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Identifying potential functional lncRNAs in metabolic syndrome by constructing a lncRNA–miRNA–mRNA network

Journal of Human Genetics volume 65, pages 927–938 (2020)Cite this article

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Abstract

The metabolic syndrome (MS) is a cluster of interrelated risk factors including diabetes mellitus, abdominal obesity, high cholesterol, and hypertension, which can significantly increase mortality and disability. Accumulating evidence suggest that long non-coding RNAs (lncRNAs) are involved in the pathogenesis of human metabolic diseases. However, little is known about the regulatory role of lncRNAs in MS. In this work, we proposed a method for identifying potential MS-associated lncRNAs by constructing an lncRNA–miRNA–mRNA network (LMMN). Firstly, we constructed LMMN by integrating MS-associated genes, miRNA–mRNA interactions, miRNA–lncRNA interactions and mRNA/miRNA expression profiles in patients with MS. Then, we predicted potential MS-associated lncRNAs based on the topological properties of LMMN. As a result, we identified XIST as the most important lncRNA in LMMN. Furthermore, we focused on XIST/miR-214-3p and mir-181a-5p/PTEN axis and validated their expression in MS using real-time quantitative polymerase chain reaction (RT-qPCR). The RT-qPCR results showed that the expression of XIST and PTEN was significantly decreased (P < 0.05) while the expression of miR-214-3p was significantly increased (P < 0.05) in peripheral blood mononuclear cells (PBMCs) of patients with MS, compared with healthy controls. In addition, correlation analysis showed that XIST was negatively correlated with serum C peptide and PTEN was positively correlated with BMI of MS patients. Our findings provided new evidence for further exploring the regulatory role of XIST and other lncRNAs in MS.

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Data availability

Users can freely access MS-associated lncRNA–miRNA–mRNA network, XIST-centered lncRNA–miRNA–mRNA network and XIST–miRNA–PTEN network through the following link: http://www.ndexbio.org/#/networkset/df19b350-593a-11ea-bfdc-0ac135e8bacf?accesskey=771d8c38dc933a19f1ccbfb5b1c3c9105177b8170879bd4215c1d0a6ec62cd16.

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Acknowledgements

This work was supported by Innovation Talents Project of Harbin Science and Technology Bureau (2017RAQXJ027), the Natural Science Foundation of Heilongjiang Province (LH2019F023), the Fundamental Research Foundation for Universities of Heilongjiang Province (LGYC2018JQ003), and China Scholarship Council.

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  1. These authors contributed equally: Dengju Yao, Zijing Lin

Authors and Affiliations

  1. School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, Heilongjiang, China

    Dengju Yao

  2. Department of Endocrinology and Metabolism, the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China

    Zijing Lin & Xiaorong Zhan

  3. College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, Heilongjiang, China

    Xiaojuan Zhan

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  1. Dengju Yao
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Correspondence to Xiaorong Zhan.

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Yao, D., Lin, Z., Zhan, X. et al. Identifying potential functional lncRNAs in metabolic syndrome by constructing a lncRNA–miRNA–mRNA network. J Hum Genet 65, 927–938 (2020). https://doi.org/10.1038/s10038-020-0753-7

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