Rapamycin inhibits oral cancer cell growth by promoting oxidative stress and suppressing ERK1/2, NF-κB and beta-catenin pathways
Bibliographic record
Abstract
Treatment of oral cancer is based exclusively on surgery combined with or without chemotherapy. However, it has several side effects. Targeting a new, more effective therapy has become an urgent matter. The purpose of this study was to evaluate the anti-tumor activity of rapamycin in oral cancer and its mechanism of action. Human gingival carcinoma cells were stimulated with different concentrations of rapamycin to assess proliferation, colony formation, cell migration, as well as apoptosis, and autophagy. The expression of proteins involved in the cell cycle (cyclin D1, p15, p21, p27) and autophagy, as well as that of oncogenes and tumor suppressor genes, were determined by quantitative PCR. The signaling pathways were evaluated by Western blotting. Our results show that rapamycin has a selective effect at a low dose on cancer cell growth/survival. This was confirmed by low colony formation and the inhibition of cell migration, while increasing cell apoptosis by activating caspase-9 and -3. Rapamycin promoted cell autophagy and increased mitochondrial oxidative stress by being involved in DNA damage in the exposed cells. Finally, rapamycin exhibits potent anti-oral cancer properties through inhibition of several cancer-promoting pathways (MAPK, NF-κB, and Wnt/beta-catenin). These results indicate that rapamycin could be a potential agent for the treatment of oral cancer and for a prevention strategy.
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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) | 0.001 | 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".