Role of circular RNAs in regulating toxicity induced by cancer therapies
Bibliographic record
Abstract
Owing to transformative improvements in diagnosis and treatment, survival rates for cancer patients have improved significantly across the globe. However, toxicity induced by oncotherapy remains a major concern and markedly affects disease prognosis. In recent years, research on the association between circular RNAs (circRNAs) and oncotherapy-induced toxicity has received extensive attention. CircRNAs are a class of single-stranded closed-loop molecules that play a regulatory role in the occurrence and development of tumors. An integral role of circRNAs in the development of cancer treatment-induced toxicity, as well as in pathological processes such as oxidative damage, mitochondrial damage, apoptosis, dysregulation of calcium homeostasis, and dysregulation of vascular homeostasis has been deciphered. With regards to chemotherapy, radiotherapy, and immunotherapy for cancer treatment, circRNAs play crucial functions in modulating the effects of oncotherapy-induced toxicity. The current review focuses on the mechanisms by which circRNAs function in regulating cancer treatment-induced toxicity, which leads to apoptosis, mitochondrial damage, oxidative stress, DNA damage, and fibrosis. In addition, this review summarizes the potential circRNA biomarkers, treatment strategies and future challenges, which may help translate circRNA research into clinical practice for early detection and improvement of cancer treatment-induced toxicity in the future.
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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 | 0.001 |
| 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) | 0.002 | 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 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".