AEBP1 down regulation induced cell death pathway depends on PTEN status of glioma cells
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common aggressive form of brain cancer with overall dismal prognosis (10-12 months) despite all current multimodal treatments. Previously we identified adipocyte enhancer binding protein 1 (AEBP1) as a differentially regulated gene in GBM. On probing the role of AEBP1 over expression in glioblastoma, we found that both cellular proliferation and survival were affected upon AEBP1 silencing in glioma cells, resulting in cell death. In the present study we report that the classical caspase pathway components are not activated in cell death induced by AEBP1 down regulation in PTEN-deficient (U87MG and U138MG) cells. PARP-1 was not cleaved but over-activated under AEBP1 down regulation which leads to the synthesis of PAR in the nucleus triggering the release of AIF from the mitochondria. Subsequently, AIF translocates to the nucleus along with MIF causing chromatinolysis. AEBP1 positively regulates PI3KinaseCβ by the binding to AE-1 binding element in the PI3KinaseCβ promoter. Loss of PI3KinaseCβ expression under AEBP1 depleted condition leads to excessive DNA damage and activation of PARP-1. Furthermore, over expression of PIK3CB (in trans) in U138MG cells prevents DNA damage in these AEBP1 depleted cells. On the contrary, AEBP1 down regulation induces caspase-dependent cell death in PTEN-proficient (LN18 and LN229) cells. Ectopic expression of wild-type PTEN in PTEN-deficient U138MG cells results in the activation of canonical caspase and Akt dependent cell death. Collectively, our findings define AEBP1 as a potential oncogenic driver in glioma, with potential implications for therapeutic intervention.
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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 | 0.000 |
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".