Emerging roles of CircRNA-miRNA networks in cancer development and therapeutic response
Bibliographic record
Abstract
The complex interplay of epigenetic factors is essential in regulating the hallmarks of cancer and orchestrating intricate molecular interactions during tumor progression. Circular RNAs (circRNAs), known for their covalently closed loop structures, are non-coding RNA molecules exceptionally resistant to enzymatic degradation, which enhances their stability and regulatory functions in cancer. Similarly, microRNAs (miRNAs) are endogenous non-coding RNAs with linear structures that regulate cellular biological processes akin to circRNAs. Both miRNAs and circRNAs exhibit aberrant expressions in various cancers. Notably, circRNAs can function as sponges for miRNAs, influencing their activity. The circRNA/miRNA interaction plays a pivotal role in the regulation of cancer progression, including in brain, gastrointestinal, gynecological, and urological cancers, influencing key processes such as proliferation, apoptosis, invasion, autophagy, epithelial-mesenchymal transition (EMT), and more. Additionally, this interaction impacts the response of tumor cells to radiotherapy and chemotherapy and contributes to immune evasion, a significant challenge in cancer therapy. Both circRNAs and miRNAs hold potential as biomarkers for cancer prognosis and diagnosis. In this review, we delve into the circRNA-miRNA circuit within human cancers, emphasizing their role in regulating cancer hallmarks and treatment responses. This discussion aims to provide insights for future research to better understand their functions and potentially guide targeted treatments for cancer patients using circRNA/miRNA-based strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".