Circular RNA, A Molecule with Potential Chemistry and Applications in RNA-based Cancer Therapeutics: An Insight into Recent Advances
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".