Circular RNAs in EMT-driven metastasis regulation: modulation of cancer cell plasticity, tumorigenesis and therapy resistance
Bibliographic record
Abstract
The non-coding RNAs comprise a large part of human genome lack of capacity in encoding functional proteins. Among various members of non-coding RNAs, the circular RNAs (circRNAs) have been of importance in the pathogenesis of human diseases, especially cancer. The circRNAs have a unique closed loop structure and due to their stability, they are potential diagnostic and prognostic factors in cancer. The increasing evidences have highlighted the role of circRNAs in the modulation of proliferation and metastasis of cancer cells. On the other hand, metastasis has been responsible for up to 90% of cancer-related deaths in patients, requiring more investigation regarding the underlying mechanisms modulating this mechanism. EMT enhances metastasis and invasion of tumor cells, and can trigger resistance to therapy. The cells demonstrate dynamic changes during EMT including transformation from epithelial phenotype into mesenchymal phenotype and increase in N-cadherin and vimentin levels. The process of EMT is reversible and its reprogramming can disrupt the progression of tumor cells. The aim of current review is to understanding the interaction of circRNAs and EMT in human cancers and such interaction is beyond the regulation of cancer metastasis and can affect the response of tumor cells to chemotherapy and radiotherapy. The onco-suppressor circRNAs inhibit EMT, while the tumor-promoting circRNAs mediate EMT for acceleration of carcinogenesis. Moreover, the EMT-inducing transcription factors can be controlled by circRNAs in different human tumors.
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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) | 0.001 | 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) | 0.002 | 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".