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

  • Regular Article - Statistical and Nonlinear Physics
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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.

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Data Availability Statement

The datasets of T2DM-related genes and related biological pathways were collected from the NCBI Gene Database and the KEGG PATHWAY Database, respectively. The main data supporting the findings of this study are included in this article. Other data generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

Notes

  1. NCBI (National Center for Biotechnology Information) website: https://www.ncbi.nlm.nih.gov/.

  2. KEGG (Kyoto Encyclopedia of Genes and Genomes) website: https://www.kegg.jp/ or https://www.genome.jp/kegg/.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) (Grant nos. 11365023 and 51866005). The authors would like to thank Professors Fukai Bao and Wen Zhang from Kunming Medical University for their helpful discussions, and the anonymous reviewer for valuable comments and suggestions.

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Authors and Affiliations

  1. Center for Nonlinear Complex Systems, Department of Physics, School of Physics and Astronomy, Yunnan University, 2 Cuihu North Road, Kunming, 650091, Yunnan, People’s Republic of China

    Xue-Yan Zhang, Tian-Yuan He, Chuan-Yun Xu & Ke-Fei Cao

  2. Department of Statistics, Modelling and Economics, UK Health Security Agency, 61 Colindale Avenue, London, NW9 5EQ, UK

    Xu-Sheng Zhang

  3. Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College of Science, Technology and Medicine, Norfolk Place, London, W2 1PG, UK

    Xu-Sheng Zhang

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Contributions

XYZ and KFC conceived and designed the research. XYZ collected the data, performed numerical calculations with the help of CYX, and drafted the original manuscript. All authors contributed to the analysis, discussion and interpretation of the results. KFC revised the manuscript and finalized the submission version of the manuscript with XSZ.

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Correspondence to Ke-Fei Cao.

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Zhang, XY., He, TY., Xu, CY. et al. Theoretical investigation of the pathway-based network of type 2 diabetes mellitus-related genes. Eur. Phys. J. B 96, 86 (2023). https://doi.org/10.1140/epjb/s10051-023-00540-z

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