Long Non-Coding RNAs: Key Regulators of Epithelial-Mesenchymal Transition, Tumour Drug Resistance and Cancer Stem Cells
Bibliographic record
Abstract
Epithelial mesenchymal transition (EMT), the adoption by epithelial cells of a mesenchymal-like phenotype, is a process co-opted by carcinoma cells in order to initiate invasion and metastasis. In addition, it is becoming clear that is instrumental to both the development of drug resistance by tumour cells and in the generation and maintenance of cancer stem cells. EMT is thus a pivotal process during tumour progression and poses a major barrier to the successful treatment of cancer. Non-coding RNAs (ncRNA) often utilize epigenetic programs to regulate both gene expression and chromatin structure. One type of ncRNA, called long non-coding RNAs (lncRNAs), has become increasingly recognized as being both highly dysregulated in cancer and to play a variety of different roles in tumourigenesis. Indeed, over the last few years, lncRNAs have rapidly emerged as key regulators of EMT in cancer. In this review, we discuss the lncRNAs that have been associated with the EMT process in cancer and the variety of molecular mechanisms and signalling pathways through which they regulate EMT, and finally discuss how these EMT-regulating lncRNAs impact on both anti-cancer drug resistance and the cancer stem cell phenotype.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".