Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) can be challenging to evaluate histologically. MicroRNAs (miRNAs) are small RNA molecules that often are excellent biomarkers due to their abundance, cell-type and disease stage specificity and stability. To evaluate miRNAs as adjunct tissue markers for classifying and grading well-differentiated GEP-NETs, we generated and compared miRNA expression profiles from four pathological types of GEP-NETs. Using quantitative barcoded small RNA sequencing and state-of-the-art sequence annotation, we generated comprehensive miRNA expression profiles from archived pancreatic, ileal, appendiceal and rectal NETs. Following data preprocessing, we randomly assigned sample profiles to discovery (80%) and validation (20%) sets prior to data mining using machine-learning techniques. High expression analyses indicated that miR-375 was the most abundant individual miRNA and miRNA cistron in all samples. Leveraging prior knowledge that GEP-NET behavior is influenced by embryonic derivation, we developed a dual-layer hierarchical classifier for differentiating GEP-NET types. In the first layer, our classifier discriminated midgut (ileum, appendix) from non-midgut (rectum, pancreas) NETs based on miR-615 and -92b expression. In the second layer, our classifier discriminated ileal from appendiceal NETs based on miR-125b, -192 and -149 expression, and rectal from pancreatic NETs based on miR-429 and -487b expression. Our classifier achieved overall accuracies of 98.5% and 94.4% in discovery and validation sets, respectively. We also found provisional evidence that low- and intermediate-grade pancreatic NETs can be discriminated based on miR-328 expression. GEP-NETs can be reliably classified and potentially graded using a limited panel of miRNA markers, complementing morphological and immunohistochemistry-based approaches to histologic evaluation.
Nicole Panarelli, Kathrin Tyryshkin, Justin Jong Mun Wong, Adrianna Majewski, Xiaojing Yang, Theresa Scognamiglio, Michelle Kang Kim, Kimberly Bogardus, Thomas Tuschl, Yao-Tseng Chen and Neil Renwick
Brendan M Finnerty, Maureen D Moore, Akanksha Verma, Anna Aronova, Shixia Huang, Dean P Edwards, Zhengming Chen, Marco Seandel, Theresa Scognamiglio, Yi-Chieh Nancy Du, Olivier Elemento, Rasa Zarnegar, Irene M Min and Thomas J Fahey III
Loss of ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) expression by CpG promoter hypermethylation is associated with metastasis in gastroenteropancreatic neuroendocrine tumors; however, the mechanism of how UCHL1 loss contributes to metastatic potential remains unclear. In this study, we first confirmed that the loss of UCHL1 expression on immunohistochemistry was significantly associated with metastatic tumors in a translational pancreatic neuroendocrine tumor (PNET) cohort, with a sensitivity and specificity of 78% and 89%, respectively. To study the mechanism driving this aggressive phenotype, BON and QGP-1 metastatic PNET cell lines, which do not produce UCHL1, were stably transfected to re-express UCHL1. In vitro assays, RNA sequencing and reverse phase protein array (RPPA) analyses were performed comparing empty-vector negative controls and UCHL1-expressing cell lines. UCHL1 re-expression is associated with lower anchorage-independent colony growth in BON cells, lower colony formation in QGP cells and a higher percentage of cells in the G0/G1 cell-cycle phase in BON and QGP cells. On RPPA proteomic analysis, there was an upregulation of cell-cycle regulatory proteins CHK2 (1.2-fold change, P = 0.004) and P21 (1.2-fold change, P = 0.023) in BON cells expressing UCHL1; western blot confirmed upregulation of phosphorylated CHK2 and P21. There were no transcriptomic differences detected on RNA sequencing between empty-vector negative controls and UCHL1-expressing cell lines. In conclusion, UCHL1 loss correlates with metastatic potential in PNETs and its re-expression induces a less aggressive phenotype in vitro, in part by inducing cell-cycle arrest through posttranslational regulation of phosphorylated CHK2. UCHL1 expression should be considered as a functional biomarker in detecting PNETs capable of metastasis.