Bibliographic record
Abstract
Astrocytic tumors are the most common neuroepithelial neoplasms with high relapse rate after surgery. Understanding the molecular mechanisms for astrocytic tumorigenesis and progression will lead to early diagnosis and effective treatment of astrocytic tumors. The DEK mRNA and protein expression in normal brain tissues and astrocytic tumors was quantified. To investigate DEK functions in tumor cells, DEK gene was silenced with siRNA in U251 glioblastoma cells. Cell proliferation, cell cycle and apoptosis were then measured. The expression and activity of key genes that regulate cell proliferation and apoptosis were also measured. We identified DEK as a high expressed gene in astrocytic tumor tissues. DEK expression level was positively correlated with the pathological grade of astrocytic tumors. Gene silencing of DEK in U251 glioblastomas inhibited cell proliferation and blocked cells at G0/G1 phase of cell cycle. DEK depletion also induced cell apoptosis, with up-regulated expression of P53 and P21 and down-regulated expression of Bcl-2 and C-myc. The Caspase-3 activity in U251 cells was also significantly increased after knockdown. Our results provided evidences that DEK regulates proliferation and apoptosis of glioblastomas. DEK gene silencing may induce apoptosis through P53-dependent pathway. Our data indicated DEK plays multiple roles to facilitate tumor growth and maintenance. It can be used as a potential target for astrocytic tumor diagnosis and gene therapy.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".