Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer

BMC Bioinformatics

Research Article

Jing Wang¹  Qi Liu¹  Qingling Zhang²  Yanqing Ding²  Jiamao Luo²  Huilin Niu²  Chun Liu²  Hao Wang²  Hua Xu³  Jingchun Sun³  Zhongming Zhao⁴ 

[1] Center for Quantitative Sciences, Vanderbilt University School of Medicine, 37232, Nashville, TN, USA;Department of Pathology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China;Department of Pathology, College of Basic Medicine, Southern Medical University, 510515, Guangzhou, China;School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA;School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA;Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 77030, Houston, TX, USA;

关键词: Colorectal cancer (CRC);    microRNA;    Transcription factor;    Feed-forward loops (FFLs);    Regulatory network;   

DOI  :  10.1186/s12859-017-1796-4

 received in 2016-10-22, accepted in 2017-08-21,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundColorectal cancer (CRC) is one of the most common malignancies worldwide with poor prognosis. Studies have showed that abnormal microRNA (miRNA) expression can affect CRC pathogenesis and development through targeting critical genes in cellular system. However, it is unclear about which miRNAs play central roles in CRC’s pathogenesis and how they interact with transcription factors (TFs) to regulate the cancer-related genes.ResultsTo address this issue, we systematically explored the major regulation motifs, namely feed-forward loops (FFLs), that consist of miRNAs, TFs and CRC-related genes through the construction of a miRNA-TF regulatory network in CRC. First, we compiled CRC-related miRNAs, CRC-related genes, and human TFs from multiple data sources. Second, we identified 13,123 3-node FFLs including 25 miRNA-FFLs, 13,005 TF-FFLs and 93 composite-FFLs, and merged the 3-node FFLs to construct a CRC-related regulatory network. The network consists of three types of regulatory subnetworks (SNWs): miRNA-SNW, TF-SNW, and composite-SNW. To enhance the accuracy of the network, the results were filtered by using The Cancer Genome Atlas (TCGA) expression data in CRC, whereby we generated a core regulatory network consisting of 58 significant FFLs. We then applied a hub identification strategy to the significant FFLs and found 5 significant components, including two miRNAs (hsa-miR-25 and hsa-miR-31), two genes (ADAMTSL3 and AXIN1) and one TF (BRCA1). The follow up prognosis analysis indicated all of the 5 significant components having good prediction of overall survival of CRC patients.ConclusionsIn summary, we generated a CRC-specific miRNA-TF regulatory network, which is helpful to understand the complex CRC regulatory mechanisms and guide clinical treatment. The discovered 5 regulators might have critical roles in CRC pathogenesis and warrant future investigation.

【 授权许可】

CC BY   
© The Author(s). 2017