Presence of sst5TMD4, a truncated splice variant of the somatostatin receptor subtype 5, is associated to features of increased aggressiveness in pancreatic neuroendocrine tumors.
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PURPOSE: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare and heterogeneous tumors, and their biological behavior is not well known. We studied the presence and potential functional roles of somatostatin receptors (sst1-5), focusing particularly on the truncated variants (sst5TMD4, sst5TMD5) and on their relationships with the angiogenic system (Ang/Tie-2 and VEGF) in human GEP-NETs. EXPERIMENTAL DESIGN: We evaluated 42 tumor tissue samples (26 primary/16 metastatic) from 26 patients with GEP-NETs, and 30 non-tumoral tissues (26 from adjacent non-tumor regions and 4 from normal controls) from a single center. Expression of sst1-5, sst5TMD4, sst5TMD5, Ang1-2, Tie-2 and VEGF was analyzed using real-time qPCR, immunofluorescence and immunohistochemistry. Expression levels were associated with tumor characteristics and clinical outcomes. Functional role of sst5TMD4 was analyzed in GEP-NET cell lines. RESULTS: sst1 exhibited the highest expression in GEP-NET, whilst sst2 was the most frequently observed sst-subtype (90.2%). Expression levels of sst1, sst2, sst3, sst5TMD4, and sst5TMD5 were significantly higher in tumor tissues compared to their adjacent non-tumoral tissue. Lymph-node metastases expressed higher levels of sst5TMD4 than in its corresponding primary tumor tissue. sst5TMD4 was also significantly higher in intestinal tumor tissues from patients with residual disease of intestinal origin compared to those with non-residual disease. Functional assays demonstrated that the presence of sst5TMD4 was associated to enhanced malignant features in GEP-NET cells. Angiogenic markers correlated positively with sst5TMD4, which was confirmed by immunohistochemical/fluorescence studies. CONCLUSIONS: sst5TMD4 is overexpressed in GEP-NETs and is associated to enhanced aggressiveness, suggesting its potential value as biomarker and target in GEP-NETs.