SET complex in serous epithelial ovarian cancer
Bibliographic record
Abstract
With low cure rates but increasing diverse treatment options that provide variable remission times, ovarian cancer is increasingly being recognized as a chronic disease. This reality indicates the need for a better understanding of factors influencing disease progression. In a previous global analysis of gene expression, we identified genes differentially expressed when comparing serous epithelial ovarian tumors of low and high malignant potential (grade 0 vs grade 3). In this analysis, 4 out of 5 members of the SET complex, SET, APE1, NM23 and HMGB2, were highly expressed in invasive grade 3 tumors. To further investigate the expression of these genes and the fifth member of the SET complex (pp32), we performed immunohistochemistry, on a tissue array composed of 235 serous tumors of different grades and disease stages. A significant correlation between expression of all SET complex proteins and the tumor differentiation was observed (p < 0.05). When combining all tumors, overexpression of Nm23 (p = 0.04), Set (p = 0.004) and Ape1 (p = 0.004) was associated with the clinical stage of the disease. No marker by itself was associated with prognosis. The combination of a high level of Nm23 in the context of a low level of Set compared to all other combinations of these markers did confer a better prognosis (p = 0.03). When combined, high expression of Hmgb2 and low expression of Ape1 was also associated with patient prognosis (p = 0.05). These findings suggest that a strategy that sums the activities of different partners within a pathway may be more appropriate in designing nomograms for patient stratification.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".