Norrin mediates tumor-promoting and -suppressive effects in glioblastoma via Notch and Wnt
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) contains a subpopulation of cells, GBM stem cells (GSCs), that maintain the bulk tumor and represent a key therapeutic target. Norrin is a Wnt ligand that binds Frizzled class receptor 4 (FZD4) to activate canonical Wnt signaling. Although Norrin, encoded by NDP, has a well-described role in vascular development, its function in human tumorigenesis is largely unexplored. Here, we show that NDP expression is enriched in neurological cancers, including GBM, and its levels positively correlated with survival in a GBM subtype defined by low expression of ASCL1, a proneural factor. We investigated the function of Norrin and FZD4 in GSCs and found that it mediated opposing tumor-suppressive and -promoting effects on ASCL1lo and ASCL1hi GSCs. Consistent with a potential tumor-suppressive effect of Norrin suggested by the tumor outcome data, we found that Norrin signaling through FZD4 inhibited growth in ASCL1lo GSCs. In contrast, in ASCL1hi GSCs Norrin promoted Notch signaling, independently of WNT, to promote tumor progression. Forced ASCL1 expression reversed the tumor-suppressive effects of Norrin in ASCL1lo GSCs. Our results identify Norrin as a modulator of human brain cancer progression and reveal an unanticipated Notch-mediated function of Norrin in regulating cancer stem cell biology. This study identifies an unanticipated role of Norrin in human brain cancer progression. In addition, we provide preclinical evidence suggesting Norrin and canonical Wnt signaling as potential therapeutic targets for GBM subtype-restricted 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) | 0.000 | 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".