VEPH1 expression decreases vascularisation in ovarian cancer xenografts and inhibits VEGFA and IL8 expression through inhibition of AKT activation
Bibliographic record
Abstract
VEPH1 is amplified in several cancers including ovarian but its impact on tumour progression is unknown. Previous work has shown that VEPH1 inhibits TGF β signalling while its Drosophila ortholog increases tissue growth, raising the possibility that VEPH1 could impact tumour growth or progression. A CRISPR approach was used to disrupt VEPH1 expression in ovarian cancer ES-2 cells, while VEPH1-negative SKOV3 cells were stably transfected with VEPH1 cDNA. The impact of altered VEPH1 expression was assessed using in vitro and in vivo assays and mechanistic studies were performed in vitro . VEPH1 expression in SKOV3 cells resulted in a reduced tumour growth rate associated with increased necrotic area, and decreased microvessel density and VEGF-A levels relative to tumours formed by mock-transfected cells. VEPH1 expression also decreased VEGFA and IL8 expression in SKOV3 cells and was associated with decreased activated AKT levels. These effects were not observed in ES-2 cells, which bear a BRAF V600E activating mutation that leads to constitutively increased IL8 and VEGFA expression. VEPH1 expression in SKOV3 ovarian cancer cells inhibits AKT activation to decrease VEGFA and IL8 expression, which leads to decreased tumour vascularisation and progression.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".