Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Highly Accessed Research article

An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells

Seungyoon Nam12*, Xinghua Long23, ChangHyuk Kwon1, Sun Kim4 and Kenneth P Nephew2

Author Affiliations

1 Cancer Genomics Branch, National Cancer Center, Goyang-si, Gyeonggi-do, 410-769, Korea

2 Department of Cellular and Integrative Physiology, Medical Sciences Program, Indiana University School of Medicine, Bloomington, IN 47405, USA

3 Zhongnan Hospital, Wuhan University, Wuhan, 430071, China

4 Department of Computer Science and Engineering, Bioinformatics Institute, Seoul National University, Seoul, 151-742, Korea

For all author emails, please log on.

BMC Genomics 2012, 13:732  doi:10.1186/1471-2164-13-732

Published: 27 December 2012

Abstract

Background

A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs) into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines) from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an “integrative network”. We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity.

Results

Based on the integrative network, we extracted “substructures” (network clusters) representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells) compared to drug sensitive state (parental MCF7 cells). We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222.

Conclusions

By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In addition, new miRNA clusters that contribute to antiestrogen resistance were identified, and they warrant further investigation.

Keywords:
Bioinformatics; miRNA; Network; Breast cancer; Antiestrogen resistance