Targeting circular RNAs as a therapeutic approach: current strategies and challenges
Bibliographic record
Abstract
Significant progress has been made in circular RNA (circRNA) research in recent years. Increasing evidence suggests that circRNAs play important roles in many cellular processes, and their dysregulation is implicated in the pathogenesis of various diseases. CircRNAs are highly stable and usually expressed in a tissue- or cell type-specific manner. Therefore, they are currently being explored as potential therapeutic targets. Gain-of-function and loss-of-function approaches are typically performed using circRNA expression plasmids and RNA interference-based strategies, respectively. These strategies have limitations that can be mitigated using nanoparticle and exosome delivery systems. Furthermore, recent developments show that the cre-lox system can be used to knockdown circRNAs in a cell-specific manner. While still in the early stages of development, the CRISPR/Cas13 system has shown promise in knocking down circRNAs with high specificity and efficiency. In this review, we describe circRNA properties and functions and highlight their significance in disease. We summarize strategies that can be used to overexpress or knockdown circRNAs as a therapeutic approach. Lastly, we discuss major challenges and propose future directions for the development of circRNA-based therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".