Circular RNAs and their associations with breast cancer subtypes
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Asha A. Nair1, Nifang Niu2, Xiaojia Tang1, Kevin J. Thompson1, Liewei Wang3, Jean-Pierre Kocher1, Subbaya Subramanian4, Krishna R. Kalari1
1Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
2Division of Genomic and Molecular Pathology, University of Chicago, Chicago, IL, USA
3Department of Pharmacology, Mayo Clinic, Rochester, MN, USA
4Division of Basic and Translational Research, University of Minnesota, Minneapolis, MN, USA
Krishna R. Kalari, email: Kalari.Krishna@mayo.edu
Subbaya Subramanian, email: firstname.lastname@example.org
Keywords: circular RNA, circ-seq, breast cancer, molecular subtypes, proliferation
Received: February 23, 2016 Accepted: October 29, 2016 Published: November 05, 2016
Circular RNAs (circRNAs) are highly stable forms of non-coding RNAs with diverse biological functions. They are implicated in modulation of gene expression thus affecting various cellular and disease processes. Based on existing bioinformatics approaches, we developed a comprehensive workflow called Circ-Seq to identify and report expressed circRNAs. Circ-Seq also provides informative genomic annotation along circRNA fused junctions thus allowing prioritization of circRNA candidates. We applied Circ-Seq first to RNA-sequence data from breast cancer cell lines and validated one of the large circRNAs identified. Circ-Seq was then applied to a larger cohort of breast cancer samples (n = 885) provided by The Cancer Genome Atlas (TCGA), including tumors and normal-adjacent tissue samples. Notably, circRNA results reveal that normal-adjacent tissues in estrogen receptor positive (ER+) subtype have relatively higher numbers of circRNAs than tumor samples in TCGA. Similar phenomenon of high circRNA numbers were observed in normal breast-mammary tissues from the Genotype-Tissue Expression (GTEx) project. Finally, we observed that number of circRNAs in normal-adjacent samples of ER+ subtype is inversely correlated to the risk-of-relapse proliferation (ROR-P) score for proliferating genes, suggesting that circRNA frequency may be a marker for cell proliferation in breast cancer. The Circ-Seq workflow will function for both single and multi-threaded compute environments. We believe that Circ-Seq will be a valuable tool to identify circRNAs useful in the diagnosis and treatment of other cancers and complex diseases.
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