Controlling Gene Expression through RNA Regulons: The Role of the Eukaryotic Translation Initiation Factor eIF4E
Bibliographic record
Abstract
The eukaryotic translation initiation factor eIF4E is a potent oncogene. In fact, its overexpression in human cancer often correlates with poor prognosis. Traditionally, eIF4E plays a role in translation initiation where it binds the 5' m7G cap found on mRNAs. More recent studies indicate that a significant fraction of eIF4E (up to 68%) resides in the nucleus where it regulates the nuclear export of specific mRNAs. Additionally, eIF4E may play a role in mRNA sequestration and stability in cytoplasmic processing bodies (P-bodies). Our recent studies suggest that eIF4E governs cell cycle progression and cellular proliferation by coordinately orchestrating the expression of several genes at the post-transcriptional level. Hence, eIF4E functions as a central node of an RNA regulon (described below), which plays an essential role in normal differentiation and development and is frequently dysregulated in cancer. Several cellular factors, such as the promyelocytic leukemia protein PML, modulate the function of this regulon by altering the activity of eIF4E. Here, the physiological implications of these observations are described and the clinical implications of directly targeting eIF4E, and the related regulon, are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".