Targeting the eIF4F Translation Initiation Complex: A Critical Nexus for Cancer Development
Bibliographic record
Abstract
Elevated protein synthesis is an important feature of many cancer cells and often arises as a consequence of increased signaling flux channeled to eukaryotic initiation factor 4F (eIF4F), the key regulator of the mRNA-ribosome recruitment phase of translation initiation. In many cellular and preclinical models of cancer, eIF4F deregulation results in changes in translational efficiency of specific mRNA classes. Importantly, many of these mRNAs code for proteins that potently regulate critical cellular processes, such as cell growth and proliferation, enhanced cell survival and cell migration that ultimately impinge on several hallmarks of cancer, including increased angiogenesis, deregulated growth control, enhanced cellular survival, epithelial-to-mesenchymal transition, invasion, and metastasis. By being positioned as the molecular nexus downstream of key oncogenic signaling pathways (e.g., Ras, PI3K/AKT/TOR, and MYC), eIF4F serves as a direct link between important steps in cancer development and translation initiation. Identification of mRNAs particularly responsive to elevated eIF4F activity that typifies tumorigenesis underscores the critical role of eIF4F in cancer and raises the exciting possibility of developing new-in-class small molecules targeting translation initiation as antineoplastic agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".