Involvement of p21 <sup>Waf1/Cip1</sup> in Protein Kinase C Alpha-Induced Cell Cycle Progression
Bibliographic record
Abstract
Protein kinase C (PKC) plays an important role in the regulation of glioma growth; however, the identity of the specific isoform and mechanism by which PKC fulfills this function remain unknown. In this study, we demonstrate that PKC activation in glioma cells increased their progression through the cell cycle. Of the six PKC isoforms that were present in glioma cells, PKC alpha was both necessary and sufficient to promote cell cycle progression when stimulated with phorbol 12-myristate 13-acetate. Also, decreased PKC alpha expression resulted in a marked decrease in cell proliferation. The only cell cycle-regulatory molecule whose expression was rapidly altered and increased by PKC alpha activity was the cyclin-cyclin-dependent kinase (CDK) inhibitor p21(Waf1/Cip1). Coimmunoprecipitation studies revealed that p21(Waf1/Cip1) upregulation was accompanied by an incorporation of p21(Waf1/Cip1) into various cyclin-CDK complexes and that the kinase activity of these complexes was increased, thus resulting in cell cycle progression. Furthermore, depletion of p21(Waf1/Cip1) by antisense strategy attenuated the PKC-induced cell cycle progression. These results suggest that PKC alpha activity controls glioma cell cycle progression through the upregulation of p21(Waf1/Cip1), which facilitates active cyclin-CDK complex formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".