Journal
NAR CANCER
Volume 2, Issue 3, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/narcan/zcaa009
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Funding
- Academic Health Sciences Center Alternative Funding Plan Innovation Fund
- Canada Foundation for Innovation John R. Evans Leaders Fund
- Carcinoid and Neuroendocrine Tumor Society Canada
- Ontario Research Fund-Research Infrastructure
- Robertson Therapeutic Development
- Rockefeller University Center for Clinical and Translational Science Award - National Center for Advancing Translational Sciences, National Institutes of Health Clinical and Translational Science Award program [UL1TR001866]
- Southeastern Ontario Academic Medical Organization
- Ontario Institute of Cancer Research - Government of Ontario
- Ontario Institute of Cancer Research
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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 andmiR-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 reflectingmorphological 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.
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