Circular RNA, A Molecule with Potential Chemistry and Applications in RNA-based Cancer Therapeutics: An Insight into Recent Advances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Among these, circular RNAs (circRNAs) represent a recently identified class of endogenous ncRNAs with a pivotal role in gene regulation, alongside short ncRNAs (e.g., microRNAs or miRNAs) and long non-coding RNAs (lncRNAs). CircRNAs are characterized by their single-stranded, covalently closed circular structure, which lacks polyadenylated tails and 5'-3' ends. This unique circular conformation makes them resistant to exonuclease degradation, rendering them more stable than linear RNAs, such as mRNAs in human blood cells, which highlights their potential as biomarkers. Both linear and circular RNAs are derived from pre-mRNA precursors. However, while linear RNAs are produced through conventional splicing, circRNAs are primarily formed through a process known as reverse splicing. CircRNAs can be categorized into five basic types: exon circRNAs, circular intronic RNAs, exon-intron circRNAs, intergenic circRNAs, and fusion circRNAs. These molecules have been shown to significantly influence key hallmarks of cancer, including sustained growth signaling, proliferation, angiogenesis, resistance to apoptosis, unlimited replicative potential, and metastasis. This article will delve into the biogenesis and functions of circRNAs, explore their roles in cancer, and discuss their potential applications as therapeutic options and diagnostic biomarkers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it