Reprogramming of the transcriptome in a novel chromosome 3 transfer tumor suppressor ovarian cancer cell line model affected molecular networks that are characteristic of ovarian cancer
Bibliographic record
Abstract
Tumor suppression as a consequence of the transfer of chromosome 3p fragments was previously observed in a novel epithelial ovarian cancer (EOC) OV-90 cell line model harboring loss of 3p. Microarray analysis revealed that tumor suppression was associated with a modified transcriptome. To investigate the relevance of the altered transcriptome, the differentially expressed genes identified by Affymetrix analysis in the 3p transfer studies, were integrated with a comparative microarray analysis of normal ovarian surface epithelial (NOSE) cells and malignant ovarian (TOV) cancers. Data from 219 significantly differentially expressed genes exhibited patterns in the direction predicted by the analysis of 3p transfer study. The 30 genes with the highest statistically significant differences (P < 1 x 10(-8)) in expression were found consistently differentially expressed between NOSE and TOV samples. The investigation of these genes in benign serous ovarian tumors and EOC cell lines also exhibited predictable expression patterns. Within the group of differentially expressed genes were SPARC, DAB2, CP, EVI1, ELF3, and EHD2, known to play a role in ovarian cancer, genes implicated in other cancers, such as GREM1 and GLIPR1, as well as genes not previously reported in a cancer context such as AKAP2 and ATAD4. A number of the differentially expressed genes are implicated in the TGF-beta signaling pathway. These findings suggest that the reprogramming of the transcriptome that occurred as a consequence of the chromosome 3 transfer and tumor suppression affected molecular networks that are characteristic of ovarian carcinogenesis thus validating our novel ovarian cancer cell line model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".