CCL18 from ascites promotes ovarian cancer cell migration through proline-rich tyrosine kinase 2 signaling
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer (OC) ascites consist in a proinflammatory tumor environment that is characterized by the presence of various cytokines, chemokines and growth factors. The presence of these inflammatory-related factors in ascites is associated with a more aggressive tumor phenotype. CCL18 is a member of CCL chemokines and its expression has been associated with poor prognosis in some cancers. However, its role in OC progression has not been established. Therefore, the aim of the current study was to elucidate the role of ascites CCL18 in OC progression. METHODS: ELISA and tissue microarrays were used to assess CCL18 in ascites and phospho-Pyk2 expression in cancer tissues respectively. Cell migration was assessed using Boyden chambers. CCL18 and ascites signaling was examined in ovarian cancer cells utilizing siRNA and exogenous gene expression. RESULTS: Here, we show that CCL18 levels are markedly increased in advanced serous OC ascites relative to peritoneal effusions from women with benign conditions. Ascites and CCL18 dose-dependently enhanced the migration of OC cell lines CaOV3 and OVCAR3. CCL18 levels in ascites positively correlated with the ability of ascites to promote cell migration. CCL18 blocking antibodies significantly attenuated ascites-induced cell migration. Ascites and CCL18 stimulated the phosphorylation of proline-rich tyrosine kinase 2 (Pyk2) in CaOV3 and OVCAR3 cells. Most importantly, the expression of phosphorylated Pyk2 in serous OC tumors was associated with shorter progression-free survival. Furthermore, enforced expression of Pyk2 promoted tumor cell migration while siRNA-mediated downregulation of Pyk2 attenuated cell migration. Downregulation of Pyk2 markedly inhibited ascites and CCL18-induced cell migration. CONCLUSIONS: Taken together, our findings establish an important role for CCL18, as a component of ascites, in the migration of tumor cells and identify Pyk2 as prognostic factor and a critical downstream signaling pathway for ascites-induced OC cell migration.
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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) | 0.002 | 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".