Hippo pathway suppression reprograms TNFα-primed glioblastoma extracellular vesicles transcripts cargo to drive mesenchymal stem/stromal cells vasculogenic mimicry
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Bibliographic record
Abstract
BACKGROUND: Glioblastoma (GBM) secrete extracellular vesicles (EVs) which play a pivotal role in brain tumor progression by mediating intercellular communication within the inflamed tumor microenvironment (TME). EVs' cargo transports biomolecules that promote tumor progression, immune evasion, and resistance to therapies. While Hippo inhibitors play a significant role in mitigating cancer inflammation, their specific impact on EVs cargo remains unknown. METHODS: Human grade IV U87 GBM-derived cells were cultured and EVs isolated from the conditioned media of tumor necrosis factor (TNF)α-primed cells. Total RNA was extracted using TRIzol™, and differential gene expression assessed through gene arrays and validated by RT-qPCR. Protein cell and EVs lysates were used for immunoblotting. 3D mesenchymal stem/stromal cells (MSC) in vitro vasculogenic mimicry (VM) was assessed using Cultrex matrices. RESULTS: Our study shows that U87 cells are responsive to pro-inflammatory stimulation by TNFα as the phosphorylation status of ERK, IκB, and NFκB increased. Among the Hippo pathway inhibitors tested, VT107 inhibited both the TNFα-induced phosphorylation, induction of the downstream Hippo pathway CYR61, and cargo of secreted EVs as assessed upon gene array screens. Pro-inflammatory genes that were reduced by VT107 in EVs included, among others, COX2, IL6, IL1B, and several members of the CCL, CXCL, and Interleukin/Interleukin receptors family. EVs isolated from VT107-treated TNFα-primed U87 cells had decreased paracrine regulation of MSC in vitro VM. CONCLUSIONS: By inhibiting the Hippo pathway and TNFα-induced pro-inflammatory cargo of GBM-derived EVs, our data support VT107 as a potential candidate to inhibit tumor-promoting processes involved in therapy resistance such as paracrine induction of MSC-mediated VM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 it