The <i>EIF4EBP1</i> gene encoding 4EBP1 is transcriptionally upregulated by MYC and linked to shorter survival in medulloblastoma
Bibliographic record
Abstract
ABSTRACT Medulloblastoma (MB) is the most common malignant brain tumor in childhood and is stratified into four molecular groups ‒ WNT, SHH, Group 3 and Group 4. Group 3 MB patients exhibit the poorest prognosis, with a 5-year overall survival of <60%, followed by Group 4 MB patients. Apart from MYC amplification in a subset of Group 3 MBs, the molecular pathomechanisms driving aggressiveness of these tumors remain incompletely characterized. The gene encoding the mTOR substrate and mRNA translation inhibitor eukaryotic translation initiation factor 4E-binding protein 1 ( EIF4EBP1 ) represents a possible MYC target gene whose corresponding protein, 4EBP1, was shown to be more active in Group 3 versus Group 4 MBs. However, the prognostic role of 4EBP1 in MB and the mechanisms supporting 4EBP1 overexpression in Group 3 MB are still elusive. We analyzed EIF4EBP1 mRNA expression in publicly available data sets and found an upregulation in MB as compared to non-neoblastic brain. EIF4EBP1 mRNA expression levels were higher in Group 3 compared to Group 4 MBs. EIF4EBP1 mRNA expression was correlated with MYC expression, most prominently in Group 3 MBs. Survival analyses highlighted that high EIF4EBP1 mRNA expression was associated with reduced overall and event-free survival across all MB patients and in Group 3/Group 4 MB patients. Immunohistochemical evaluation of 4EBP1 protein expression in MB tissues confirmed that high levels of 4EBP1 are associated with poor outcome. Functional analyses revealed that MYC directly regulates EIF4EBP1 promoter activity, providing a mechanism for increased EIF4EBP1 mRNA levels in Group 3 MBs. Finally, we observed that 4EBP1 may support colony formation of in vitro cultured MB cells. Our data highlight that transcriptional upregulation of EIF4EBP1 by MYC promotes in vitro tumorigenicity of MB cells and associates with shorter survival of MB patients.
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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 | 0.001 | 0.001 |
| 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".