of the mTOR signaling pathway. In turn, pharmacologic and genetic inhibition of SREBF1 activity impairs the androgen-mediated induction of the key lipogenic genes fatty acid synthase (FASN) and stearoyl-CoA desaturase (SCD1). Consistent with these observations, the expression of the SREBF1, FASN, and SCD1 genes is significantly correlated in human prostate cancer tumor clinical specimens. Functionally, blockade of SREBF1 activity reduces the androgen-driven lipid accumulation. Interestingly, decreased triglyceride accumulation observed upon SREBF1 inhibition is paralleled by an increase in mitochondrial respiration, indicating a potential rewiring of citrate metabolism in prostate cancer cells. Altogether, these data define an AR/mTOR nuclear axis, in the context of prostate cancer, as a novel pathway regulating SREBF1 activity and citrate metabolism. Implications: The finding that an AR/mTOR complex promotes SREBF1 expression and activity enhances our understanding of the metabolic adaptation necessary for prostate cancer cell growth and suggests novel therapeutic approaches to target metabolic vulnerabilities in tumors. Mol Cancer Res; 16(9); 1396–405. ©2018 AACR.
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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 | 0.000 |
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".