miRNA As Therapeutics for The Management of Cancer, A Comprehensive Review.
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression and critical cellular processes like proliferation, apoptosis, and differentiation. They have emerged as promising therapeutic agents for cancer management due to their ability to regulate multiple oncogenes and tumor suppressor genes simultaneously. This review examines the therapeutic potential of miRNAs in cancer treatment, including their roles as tumor suppressors and oncomiRs. miRNAs can be utilized through replacement or inhibition strategies to target cancer progression. However, the clinical application of miRNA-based therapies faces challenges such as efficient delivery, stability in vivo, and off-target effects. Various delivery systems, including lipid nanoparticles, viral vectors, and exosomes, are being explored to enhance miRNA stability and specificity. Additionally, miRNA mimics, inhibitors, and chemical modifications are under development to reduce off-target effects. Clinical trials on miR-34 and miR-21 have shown promising results, but further research is needed to overcome delivery and specificity issues before miRNA-based therapies become widely applicable in clinical practice. The future of miRNA therapeutics holds promise for providing targeted, less toxic, and more effective treatment options for cancer patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".