Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus

  • Angga Aditya Permana Universitas Multimedia Nusantara
  • Muhammad Fahrury Romdendine Universitas Multimedia Nusantara
  • Analekta Tiara Perdana UIN Sultan Maulana Hasanuddin Banten
Keywords: Enrichment analysis, graph analysis, protein-protein interactions, type 2 diabetes mellitus


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.


Download data is not yet available.


[1] E. A. Finkelstein, J. Chay, and S. Bajpai, “The Economic Burden of Self-Reported and Undiagnosed Cardiovascular Diseases and Diabetes on Indonesian Households,” PLoS One, vol. 9, no. 6, p. e99572, Jun. 2014, doi: 10.1371/journal.pone.0099572.
[2] B. Arifin et al., “Health-related quality of life in Indonesian type 2 diabetes mellitus outpatients measured with the Bahasa version of EQ-5D,” Qual. Life Res., vol. 28, no. 5, pp. 1179–1190, May 2019, doi: 10.1007/s11136-019-02105-z.
[3] F. V. Ferdinand, J. Sebastian, and F. Natalia, “Predicting stroke, hypertension, and diabetes diseases based on individual characteristics,” ICIC Express Lett. Part B Appl., vol. 12, no. 8, pp. 723–731, 2021, doi: 10.24507/icicelb.12.08.723.
[4] H. Sofyan et al., “The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study,” BMC Health Serv. Res., vol. 23, no. 1, p. 271, Mar. 2023, doi: 10.1186/s12913-023-09288-9.
[5] S. E. Kahn, M. E. Cooper, and S. Del Prato, “Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future.,” Lancet, vol. 383, no. 9922, pp. 1068–83, Mar. 2014, doi: 10.1016/S0140-6736(13)62154-6.
[6] R. A. DeFronzo et al., “Type 2 diabetes mellitus,” Nat. Rev. Dis. Prim., vol. 1, no. 15019, Jul. 2015, doi: 10.1038/nrdp.2015.19.
[7] Y. Du, J. Zhou, J. Fan, Z. Shen, and X. Chen, “Streamline proteomic approach for characterizing protein-protein interaction network in a RAD52 protein complex.,” J. Proteome Res., vol. 8, no. 5, pp. 2211–7, May 2009, doi: 10.1021/pr800662x.
[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.
[9] S. Zhao and R. Iyengar, “Systems pharmacology: network analysis to identify multiscale mechanisms of drug action.,” Annu. Rev. Pharmacol. Toxicol., vol. 52, pp. 505–21, 2012, doi: 10.1146/annurev-pharmtox-010611-134520.
[10] M. Adhami, B. Sadeghi, A. Rezapour, A. A. Haghdoost, and H. MotieGhader, “Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis.,” BMC Biotechnol., vol. 21, no. 1, p. 22, Mar. 2021, doi: 10.1186/s12896-021-00680-z.
[11] R. Ferrari et al., “Stratification of candidate genes for Parkinson’s disease using weighted protein-protein interaction network analysis.,” BMC Genomics, vol. 19, no. 1, p. 452, Jun. 2018, doi: 10.1186/s12864-018-4804-9.
[12] N. A. Moumi, C. L. Brown, P. J. Vikesland, A. Pruden, and L. Zhang, “Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes,” in 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec. 2022, pp. 73–76, doi: 10.1109/BIBM55620.2022.9995224.
[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.
[15] J. H. Prieto, S. Koncarevic, S. K. Park, J. Yates, and K. Becker, “Large-scale differential proteome analysis in Plasmodium falciparum under drug treatment.,” PLoS One, vol. 3, no. 12, p. e4098, 2008, doi: 10.1371/journal.pone.0004098.
[16] M. Uhlén et al., “A Human Protein Atlas for Normal and Cancer Tissues Based on Antibody Proteomics,” Mol. Cell. Proteomics, vol. 4, no. 12, pp. 1920–1932, Dec. 2005, doi: 10.1074/mcp.M500279-MCP200.
[17] H. C. Rustamaji et al., “A network analysis to identify lung cancer comorbid diseases,” Appl. Netw. Sci., vol. 7, no. 1, p. 30, Dec. 2022, doi: 10.1007/s41109-022-00466-y.
[18] S. L. Gan and M. A. Djauhari, “An Overall Centrality Measure : The Case of U . S Stock Market,” Int. J. Basic Appl. Sci., vol. 12, no. 06, pp. 99–104, 2012.
[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.
[28] G. R. Iyer et al., “Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations.,” Metabolites, vol. 10, no. 12, Nov. 2020, doi: 10.3390/metabo10120479.
[29] J. Taneera, P. Storm, and L. Groop, “Downregulation of type II diabetes mellitus and maturity onset diabetes of young pathways in human pancreatic islets from hyperglycemic donors.,” J. Diabetes Res., vol. 2014, p. 237535, 2014, doi: 10.1155/2014/237535.
[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.
[31] D. J. Michael, H. Cai, W. Xiong, J. Ouyang, and R. H. Chow, “Mechanisms of peptide hormone secretion,” Trends Endocrinol. Metab., vol. 17, no. 10, pp. 408–415, Dec. 2006, doi: 10.1016/j.tem.2006.10.011.
[32] S. V. Moelands, P. L. Lucassen, R. P. Akkermans, W. J. De Grauw, and F. A. Van de Laar, “Alpha-glucosidase inhibitors for prevention or delay of type 2 diabetes mellitus and its associated complications in people at increased risk of developing type 2 diabetes mellitus.,” Cochrane database Syst. Rev., vol. 12, no. 12, p. CD005061, Dec. 2018, doi: 10.1002/14651858.CD005061.pub3.
[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.
How to Cite
A. A. Permana, M. F. Romdendine, and A. T. Perdana, “Graph Analysis for the Discovery of Key Proteins in Type 2 Diabetes Mellitus”,, vol. 5, no. 4, Oct. 2023.
Research Article