Novel combination of Celecoxib and proteasome inhibitor MG132 provides synergistic antiproliferative and proapoptotic effects in human liver tumor cells
Bibliographic record
Abstract
Molecular targeted therapy has shown promise as a treatment for advanced hepatocellular carcinoma (HCC). Celecoxib (Celebrex®) exhibits antitumor effects in human HCC cells, and its mechanism of action is mediated either by its ability to inhibit cyclooxygenase 2 (COX-2) or by a number of various other COX-2 independent effects. Proteasome inhibitors (PIs) can exert cell growth inhibitory and apoptotic effects in different tumor cell types, including HCC cells. The present study examined the interaction between celecoxib and the PI MG132 in two human liver tumor cell lines HepG2 and HA22T/VGH. Our data showed that each inhibitor reduced proliferation and induced apoptosis in a dose-dependent manner in both cell lines. Moreover, the combination of celecoxib with MG132 synergistically inhibited cell viability and increased apoptosis, as documented by caspase 3 and 7 activation, PARP cleavage, and down-regulation of Bcl-2. Celecoxib and MG132, both alone and synergistically in combination, induced expression of the endoplasmic reticulum (ER) stress genes ATF4, CHOP, TRB3 and promoted the splicing of XBP1 mRNA. Knockdown of TRB3 mRNA expression by small interference RNA significantly decreased combination-induced cell death in HA22T/VGH cells, whereas it increased combination-induced cell death in HepG2 cells, suggesting that activation of the ER stress response might have either a detrimental or a protective role in liver tumor cell survival. In conclusion, our data indicate that combination treatment with celecoxib and MG132 resulted in synergistic antiproliferative and proapoptotic effects against liver cancer cells, providing a rational basis for the clinical use of this combination in the treatment of liver 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) | 0.001 | 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".