Research article

Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing

Sanne Nygaard^1,2 , Anders Jacobsen^1,2 , Morten Lindow^1,2,7 , Jens Eriksen³ , Eva Balslev⁴ , Henrik Flyger⁵ , Niels Tolstrup⁶ , Søren Møller⁶ , Anders Krogh¹ and Thomas Litman⁶

¹The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark

²The Biotech Research and Innovation Centre (BRIC), Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark

³Laboratory of Oncology, Herlev University Hospital, 2730 Herlev, Denmark

⁴Department of Pathology, Herlev University Hospital, 2730 Herlev, Denmark

⁵Department of Breast Surgery, Herlev University Hospital, 2730 Herlev, Denmark

⁶Exiqon A/S, Bygstubben 9, 2950 Vedbæk, Denmark

⁷Santaris Pharma A/S, Bøge Allé 3-5, 2970 Hørsholm, Denmark

author email corresponding author email

BMC Medical Genomics 2009, 2:35doi:10.1186/1755-8794-2-35

Published:	9 June 2009

Abstract

Background

MiRNAs play important roles in cellular control and in various disease states such as cancers, where they may serve as markers or possibly even therapeutics. Identifying the whole repertoire of miRNAs and understanding their expression patterns is therefore an important goal.

Methods

Here we describe the analysis of 454 pyrosequencing of small RNA from four different tissues: Breast cancer, normal adjacent breast, and two teratoma cell lines. We developed a pipeline for identifying new miRNAs, emphasizing extracting and retaining as much data as possible from even noisy sequencing data. We investigated differential expression of miRNAs in the breast cancer and normal adjacent breast samples, and systematically examined the mature sequence end variability of miRNA compared to non-miRNA loci.

Results

We identified five novel miRNAs, as well as two putative alternative precursors for known miRNAs. Several miRNAs were differentially expressed between the breast cancer and normal breast samples. The end variability was shown to be significantly different between miRNA and non-miRNA loci.

Conclusion

Pyrosequencing of small RNAs, together with a computational pipeline, can be used to identify miRNAs in tumor and other tissues. Measures of miRNA end variability may in the future be incorporated into the discovery pipeline as a discriminatory feature. Breast cancer samples show a distinct miRNA expression profile compared to normal adjacent breast.