Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing. Issue 1 (January 2019)

• Record Type: Journal Article
• Authors: Panarelli, Nicole; Tyryshkin, Kathrin; Wong, Justin Jong Mun; Majewski, Adrianna; Yang, Xiaojing; Scognamiglio, Theresa; Kim, Michelle Kang; Bogardus, Kimberly; Tuschl, Thomas; Chen, Yao-Tseng; Renwick, Neil
• Is Part Of: Endocrine-related cancer. Volume 26:Issue 1(2019)
• Page Start: 47
• Page End: 57
• Publication Date: 2019-01
• Subjects: gastroenteropancreatic neuroendocrine tumors -- classification -- biomarkers -- microRNA -- small RNA sequencing; Endocrine glands -- Cancer -- Periodicals; Endocrinology -- Periodicals; Cancer -- Endocrine aspects -- Periodicals; 616.9944005
• DOI: 10.1530/ERC-18-0244
• Languages: English
• ISSNs: 1351-0088

Abstract

Abstract : 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.