Abstract A17: A lincRNA orchestrates glioma stem cell-mediated angiogenesis in glioblastoma
Bibliographic record
Abstract
Abstract Angiogenesis is a hallmark of glioblastoma. Recent data suggest that glioma stem cells (GSCs) contribute to pathologic angiogenesis through transdifferentiation into endothelial cells or vascular pericytes. Using an unbiased screen, we identified a lincRNA near the VEGFR1 gene, which we named LIVE (lincRNA-VEGFR1), that directs physiologic vascular network formation through global effects on gene expression. LIVE is highly expressed in glioblastoma and enriched in GSCs. Over-expression of LIVE results in specification of GSCs along a vascular lineage. LIVE associates with PARP1 in GSCs and RNA helicase A in endothelial cells to drive pericyte-specific and endothelial-associated gene expression, respectively. Depletion of LIVE in a xenograft model of glioblastoma results in decreased microvascular density, vascular perfusion and pericyte coverage, and depletion of GSCs within the vascular niche. Our findings demonstrate a novel role for lincRNAs in the orchestration of tumour angiogenesis and reveal a therapeutic potential for targeting LIVE in glioblastoma. Citation Format: Jenny J. Wang, Megan Wu, Christopher Li, Uswa Shahzad, Jason Karamchandani, Phil Marsden, Sunit Das. A lincRNA orchestrates glioma stem cell-mediated angiogenesis in glioblastoma. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr A17.
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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 | 0.001 | 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) | 0.003 | 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".