Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining

Simple Summary Lung neuroendocrine neoplasms (NENs) are a subset of lung cancer that is difficult to diagnose. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many cancers. In this study, we generated miRNA profiles for 55 preserved lung NEN samples (14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC)), and randomly assigned them to either discovery or validation sets. We used machine learning and data mining algorithms to identify important miRNA that can distinguish between the types. Using the miRNAs identified with these algorithms, we were able to distinguish between carcinoids (TC and AC) and neuroendocrine carcinomas (SCLC and LCNEC) in the discovery set with 93% accuracy; in the validation set, we were able to distinguish between these groups with 100% accuracy. Using the same machine learning and data mining techniques, we also identified miRNAs that can distinguish between TC and AC, and SCLC and LCNEC, however more samples are needed to validate these findings. 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 identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size.