CCL18 from tumor‐cells promotes epithelial ovarian cancer metastasis via mTOR signaling pathway
Bibliographic record
Abstract
CCL18 is a chemotactic cytokine involved in the pathogenesis and progression of various disorders, including cancer. Previously, our results showed high levels of CCL18 in the serum of epithelial ovarian carcinoma patients suggesting its potential as a circulating biomarker. In this study, we determined that CCL18 expression was up-regulated in ovarian carcinoma compared with adjacent tissue and was expressed in carcinoma cells in the tumor and not in normal ovarian epithelial cells by laser capture microdissection coupled with real-time RT-PCR. Moreover, correlation analysis showed that the CCL18 level was positively correlated with the metastasis of patients with ovarian cancer. Survival analysis also revealed that an increased level of CCL18 was associated with worse survival time in ovarian cancer patients. Over-expression of CCL18 led to enhanced migration and invasion of the Skov3 ovarian cancer cell line in vitro and in vivo. Finally, proteomics analysis demonstrated that CCL18-mediated ovarian cancer invasiveness was strongly correlated with the mTORC2 pathway. These findings suggest that the CCL18 chemokine has an important role in chemokine-mediated tumor metastasis, and may serve as a potential predictor for poor survival outcomes for ovarian cancer. © 2015 The Authors. Molecular Carcinogenesis published by Wiley Periodicals, Inc.
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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) | 0.004 | 0.001 |
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".