Effect of Ovarian Cancer Ascites on Cell Migration and Gene Expression in an Epithelial Ovarian Cancer In Vitro Model
Bibliographic record
Abstract
A third of patients with epithelial ovarian cancer (EOC) present ascites. The cellular fraction of ascites often consists of EOC cells, lymphocytes, and mesothelial cells, whereas the acellular fraction contains cytokines and angiogenic factors. Clinically, the presence of ascites correlates with intraperitoneal and retroperitoneal tumor spread. We have used OV-90, a tumorigenic EOC cell line derived from the malignant ascites of a chemonaive ovarian cancer patient, as a model to assess the effect of ascites on migration potential using an in vitro wound-healing assay. A recent report of an invasion assay described the effect of ascites on the invasion potential of the OV-90 cell line. Ascites sampled from 31 ovarian cancer patients were tested and compared with either 5% fetal bovine serum or no serum for their nonstimulatory or stimulatory effect on the migration potential of the OV-90 cell line. A supervised analysis of data generated by the Affymetrix HG-U133A GeneChip identified differentially expressed genes from OV-90 cells exposed to ascites that had either a nonstimulatory or a stimulatory effect on migration. Ten genes (IRS2, CTSD, NRAS, MLXIP, HMGCR, LAMP1, ETS2, NID1, SMARCD1, and CD44) were upregulated in OV-90 cells exposed to ascites, allowing a nonstimulatory effect on cell migration. These findings were validated by quantitative polymerase chain reaction. In addition, the gene expression of IRS2 and MLXIP each correlated with prognosis when their expression was assessed in an independent set of primary cultures established from ovarian ascites. This study revealed novel candidates that may play a role in ovarian cancer 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) | 0.001 | 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".