Molecular Mechanisms Regulating Epithelial Mesenchymal Transition (EMT) to Promote Cancer Progression
Bibliographic record
Abstract
The process of epithelial-mesenchymal transition (EMT) is crucial in various physiological/pathological circumstances such as development, wound healing, stem cell behavior, and cancer progression. It involves the conversion of epithelial cells into a mesenchymal phenotype, which causes the cells to become highly motile. This reprogramming is initiated and controlled by various signaling pathways and governed by several key transcription factors, including Snail 1, Snail 2 (Slug), TWIST 1, TWIST2, ZEB1, ZEB2, PRRX1, GOOSECOID, E47, FOXC2, SOX4, SOX9, HAND1, and HAND2. The intracellular signaling pathways are activated/inactivated by signals received from the extracellular environment and the transcription factors are carefully regulated at the transcriptional, translational, and post-translational levels to maintain tight regulatory control of EMT. One of the most important pathways involved in this process is the transforming growth factor-β (TGFβ) family signaling pathway. This review will discuss the role of EMT in promoting epithelial cancer progression and the convergence/interplay of multiple signaling pathways and transcription factors that regulate this phenomenon.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".