c-Jun promotes neuroblastoma cell differentiation by inhibiting APC formation via CDC16 and reduces neuroblastoma malignancy
Bibliographic record
Abstract
Neuroblastoma is a pediatric embryonal malignancy characterized by impaired neuronal differentiation. Differentiation status in neuroblastoma strongly affects the clinical outcome, thus, enforcement of differentiation becomes a treatment strategy for this disease. However, the molecular mechanisms that control neuroblastoma differentiation are poorly understood. As an extensively studied protein of the activator protein-1 (AP-1) complex, c-Jun is involved in numerous cell regulations such as proliferation, survival and differentiation. In the current study, we demonstrated that c-Jun expression was upregulated by retinoic acid (RA) and flow cytometry assay indicated c-Jun overexpression arrested cell cycle to G1 phase, which, in turn, promoted the initiation of neuroblastoma cell differentiation. Co-immunoprecipitation (co-IP) assay showed that c-Jun competitively interacted with CDC16, a key subunit in anaphase-promoting complex (APC), resulting in reduced APC formation and inhibition of cell cycle progression. Furthermore, EdU proliferation assay and transwell experiment showed that c-Jun overexpression inhibited neuroblastoma cell proliferation and migration via interacting and sequestering CDC16. These findings identify c-Jun as a key regulator of neuroblastoma cell cycle and differentiation and may represent a promising therapeutic target to induce neuroblastoma differentiation via the interaction between c-Jun and CDC16.
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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 | 0.001 |
| 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".