Modulation of invasive properties of CD133(+) glioblastoma stem cells: A role for MT1‐MMP in bioactive lysophospholipid signaling
Bibliographic record
Abstract
Future breakthroughs in cancer therapy must accompany targeted agents that will neutralize cancer stem cells response to circulating growth factors. Since the brain tissue microenvironmental niche is a prerequisite for expression of the stem cell marker CD133 antigen in brain tumors, we investigated the invasion mechanisms specific to CD133(+) U87 glioblastoma cells in response to lysophosphatidic acid (LPA) and sphingosine 1-phosphate (S1P), two circulating bioactive lysophospholipids and potent inducers of cancer. A CD133(+) U87 glioma cell population was isolated from parental U87 glioblastoma cells using magnetic cell sorting technology. The CD133(+)-enriched cell population grew as neurospheres and showed enhanced maximal response to both LPA (approximately 5.0-fold) and S1P (approximately 2.5-fold) at 1 microM when compared to parental U87 cells. The increased response to LPA in CD133(+) cells, reflected by increased levels of phosphorylated ERK, was found independent of the cooperative functions of the membrane-type-1 matrix metalloproteinase (MT1-MMP), while this cooperativity was essential to the S1P response. Quantitative RT-PCR was performed and we found higher gene expression levels of the S1P receptors S1P1 and S1P2, and of the LPA receptor LPA1 in CD133(+) cells than in their parental U87 cells. These increased levels reflected those observed from in vivo experimental U87 tumor implants. Our data suggest that the CD133(+) cell subpopulation evokes most of the lysophospholipid response within brain tumors through a combined regulation of S1P/LPA cell surface receptors signaling and by MT1-MMP. The emergence of lead compounds targeting the stem cell niche and S1P/LPA signaling in CD133(+) cancer cells is warranted.
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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".