Resveratrol enhances prostate cancer cell response to ionizing radiation. Modulation of the AMPK, Akt and mTOR pathways
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PrCa) displays resistance to radiotherapy (RT) and requires radiotherapy dose escalation which is associated with greater toxicity. This highlights a need to develop radiation sensitizers to improve the efficacy of RT in PrCa. Ionizing radiation (IR) stimulates pathways of IR-resistance and survival mediated by the protein kinase Akt but it also activates the metabolic energy sensor and tumor suppressor AMP-Activated Protein Kinase (AMPK). Here, we examined the effects of the polyphenol resveratrol (RSV) on the IR-induced inhibition of cell survival, modulation of cell cycle and molecular responses in PrCa cells. METHODS: Androgen-insensitive (PC3), sensitive (22RV1) PrCa and PNT1A normal prostate epithelial cells were treated with RSV alone (2.5-10 μM) or in combination with IR (2-8 Gy). Clonogenic assays, cell cycle analysis, microscopy and immunoblotting were performed to assess survival, cell cycle progression and molecular responses. RESULTS: RSV (2.5-5 μM) inhibited clonogenic survival of PC3 and 22RV1 cells but not of normal prostate PNT1A cells. RSV specifically sensitized PrCa cells to IR, induced cell cycle arrest at G1-S phase and enhanced IR-induced nuclear aberrations and apoptosis. RSV enhanced IR-induced expression of DNA damage (γH2Ax) and apoptosis (cleaved-caspase 3) markers as well as of the cell cycle regulators p53, p21(cip1) and p27(kip1). RSV enhanced IR-activation of ATM and AMPK but inhibited basal and IR-induced phosphorylation of Akt. CONCLUSIONS: Our results suggest that RSV arrests cell cycle, promotes apoptosis and sensitizes PrCa cells to IR likely through a desirable dual action to activate the ATM-AMPK-p53-p21(cip1)/p27(kip1) and inhibit the Akt signalling pathways.
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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".