Extra-Ribosomal Roles for Ribosomal Proteins and Their Relevance to Tumour Suppression, Carcinogenesis and Cancer Progression
Bibliographic record
Abstract
Protein translation by ribosomes is one of the most energetically costly cellular processes. Consequently, the number and activity of ribosomes in cells and tissues are precisely tailored to match metabolic demands. While ribosomal proteins (RPs) play essential roles in facilitating and regulating the translation of mRNA transcripts into protein, there is increasing evidence that free RPs not bound to ribosomes can play important roles in cellular regulation. Often, free RPs act as tumour suppressors by multiple mechanisms, for example, by inducing cell cycle arrest through their ability to bind and inhibit MDM2-mediated p53 degradation. Dysregulation of these RPs, however, can result in various diseases like Diamond-Blackfan anemia, ribosomopathies, and other diseases. In cancer, epigenetic modifications, altered transcription, and processing defects in the rRNAs create "onco-ribosomes" that strongly support tumour cell replication, invasion and metastasis. In this context, free RPs in tumour cells (often mutated or post-translationally modified) further promote tumour cell proliferation, invasion, and metastasis. This review focuses specifically on extra-ribosomal roles for RPs, where depending upon cellular context, they act outside of the ribosome to either suppress tumorigenesis in normal tissues or promote tumour proliferation and progression. This new understanding of the interplay between RPs and pathways suppressing or promoting tumorigenesis further emphasizes why the ribosome is increasingly being seen as an important therapeutic target in human cancers.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".