Metformin Elicits Antitumor Effects and Downregulates the Histone Methyltransferase Multiple Myeloma SET Domain (MMSET) in Prostate Cancer Cells
Bibliographic record
Abstract
BACKGROUND: This study explored the biological effects of metformin on prostate cancer (PCa) cells and determined molecular pathways and epigenetic regulators implicated in its mechanism of action. METHODS: We performed mRNA expression profiling in 22Rv1 cells following 2.5 mM and 5 mM metformin treatment. Genes significantly modified by metformin treatment were ranked based on altered expression, involvement with cancer-related processes, and reported dysregulation in PCa. The effects of the top ranked gene, MMSET, on the proliferative and invasive capabilities of PCa cells were investigated via siRNA knockdown alone and also combined with metformin treatment. RESULTS: Metformin treatment decreased cell growth of PCa cell line 22Rv1 and stalled cells at the G1/S checkpoint in a time- and dose-dependent manner, resulting in increased cells in G1 (P < 0.05) and decreased cells in S (P < 0.05) phase. Metformin activated the AMPK/mTOR signaling pathway as shown by increased p-AMPK and decreased p-p70S6K. mRNA expression profiling following metformin treatment identified significant changes in 136 chromatin-modifying genes. The top ranked gene, multiple myeloma SET domain (MMSET) showed increased expression in PCa cell lines (22Rv1 and DU145) when compared to the benign prostate epithelium-derived cell-line RWPE-1, and its expression was decreased upon metformin treatment. siRNA-mediated knockdown of MMSET showed decreased cellular migration and invasion in DU-145 cells. MMSET knockdown in combination with metformin treatment resulted in further reduction in the capacity of PCa cells to migrate and invade. CONCLUSIONS: These data suggest MMSET may play a role in the inhibitory effect of metformin on PCa and could serve as a potential novel therapeutic target for PCa. Prostate 76:1507-1518, 2016. © 2016 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".