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Silencing of UCHL1 by CpG Promoter Hyper-methylation is Associated with Metastatic Gastroenteropancreatic Well-differentiated Neuroendocrine (carcinoid) Tumors

Overview
Journal Ann Surg Oncol
Publisher Springer
Specialty Oncology
Date 2014 May 24
PMID 24854489
Citations 10
Authors
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Abstract

Background: Well-differentiated gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare tumors with varying metastatic potential. The underlying molecular basis for metastasis by GEP-NETs remains undefined.

Methods: Quantitative PCR and immunohistochemistry (IHC) staining for ubiquitin carboxyl-terminal esterase L1 (UCHL1) gene and protein expression was performed on a group of localized and metastatic well-differentiated GEP-NET samples acquired from a prospectively maintained tissue bank. The ability of extent of UCHL1 IHC staining to differentiate localized and metastatic tumors was compared with Ki-67 index.

Results: Among 46 total samples, UCHL1 expression at both the gene and protein level was significantly greater among localized GEP-NETs compared with metastatic tumors and metastases (p < 0.001). Hypermethylation of the UCHL1 promoter was commonly observed among metastatic primary tumors and metastases (those with the lowest UCHL1 expression) but not among localized tumors (p < 0.001). Poor staining (<50 %) for UCHL1 was observed in 27 % of localized tumors compared with 87 % of metastatic tumors (p = 0.001). The presence of <50 % staining for UCHL1 was 88 % sensitive and 73 % specific for identifying metastatic disease. In contrast, there was no association between Ki-67 index and metastatic disease. In multivariable analysis, only UCHL1 staining <50 % [odds ratio (OR) 24.5, p = 0.035] and vascular invasion (OR 38.4, p = 0.03) were independent risk factors for metastatic disease at the time of initial surgery.

Conclusions: Loss of UCHL1 expression by CpG promoter hypermethylation is associated with metastatic GEP-NETs. Extent of UCHL1 staining should be explored as a potentially clinically useful adjunct to Ki-67 index in evaluating GEP-NETs for aggressive features.

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