Epigenetic regulation of temozolomide resistance in human cancers with an emphasis on brain tumors: Function of non-coding RNAs
Bibliographic record
Abstract
Brain tumors, which are highly malignant, pose a significant threat to health and often result in substantial rates of mortality and morbidity worldwide. The brain cancer therapy has been challenging due to obstacles such as the BBB, which hinders effective delivery of therapeutic agents. Additionally, the emergence of drug resistance further complicates the management of brain tumors. TMZ is utilized in brain cancer removal, but resistance is a drawback. ncRNAs are implicated in various diseases, and their involvement in the cancer is particularly noteworthy. The focus of the current manuscript is to explore the involvement of ncRNAs in controlling drug resistance, specifically in the context of resistance to the chemotherapy drug TMZ. The review emphasizes the function of ncRNAs, particularly miRNAs, in modulating the growth and invasion of brain tumors, which significantly influences their response to TMZ treatment. Through their interactions with various molecular pathways, miRNAs are modulators of TMZ response. Similarly, lncRNAs also associate with molecular pathways and miRNAs, affecting the efficacy of TMZ chemotherapy. Given their functional properties, lncRNAs can either induce or suppress TMZ resistance in brain tumors. Furthermore, circRNAs, which are cancer controllers, regulate miRNAs by acting as sponges, thereby impacting the response to TMZ chemotherapy. The review explores the correlation between ncRNAs and TMZ chemotherapy, shedding light on the underlying molecular pathways involved in this process.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".