Tenascin-C Stimulates Glioma Cell Invasion through Matrix Metalloproteinase-12
Bibliographic record
Abstract
The capacity of glioma cells to invade extensively within the central nervous system is a major cause of the high morbidity rate of primary malignant brain tumors. Glioma cell invasion involves the attachment of tumor cells to extracellular matrix (ECM), degradation of ECM components, and subsequent penetration into adjacent brain structures. These processes are accomplished in part by matrix metalloproteinases (MMP) within a three-dimensional milieu of the brain parenchyma. As the majority of studies have used a two-dimensional monolayer culture system, we have used a three-dimensional matrix of collagen type I gel to address glioma-secreted proteases, ECM, and invasiveness of glioma cells. We show that in a three-dimensional collagen type I matrix, the presence of tenascin-C, commonly elevated in high-grade gliomas, increased the invasiveness of glioma cells. The tenascin-C-mediated invasiveness was blocked by metalloproteinase inhibitors, but this did not involve the gelatinases (MMP-2 and MMP-9) commonly implicated in two-dimensional glioma growth. A thorough analysis of 21 MMPs and six members of a disintegrin and metalloproteinase domain showed that MMP-12 was increased in gliomas by tenascin-C in three-dimensional matrix. Furthermore, examinations of resected specimens revealed high MMP-12 levels in the high-grade glioblastoma multiforme tumors. Finally, a function-blocking antibody as well as small interfering RNA to MMP-12 attenuated the tenascin-C-stimulated glioma invasion. These results identify a new factor, MMP-12, in regulating glioma invasiveness through interaction with tenascin-C.
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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".