Transcriptome analysis of serous ovarian cancers identifies differentially expressed chromosome 3 genes
Bibliographic record
Abstract
Cytogenetic, molecular genetic and functional analyses have implicated chromosome 3 genes in epithelial ovarian cancers (EOC). To further characterize their contribution to EOC, the Affymetrix U133A GeneChip(R) was used to perform transcriptome analyses of chromosome 3 genes in primary cultures of normal ovarian surface epithelial (NOSE) cells (n = 14), malignant serous epithelial ovarian tumors (TOV) (n = 17), and four EOC cell lines (TOV-81D, TOV-112D, TOV-21G, and OV-90). A two-way comparative analysis of 735 known genes and expressed sequences identified 278 differentially expressed genes, where 43 genes were differentially expressed in at least 50% of the TOV samples. Three genes, RIS1 (at 3p21.31), GBE1 (at 3p12.2), and HEG1 (at 3q21.2), were similarly underexpressed in all TOV samples. Deregulation of the expression of these genes was not associated with loss of heterozygosity (LOH) of the genetic loci harboring them. LOH analysis of the RIS1, GBE1, and HEG1 loci was observed at frequencies of 14.3%, 13.7%, and 9.2%, respectively, in a series of 66 malignant TOV samples of the serous subtype. Reduced expression levels of RIS1, GBE1, and HEG1 were observed only in the tumorigenic EOC cell lines (TOV-21G, TOV-112D, and OV-90) and did not correlate with LOH. These results combined suggest that RIS1, GBE1, and HEG1, unlike classical tumor suppressor genes, are not likely to be primary targets of inactivation. This study provides a comprehensive analysis of chromosome 3 gene expression in NOSE and in EOC samples and identifies chromosome 3 gene candidates for further study.
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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".