Novel Combination of Sorafenib and Celecoxib Provides Synergistic Anti-Proliferative and Pro-Apoptotic Effects in Human Liver Cancer Cells
Bibliographic record
Abstract
Molecular targeted therapy has shown promise as a treatment for advanced hepatocellular carcinoma (HCC). Sorafenib, a multikinase inhibitor, recently received FDA approval for the treatment of advanced HCC. However, although sorafenib is well tolerated, concern for its safety has been expressed. Celecoxib (Celebrex®) is a selective cyclooxygenase-2 (COX-2) inhibitor which exhibits antitumor effects in human HCC cells. The present study examined the interaction between celecoxib and sorafenib in two human liver tumor cell lines HepG2 and Huh7. Our data showed that each inhibitor alone reduced cell growth and the combination of celecoxib with sorafenib synergistically inhibited cell growth and increased apoptosis. To better understand the molecular mechanisms underlying the synergistic antitumor activity of the combination, we investigated the expression profile of the combination-treated liver cancer cell lines using microarray analysis. Combination treatment significantly altered expression levels of 1,986 and 2,483 transcripts in HepG2 and Huh7 cells, respectively. Genes functionally involved in cell death, signal transduction and regulation of transcription were predominantly up-regulated, while genes implicated in metabolism, cell-cycle control and DNA replication and repair were mainly down-regulated upon treatment. However, combination-treated HCC cell lines displayed specificity in the expression and activity of crucial factors involved in hepatocarcinogenesis. The altered expression of some of these genes was confirmed by semi-quantitative and quantitative RT-PCR and by Western blotting. Many novel genes emerged from our transcriptomic analyses, and further functional analyses may determine whether these genes can serve as potential molecular targets for more effective anti-HCC strategies.
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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 | 0.000 | 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) | 0.000 | 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".