Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining

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 anatomic 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 five 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 anatomic
sites have convergent miRNA expression programs, likely reflecting
morphologic and functional similarities. Using machine learning
approaches, we identified 17 miRNAs to discriminate 15 NEN pathological
types and subsequently constructed a multi-layer classifier, correctly
identifying 217 (98%) of 221 samples and overturning one histologic
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.