The Role of Endogenous Circular RNAs in Glioblastoma Treatment Resistance: A Literature Review
Bibliographic record
Abstract
Introduction: Glioblastomas are a high-grade primary brain cancer of astrocytes, a glial cell. The average survival upon diagnosis is 15 months. Researchers have been continuously attempting to develop new drugs and therapies that would exhibit greater efficiency against this cancer. However, tumor resistance is often observed in glioblastomas. Circular RNA is a relatively novel finding of non-coding RNA that is highly stable compared to linear RNA. Recent studies have found a variation in circular RNA expression levels in resistant versus non-resistant cancer cell lines. Thus, researchers have been exploring this endogenous entity to understand the role it may play in chemo- and radioresistant glioblastomas. Methods: A comprehensive look into the most recent findings on this topic was done using Google Scholar and PubMed search engines. Key words used include “circRNA,” “glioblastoma,” and “chemo- and radio-resistance”. This review mainly stems from primary research published between 2017 and 2024. Results: Only a few specific circular RNAs have been found to be implicated in chemoresistance. Research has focused on understanding resistance to temozolomide. However, variation in certain circular RNA expression has also been noted in cisplatin and histone deacytlase inhibitor therapies for glioblastoma treatment. These circular RNAs inhibit the cytotoxic effects of chemotherapeutics primarily though microRNA sponging. Additionally, similar results were found in radioresistant glioblastoma tumors, whereby specific circular RNAs block apoptosis by altering various protein mechanisms through microRNA sponging and inhibiting translocation. Discussion: Developing methods that inhibit this tumor’s ability to resist otherwise effective therapies may be an ideal method of treatment. Many studies have attempted to modify cellular pathways to facilitate apoptosis of cancer cells. However, due to complex interactions and possible off-target effects, these methods are inefficient. Circular RNAs may serve as improved therapeutic targets due to their tissue specificity and increased stability. Conclusion: The newly identified circular RNAs involved in glioblastoma therapy resistance may be used to increase cancer cell susceptibility to treatments. Knock-out and overexpression analysis indicate these molecules to be sufficient in altering resistance abilities of these cells. Highly specific targeting techniques may therefore be a viable solution for chemo- and radioresistance.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".