Non-coding RNAs as mediators of epithelial to mesenchymal transition in metastatic colorectal cancers
Bibliographic record
Abstract
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality globally, necessitating the development of innovative treatment strategies. Recent research has underscored the significant role of non-coding RNAs (ncRNAs) in CRC pathogenesis, offering new avenues for diagnosis and therapy. In this review, we delve into the intricate roles of various ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), in CRC progression, epithelial-mesenchymal transition (EMT), metastasis, and drug resistance. We highlight the interaction of these ncRNAs with and regulation of key signaling pathways, such as Wnt/β-catenin, Notch, JAK-STAT, EGFR, and TGF-β, and the functional relevance of these interactions in CRC progression. Additionally, the review highlights the emerging applications of nanotechnology in enhancing the delivery and efficacy of ncRNA-based therapeutics, which could address existing challenges related to specificity and side effects. Future research directions, including advanced diagnostic tools, targeted therapeutics, strategies to overcome drug resistance, and the integration of personalized medicine approaches are discussed. Integrating nanotechnology with a deeper understanding of CRC biology offers the potential for more effective, targeted, and personalized strategies, though further research is essential to validate these approaches.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".