eIF4B controls survival and proliferation and is regulated by proto-oncogenic signaling pathways
Bibliographic record
Abstract
Messenger RNA translation or protein synthesis, is a fundamental biological process affecting cell growth, survival and proliferation. Initiation is the rate limiting and hence the most regulated step of translation. In eukaryotes, translation initiation is facilitated by multiple protein factors collectively called eIFs (for eukaryotic translation initiation factors). The complex consisting of the eIF4 group factors including the mRNA cap-binding eIF4E protein, large scaffolding protein eIF4G and RNA helicase eIF4A is assisted by the eIF4B co-factor to unwind local secondary structures and create a ribosome landing pad on mRNA. Recruitment of the ribosome and augmentation in the mRNA scanning process culminates in the positioning of the ribosome over the start codon. Deregulated translational control is believed to play an important role in oncogenic transformation. Indeed, many eIFs are bona fide proto-oncogenes. In many types of human cancers, eIFs are either overexpressed or ectopically activated by Ras-MAPK and PI3K-mTOR signaling cascades, resulting in increased survival and accelerated proliferation. In this review we will analyze the bulk of data describing eIF4B and its role in cell survival and proliferation. Recent studies have shown that eIF4B is phosphorylated and activated by Ras-MAPK and PI3K-mTOR signaling cascades. In addition, eIF4B regulates translation of proliferative and pro-survival mRNAs. Moreover, eIF4B depletion in cancer cells attenuates proliferation, sensitizes them to genotoxic stress-driven apoptosis. Taken together, these findings identify eIF4B as a potential target for development of anti-cancer therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".