Crucial roles of exosomes secreted from ganglioside GD3/GD2-positive glioma cells in enhancement of the malignant phenotypes and signals of GD3/GD2-negative glioma cells.
Bibliographic record
Abstract
Neuroectoderm-derived tumors characteristically express gangliosides such as GD3 and GD2. Many studies have reported that gangliosides GD3/GD2 enhance malignant phenotypes of cancers. Recently, we reported that human gliomas expressing GD3/GD2 exhibited enhanced malignant phenotypes. Here, we investigated the function of GD3/GD2 in glioma cells and GD3/GD2-expressing glioma-derived exosomes. As reported previously, transfectant cells of human glioma U251 MG expressing GD3/GD2 showed enhanced cancer phenotypes compared with GD3/GD2-negative controls. When GD3/GD2-negative cells were treated with exosomes secreted from GD3/GD2-positive cells, clearly increased malignant properties were observed. Furthermore, increased phosphorylation of signaling molecules was detected after 5-15 min of exosome treatment, ie, higher tyrosine phosphorylation of platelet-derived growth factor receptor, focal adhesion kinase, and paxillin was found in treated cells than in controls. Phosphorylation of extracellular signal-regulated kinase-1/2 was also enhanced. Consequently, it is suggested that exosomes secreted from GD3/GD2-positive gliomas play important roles in enhancement of the malignant properties of glioma cells, leading to total aggravation of heterogenous cancer tissues, and also in the regulation of tumor microenvironments.
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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".