Enhancer of Zeste Homolog 2 Expression Is Associated With Metastasis and Adverse Clinical Outcome in Clear Cell Renal Cell Carcinoma: A Comparative Study and Review of the Literature
Bibliographic record
Abstract
CONTEXT: Enhancer of zeste homolog 2 (EZH2), a histone methyltransferase mediating chromatin condensation and epigenetic modulation, is overexpressed in various human carcinomas and is associated with adverse clinicopathologic characteristics and biologic behavior. The expression of EZH2 in renal cell carcinomas (RCCs) has not been fully characterized yet. OBJECTIVE: To evaluate the prognostic role of EZH2 in RCC by analyzing the immunohistochemical staining pattern of the marker in relation to pathologic features and clinical outcome. DESIGN: We correlated the immunolabeling of EZH2 with multiple clinicopathologic features, including Fuhrman nuclear grade, pathologic stage, metastatic status, and clinical outcome in 223 clear cell RCCs (CRCCs) and 21 papillary RCCs, by using tissue microarrays of primary and metastatic cases. RESULTS: Most CRCCs (75%) showed positive EZH2 staining, with most primary tumors showing focal staining in comparison to nonfocal staining in metastatic cases. In primary tumors, EZH2 expression was associated with higher nuclear grade and lower pathologic stage. Metastatic tumors showed a higher number of positive cases (81% versus 67%) and a more diffuse and more intense pattern of staining than primary CRCCs. For the 22 locally advanced primary tumors (T3/4) and 43 metastatic RCCs, patients who experienced RCC-related deaths significantly overexpressed the marker in comparison to patients who did not experience RCC-related mortality. CONCLUSIONS: By showing that EZH2 expression is associated with increased metastatic potential and a worse clinical outcome, this study suggests that EZH2 can serve as a prognostic biomarker for RCC, thus confirming it as a key molecule driving oncogenesis and metastasis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".