Genome-wide small nucleolar RNA expression analysis of lung cancer by next-generation deep sequencing.

Abstract

Emerging evidence indicates that small nucleolar RNAs (snoRNAs), a class of small noncoding RNAs, may play important function in tumorigenesis. Nonsmall-cell lung cancer (NSCLC) is the number one cancer killer for men and women. Systematically characterizing snoRNAs in NSCLC will develop biomarkers for its early detection and prognostication. We used next-generation deep sequencing to comprehensively characterize snoRNA profiles in 12 NSCLC tissues. We used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the findings in 40 surgical Stage I NSCLC specimens and 126 frozen NSCLC tissues of different stages. The 126 NSCLC tissues were divided into a training set and a testing set. Deep sequencing identified 458 snoRNAs, of which, 29 had a ≥3.0-fold expression level change in Stage I NSCLC tissues versus normal tissues. qRT-PCR analysis showed that 16 of 29 snoRNAs exhibited consistent changes with deep sequencing data. The 16 snoRNAs exhibited 0.75-0.94 area under receiver-operator characteristic curve values in distinguishing lung tumor from normal lung tissues (all ≤0.0001) with 70.0-95.0% sensitivity and 70.0-95.0% specificity. Six genes (snoRA47, snoRA68, snoRA78, snoRA21, snoRD28 and snoRD66) were identified whose expressions were associated with overall survival of the NSCLC patients. A prediction model consisting of three genes (snoRA47, snoRA68 and snoRA78) was developed in the training set of 77 cases, which could significantly predict overall survival of the NSCLC patients (p < 0.0001). The prognostic performance of the prediction model was confirmed in the testing set of 49 NSCLC patients. The identified snoRNA signatures may provide potential biomarkers for the early detection and prognostication of NSCLC.