Molecular mechanisms of sorafenib action in liver cancer cells
Bibliographic record
Abstract
Sorafenib, a multikinase inhibitor, recently received FDA approval for the treatment of advanced hepatocellular carcinoma (HCC). However, as the clinical application of sorafenib evolves, there is increasing interest in defining the mechanisms underlying its anti-tumor activity. Considering that this specific inhibitor could target unexpected molecules depending on the biologic context, a precise understanding of its mechanism of action could be critical to maximize its treatment efficacy, while minimizing adverse effects. Two human HCC cell lines (HepG2 and Huh7), carrying different biological and genetic characteristics, were used in this study to examine the intracellular events leading to sorafenib-induced HCC cell-growth inhibition. Sorafenib inhibited cell growth in both cell lines in a dose- and time-dependent manner and significantly altered expression levels of 826 and 2011 transcripts in HepG2 and Huh7 cells, respectively. Genes functionally involved in angiogenesis, apoptosis, transcription regulation, signal transduction, protein biosynthesis and modification were predominantly upregulated, while genes implicated in cell cycle control, DNA replication recombination and repair, cell adhesion, metabolism and transport were mainly downregulated upon treatment. However, each sorafenib-treated HCC cell line displayed specificity in the expression and activity of crucial factors involved in hepatocarcinogenesis. The altered expression of some of these genes was confirmed by semiquantitative and quantitative RT-PCR and by western blotting. Many novel genes emerged from our transcriptomics analysis that had not previously been reported to be effected by sorafenib. 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 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 | 0.001 |
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".