Versican G3 Promotes Mouse Mammary Tumor Cell Growth, Migration, and Metastasis by Influencing EGF Receptor Signaling
Bibliographic record
Abstract
Increased versican expression in breast tumors is predictive of relapse and has negative impact on survival rates. The C-terminal G3 domain of versican influences local and systemic tumor invasiveness in pre-clinical murine models. However, the mechanism(s) by which G3 influences breast tumor growth and metastasis is not well characterized. Here we evaluated the expression of versican in mouse mammary tumor cell lines observing that 4T1 cells expressed highest levels while 66c14 cells expressed low levels. We exogenously expressed a G3 construct in 66c14 cells and analyzed its effects on cell proliferation, migration, cell cycle progression, and EGFR signaling. Experiments in a syngeneic orthotopic animal model demonstrated that G3 promoted tumor growth and systemic metastasis in vivo. Activation of pERK correlated with high levels of G3 expression. In vitro, G3 enhanced breast cancer cell proliferation and migration by up-regulating EGFR signaling, and enhanced cell motility through chemotactic mechanisms to bone stromal cells, which was prevented by inhibitor AG 1478. G3 expressing cells demonstrated increased CDK2 and GSK-3β (S9P) expression, which were related to cell growth. The activity of G3 on mouse mammary tumor cell growth, migration and its effect on spontaneous metastasis to bone in an orthotopic model was modulated by up-regulating the EGFR-mediated signaling pathway. Taken together, EGFR-signaling appears to be an important pathway in versican G3-mediated breast cancer tumor invasiveness and metastasis.
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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".