The Role of Fascin in the Migration and Invasiveness of Malignant Glioma Cells
Bibliographic record
Abstract
Malignant glioma is the most common primary brain tumor, and its ability to invade the surrounding brain parenchyma is a leading cause of tumor recurrence and treatment failure. Whereas the molecular mechanisms of glioma invasion are incompletely understood, there is growing evidence that cytoskeletal-matrix interactions contribute to this process. Fascin, an actin-bundling protein, induces parallel actin bundles in cell protrusions and increases cell motility in multiple human malignancies. The role of fascin in glioma invasion remains unclear. We demonstrate that fascin is expressed in a panel of human malignant glioma cell lines, and downregulation of fascin expression in glioma cell lines by small interfering RNA (siRNA) is associated with decreased cellular attachment to extracellular matrix (ECM) and reduced migration. Using immunofluorescence analysis, we show that fascin depletion results in a reduced number of filopodia as well as altered glioma cell shape. In vitro invasiveness of U251, U87, and SNB19 glioma cells was inhibited by fascin siRNA treatment by 52.2%, 40.3%, and 23.8% respectively. Finally, we show a decreased invasiveness of U251-GFP cells by fascin knockdown in an ex vivo rat brain slice model system. This is the first study to demonstrate a role for fascin in glioma cell morphology, motility, and invasiveness.
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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) | 0.000 | 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".