Differential regulation of the activity of deleted in liver cancer 1 (DLC1) by tensins controls cell migration and transformation
Bibliographic record
Abstract
The epithelial growth factor receptor plays an important role in cell migration and cancer metastasis, but the underlying molecular mechanism is not fully understood. We show here that differential regulation of the Ras-homology-GTPase-activating protein [corrected] (Rho-GAP) activity of deleted in liver cancer 1 (DLC1) by tensin3 and COOH-terminal tensin-like protein (cten) controls EGF-driven cell migration and transformation. Tensin3 binds DLC1 through its actin-binding domain, a region that is missing in cten, and thereby releases an autoinhibitory interaction between the sterile alpha motif and Rho-GAP domains of DLC1. Consequently, tensin3, but not cten, promotes the activation of DLC1, which, in turn, leads to inactivation of RhoA and decreased cell migration. Depletion of endogenous tensin3, but not cten, augmented the formation of actin stress fibers and focal adhesions and enhanced cell motility. These effects were, however, ablated by an inhibitor of the Rho-associated protein kinase. Importantly, activation of DLC1 by tensin3 or its actin-binding domain drastically reduced the anchorage-independent growth of transformed cells. Our study therefore links dynamic regulation of tensin family members by EGF to Rho-GAP through DLC1 and suggests that the tensin-DLC1-RhoA signaling axis plays an important role in tumorigenesis and cancer metastasis, and may be explored for cancer intervention.
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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".