The multifunctional RNA-binding protein Staufen1: an emerging regulator of oncogenesis through its various roles in key cellular events
Bibliographic record
Abstract
The double-stranded multifunctional RNA-binding protein (dsRBP) Staufen was initially discovered in insects as a regulator of mRNA localization. Later, its mammalian orthologs have been described in different organisms, including humans. Two human orthologues of Staufen, named Staufen1 (STAU1) and Staufen2 (STAU2), share some structural and functional similarities. However, given their different spatio-temporal expression patterns, each of these orthologues plays distinct roles in cells. In the current review, we focus on the role of STAU1 in cell functions and cancer development. Since its discovery, STAU1 has mostly been studied for its involvement in various aspects of RNA metabolism. Given the pivotal role of RNA metabolism within cells, recent studies have explored the mechanistic impact of STAU1 in a wide variety of cell functions ranging from cell growth to cell death, as well as in various disease states. In particular, there has been increasing attention on the role of STAU1 in neuromuscular disorders, neurodegeneration, and cancer. Here, we provide an overview of the current knowledge on the role of STAU1 in RNA metabolism and cell functions. We also highlight the link between STAU1-mediated control of cellular functions and cancer development, progression, and treatment. Hence, our review emphasizes the potential of STAU1 as a novel biomarker and therapeutic target for cancer diagnosis and treatment, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".