Oncogenic Regulation of Tissue Factor Expression
Bibliographic record
Abstract
Abstract Abstract SCI-16 Coagulation system plays a long-recognized role in cancer progression and in the related morbidity and mortality (1, 2). Once regarded as an unspecific epiphenomenon of the underlying disease, this involvement is now viewed as a direct consequence of oncogenic mutations and the resulting acquisition of the procoagulant phenotype by cancer cells followed by local and systemic vascular consequences (Trousseau syndrome) (3). Tissue factor (TF) represents an illuminating molecular paradigm of these changes, acting as both the central regulator of the coagulation system circuitry and an emerging regulatory target of several oncogenic lesions. Thus, oncogene-driven TF upregulation has been documented in a wide spectrum of human cancer cells, including: colorectal (CRC), lung, breast, and skin cancer, as well as glioblastoma (GBM), medulloblastoma (MB), and hematopoietic malignancies. This is linked to activation of several dominant acting oncogenes, such as: K-ras, epidermal growth factor receptor (EGFR), mutant EGFR (EGFRvIII), HER-2, MET, retinoid acid receptor (RAR), and several others (4). These effects are also enabled and amplified by losses of tumor suppressor genes, such as p53 and PTEN, and modulated by microRNA (miR) networks, as well as microenvironmental and regulatory factors, such as hypoxia, inflammation, differentiation, and epithelial-to-mesenchymal transition (EMT) (5). In addition, oncogenic mutations activate several mechanisms that may sensitize cancer cells to extracellular stimuli, including the exposure to circulating coagulation factors that may access cancer cells through leaky tumor blood vessels. For instance, the expression of EGFRvIII in human GBM cells leads not only to a dramatic increase in TF levels, but also to the ectopic expression of coagulation factor VII, the main TF ligand. Simultaneously, oncogenic events (e.g., EGFRvIII or K-ras) induce marked upregulation of protease activated receptors 1 and 2 (PAR1/2), which further enhance the transmission of intracellular signals from the TF/FVIIa complex (6). Furthermore, oncogenes provoke cellular vesiculation whereby TF and other signaling proteins (including oncoproteins themselves) are released into the extracellular space and to the systemic circulation. As a result, these signaling proteins may be transferred to other cells, and modify their properties locally, regionally, and systemically (4, 7). These changes are a part of the signaling network that affects tumor growth, invasion, and metastasis, and acts through generation of a procoagulant, proinflammatory, and pro-angiogenic microenvironment, which contain niches for tumor-initiating cells (TICs). TICs (cancer stem cells) are key targets for oncogenic transformation and essential drivers of the malignant process. Their responses to the coagulation system may be altered by changes in TF and PAR status. TF targeting through genetic and pharmacological approaches results in impaired tumor initiation and growth in various experimental settings, including in transgenic models of GBM. In some instances host cell-associated TF may also play a role in disease progression, while in other cases tumor stroma and inflammatory cells are modulated indirectly by TF-expressing cancer cells (7). Collectively, oncogene-dependent deregulation of TF and activation of the coagulation system circuitry represents a unique biological effector mechanism, which likely promotes progression of human cancers, and thereby may serve as a potential therapeutic target. Disclosures: No relevant conflicts of interest to declare.
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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) | 0.006 | 0.001 |
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".