Open Access Highly Accessed Research article

Functional analysis of microRNA and transcription factor synergistic regulatory network based on identifying regulatory motifs in non-small cell lung cancer

Kening Li1, Zihui Li1, Ning Zhao1, Yaoqun Xu2, Yongjing Liu1, Yuanshuai Zhou1, Desi Shang1, Fujun Qiu1, Rui Zhang1, Zhiqiang Chang1 and Yan Xu1*

Author Affiliations

1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China

2 Institute of System Engineering, Harbin University of Commerce, Harbin 150028, China

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BMC Systems Biology 2013, 7:122  doi:10.1186/1752-0509-7-122

Published: 7 November 2013

Abstract

Background

Lung cancer, especially non-small cell lung cancer, is a leading cause of malignant tumor death worldwide. Understanding the mechanisms employed by the main regulators, such as microRNAs (miRNAs) and transcription factors (TFs), still remains elusive. The patterns of their cooperation and biological functions in the synergistic regulatory network have rarely been studied.

Results

Here, we describe the first miRNA-TF synergistic regulation network in human lung cancer. We identified important regulators (MYC, NFKB1, miR-590, and miR-570) and significant miRNA-TF synergistic regulatory motifs by random simulations. The two most significant motifs were the co-regulation of miRNAs and TFs, and TF-mediated cascade regulation. We also developed an algorithm to uncover the biological functions of the human lung cancer miRNA-TF synergistic regulatory network (regulation of apoptosis, cellular protein metabolic process, and cell cycle), and the specific functions of each miRNA-TF synergistic subnetwork. We found that the miR-17 family exerted important effects in the regulation of non-small cell lung cancer, such as in proliferation and cell cycle regulation by targeting the retinoblastoma protein (RB1) and forming a feed forward loop with the E2F1 TF. We proposed a model for the miR-17 family, E2F1, and RB1 to demonstrate their potential roles in the occurrence and development of non-small cell lung cancer.

Conclusions

This work will provide a framework for constructing miRNA-TF synergistic regulatory networks, function analysis in diseases, and identification of the main regulators and regulatory motifs, which will be useful for understanding the putative regulatory motifs involving miRNAs and TFs, and for predicting new targets for cancer studies.

Keywords:
Regulatory network; MicroRNA; Transcription factor; Motif; Cell cycle; miR-17 family; Non-small cell lung cancer