Stability of Eukaryotic Translation Initiation Factor 4E mRNA Is Regulated by HuR, and This Activity Is Dysregulated in Cancer
Bibliographic record
Abstract
Eukaryotic translation initiation factor 4E (eIF4E) is encoded by a potent oncogene which is highly elevated in many human cancers. Few studies have investigated how the level, and thus activity, of eIF4E is regulated in healthy (noncancerous) cells and how they become elevated in malignant cells. Here, our studies reveal a novel mechanism by which eIF4E levels are regulated at the level of mRNA stability. Two factors known to modulate transcript stability, HuR and the p42 isoform of AUF1, compete for binding to the 3' untranslated regions (3'UTRs) of eIF4E mRNAs. We identified a distinct AU-rich element in the 3'UTR of eIF4E which is responsible for HuR-mediated binding and stabilization. Our studies show that HuR is upregulated in malignant cancer specimens characterized by high eIF4E levels and that its depletion leads to reduction in eIF4E levels. Further, HuR and eIF4E regulate a common set of transcripts involved in cellular proliferation (cyclin D1 and c-myc) and neoangiogenesis (vascular endothelial growth factor), which suggests a functional connection between HuR and eIF4E in the regulation of these important processes. In summary, we present a novel model for the regulation of eIF4E expression and show that this model is relevant to elevation of eIF4E levels in malignant cells.
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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) | 0.001 | 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".