Attenuation of PTEN increases p21 stability and cytosolic localization in kidney cancer cells: a potential mechanism of apoptosis resistance
Bibliographic record
Abstract
BACKGROUND: The PTEN (Phosphatase and Tensin homolog deleted on chromosome Ten) tumor suppressor gene is frequently mutated or deleted in a wide variety of solid tumors, and these cancers are generally more aggressive and difficult to treat than those possessing wild type PTEN. While PTEN lies upstream of the phosphoinositide-3 kinase signaling pathway, the mechanisms that mediate its effects on tumor survival remain incompletely understood. Renal cell carcinoma (RCC) is associated with frequent treatment failures (approximately 90% in metastatic cases), and these tumors frequently contain PTEN abnormalities. RESULTS: Using the ACHN cell line containing wild type PTEN, we generated a stable PTEN knockdown RCC cell line using RNA interference. We then used this PTEN knockdown cell line to show that PTEN attenuation increases resistance to cisplatin-induced apoptosis, a finding associated with increased levels of the cyclin kinase inhibitor p21. Elevated levels of p21 result from stabilization of the protein, and they are dependent on the activities of phosphoinositide-3 kinase and Akt. More specifically, the accumulation of p21 occurs preferentially in the cytosolic compartment, which likely contributes to both cell cycle progression and resistance to apoptosis. CONCLUSION: Since p21 regulates a decision point between repair and apoptosis after DNA damage, our data suggest that p21 plays a key role in mechanisms used by PTEN-deficient tumors to escape chemotherapy. This in turn raises the possibility to use p21 attenuators as chemotherapy sensitizers, an area under active continuing investigation in our laboratories.
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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".