Prognostic Significance of Cytoplasmic p27 Expression in Human Melanoma
Bibliographic record
Abstract
BACKGROUND: The cyclin-dependent kinase inhibitor p27 plays important roles in cell proliferation, cell motility, and apoptosis. Interestingly, the nuclear and cytoplasmic p27 exert opposite biological functions. In this study, we investigated the prognostic impact of subcellular p27 expression. METHODS: We constructed melanoma tissue microarrays in a large series of melanoma patients, including 29 normal nevi, 52 dysplastic nevi, 270 primary melanomas, and 148 metastatic melanomas. The expression level of subcellular p27 in different stages of melanocytic lesions and its prognostic significance were evaluated. RESULTS: Compared with dysplastic nevi, nuclear p27 expression was remarkably reduced in primary melanomas and further reduced in metastatic melanoma (P < 0.001 for both), whereas cytoplasmic p27 expression is significantly increased from dysplastic nevi to primary melanomas (P = 0.032) and further increased in melanoma metastases (P = 0.037). Although loss of nuclear p27 expression is correlated with a worse 5-year survival of primary melanoma patients in Kaplan-Meier analysis (P = 0.046), it is not a prognostic factor by multivariate Cox regression analysis. On the contrary, Kaplan-Meier analysis showed that gain of cytoplasmic p27 was associated with a poor 5-year survival of metastatic melanoma patients (P < 0.001). Multivariate Cox regression analysis revealed that positive cytoplasmic p27 expression is an independent prognostic factor to predict metastatic melanoma patient outcome. CONCLUSION: Cytoplasmic p27 may serve as a promising prognostic marker for metastatic melanoma. IMPACT: Because there is no reliable prognostic marker for metastatic melanoma, our finding may have important clinical implications using cytoplasmic p27 as a prognostic biomarker for advanced melanoma.
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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 | 0.000 | 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".