Locking Src/Abl Tyrosine Kinase Activities Regulate Cell Differentiation and Invasion of Human Cervical Cancer Cells Expressing E6/E7 Oncoproteins of High-Risk HPV
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| 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 it