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Eurasian Journal of
            Medicine and Oncology                                          Mapping breast cancer PPI networks for targets



            Availability of data                               11.  Ideker T, Sharan R. Protein networks in disease.  Genome
                                                                  Res. 2008;18(4):644-652.
            This study relies on publicly accessible databases, where
            users can freely access and download relevant data for      doi: 10.1101/gr.071852.107
            research purposes and publish associated articles.  12.  Cowen L, Ideker T, Raphael BJ, Sharan R. Network
                                                                  propagation: A universal amplifier of genetic associations.
            References                                            Nat Rev Genet. 2017;18(9):551-562.

            1.   Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics,      doi: 10.1038/nrg.2017.38
               2023. CA Cancer J Clin. 2023;73(1):17-48.
                                                               13.  Csermely P, Korcsmáros T, Kiss HJ, London G, Nussinov R.
            2.   Hanahan D, Weinberg RA. Hallmarks of cancer: The next   Structure  and  dynamics  of  molecular  networks:  A  novel
               generation. Cell. 2011;144(5):646-674.             paradigm of drug discovery: A  comprehensive review.
               doi: 10.1016/j.cell.2011.02.013                    Pharmacol Ther. 2013;138(3):333-408.
            3.   Barabási AL, Oltvai ZN. Network biology: Understanding      doi: 10.1016/j.pharmthera.2013.01.016
               the cell’s functional organization. Nat Rev Genet. 2004;5(2):   14.  Menche J, Sharma A, Kitsak M, et al. Uncovering disease-
               101-113.                                           disease relationships through the incomplete interactome.
               doi: 10.1038/nrg1272                               Science. 2015;347(6224):1257601.
            4.   Pavlopoulos GA, Secrier M, Moschopoulos CN, et al. Using      doi: 10.1126/science.1257601
               graph theory to analyze biological networks. BioData Min.   15.  Borgatti SP, Everett MG. A graph-theoretic perspective on
               2011;4:10.                                         centrality. Soc Netw. 2006;28(4):466-484.
               doi: 10.1186/1756-0381-4-10                     16.  Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and
            5.   Zhang Y, Xiang J, Tang L, et al. Identifying breast cancer-  centrality in protein networks. Nature. 2001;411(6833):41-42.
               related genes based on a novel computational framework      doi: 10.1038/35075138
               involving KEGG pathways and PPI network modularity.
               Front Genet. 2021;12:596794.                    17.  Network YI. High-quality binary protein interaction map of
                                                                  the. Science. 2008;1158684(104):322.
               doi: 10.3389/fgene.2021.596794
                                                                  doi: 10.1126/science.1158684
            6.   Shi K, Li Y, Yang M, Li W. Identification of key genes and
               pathways in female lung cancer patients who never smoked   18.  Batada NN, Hurst LD, Tyers M. Evolutionary and
               by a bioinformatics analysis. J Cancer. 2019;10(1):51-59.  physiological importance of hub proteins.  PLoS Comput
                                                                  Biol. 2006;2(7):e88.
               doi: 10.7150/jca.26908
                                                                  doi: 10.1371/journal.pcbi.0020088
            7.   Liu QQ, Ren K, Liu SH, Li WM, Huang CJ, Yang XH.
               MicroRNA-140-5p aggravates hypertension and oxidative   19.  Fadhal E, Gamieldien J, Mwambene EC. Protein interaction
               stress of atherosclerosis via targeting Nrf2 and Sirt2. Int J   networks as metric spaces: A  novel perspective on
               Mol Med. 2019;43(2):839-849.                       distribution of hubs. BMC Syst Biol. 2014;8:6.
               doi: 10.3892/ijmm.2018.3996.                       doi: 10.1186/1752-0509-8-6
            8.   Wang E, Zaman N, Mcgee S, Milanese JS, Masoudi-Nejad A,   20.  Fadhal E. Exploring lung cancer protein network:
               O’Connor-McCourt M. Predictive genomics: A  cancer   Understanding structure and function through metric space
               hallmark network framework for predicting tumor clinical   modeling. Eur J Pure Appl Math. 2024;17(2):905-921.
               phenotypes using genome sequencing data. Semin Cancer   21.  Wu G, Feng X, Stein L. A  human functional protein
               Biol. 2015;30:4-12.                                interaction network and its application to cancer data
               doi: 10.1016/j.semcancer.2014.04.002               analysis. Genome Biol. 2010;11(5):R53.
            9.   Chen J, Riazifar H, Guan MX, Huang T. Modeling autosomal      doi: 10.1186/gb-2010-11-5-r53
               dominant optic atrophy using induced pluripotent stem   22.  Fadhal E, Mwambene EC, Gamieldien J. Modelling human
               cells and identifying potential therapeutic targets. Stem Cell   protein interaction networks as metric spaces has potential
               Res Ther. 2016;7:2.                                in disease research and drug target discovery. BMC Syst Biol.
               doi: 10.1186/s13287-015-0264-1                     2014;8:68.
            10.  Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL.      doi: 10.1186/1752-0509-8-68
               The human disease network.  Proc  Natl  Acad  Sci  U  S  A.   23.  Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S,
               2007;104(21):8685-8690.
                                                                  Diaz LA Jr., Kinzler KW. Cancer genome landscapes. Science.
               doi: 10.1073/pnas.0701361104                       2013;339(6127):1546-1558.


            Volume 9 Issue 3 (2025)                         83                              doi: 10.36922/ejmo.8208
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