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Theoretical investigation of the pathway-based network of type 2 diabetes mellitus-related genes

Author

Listed:
  • Xue-Yan Zhang

    (Yunnan University)

  • Tian-Yuan He

    (Yunnan University)

  • Chuan-Yun Xu

    (Yunnan University)

  • Ke-Fei Cao

    (Yunnan University)

  • Xu-Sheng Zhang

    (UK Health Security Agency
    Imperial College of Science, Technology and Medicine)

Abstract

Complex network is an effective approach to studying the characteristics and interactions of complex systems, which can be used to analyze the core functions and global behavior of complex biological systems. Type 2 diabetes mellitus (T2DM), the most common type of diabetes mellitus, is a complex polygenic metabolic disease associated with genetic and environmental factors. How the complex interactions between T2DM-related genes affect the pathogenesis and treatment of T2DM is not yet fully understood. By applying the network approach to biological data, this study constructs a pathway-based network model of T2DM-related genes to explore the interrelationships between genes. Analysis of statistical and topological characteristics shows that the network exhibits the small-world rather than scale-free property, with a high average degree of 99.22, revealing close and complex connections between these genes. To determine the key hub genes of the network, an integrated centrality is used to comprehensively reflect the contribution of the three centrality indices (degree centrality, betweenness centrality and closeness centrality) of nodes; by taking the threshold of 0.70 for integrated centrality, nine key hub genes are identified: PIK3CD, PIK3CA, MAPK1, PIK3R1, PRKCA, AKT2, AKT1, TNF and KRAS. These genes should play an important role in the occurrence and development of T2DM, and their identification will provide relevant and useful knowledge for further biological and medical research on their functions in T2DM (especially in the development of multi-target drugs for T2DM). This further provides clues for exploring the pathogenesis and treatment of T2DM. Graphic abstract

Suggested Citation

  • Xue-Yan Zhang & Tian-Yuan He & Chuan-Yun Xu & Ke-Fei Cao & Xu-Sheng Zhang, 2023. "Theoretical investigation of the pathway-based network of type 2 diabetes mellitus-related genes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(6), pages 1-13, June.
    • Handle: RePEc:spr:eurphb:v:96:y:2023:i:6:d:10.1140_epjb_s10051-023-00540-z
    DOI: 10.1140/epjb/s10051-023-00540-z
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    References listed on IDEAS

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    1. Wang, Huan & Hu, Jing-Bo & Xu, Chuan-Yun & Zhang, De-Hai & Yan, Qian & Xu, Ming & Cao, Ke-Fei & Zhang, Xu-Sheng, 2016. "A pathway-based network analysis of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 928-939.
    2. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    3. Wang, Huan & Xu, Chuan-Yun & Hu, Jing-Bo & Cao, Ke-Fei, 2014. "A complex network analysis of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 166-176.
    4. Diambra, L., 2011. "Coarse-grain reconstruction of genetic networks from expression levels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2198-2207.
    5. Tsuchiya, Masa & Selvarajoo, Kumar & Piras, Vincent & Tomita, Masaru & Giuliani, Alessandro, 2009. "Local and global responses in complex gene regulation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1738-1746.
    6. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    7. Hu, Jing-Bo & Wang, Huan & Wang, Le & Xu, Chuan-Yun & Cao, Ke-Fei & Zhang, Xu-Sheng, 2019. "Characteristic analysis of the pathway-based weighted network of hypertension-related genes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    8. Karlsson, Fredrik & Hörnquist, Michael, 2007. "Order or chaos in Boolean gene networks depends on the mean fraction of canalizing functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 747-757.
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