SIRT1 is required for oncogenic transformation of neural stem cells and for the survival of “cancer cells with neural stemness” in a p53-dependent manner
Bibliographic record
Abstract
BACKGROUND: Cancer stemness, observed in several types of glioma stem cells (GSCs), has been demonstrated to be an important barrier for efficient cancer therapy. We have previously reported that cancerous neural stem cells (F3.Ras.CNSCs), derived from immortalized human neural stem cells by a single oncogenic stimulation, form glial tumors in vivo. METHOD: We searched for a commonly expressed stress modulator in both F3.Ras.CNSCs and GSCs and identified silent mating type information regulation 2, homolog (SIRT1) as a key factor in maintaining cancer stemness. RESULT: We demonstrate that the expression of SIRT1, expressed in "cancer cells with neural stemness," is critical not only for the maintenance of stem cells, but also for oncogenic transformation. Interestingly, SIRT1 is essential for the survival and tumorigenicity of F3.Ras.CNSCs and GSCs but not for the U87 glioma cell line. CONCLUSION: These results indicate that expression of SIRT1 in cancer cells with neural stemness plays an important role in suppressing p53-dependent tumor surveillance, the abrogation of which may be responsible not only for inducing oncogenic transformation but also for retaining the neural cancer stemness of the cells, suggesting that SIRT1 may be a putative therapeutic target in GSCs.
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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) | 0.002 | 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".