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

• DOI: 10.1186/s12859-017-1796-4
• Publication Date: 2017-09-02T10:13:58Z
• Authors: Hao Wang; Jiamao Luo; Chun Liu; Huilin Niu; Jing Wang; Qi Liu; Zhongming Zhao; Hua Xu; Yanqing Ding; Jingchun Sun; Qingling Zhang
• Keywords: 0301 basic medicine; microRNA; Transcription, Genetic; QH301-705.5; Computer applications to medicine. Medical informatics; R858-859.7; Regulatory network; 3. Good health; Feed-forward loops (FFLs); Gene Expression Regulation, Neoplastic; Colorectal cancer (CRC); MicroRNAs; 03 medical and health sciences; Humans; Gene Regulatory Networks; Transcription factor; Biology (General); Colorectal Neoplasms; Research Article; Transcription Factors

Colorectal 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.To 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.In 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.