Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining. (15th July 2020)

• Record Type: Journal Article
• Authors: Nanayakkara, Jina; Tyryshkin, Kathrin; Yang, Xiaojing; Wong, Justin J M; Vanderbeck, Kaitlin; Ginter, Paula S; Scognamiglio, Theresa; Chen, Yao-Tseng; Panarelli, Nicole; Cheung, Nai-Kong; Dijk, Frederike; Ben-Dov, Iddo Z; Kim, Michelle Kang; Singh, Simron; Morozov, Pavel; Max, Klaas E A; Tuschl, Thomas; Renwick, Neil
• Is Part Of: NAR cancer. Volume 2:Number 3(2020)
• Publication Date: 2020-07-15
• Subjects: Cancer -- Periodicals; Cancer -- Genetic aspects -- Periodicals; Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 616.994
• DOI: 10.1093/narcan/zcaa009
• Languages: English
• ISSNs: 2632-8674

Abstract

Abstract: Neuroendocrine neoplasms (NENs) are clinically diverse and incompletely characterized cancers that are challenging to classify. MicroRNAs (miRNAs) are small regulatory RNAs that can be used to classify cancers. Recently, a morphology-based classification framework for evaluating NENs from different anatomical sites was proposed by experts, with the requirement of improved molecular data integration. Here, we compiled 378 miRNA expression profiles to examine NEN classification through comprehensive miRNA profiling and data mining. Following data preprocessing, our final study cohort included 221 NEN and 114 non-NEN samples, representing 15 NEN pathological types and 5 site-matched non-NEN control groups. Unsupervised hierarchical clustering of miRNA expression profiles clearly separated NENs from non-NENs. Comparative analyses showed that miR-375 and miR-7 expression is substantially higher in NEN cases than non-NEN controls. Correlation analyses showed that NENs from diverse anatomical sites have convergent miRNA expression programs, likely reflecting morphological and functional similarities. Using machine learning approaches, we identified 17 miRNAs to discriminate 15 NEN pathological types and subsequently constructed a multilayer classifier, correctly identifying 217 (98%) of 221 samples and overturning one histological diagnosis. Through our research, we have identified common and type-specific miRNA tissue markers and constructed an accurate miRNA-based classifier, advancing our understanding of NEN diversity.