Locking Src/Abl Tyrosine Kinase Activities Regulate Cell Differentiation and Invasion of Human Cervical Cancer Cells Expressing E6/E7 Oncoproteins of High-Risk HPV
Bibliographic record
Abstract
In this study, we compared the effects of SKI-606 with Iressa, Src/Abl and EGF-R kinase inhibitors, respectively, on selected parameters in HeLa and SiHa cervical cancer cell lines, which express E6/E7 oncoproteins of high-risk HPV types 18 and 16, respectively. Our results show that SKI-606 and Iressa inhibit cell proliferation and provoke G(0)-G(1) cell cycle arrest and reduction of S and G(2)-M phase using 2 and 5 μM concentrations of these inhibitors. In contrast, SKI-606 induces differentiation to an epithelial phenotype "mesenchymal-epithelial transition"; thus SKI-606 causes a dramatic decrease in cell motility and invasion abilities of HeLa and SiHa cancer cells, in comparison to untreated cells and Iressa-treated cells in which these parameters are only slightly affected. These changes are accompanied by a regulation of the expression patterns of E-cadherin and catenins. The molecular pathway analysis of Src/Abl inhibitor revealed that SKI-606 blocks the phosphorylation of β-catenin and consequently converts its role from a transcriptional regulator to a cell-cell adhesion molecule. Our findings indicate that SKI-606 inhibits signaling pathways involved in regulating tumor cell migration and invasion genes via β-catenin alteration, suggesting that Src inhibitor, in comparison to EGF-R, is a promising therapeutic agent for human cervical 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) | 0.001 | 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".