Differential Inhibition of Protein Translation Machinery by Curcumin in Normal, Immortalized, and Malignant Oral Epithelial Cells
Bibliographic record
Abstract
Curcumin has shown some promise in the prevention of oral carcinogenesis by mechanism(s) that are still not completely resolved. Messenger RNA translation is mediated in eukaryotes by the eIF4F complex composed of eukaryotic translation initiation factors eIF4E, eIF4G, and eIF4A. Overexpression of some of these components or the inactivation of initiation repressor proteins (4E-BP1) has been implicated in cancer development including oral carcinogenesis by affecting cell survival, angiogenesis, and tumor growth and invasion. In this study, we examined the possibility that curcumin affects the translational machinery differently in normal, immortalized normal, leukoplakia, and malignant cells. Curcumin treatment in vitro inhibited the growth of immortalized oral mucosa epithelial cells (NOM9-CT) and the leukoplakia cells (MSK-Leuk1s) as well as in the UMSCC22B and SCC4 cells derived from head and neck squamous cell carcinoma. Curcumin only exerted minor effects on the growth of normal oral epithelial cells (NOM9). In the immortalized, leukoplakia, and cancer cells, curcumin inhibited cap-dependent translation by suppressing the phosphorylation of 4E-BP1, eIF4G, eIF4B, and Mnk1, and also reduced the total levels of eIF4E and Mnk1. Our findings show that immortalized normal, leukoplakia, and malignant oral cells are more sensitive to curcumin and show greater modulation of protein translation machinery than the normal oral cells, indicating that targeting this process may be an important approach to chemoprevention in general and for curcumin in particular.
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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".