Translational control of aberrant stress responses as a hallmark of cancer
Bibliographic record
Abstract
Altered mRNA translational control is emerging as a critical factor in cancer development and progression. Targeting specific elements of the translational machinery, such as mTORC1 or eIF4E, is emerging as a new strategy for innovative cancer therapy. While translation of most mRNAs takes place through cap-dependent mechanisms, a sub-population of cellular mRNA species, particularly stress-inducible mRNAs with highly structured 5'-UTR regions, are primarily translated through cap-independent mechanisms. Intriguingly, many of these mRNAs encode proteins that are involved in tumour cell adaptation to microenvironmental stress, and thus linked to aggressive behaviour including tumour invasion and metastasis. This necessitates a rigorous search for links between microenvironmental stress and aggressive tumour phenotypes. Under stress, cells block global protein synthesis to preserve energy while maintaining selective synthesis of proteins that support cell survival. One highly conserved mechanism to regulate protein synthesis under cell stress is to sequester mRNAs into cytosolic aggregates called stress granules (SGs), where their translation is silenced. SGs confer survival advantages and chemotherapeutic resistance to tumour cells under stress. Recently, it has been shown that genetically blocking SG formation dramatically reduces tumour invasive and metastatic capacity in vivo. Therefore, targeting SG formation might represent a potential treatment strategy to block cancer metastasis. Here, we present the critical link between selective mRNA translation, stress adaptation, SGs, and tumour progression. Further, we also explain how deciphering mechanisms of selective mRNA translation occurs under cell stress holds great promise for the identification of new targets in the treatment of cancer. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".