FoxG1 Interacts with Bmi1 to Regulate Self-Renewal and Tumorigenicity of Medulloblastoma Stem Cells
Bibliographic record
Abstract
Brain tumors represent the leading cause of childhood cancer mortality, of which medulloblastoma (MB) is the most frequent malignant tumor. Recent studies have demonstrated the presence of several MB molecular subgroups, each distinct in terms of prognosis and predicted therapeutic response. Groups 1 and 2 are characterized by relatively good clinical outcomes and activation of the Wnt and Shh pathways, respectively. In contrast, groups 3 and 4 ("non-Shh/Wnt MBs") are distinguished by metastatic disease, poor patient outcome, and lack a molecular pathway phenotype. Current gene expression platforms have not detected brain tumor-initiating cell (BTIC) self-renewal genes in groups 3 and 4 MBs as BTICs typically comprise a minority of tumor cells and may therefore go undetected on bulk tumor analyses. Since increasing BTIC frequency has been associated with increasing tumor aggressiveness and poor patient outcome, we investigated the subgroup-specific gene expression profile of candidate stem cell genes within 251 primary human MBs from four nonoverlapping MB transcriptional databases (Amsterdam, Memphis, Toronto, Boston) and 74 NanoString-subgrouped MBs (Vancouver). We assessed the functional relevance of two genes, FoxG1 and Bmi1, which were significantly enriched in non-Shh/Wnt MBs and showed these genes to mediate MB stem cell self-renewal and tumor initiation in mice. We also identified their transcriptional regulation through reciprocal promoter occupancy in CD15+ MB stem cells. Our work demonstrates the application of stem cell data gathered from genomic platforms to guide functional BTIC assays, which may then be used to develop novel BTIC self-renewal mechanisms amenable to therapeutic targeting.
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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".