Characterization of Ovarian Cancer Ascites on Cell Invasion, Proliferation, Spheroid Formation, Gene Expression in an In Vitro Model of Epithelial Ovarian Cancer
Bibliographic record
Abstract
At least one third of all cases of epithelial ovarian cancer are associated with the production of ascites, although its effect on tumor cell microenvironment remains poorly understood. This study addresses the effect of the heterologous acellular fraction of ovarian cancer-derived ascites on a cell line (OV-90) derived from the chemotherapy-naïve ovarian cancer patient. Ascites were assayed for their effect on cell invasion, growth, and spheroid formation. When compared to either no serum or 5% serum, ascites fell into one of two categories: stimulatory or inhibitory. RNA from OV-90 cells exposed to selected ascites were arrayed on an Affymetrix HG-U133A GeneChip. A supervised analysis identified a number of differentially expressed genes and quantitative polymerase chain reaction validation based on OV-90 cells exposed to 54 independent ascites demonstrated that stimulatory ascites affected the expression of ISGF3G, TRIB1, MKP1, RGS4, PLEC1, and MOSPD1 genes. In addition, TRIB1 expression was shown to independently correlate with prognosis when its expression was ascertained in an independent set of primary cultures established from ovarian ascites. The data support the validity of the strategy to uncover molecular events that are associated with tumor cell behavior and highlight the impact of ascites on the cellular and molecular parameters of 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) | 0.000 | 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".