Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing

Nicole Panarelli, Kathrin Tyryshkin, Justin Jong Mun Wong, Adrianna Majewski, Xiaojing Yang, Theresa Scognamiglio, Michelle Kang Kim, Kimberly Bogardus, Thomas Tuschl, Yao Tseng Chen, Neil Renwick

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


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.

Original languageEnglish
Pages (from-to)47-57
Number of pages11
JournalEndocrine-Related Cancer
Issue number1
StatePublished - Jan 2019


  • Biomarkers
  • Classification
  • Gastroenteropancreatic neuroendocrine tumors
  • MicroRNA
  • Small RNA sequencing


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