Multiple roles of the candidate oncogene <i>ZNF217</i> in ovarian epithelial neoplastic progression
Bibliographic record
Abstract
The transcription factor ZNF217 is often amplified in ovarian cancer, but its role in neoplastic progression is unknown. We introduced ZNF217-HA by adenoviral and retroviral infection into normal human ovarian surface epithelial cells (OSE), i.e., the source of ovarian cancer, and into SV40 Tag/tag expressing, p53/pRB-deficient OSE with extended but finite life spans (IOSE). In OSE, ZNF217-HA reduced cell-substratum adhesion and accelerated loss of senescent cells, but caused no obvious proneoplastic changes. In contrast, ZNF217-HA transduction into IOSE yielded two permanent lines, I-80RZ and I-144RZ, which exhibited telomerase activity, stable telomere lengths, anchorage independence and reduced serum dependence, but were not tumorigenic in SCID mice. This immortalization required short-term EGF treatment near the time of crisis. The permanent lines were EGF-independent, but ZNF217-dependent since siRNA to ZNF217 inhibited anchorage independence and arrested growth. Array CGH revealed genomic changes resembling those of ovarian carcinomas, such as amplicons at 3q and 20q, and deletions at 4q and 18, associated with underexpressed annexin A10, N-cadherin, desmocollin 3 and PAI-2, which have been reported as tumor suppressors. The lines overexpressed EEF1A2, SMARA3 and STAT1 and underexpressed other oncogenes, tumor suppressors and extracellular matrix/adhesion genes. The results implicate ZNF217 as an ovarian oncogene, which is detrimental to senescing normal OSE cells but contributes to neoplastic progression in OSE with inactivated p53/RB. The resemblance of the genomic changes in the ZNF217-overexpressing lines to ovarian carcinomas provides a unique model to investigate interrelationships between these changes and ovarian neoplastic phenotypes.
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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".