Circular RNAs in cancer: new insights into functions and implications in ovarian cancer
Bibliographic record
Abstract
Circular RNAs (circRNAs) are a class of long non-coding RNAs (lncRNAs) which have a circular and closed loop structure. They are ubiquitous, stable, conserved and diverse RNA molecules with a range of activities such as translation and splicing regulation, which are able to interacting with RNA-binding proteins and specially miRNA sponge. The expression patterns of the circRNAs exhibited tissue specificity and also, step and stage specificity. Accumulating evidences approved the critical role of circular RNAs in many cancers such as ovarian cancer. Given that these molecules exert their effects through multiple cellular and molecular mechanisms (i.e., angiogenesis, apoptosis, growth, and metastasis) which are involved in cancer pathogenesis, circular RNAs, in particular, act by controlling cell proliferation in ovarian cancer, so that, it has been shown that the deregulation of these molecules is associated with initiation and progression of ovarian cancer. Therefore, they are attractive molecules which have introduced them as cancer biomarkers. Moreover, they could be used as new therapeutic candidates for developing novel treatment strategies. Here, for first time, we have provided a comprehensive review on the recent knowledge of circular RNAs and their pathological roles in the ovarian cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".