Cancer Stem Cells Are Enriched in the Side Population Cells in a Mouse Model of Glioma
Bibliographic record
Abstract
The recent identification of cancer stem cells (CSCs) in multiple human cancers provides a new inroad to understanding tumorigenesis at the cellular level. CSCs are defined by their characteristics of self-renewal, multipotentiality, and tumor initiation upon transplantation. By testing for these defining characteristics, we provide evidence for the existence of CSCs in a transgenic mouse model of glioma, S100beta-verbB;Trp53. In this glioma model, CSCs are enriched in the side population (SP) cells. These SP cells have enhanced tumor-initiating capacity, self-renewal, and multipotentiality compared with non-SP cells from the same tumors. Furthermore, gene expression analysis comparing fluorescence-activated cell sorting-sorted cancer SP cells to non-SP cancer cells and normal neural SP cells identified 45 candidate genes that are differentially expressed in glioma stem cells. We validated the expression of two genes from this list (S100a4 and S100a6) in primary mouse gliomas and human glioma samples. Analyses of xenografted human glioblastoma multiforme cell lines and primary human glioma tissues show that S100A4 and S100A6 are expressed in a small subset of cancer cells and that their abundance is positively correlated to tumor grade. In conclusion, this study shows that CSCs exist in a mouse glioma model, suggesting that this model can be used to study the molecular and cellular characteristics of CSCs in vivo and to further test the CSC hypothesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".