Tissue Factor Regulation by Epidermal Growth Factor Receptor and Epithelial-to-Mesenchymal Transitions: Effect on Tumor Initiation and Angiogenesis
Bibliographic record
Abstract
ErbB oncogenes drive the progression of several human cancers. Our study shows that in human carcinoma (A431) and glioma (U373) cells, the oncogenic forms of epidermal growth factor receptor (EGFR; including EGFRvIII) trigger the up-regulation of tissue factor (TF), the transmembrane protein responsible for initiating blood coagulation and signaling through interaction with coagulation factor VIIa. We show that A431 cancer cells in culture exhibit a uniform TF expression profile; however, these same cells in vivo exhibit a heterogeneous TF expression and show signs of E-cadherin inactivation, which is coupled with multilineage (epithelial and mesenchymal) differentiation. Blockade of E-cadherin in vitro, leads to the acquisition of spindle morphology and de novo expression of vimentin, features consistent with epithelial-to-mesenchymal transition. These changes were associated with an increase in EGFR-dependent TF expression, and with enhanced stimulation of vascular endothelial growth factor production, particularly following cancer cell treatment with coagulation factor VIIa. In vivo, cells undergoing epithelial-to-mesenchymal transition exhibited an increased metastatic potential. Furthermore, injections of the TF-blocking antibody (CNTO 859) delayed the initiation of A431 tumors in immunodeficient mice, and reduced tumor growth, vascularization, and vascular endothelial growth factor expression. Collectively, our data suggest that TF is regulated by both oncogenic and differentiation pathways, and that it functions in tumor initiation, tumor growth, angiogenesis, and metastasis. Thus, TF could serve as a therapeutic target in EGFR-dependent malignancies.
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) | 0.001 | 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".