Identification and analysis of miRNAs in human breast cancer and teratoma samples using deep sequencing
1 The Bioinformatics Centre, Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark
2 The Biotech Research and Innovation Centre (BRIC), Department of biology, University of Copenhagen, 2200 Copenhagen N, Denmark
3 Laboratory of Oncology, Herlev University Hospital, 2730 Herlev, Denmark
4 Department of Pathology, Herlev University Hospital, 2730 Herlev, Denmark
5 Department of Breast Surgery, Herlev University Hospital, 2730 Herlev, Denmark
6 Exiqon A/S, Bygstubben 9, 2950 Vedbæk, Denmark
7 Santaris Pharma A/S, Bøge Allé 3-5, 2970 Hørsholm, Denmark
BMC Medical Genomics 2009, 2:35 doi:10.1186/1755-8794-2-35Published: 9 June 2009
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.
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.
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.
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.