Immunohistochemical profiling of benign, low malignant potential and low grade serous epithelial ovarian tumors
Bibliographic record
Abstract
BACKGROUND: Serous epithelial ovarian tumors can be subdivided into benign (BOV), low malignant potential (LMP) or borderline and invasive (TOV) tumors. Although the molecular characteristics of serous BOV, LMP and low grade (LG) TOV tumors has been initiated, definitive immunohistochemical markers to distinguish between these tumor types have not been defined. METHODS: In the present study, we used a tissue array composed of 27 BOVs, 78 LMPs and 23 LG TOVs to evaluate the protein expression of a subset of selected candidates identified in our previous studies (Ape1, Set, Ran, Ccne1 and Trail) or known to be implicated in epithelial ovarian cancer disease (p21, Ccnb1, Ckd1). RESULTS: Statistically significant difference in protein expression was observed for Ccnb1 when BOV tumors were compared to LMP tumors (p = 0.003). When BOV were compared to LG TOV tumors, Trail was significantly expressed at a higher level in malignant tumors (p = 0.01). Expression of p21 was significantly lower in LG tumors when compared with either BOVs (p = 0.03) or LMPs (p = 0.001). We also observed that expression of p21 was higher in LMP tumors with no (p = 0.02) or non-invasive (p = 0.01) implants compared to the LMP associated with invasive implants. CONCLUSION: This study represents an extensive analyse of the benign and highly differentiated ovarian disease from an immunohistochemical perspective.
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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 | 0.000 |
| 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".