Function of noncoding RNA in regulating cancer cell plasticity
Bibliographic record
Abstract
Recent advances have brought non-coding RNAs (ncRNAs) into the spotlight, revealing their critical regulatory roles in cancer cell plasticity. ncRNAs, such as microRNAs (miRNAs), transfer RNAs (tRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are now recognized as key players in cellular processes such as chromatin remodeling, mRNA stability, and translation. This review delves into the diverse functions of ncRNAs in stem cells and cancer stem cells (CSCs) biology, emphasizing their impact on maintaining and modulating cellular states. We explore the mechanisms by which ncRNAs influence stem cell self-renewal and differentiation, including their roles in establishing pluripotency and directing differentiation. In the context of cancer, ncRNAs are pivotal in driving processes like epithelial-mesenchymal transition (EMT), which underlies metastasis and therapy resistance. By regulating gene expression and epigenetic landscapes, ncRNAs sustain the dynamic nature of CSCs, facilitating tumor growth and heterogeneity. The review also highlights the potential clinical applications of ncRNAs as biomarkers and therapeutic targets. Advances in ncRNA detection and manipulation have opened new avenues for developing diagnostic tools and innovative treatments. Liquid biopsies, which utilize ncRNAs from biological fluids, provide a minimally invasive approach to monitor tumor dynamics and progression. Uncovering the intricate networks regulated by ncRNAs makes it evident that these molecules play central roles in understanding cancer cell plasticity. Insights into their functions offer promising strategies for targeted cancer therapies, aiming to disrupt the adaptability of cancer cells and improve treatment outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".