The curcumin analog (PAC) suppressed cell survival and induced apoptosis and autophagy in oral cancer cells
Bibliographic record
Abstract
Abstract PAC (3,5- Bis (4-hydroxy-3-methoxybenzylidene)- N -methyl-4-piperidone), a novel bioactive curcumin analog, has been reported to have anticancer properties against various tumors. However, the anti-cancer effects of PAC on oral cavity squamous cell carcinoma were not studied yet. Our aim is to investigate the anti-oral cancer properties of PAC in vitro, and determine the molecular mechanisms underlying these effects. Viability assays including MTT and LDH were conducted to measure cell proliferation. Flow cytometry-based cytotoxicity assay was performed to detect autophagic cell death and oxidative stress markers. Western blotting was used for measuring protein expression/activation in apoptotic, autophagic and pro-carcinogenic cellular signaling pathways. We demonstrated that PAC preferentially and, in a dose, -dependent way kills oral cancer cells, but was not toxic to normal human gingival cells. PAC destabilizes cell-cycle distributions, inhibits the expression of oncogenes (cyclin D1) and that of cyclin-dependent kinase inhibitor (p21 WAF1 ) is upregulated, increases the expression of p53 gene, and inhibits epithelial-mesenchymal transition markers in oral cancer cells. The PAC effect involve various signaling pathways including NF-κB, MAPK, Wnt, caspase-3/9 and PARP1. Finally, PAC demonstrated ability to induce autophagy, decrease production of reactive oxygen species, increase intracellular glutathione (GSH) activity, and reduce mitochondrial membrane potential in oral cancer cells. In conclusion, PAC inhibits the proliferation and increases the apoptosis and autophagy and oxidative stress of oral cancer cells. These effects involve ERK1/2, p38/JNK, NF-κB and Wnt cellular signaling pathways. Overall, our study suggests the potential use of PAC to treat oral cancer.
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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".