Cancers, Vol. 12, Pages 2653: Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining

Cancers, Vol. 12, Pages 2653: Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining Cancers doi: 10.3390/cancers12092653 Authors: Justin J. M. Wong Paula S. Ginter Kathrin Tyryshkin Xiaojing Yang Jina Nanayakkara Zier Zhou Thomas Tuschl Yao-Tseng Chen Neil Renwick Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs; SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identifi...
Source: Cancers - Category: Cancer & Oncology Authors: Tags: Article Source Type: research