{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:43:09Z","timestamp":1790718189678,"version":"4.1.0"},"reference-count":46,"publisher":"Bioscientifica","issue":"1","content-domain":{"domain":["erc.bioscientifica.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1530\/erc-18-0244","type":"journal-article","created":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T08:54:05Z","timestamp":1531904045000},"page":"47-57","update-policy":"https:\/\/doi.org\/10.1530\/crossmarkpolicy-8","source":"Crossref","is-referenced-by-count":47,"title":["Evaluating gastroenteropancreatic neuroendocrine tumors through microRNA sequencing"],"prefix":"10.1530","volume":"26","author":[{"given":"Nicole","family":"Panarelli","sequence":"first","affiliation":[{"name":"1Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathrin","family":"Tyryshkin","sequence":"additional","affiliation":[{"name":"2Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin Jong Mun","family":"Wong","sequence":"additional","affiliation":[{"name":"2Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrianna","family":"Majewski","sequence":"additional","affiliation":[{"name":"2Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojing","family":"Yang","sequence":"additional","affiliation":[{"name":"2Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen\u2019s University, Kingston, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Theresa","family":"Scognamiglio","sequence":"additional","affiliation":[{"name":"1Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle Kang","family":"Kim","sequence":"additional","affiliation":[{"name":"3Center for Carcinoid and Neuroendocrine Tumors of Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kimberly","family":"Bogardus","sequence":"additional","affiliation":[{"name":"4HHMI, Laboratory of RNA Molecular Biology, The Rockefeller University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Tuschl","sequence":"additional","affiliation":[{"name":"4HHMI, Laboratory of RNA Molecular Biology, The Rockefeller University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao-Tseng","family":"Chen","sequence":"additional","affiliation":[{"name":"1Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Renwick","sequence":"additional","affiliation":[{"name":"2Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen\u2019s University, Kingston, Ontario, Canada"},{"name":"4HHMI, Laboratory of RNA Molecular Biology, The Rockefeller University, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"416","reference":[{"key":"ref341","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1038\/nprot.2008.73","article-title":"Analyzing real-time PCR data by the comparative C(T) method","volume":"3","year":"2008","journal-title":"Nature 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