NuMA Overexpression in Epithelial Ovarian Cancer
Bibliographic record
Abstract
Highly aneuploid tumours are common in epithelial ovarian cancers (EOC). We investigated whether NuMA expression was associated with this phenomenon.NuMA protein levels in normal and tumour tissues, ovarian cell lines and primary cultures of malignant cells derived from ovarian ascitic fluids were analysed by Affymetrix microarray analysis, immunoblotting, immunohistochemistry (IHC) and immunofluorescence (IF), with results correlated to associated clinical data. Aneuploidy status in primary cultures was determined by FACS analysis.Affymetrix microarray data indicated that NuMA was overexpressed in tumour tissue, primary cultures and cell lines compared to normal ovarian tissue. IHC revealed low to weak NuMA expression in normal tissues. Expression was upregulated in tumours, with a significant association with disease stage in mucinous EOC subtypes (p = 0.009), lymph node involvement (p = 0.03) and patient age (p = 0.04). Additional discontinuous data analysis revealed that high NuMA levels in tumours decreased with grade (p = 0.02) but increased with disease stage (p = 0.04) in serous EOC. NuMA expression decreased in late disease stage 4 endometrioid EOCs. High NuMA levels decreased with increased tumour invasion in all subtypes (p = 0.03). IF of primary cultures revealed that high NuMA levels at mitotic spindle poles were significantly associated with a decreased proportion of cells in cytokinesis (p = 0.05), increased binucleation (p = 0.021) and multinucleation (p = 0.007), and aneuploidy (p = 0.008).NuMA is highly expressed in EOC tumours and high NuMA levels correlate with increases in mitotic defects and aneuploidy in primary cultures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".