Circular RNAs in Epithelial Ovarian Cancer: From Biomarkers to Therapeutic Targets
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer, and more than 70% of patients are diagnosed at advanced stages. Despite the application of surgery and chemotherapy, the prognosis remains poor due to the high relapse rate. It is urgent to identify novel biomarkers and develop novel therapeutic strategies for EOC. Circular RNAs (circRNAs) are a class of noncoding RNAs generated from the "back-splicing" of precursor mRNA. CircRNAs exert their functions via several mechanisms, including acting as miRNA sponges, interacting with proteins, regulating transcription, and encoding functional proteins. Recent studies have identified many circRNAs that are dysregulated in EOC and may be used as diagnostic and prognostic markers. Increasing evidence has revealed that circRNAs play a critical role in ovarian cancer progression by regulating various cellular processes, including proliferation, apoptosis, metastasis, and chemosensitivity. The circRNA-based therapy may be a novel strategy that is worth exploring in the future. Here, we provide an overview of EOC and circRNA biogenesis and functions. We then discuss the dysregulations of circRNAs in EOC and the possibility of using them as diagnostic/prognostic markers. We also summarize the role of circRNAs in regulating ovarian cancer development and speculate their potential as therapeutic targets.
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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 | 0.000 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".