Vimentin‐mediated myosin 10 aggregation at tips of cell extensions drives <scp>MT1‐MMP</scp> ‐dependent collagen degradation in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is a high prevalence adenocarcinoma with progressive increases in metastasis-related mortality, but the mechanisms governing the extracellular matrix (ECM) degradation important for metastasis in CRC are not well-defined. We investigated a functional relationship between vimentin (Vim) and myosin 10 (Myo10), and whether this relationship is associated with cancer progression. We tested the hypothesis that Vim regulates the aggregation of Myo10 at the tips of cell extensions, which increases membrane-type 1 matrix metalloproteinase (MT1-MMP)-associated local collagen proteolysis and ECM degradation. Analysis of CRC samples revealed colocalization of Vim with Myo10 and MT1-MMP in cell extensions adjacent to sites of collagen degradation, suggesting an association with local cell invasion. We analyzed cultured CRC cells and fibroblasts and found that Vim accelerates aggregation of Myo10 at cell tips, which increases the cell extension rate. Vim stabilizes the interaction of Myo10 with MT1-MMP, which in turn increases collagenolysis. Vim depletion reduced the aggregation of Myo10 at the cell extension tips and MT1-MMP-dependent collagenolysis. We propose that Vim interacts with Myo10, which in turn associates with MT1-MMP to facilitate the transport of these molecules to the termini of cell extensions and there enhance cancer invasion of soft connective tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".