Homo and Heterotypic Cellular Cross-Talk in Epithelial Ovarian Cancer Impart Pro-Tumorigenic Properties through Differential Activation of the Notch3 Pathway
Bibliographic record
Abstract
An active fluidic microenvironment governs peritoneal metastasis in epithelial ovarian cancer (EOC), but its critical functional/molecular cues are not fully understood. Utilizing co-culture models of NIH3T3 cells (differentially overexpressing Jagged1) and SKOV3 cells expressing a Notch3 luciferase reporter-sensor (SNFT), we showed that incremental expression of Jagged1 led to proportional Notch3 activation in SNFT. With no basal luciferase activity, this system efficiently recorded dose-dependent Notch3 activation by rh-Jag1 peptide and the non-appearance of such induction in co-culture with NIH3T3Δjag1 cells indicates its sensitivity and specificity. Similar Notch3 modulation was shown for the first time in co-cultures with HGSOC patients’ ascites-derived cancer-associated fibroblasts and Jagged1-expressing EOC cell lines. NIH3T3J1-A and OVCAR3 co-cultured SNFT cells showed maximum proliferation, invasion, and cisplatin resistance among all the heterotypic/homotypic cellular partners. VEGFA and CDKN1A are the two most upregulated genes identified across co-cultures by the gene profiler array. Co-culture induced VEGFA secretion from SNFT cells which also reduced cancer stem cell differentiation in platinum-resistant A2780 cells. rh-Jag1-peptide promoted enhanced nuclear-cytoplasmic p21 expression. Additionally, metastatic HGSOC tumors had higher VEGFA than corresponding primary tumors. This study thus demonstrates the tumoral and non-tumoral cell-mediated differential Notch3 activation imparting its tumorigenic effects through two critical molecular regulators, VEGFA and p21, during EOC 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 | 0.000 |
| 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".