Role of PELP1/MNAR Signaling in Ovarian Tumorigenesis
Bibliographic record
Abstract
Emerging evidence suggests that nuclear receptor (NR) coregulators have potential to act as master genes and their deregulation can promote oncogenesis. Proline-, glutamic acid-, and leucine-rich protein-1 (PELP1/MNAR) is a novel NR coregulator. Its expression is deregulated in hormone-driven cancers. However, the role of PELP1/MNAR in ovarian cancer progression remains unknown. Analysis of serial analysis of gene expression data suggested deregulation of PELP1/MNAR expression in ovarian tumors. Western analysis of PELP1/MNAR in normal and serous ovarian tumor tissues showed 3- to 4-fold higher PELP1/MNAR expression in serous tumors compared with normal ovarian tissues. To examine the significance of PELP1/MNAR in ovarian cancer progression, we have generated model cells that overexpress PELP1/MNAR and ovarian cancer cells in which PELP1/MNAR expression is down-regulated by stable expression of PELP1/MNAR-specific shRNA. Down-regulation of PELP1/MNAR in cancerous ovarian model cells (OVCAR3) resulted in reduced proliferation, affected the magnitude of c-Src and protein kinase B (AKT) signaling, and reduced tumorigenic potential of ovarian cancer cells in a nude mouse model. PELP1/MNAR overexpression in nontumorigenic immortalized surface epithelial cells (IOSE cells) promoted constitutive activation of c-Src and AKT signaling pathways and promoted anchorage-independent growth. Immunohistochemical studies using human ovarian cancer tissue arrays (n = 123) showed that PELP1/MNAR is 2- to 3-fold overexpressed in 60% of ovarian tumors, and PELP1/MNAR deregulation occurs in all different types of ovarian cancer. Collectively, these results suggest that PELP1/MNAR signaling plays a role in ovarian cancer cell proliferation and survival, and that its expression is deregulated in ovarian carcinomas.
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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".