Molecular description of a 3D in vitro model for the study of epithelial ovarian cancer (EOC)
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) cell lines are useful tools for the molecular and biological characterization of ovarian cancer. The use of an in vitro multidimensional (3-D) culture model recapitulates some of the growth conditions encountered by tumor cells in vivo. Here we describe a molecular comparison of spheroid based 3D EOC models versus monolayer cultures and xenografts using cell lines from malignant ovarian tumors (TOV-21G and TOV-112D) and ascites (OV-90) previously established and characterized in our laboratory. Gene expression analyses of the three models were performed using the Affymetrix HG-U133A high density DNA array. Cluster analysis identified a set of genes that stratified expression profiles from the EOC cell lines grown as spheroids and xenografts from that of monolayer cultures. The gene expression analysis results were validated by Q-PCR analyses on an independent set of RNAs. Differential expression observed for the S100A6 gene between the monolayer, spheroid cultures and xenografts was confirmed at the protein level by immunohistochemistry. The analysis was extended to various ovarian tumor tissues using an EOC tissue array. This result represents an example of a gene that, if studied in vitro, is more representative of the in vivo disease in a 3D model rather than the monolayer culture. Identification of genes in spheroid models that mimic the in vivo tumor gene expression patterns may allow a better understanding of the community effect observed in human disease that is determined by direct or indirect interactions of cells with their environment or other surrounding cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".