Ribavirin suppresses eIF4E-mediated oncogenic transformation by physical mimicry of the 7-methyl guanosine mRNA cap
Bibliographic record
Abstract
The eukaryotic translation initiation factor eIF4E is deregulated in many human cancers, and its overexpression in cells leads to malignant transformation. Oncogenic properties of eIF4E are directly linked to its ability to bind 7-methyl guanosine of the 5' mRNA. Here, we observe that the antiviral guanosine analogue ribavirin binds to eIF4E with micromolar affinity at the functional site used by 7-methyl guanosine mRNA cap, competes with eIF4E:mRNA binding, and, at low micromolar concentrations, selectively disrupts eIF4E subcellular organization and transport and translation of mRNAs posttranscriptionally regulated by eIF4E, thereby reducing levels of oncogenes such as cyclin D1. Ribavirin potently suppresses eIF4E-mediated oncogenic transformation of murine cells in vitro, of tumor growth of a mouse model of eIF4E-dependent human squamous cell carcinoma in vivo, and of colony formation of eIF4E-dependent acute myelogenous leukemia cells derived from human patients. These findings describe a specific, potent, and unforeseen mechanism of action of ribavirin. Quantum mechanical and NMR structural studies offer directions for the development of derivatives with improved cytostatic and antiviral properties. In all, ribavirin's association with eIF4E may provide a pharmacologic means for the interruption of posttranscriptional networks of oncogenes that maintain and enhance neoplasia and malignancy in human cancer.
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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".