Emerging roles of CircRNA-miRNA networks in cancer development and therapeutic response
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
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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