The role of Norrie Disease Pseudoglioma (NDP) signaling in glioblastoma
Bibliographic record
Abstract
Norrin is a WNT ligand that binds Frizzled-4 (FZD4) and Low-density lipoprotein receptor-related protein (LRP5/6) receptor complex to activate canonical WNT/ β-Catenin signaling. Norrin/FZD4 signaling is involved in the regulation of vasculature in several tissues including retina, inner ear and for blood-brain barrier function. The role of Norrin in cancer is not very well characterized. Here, we show that NDP is expressed in a wide range of cancer types, with a particular enrichment in glioblastoma (GBM) and lower grade glioma (LGG). Kaplan-Meier survival analysis of publicly available datasets revealed a significant correlation between NDP expression and survival in GBM, LGG and neuroblastoma. To investigate the function of NDP in GBM, we performed a set of NDP and FZD4 gain and loss of function experiments in patient-derived GBM stem cell (GNS) lines. Recently ASCL1 expression was shown to stratify GNS lines into two cohorts with different tumorigenic, proliferation and differentiation dynamics. Surprisingly, we found that NDP manipulation resulted in opposite effects in ASCL1hi versus ASCL1lo lines. NDP inhibited proliferation and sphere formation in ASCL1lo lines through WNT- dependent mechanisms, while it stimulated proliferation and sphere formation in ASCL1hi lines through WNT-independent mechanisms. Immunocytochemistry staining for proliferation markers indicated that NDP affects cycle kinetics and cell cycle exit in both cohorts. Interestingly, RNA-Seq analysis of NDP knockdown ASCL1hi and ASCL1lo lines revealed a remarkable effect of NDP knockdown on cell cycle controlling genes. In addition, the library revealed a significant number of uniquely expressed genes in each cell line, consistent with the divergence of NDP molecular functions between the two lines. Collectively, our results indicate that NDP is involved in the regulation of GBM progression, and that NDP function in GBM stratifies with ASCL1 expression.
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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 | 0.001 | 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".