Circular RNAs in cancer: Limitations in functional studies and diagnostic potential
Bibliographic record
Abstract
Circular RNAs (circRNAs) are a large class of noncoding RNAs, generated from a process called back-splicing, that possess critical regulatory functions in many cellular events. A large body of literature has reported various circRNA functions and their underlying mechanisms, including sponging miRNA, exerting transcriptional and translational regulation, interacting with proteins, and translating into peptides and proteins. CircRNA dysregulation has been implicated in many cancers, including lung, breast, liver, gastric, colorectal, and ovarian cancer. They are detectable in bodily fluids and relatively stable, making them potential cancer biomarker candidates. Furthermore, targeting circRNA expression levels is a potential therapeutic approach for treating cancers. In this review, we describe the functional mechanisms of circRNAs and discuss limitations of current mechanism studies. Following this, we outline the potential of circRNAs to be effective biomarkers in various cancers and present circRNA-based therapeutic approaches. Finally, we discuss challenges in using circRNAs as diagnostic and therapeutic tools and propose future research directions.
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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 | 0.002 | 0.003 |
| 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 | 0.002 | 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".