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. 2008 Mar;15(1):243-56.
doi: 10.1677/ERC-07-0194.

Defining molecular classifications and targets in gastroenteropancreatic neuroendocrine tumors through DNA microarray analysis

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Defining molecular classifications and targets in gastroenteropancreatic neuroendocrine tumors through DNA microarray analysis

Eva-Maria Duerr et al. Endocr Relat Cancer. 2008 Mar.

Abstract

Current classifications of human gastroenteropancreatic neuroendocrine tumors (NETs) are inconsistent and based upon histopathologic but not molecular features. We sought to compare a molecular classification with the World Health Organization (WHO) histologic classification, identify genes that may be important for tumor progression, and determine whether gastrointestinal NETs (GI-NETs) differ in their molecular profile from pancreatic NETs (PNETs). DNA microarray analysis was performed to identify differentially expressed genes in PNETs and GI-NETs. Confirmation of expression levels was obtained by quantitative real-time PCR. Immunoblotting and mutational analysis were performed for selected genes. Hierarchical clustering of 19 PNETs revealed a 'benign' and 'malignant' cluster that corresponded well with the WHO categories of well-differentiated endocrine tumor (WDET) and well-differentiated endocrine carcinoma (WDEC) respectively. FEV, adenylate cyclase 2 (ADCY2), nuclear receptor subfamily 4, group A, member 2 (NR4A2), and growth arrest and DNA-damage-inducible, beta (GADD45b) were the most highly up-regulated genes in the malignant group of PNETs. Platelet-derived growth factor receptor (PDGFR) was expressed in both WDETs and WDECs, and phosphorylation of PDGFR-beta was observed in 83% of all PNETs. Malignant ileal GI-NETs exhibited a distinctive gene expression profile, and extracellular matrix protein 1 (ECM), vesicular monoamine member 1 (VMAT1), galectin 4 (LGALS4), and RET Proto-oncogene (RET) were highly up-regulated genes. Gene expression profiles reflect the current WHO classification and can distinguish benign from malignant PNETs and also PNETs from GI-NETs. This suggests that molecular profiling may enhance tumor classification schemes. Potential gene targets have also been identified, and PDGFR and RET are candidates that may represent novel therapeutic targets.

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