Role of Eotaxin-1 Signaling in Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Tumor cell growth and migration can be directly regulated by chemokines. In the present study, the association of CCL11 with ovarian cancer has been investigated. EXPERIMENTAL DESIGN AND RESULTS: Circulating levels of CCL11 in sera of patients with ovarian cancer were significantly lower than those in healthy women or women with breast, lung, liver, pancreatic, or colon cancer. Cultured ovarian carcinoma cells absorbed soluble CCL11, indicating that absorption by tumor cells could be responsible for the observed reduction of serum level of CCL11 in ovarian cancer. Postoperative CCL11 levels in women with ovarian cancer negatively correlated with relapse-free survival. Ovarian tumors overexpressed three known cognate receptors of CCL11, CC chemokine receptors (CCR) 2, 3, and 5. Strong positive correlation was observed between expression of individual receptors and tumor grade. CCL11 potently stimulated proliferation and migration/invasion of ovarian carcinoma cell lines, and these effects were inhibited by neutralizing antibodies against CCR2, CCR3, and CCR5. The growth-stimulatory effects of CCL11 were likely associated with activation of extracellular signal-regulated kinase 1/2, MEK1, and STAT3 phosphoproteins and with increased production of multiple cytokines, growth factors, and angiogenic factors. Inhibition of CCL11 signaling by the combination of neutralizing antibodies against the ligand and its receptors significantly increased sensitivity to cisplatin in ovarian carcinoma cells. CONCLUSION: We conclude that CCL11 signaling plays an important role in proliferation and invasion of ovarian carcinoma cells and CCL11 pathway could be targeted for therapy in ovarian cancer. Furthermore, CCL11 could be used as a biomarker and a prognostic factor of relapse-free survival in ovarian cancer.
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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".