Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus
Keywords:
Enrichment analysis, graph analysis, protein-protein interactions, type 2 diabetes mellitus
Abstract
One of the metabolic diseases with a rising prevalence in Indonesia is Type 2 Diabetes Mellitus (T2DM). A collective effort from various sectors is required to seek solutions for T2DM. The proteomic approach, which focuses on proteins and their interactions related to T2DM, can be used to understand this condition. This research aims to model protein interactions associated with T2DM using a network graph, enabling the identification of key proteins that have the potential to serve as therapeutic targets or T2DM biomarkers. The study begins by acquiring data on T2DM-related proteins and their interactions. Subsequently, data preparation is performed to construct an interaction network graph. The graph is then analyzed using four centrality measures: degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality, to identify key proteins. Finally, gene set enrichment analysis (GSEA) is conducted on the identified key proteins. The analysis of the interaction network graph successfully identifies key proteins associated with T2DM. Out of 27 T2DM-related proteins, seven are selected by all four centrality measures: ABCC8, HNF4A, INS, KCNJ11, NEUROD1, PDX1, and SLC30A8. CAPN10, IL6, and WFS1 are chosen by all centrality measures except for betweenness centrality, while PTRN is only selected by betweenness and eigenvector centrality. GSEA results also indicate that these key proteins play roles in various biological mechanisms related to T2DM. This research successfully identifies T2DM key proteins using graph analysis, potentially serving as therapeutic targets or T2DM biomarkers. However, medical validation of the identified key proteins is necessary.
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References
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[8] D. Szklarczyk et al., “The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest,” Nucleic Acids Res., vol. 51, no. D1, pp. D638–D646, Jan. 2023, doi: 10.1093/nar/gkac1000.
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[13] N. Zaki, H. Singh, and E. A. Mohamed, “Identifying Protein Complexes in Protein-Protein Interaction Data Using Graph Convolutional Network,” IEEE Access, vol. 9, pp. 123717–123726, 2021, doi: 10.1109/ACCESS.2021.3110845.
[14] Y. Zhang, C.-Y. Li, W. Ge, and Y. Xiao, “Exploration of the Key Proteins in the Normal-Adenoma-Carcinoma Sequence of Colorectal Cancer Evolution Using In-Depth Quantitative Proteomics.,” J. Oncol., vol. 2021, p. 5570058, 2021, doi: 10.1155/2021/5570058.
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[19] Z. A. Rachman, W. Maharani, and Adiwijaya, “The analysis and implementation of degree centrality in weighted graph in Social Network Analysis,” in 2013 International Conference of Information and Communication Technology (ICoICT), Mar. 2013, pp. 72–76, doi: 10.1109/ICoICT.2013.6574552.
[20] O. Ledesma González, R. Merinero‐Rodríguez, and J. I. Pulido‐Fernández, “Tourist destination development and social network analysis: What does degree centrality contribute?,” Int. J. Tour. Res., vol. 23, no. 4, pp. 652–666, Jul. 2021, doi: 10.1002/jtr.2432.
[21] M. Ashtiani et al., “A systematic survey of centrality measures for protein-protein interaction networks,” BMC Syst. Biol., vol. 12, no. 1, p. 80, Dec. 2018, doi: 10.1186/s12918-018-0598-2.
[22] M. Neupane, J. N. Kiser, Bovine Respiratory Disease Complex Coordinated Agricultural Project Research Team, and H. L. Neibergs, “Gene set enrichment analysis of SNP data in dairy and beef cattle with bovine respiratory disease.,” Anim. Genet., vol. 49, no. 6, pp. 527–538, Dec. 2018, doi: 10.1111/age.12718.
[23] M. V. Kuleshov et al., “Enrichr: a comprehensive gene set enrichment analysis web server 2016 update,” Nucleic Acids Res., vol. 44, no. W1, pp. W90–W97, Jul. 2016, doi: 10.1093/nar/gkw377.
[24] P. Shannon et al., “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,” Genome Res., vol. 13, no. 11, pp. 2498–2504, Nov. 2003, doi: 10.1101/gr.1239303.
[25] A. Gabryelska, F. F. Karuga, B. Szmyd, and P. Białasiewicz, “HIF-1α as a Mediator of Insulin Resistance, T2DM, and Its Complications: Potential Links With Obstructive Sleep Apnea.,” Front. Physiol., vol. 11, p. 1035, 2020, doi: 10.3389/fphys.2020.01035.
[26] A. Sinha and H. A. Nagarajaram, “Nodes occupying central positions in human tissue specific PPI networks are enriched with many splice variants,” Proteomics, vol. 14, no. 20, pp. 2242–2248, Oct. 2014, doi: 10.1002/pmic.201400249.
[27] A. Zito et al., “Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality.,” Front. Genet., vol. 12, p. 577623, 2021, doi: 10.3389/fgene.2021.577623.
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[30] K. Yang et al., “Exploring the Regulatory Mechanism of Hedysarum Multijugum Maxim.-Chuanxiong Rhizoma Compound on HIF-VEGF Pathway and Cerebral Ischemia-Reperfusion Injury’s Biological Network Based on Systematic Pharmacology.,” Front. Pharmacol., vol. 12, p. 601846, 2021, doi: 10.3389/fphar.2021.601846.
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[33] T. Sadlon et al., “Unravelling the molecular basis for regulatory T-cell plasticity and loss of function in disease.,” Clin. Transl. Immunol., vol. 7, no. 2, p. e1011, 2018, doi: 10.1002/cti2.1011.
[34] The Gene Ontology Consortium, “The Gene Ontology Resource: 20 years and still GOing strong.,” Nucleic Acids Res., vol. 47, no. D1, pp. D330–D338, Jan. 2019, doi: 10.1093/nar/gky1055.
[35] T. Nugroho and S. Prastowo, “Protein-to-protein interaction of genes responsible for the economic trait of Madura Cattle: an in silico analysis,” IOP Conf. Ser. Earth Environ. Sci., vol. 1114, no. 1, p. 012084, Dec. 2022, doi: 10.1088/1755-1315/1114/1/012084.
Published
2023-10-25
How to Cite
[1]
A. A. Permana, M. F. Romdendine, and A. T. Perdana, “Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus”, Indones.J.electronic.electromed.med.inf, vol. 5, no. 4, Oct. 2023.
Section
Research Article
Copyright (c) 2023 Angga Aditya Permana, Muhammad Fahrury Romdendine, Analekta Tiara Perdana
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