Pseudopodial Actin Dynamics Control Epithelial-Mesenchymal Transition in Metastatic Cancer Cells
Bibliographic record
Abstract
A key cellular process associated with the invasive or metastatic program in many cancers is the transformation of epithelial cells toward a mesenchymal state, a process called epithelial to mesenchymal transition or EMT. Actin-dependent protrusion of cell pseudopodia is a critical element of mesenchymal cell migration and therefore of cancer metastasis. However, whether EMT occurs in human cancers and, in particular, whether it is a prerequisite for tumor cell invasion and metastasis, remains a subject of debate. Microarray and proteomic analysis of actin-rich pseudopodia from six metastatic human tumor cell lines identified 384 mRNAs and 64 proteins common to the pseudopodia of six metastatic human tumor cell lines of various cancer origins leading to the characterization of 19 common pseudopod-specific proteins. Four of these (AHNAK, septin-9, eIF4E, and S100A11) are shown to be essential for pseudopod protrusion and tumor cell migration and invasion. Knockdown of each of these proteins in metastatic cells resulted in reduced actin cytoskeleton dynamics and induction of mesenchymal-epithelial transition (MET) that could be prevented by the stabilization of the actin cytoskeleton. Actin-dependent pseudopodial protrusion and tumor cell migration are therefore determinants of EMT. Protein regulators of pseudopodial actin dynamics may represent unique molecular targets to induce MET and thereby inhibit the metastatic potential of tumor cells.
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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 | 0.000 | 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 | 0.000 |
| 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".