A MT1-MMP/NF-κB signaling axis as a checkpoint controller of COX-2 expression in CD133(+) U87 glioblastoma cells
Bibliographic record
Abstract
BACKGROUND: The CD133(+) stem cell population in recurrent gliomas is associated with clinical features such as therapy resistance, blood-brain barrier disruption and, hence, tumor infiltration. Screening of a large panel of glioma samples increasing histological grade demonstrated frequencies of CD133(+) cells which correlated with high expression of cyclooxygenase (COX)-2 and of membrane type-1 matrix metalloproteinase (MT1-MMP). METHODS: We used qRT-PCR and immunoblotting to examine the molecular interplay between MT1-MMP and COX-2 gene and protein expression in parental, CD133(+), and neurospheres U87 glioma cell cultures. RESULTS: We found that CD133, COX-2 and MT1-MMP expression were enhanced when glioma cells were cultured in neurosphere conditions. A CD133(+)-enriched U87 glioma cell population, isolated from parental U87 cells with magnetic cell sorting technology, also grew as neurospheres and showed enhanced COX-2 expression. MT1-MMP gene silencing antagonized COX-2 expression in neurospheres, while overexpression of recombinant MT1-MMP directly triggered COX-2 expression in U87 cells independent from MT1-MMP's catalytic function. COX-2 induction by MT1-MMP was also validated in wild-type and in NF-kappaB p65-/- mutant mouse embryonic fibroblasts, but was abrogated in NF-kappaB 1 (p50-/-) mutant cells. CONCLUSION: We provide evidence for enhanced COX-2 expression in CD133(+) glioma cells, and direct cell-based evidence of NF-kappaB-mediated COX-2 regulation by MT1-MMP. The biological significance of such checkpoint control may account for COX-2-dependent mechanisms of inflammatory balance responsible of therapy resistance phenotype of cancer stem cells.
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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".