Oligonucleotide microarray analysis of chromosome 17 gene expression in a model human epithelial ovarian cancer cell line, TOV112D, and in epithelial ovarian tumors and ovarian malignant ascites
Bibliographic record
Abstract
The importance of developing relevant ovarian cancer models led us to test the applicability of a system comprised of epithelial ovarian cancer cell lines. The high frequency of loss of heterozygosity (LOH) and rearrangements of chromosome 17 in ovarian tumors provide evidence of a role of chromosome 17 genes in ovarian tumorigenesis. Oligonucleotide microarray expression analysis was applied to assess the expression profiles of 864 probe sets that map to chromosome 17. The TOV112D ovarian cancer cell line, a spontaneously immortalized and tumorigenic ovarian cancer cell line derived from an endometrioid histopathological subtype, which has been shown to exhibit LOH of chromosome 17, was used as a model to identify candidate genes based on Affymetrix expression microarray analyses in comparative analysis with three primary cultures derived from normal ovarian surface epithelium (NOSE). Two-way comparative analyses identified 81 probe sets, representing 64 differentially expressed genes, which exhibited at least a three-fold difference in expression relative to the mean of NOSE samples. The expression of these 64 candidate genes was investigated by microarray analysis in 31 fresh solid malignant ovarian tumors of different histopathologies, six ovarian tumors of borderline pathology, 32 primary cultures of ovarian tumors, 28 primary cultures of malignant ovarian ascites, and 16 NOSE samples. The chromosome 17 expression profile of TOV112D monolayer was compared with this cell line grown as a three-dimensional spheroid, solid tumors and monolayer cultures of these tumors from intraperitoneal and subcutaneous injection into nude mice. The expression profiles of selected candidates were validated by RT-PCR. About 63% of the candidates overexpressed at least three-fold relative to TOV112D were also overexpressed in some of the solid malignant ovarian tumors, and about 91% of the candidates that were underexpressed at least three-fold in TOV112D were also underexpressed in some of these tumors. The same differential pattern of gene expression of candidates was also observed in primary cultures of ovarian tumors and ovarian ascites, however, the effect in primary cultures was reduced. These results indicate that TOV112D, representing a spontaneously immortalized long-term passage, was more representative of solid ovarian tumors compared with the primary cultures. Growth conditions showed little impact on the expression profile of candidates when TOV112D was grown in different culture environments such as in vitro monolayer or mouse tumor xenograft. The finding that TOV112D identified differentially expressed genes in ovarian tumor samples regardless of histopathological subtype indicates that it is a useful model, not only to study the endometrioid subtype, but also serous and clear cell subtypes of epithelial ovarian cancer. Comparison of the expression profiles of our candidate genes identified by microarray analysis with published reports revealed that eight genes (ACACA, SFRS2, CCL2, CSF3, IGFBP4, KRT19, ITGA3, and TIMP2) were previously implicated in ovarian cancer, and 23 including MAC30 and TBX2 were implicated in tumorigenesis of other types of cancers. The use of long-term ovarian cancer cultures provides scientists with a model to study candidate genes, some of which may prove important for early detection or represent targets for the development of new ovarian cancer treatments.
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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".