MicroRNA–mRNA expression profiles associated with medulloblastoma subgroup 4
Received 10 November 2017
Accepted for publication 22 December 2017
Published 16 February 2018 Volume 2018:10 Pages 339—352
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Nakshatri
Sivan Gershanov,1 Helen Toledano,2,3 Shalom Michowiz,3,4 Orit Barinfeld,3,5 Albert Pinhasov,1 Nitza Goldenberg-Cohen,5–7 Mali Salmon-Divon1
1Genomic Bioinformatics Laboratory, Department of Molecular Biology, Ariel University, Ariel, Israel; 2Department of Pediatric Oncology, Schneider Children’s Medical Center of Israel, Petah Tikva, Israel; 3Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; 4Department of Pediatric Neurosurgery, Schneider Children’s Medical Center of Israel, Petah Tikva, Israel; 5The Krieger Eye Research Laboratory, Felsenstein Medical Research Center, Beilinson Hospital, Petah Tikva, Tel Aviv, Israel; 6Department of Ophthalmology, Bnai Zion Medical Center, Haifa, Israel; 7The Ruth and Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel
Purpose: Medulloblastoma (MB), the most common malignant brain tumor in children, is divided into four tumor subgroups: wingless-type (WNT), sonic hedgehog (SHH), Group 3, and Group 4. Ideally, clinical practice and treatment design should be subgroup specific. While WNT and SHH subgroups have well-defined biomarkers, distinguishing Group 3 from Group 4 is not straightforward. MicroRNAs (miRNAs), which regulate posttranscriptional gene expression, are involved in MB tumorigenesis. However, the miRNA–messenger RNA (mRNA) regulatory network in MB is far from being fully understood. Our aims were to investigate miRNA expression regulation in MB subgroups, to assess miRNA target relationships, and to identify miRNAs that can distinguish Group 3 from Group 4.
Patients and methods: With these aims, integrated transcriptome mRNA and miRNA expression analysis was performed on primary tumor samples collected from 18 children with MB, using miRNA sequencing (miRNA-seq), RNA sequencing (RNA-seq), and quantitative PCR.
Results: Of all the expressed miRNAs, 19 appeared to be significantly differentially expressed (DE) between Group 4 and non-Group 4 subgroups (false discovery rate [FDR] <0.05), including 10 miRNAs, which, for the first time, are reported to be in conjunction with MB. RNA-seq analysis identified 165 genes that were DE between Group 4 and the other subgroups (FDR <0.05), among which seven are predicted targets of five DE miRNAs and exhibit inverse expression pattern.
Conclusion: This study identified miRNA molecules that may be involved in Group 4 etiology, in general, and can distinguish between Group 3 and Group 4, in particular. In addition, understanding the involvement of miRNAs and their targets in MB may improve diagnosis and advance the development of targeted treatment for MB.
Keywords: RNA-seq, pediatric brain tumor, differential expression, tumor subgroups
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