Epidermal Growth Factor Receptor Variant III–Induced Glioma Invasion Is Mediated through Myristoylated Alanine-Rich Protein Kinase C Substrate Overexpression
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most common and most malignant adult brain tumor. A characteristic of GBM is their highly invasive nature, making complete surgical resection impossible. The most common gain-of-function alteration in GBM is amplification, overexpression, and mutations of the epidermal growth factor receptor (EGFR). The constitutively activated mutant EGFR variant III (EGFRvIII), found in approximately 20% of GBM, confers proliferative and invasive advantage. The signaling cascades downstream of aberrant EGFR activation contributing to the invasive phenotype are not completely understood. Here, we show myristoylated alanine-rich protein kinase C substrate (MARCKS), previously implicated in cell adhesion and motility, contributes to EGFR-mediated invasion of human GBM cells. EGFRvIII-expressing or EGF-stimulated human GBM cells increased expression, phosphorylation, and cytosolic translocation of MARCKS in a protein kinase C-alpha-dependent manner. Down-regulation of MARCKS expression with small interfering RNA in GBM cells expressing EGFRvIII led to decreased cell adhesion, spreading, and invasion. Elucidation of mechanisms that promote EGFRvIII-mediated tumorigenesis in GBM, such as MARCKS, provides additional understanding and potential biological targets against this currently terminal human cancer.
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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".