Tissue array analysis of expression microarray candidates identifies markers associated with tumor grade and outcome in serous epithelial ovarian cancer
Bibliographic record
Abstract
Molecular profiling is a powerful approach to identify potential clinical markers for diagnosis and prognosis as well as providing a better understanding of the biology of epithelial ovarian cancer. On the basis of the analysis of HuFL expression data, we have previously identified genes that distinguish low malignant potential and invasive serous epithelial ovarian tumors. In this study, we used immunohistochemistry to monitor a subset of differently expressed candidates (Ahr, Paep, Madh3, Ran, Met, Mek1, Ccne1, Ccd20, Cks1 and Cas). A tissue array composed of 244 serous tumors of different grades (0-3) and stages (I-IV) was used in this analysis. All markers assayed presented differential protein expression between serous tumors of low and high grade. Significant differences in Ccne1 and Ran expression were observed in a comparison of low malignant potential and grade 1 tumor samples (p<0.01). In addition, irrespective of the grade, Ccne1, Ran, Cdc20 and Cks1 showed significant differences of expression in association with the clinical stage of disease. While high level of Ccne1 have previously been associated with poor outcomes, here we found that high level of either Ran or Cdc20 appear to be more tightly associated with a poor prognosis (p<0.001, 0.03, respectively). The application of these biomarkers in both the initial diagnosis and prognostic attributes of patients with epithelial ovarian tumors should prove to be useful in patient management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".