A Role for the Hippo/<scp>YAP1</scp> Pathway in the Regulation of In Vitro Vasculogenic Mimicry in Glioblastoma Cells
Bibliographic record
Abstract
The Hippo pathway plays a tumorigenic role in highly angiogenic glioblastoma (GBM), whereas little is known about clinically relevant Hippo pathway inhibitors' ability to target adaptive mechanisms involved in GBM chemoresistance. Their molecular impact was investigated here in vitro against an alternative process to tumour angiogenesis termed vasculogenic mimicry (VM) in GBM-derived cell models. In silico analysis of the downstream Hippo signalling members YAP1, TAZ and TEAD1 transcript levels in low-grade glioblastoma (LGG) and GBM tumour tissues was performed using GEPIA. TAZ transcript levels did not differ between the healthy and tumour tissues data analysed. In contrast, YAP1 transcript levels were elevated in GBM tissues, whereas TEAD1 levels were high in both LGG and GBM. All three Hippo pathway inhibitors tested, GNE7883, VT107 and IAG933 effectively inhibited U87 and U251 cell migration and in vitro VM as assessed on Cultrex matrix. YAP1 gene and protein expression were induced upon VM, and its translocation to the nucleus was inhibited by the Hippo pathway inhibitors tested. SiRNA-mediated transient silencing of YAP1 repressed cell migration, VM formation and CTGF and Cyr61 transcription. In conclusion, targeting of VM using Hippo pathway inhibitors could help circumvent GBM chemoresistance and effectively complement other brain cancer treatments.
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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".