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Comput Biol Chem. 2019 Feb;78:1-7. doi: 10.1016/j.compbiolchem.2018.11.008. Epub 2018 Nov 16.

Identification of transcription factor-miRNA-lncRNA feed-forward loops in breast cancer subtypes.

Author information

1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
2
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
3
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China.
4
Department of Pathophysiology, School of Medicine, Southeast University, Nanjing, 210009, China. Electronic address: lw2247@yeah.net.
5
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. Electronic address: weijiang@nuaa.edu.cn.

Abstract

Previous studies have demonstrated that transcription factor-miRNA-gene feed-forward loops (FFLs) played important roles in tumorigenesis. However, the lncRNA-involved FFLs have not been explored very well. Understanding the characteristics of lncRNA-involved FFLs in breast cancer subtypes may be a key question with clinical implications. In this study, we firstly constructed an integrated background regulatory network. Then, based on mRNA, miRNA, and lncRNA differential expression, we identified 147, 140, 284, 1031 dysregulated FFLs for luminal A, luminal B, HER2+ and basal-like subtype of breast cancer, respectively. Importantly, the known breast cancer-associated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. Through merging the dysregulated FFLs, we constructed the regulatory sub-network for each subtype. We found that all sub-networks were enriched in the well-known cancer-related pathways, such as cell cycle, pathways in cancer. Next, we also identified potential prognostic FFLs for subtypes of breast cancer, such as the hsa-miR-182-5p_JUN_XIST in basal-like subtype. Finally, we also discussed the potential application of inferring the candidate drugs for breast cancer treatment through modulating the lncRNA expression in the dysregulated FFLs. Collectively, this study elucidated the roles of lncRNA-involved FFLs in breast cancer subtypes, which could contribute to understanding breast cancer pathogenesis and improving the treatment.

KEYWORDS:

Breast cancer; Feed-forward loop; Regulatory network; lncRNA; microRNA

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