Role of Eukaryotic Initiation Factors during Cellular Stress and Cancer Progression
Bibliographic record
Abstract
Protein synthesis can be segmented into distinct phases comprising mRNA translation initiation, elongation, and termination. Translation initiation is a highly regulated and rate-limiting step of protein synthesis that requires more than 12 eukaryotic initiation factors (eIFs). Extensive evidence shows that the transcriptome and corresponding proteome do not invariably correlate with each other in a variety of contexts. In particular, translation of mRNAs specific to angiogenesis, tumor development, and apoptosis is altered during physiological and pathophysiological stress conditions. In cancer cells, the expression and functions of eIFs are hampered, resulting in the inhibition of global translation and enhancement of translation of subsets of mRNAs by alternative mechanisms. A precise understanding of mechanisms involving eukaryotic initiation factors leading to differential protein expression can help us to design better strategies to diagnose and treat cancer. The high spatial and temporal resolution of translation control can have an immediate effect on the microenvironment of the cell in comparison with changes in transcription. The dysregulation of mRNA translation mechanisms is increasingly being exploited as a target to treat cancer. In this review, we will focus on this context by describing both canonical and noncanonical roles of eIFs, which alter mRNA translation.
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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 | 0.002 | 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 | 0.001 |
| 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".