Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray Yoshiki Murakami 1 *, Toshihito Tanahashi 2,3 , Rina Okada 3 , Hidenori Toyoda 4 , Takashi Kumada 4 , Masaru Enomoto 1 , Akihiro Tamori 1 , Norifumi Kawada 1 , Y-h Taguchi 5 , Takeshi Azuma 3 1 Department of Hepatology, Osaka City University Graduate School of Medicine, Osaka, Japan, 2 Department of Medical Pharmaceutics, Kobe Pharmaceutical University, Kobe, Japan, 3 Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan, 4 Department of Gastroenterology, Ogaki Municipal Hospital, Ogaki, Japan, 5 Department of Physics, Chuo University, Tokyo, Japan Abstract MicroRNA (miRNA) expression profiling has proven useful in diagnosing and understanding the development and progression of several diseases. Microarray is the standard method for analyzing miRNA expression profiles; however, it has several disadvantages, including its limited detection of miRNAs. In recent years, advances in genome sequencing have led to the development of next-generation sequencing (NGS) technologies, which significantly advance genome sequencing speed and discovery. In this study, we compared the expression profiles obtained by next generation sequencing (NGS) with the profiles created using microarray to assess if NGS could produce a more accurate and complete miRNA profile. Total RNA from 14 hepatocellular carcinoma tumors (HCC) and 6 matched non-tumor control tissues were sequenced with Illumina MiSeq 50-bp single-end reads. Micro RNA expression profiles were estimated using miRDeep2 software. As a comparison, miRNA expression profiles for 11 out of 14 HCCs were also established by microarray (Agilent human microRNA microarray). The average total sequencing exceeded 2.2 million reads per sample and of those reads, approximately 57% mapped to the human genome. The average correlation for miRNA expression between microarray and NGS and subtraction were 0.613 and 0.587, respectively, while miRNA expression between technical replicates was 0.976. The diagnostic accuracy of HCC, p-value, and AUC were 90.0%, 7.22 6 10 24 , and 0.92, respectively. In summary, NGS created an miRNA expression profile that was reproducible and comparable to that produced by microarray. Moreover, NGS discovered novel miRNAs that were otherwise undetectable by microarray. We believe that miRNA expression profiling by NGS can be a useful diagnostic tool applicable to multiple fields of medicine. Citation: Murakami Y, Tanahashi T, Okada R, Toyoda H, Kumada T, et al. (2014) Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray. PLoS ONE 9(9): e106314. doi:10.1371/journal.pone.0106314 Editor: Max Costa, New York University School of Medicine, United States of America Received March 10, 2014; Accepted July 29, 2014; Published September 12, 2014 Copyright: ß 2014 Murakami et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors have no support or funding to report. Competing Interests: The authors declare that they have no competing interests. * Email: m2079633@med.osaka-cu.ac.jp Introduction MicroRNAs (miRNAs) are an abundant class of small (19– 24 nt) and highly conserved, non-coding RNA. They act as post- transcriptional regulators of gene expression, altering mRNA transcription and translation by hybridizing to the untranslated regions (UTRs) of certain subsets of mRNAs [1] [2]. Since their initial discovery in Caenorhabditis elegans in 1993 [3], researchers have gained much insight into the prevalence of miRNAs in other species. The latest miRBase database (release 20) contains 1827 precursor miRNAs and 2578 mature miRNA products in Homo sapiens (http://www.mirbase.org/index.shtml). Hepatocellular carcinoma (HCC) is a common cause of cancer- related deaths worldwide. There are more than 250,000 new HCC cases and an estimated 500,000–600,000 HCC deaths annually [4] [5]. The most frequent etiology of HCC is chronic hepatitis B and C (CHB, CHC), or alcoholic liver disease. Although recent advances in functional genomics provide a deeper understanding of viral associated hepatocarcinogenesis (review in [6]), the molecular pathogenesis of HCC remains unclear. Altered miRNA expression has been observed in a large variety of HCC and a correlation has been found between miRNA expression and histological differentiation [7] [8]. For example, the expression level of miR-26 has been associated with hepatocarcinogenesis and response to interferon therapy [9]. Moreover recently, miR-122 expression was associated with hepatocarcinogenesis, liver homeostasis, and essential liver me- tabolism [10] [11]. miR-18 has also been highly associated with the occurrence and progression of different types of cancer [12] [13]. In other research, miRNA expression profiles were associated with vascular invasion, the levels of alpha-fetoprotein, and large tumor size [14]. To date, studies exploring the role of miRNAs in hepatocar- cinogenesis have relied on microarrays to assay miRNA expres- sion. Deep sequencing, a set of technologies that produce large amounts of sequence data from nucleic acid specimens, is rapidly replacing microarrays as the technology of choice for quantifying and annotating miRNAs [15] [16]. Deep sequencing has the superior ability to capture the scale and complexity of whole transcriptomes [17]. In particular, short read deep sequencing PLOS ONE | www.plosone.org 1 September 2014 | Volume 9 | Issue 9 | e106314