Role of Ubiquitin-regulated EMT in Cancer Metastasis and Chemoresistance
Bibliographic record
Abstract
Epithelial-mesenchymal transition (EMT) is a fundamental biological process that promotes cancer metastasis and chemoresistance. However, the therapeutic efficacy of EMT inhibitors remains limited. Ubiquitination, a critical post-translational modification, involves attaching ubiquitin molecules to proteins to regulate their function and stability. It modulates EMT by controlling key EMT transcription factors (EMT-TFs) and associated signaling pathways. Evidence indicates that ubiquitination-dependent regulation of EMT serves as a central mechanism underlying tumor metastasis and chemoresistance. Targeting specific deubiquitinases (DUBs) or E3 ligases can effectively reverse EMT-induced cancer progression and treatment resistance. These findings highlight the therapeutic potential of E3 ligase and DUB inhibitors in oncology. Collectively, ubiquitination-regulated EMT is pivotal in mediating metastasis and chemoresistance in malignant tumors. This review summarizes the molecular mechanisms of EMT and emphasizes ubiquitination's essential role in regulating EMT to promote tumor metastasis and chemoresistance. Consequently, developing inhibitors against specific E3 ligases and DUBs offers a promising strategy to improve cancer 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 | 0.001 | 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 | 0.001 |
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".