The Guanine Nucleotide Exchange Factor SWAP-70 Modulates the Migration and Invasiveness of Human Malignant Glioma Cells
Bibliographic record
Abstract
The malignant glioma is the most common primary human brain tumor. Its tendency to invade away from the primary tumor mass is considered a leading cause of tumor recurrence and treatment failure. Accordingly, the molecular pathogenesis of glioma invasion is currently under investigation. Previously, we examined a gene expression array database comparing human gliomas to nonneoplastic controls and identified several Rac guanine nucleotide exchange factors with differential expression. Here, we report that the guanine nucleotide exchange factor SWAP-70 has increased expression in malignant gliomas and strongly correlates with lowered patient survival. SWAP-70 is a multifunctional signaling protein involved in membrane ruffling that works cooperatively with activated Rac. Using a glioma tissue microarray, we validated that SWAP-70 demonstrates higher expression in malignant gliomas compared with low-grade gliomas or nonneoplastic brain tissue. Through immunofluorescence, SWAP-70 localizes to membrane ruffles in response to the growth factor, epidermal growth factor. To assess the role of SWAP-70 in glioma migration and invasion, we inhibited its expression withsmall interfering RNAs and observed decreased glioma cell migration and invasion. SWAP-70 overexpression led to increased levels of active Rac even in low-serum conditions. In addition, when SWAP-70 was overexpressed in glioma cells, we observed enhanced membrane ruffle formation followed by increased cellmigration and invasiveness. Taken together, our findings suggest that the guanine nucleotide exchange factor SWAP-70 plays an important role in the migration and invasion of human gliomas into the surrounding tissue.
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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".