eIF4B controls survival and proliferation and is regulated by proto-oncogenic signaling pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it