Downregulation of the c-Fes protein-tyrosine kinase inhibits the proliferation of human renal carcinoma cells
Bibliographic record
Abstract
The c-Fes protein-tyrosine kinase is associated with growth and differentiation of hematopoietic, neuronal, vascular endothelial and epithelial cell types.In this study, we investigated whether small interfering RNA (siRNA)mediated knockdown of c-Fes expression affected proliferation of the human renal carcinoma cell lines, ACHN and VMRC-RCW.Immunofluorescence microscopy showed that c-Fes was expressed in both the cytosol and nuclei of these cells, and siRNA treatment preferentially downregulated c-Fes expression in the cytosol.Knock-down of c-Fes inhibited cellular proliferation in a dose-dependent manner with minimal increase in cell death.c-Fes siRNA treatment also downregulated the phosphorylation of Akt1 on S473 and IKK• on T23, and cyclin D1 expression, enhanced the expression of IκB•, and prevented the nuclear localization of NFκB.Treatment with an NFκB inhibitory peptide (SN50) also blocked the proliferation and nuclear localization of NFκB in these cells.The effect of SN50 treatment was not enhanced by c-Fes siRNA, suggesting that downregulation of c-Fes expression inhibited cell cycle progression through the Akt1/NFκB pathway.In contrast to siRNA-mediated knockdown, ectopic expression of either wild-type or kinaseinactive c-Fes in renal carcinoma cells failed to alter their proliferation in vitro and in vivo.Thus, suppression of proliferation resulting from siRNA-mediated knockdown may depend upon an expression of c-Fes protein rather than its kinase activity.Taken together, our results indicate that downregulation of c-Fes expression may be a potential therapeutic strategy for advanced human renal cell carcinoma and inhibition of its kinase activity as an antiangiogenic therapy does not seem to induce the growth of human renal carcinoma cells.
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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".