Ovarian cancer ascites enhance the migration of patient‐derived peritoneal mesothelial cells <i>via</i><scp>cMet</scp> pathway through <scp>HGF</scp>‐dependent and ‐independent mechanisms
Bibliographic record
Abstract
Ovarian cancer ascites consist of a proinflammatory environment that is characterized by the presence of abundant human peritoneal mesothelial cells (HPMCs). Cytokines and growth factors in ascites modulate cell activities of tumor cells. The expression of proinflammatory cytokines in ascites is associated with a more aggressive tumor phenotype. The effect of ascites on HPMCs is for the most part unknown but this interplay is thought to be important for epithelial ovarian cancer (EOC) progression. Here, we examine the components of ascites, which stimulate patient-derived HPMC migration, from women with advanced EOC. We show that ovarian cancer ascites enhanced the migration of HPMCs. This effect was inhibited by heat treatment, hepatocyte growth factor (HGF) blocking antibodies and a HGF receptor (cMet) inhibitor. In ovarian cancer ascites, HGF is present at high concentration compared to benign fluids. Ascites-mediated activation of cMet was associated with Akt and EKR1/2 phosphorylation. This response was partly inhibited by heat treatment and cMet inhibitor. Ascites-induced migration and a cMet phosphorylation were strongly inhibited by epidermal growth factor receptor (EGFR) inhibitor PD153035, suggesting the transactivation of cMet by EGFR. Our study suggests that HGF and ligands of EGFR are factors that mediate ovarian cancer ascites-mediated migration of HPMCs by activating cMet and possibly downstream ERK1/2 and Akt pathways. The study provides evidence for the first time that ascites not only support tumor growth but also enhance the migratory potential of cancer-associated mesothelial cells, which in turn may support cancer progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".