Research article Special Issues

Mathematical analysis of a SIPC age-structured model of cervical cancer


  • Received: 22 February 2022 Revised: 26 March 2022 Accepted: 31 March 2022 Published: 12 April 2022
  • Human Papillomavirus (HPV), which is the main causal factor of cervical cancer, infects normal cervical cells on the specific cell's age interval, i.e., between the $ G_1 $ to $ S $ phase of cell cycle. Hence, the spread of the viruses in cervical tissue not only depends on the time, but also the cell age. By this fact, we introduce a new model that shows the spread of HPV infections on the cervical tissue by considering the age of cells and the time. The model is a four dimensional system of the first order partial differential equations with time and age independent variables, where the cells population is divided into four sub-populations, i.e., susceptible cells, infected cells by HPV, precancerous cells, and cancer cells. There are two types of the steady state solution of the system, i.e., disease-free and cancerous steady state solutions, where the stability is determined by using Fatou's lemma and solving some integral equations. In this case, we use a non-standard method to calculate the basic reproduction number of the system. Lastly, we use numerical simulations to show the dynamics of the age-structured system.

    Citation: Eminugroho Ratna Sari, Fajar Adi-Kusumo, Lina Aryati. Mathematical analysis of a SIPC age-structured model of cervical cancer[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 6013-6039. doi: 10.3934/mbe.2022281

    Related Papers:

  • Human Papillomavirus (HPV), which is the main causal factor of cervical cancer, infects normal cervical cells on the specific cell's age interval, i.e., between the $ G_1 $ to $ S $ phase of cell cycle. Hence, the spread of the viruses in cervical tissue not only depends on the time, but also the cell age. By this fact, we introduce a new model that shows the spread of HPV infections on the cervical tissue by considering the age of cells and the time. The model is a four dimensional system of the first order partial differential equations with time and age independent variables, where the cells population is divided into four sub-populations, i.e., susceptible cells, infected cells by HPV, precancerous cells, and cancer cells. There are two types of the steady state solution of the system, i.e., disease-free and cancerous steady state solutions, where the stability is determined by using Fatou's lemma and solving some integral equations. In this case, we use a non-standard method to calculate the basic reproduction number of the system. Lastly, we use numerical simulations to show the dynamics of the age-structured system.



    加载中


    [1] S. V. Graham, The human papillomavirus replication cycle, and its links to cancer progression: a comprehensive review, Clin. Sci., 131 (2017), 2201–2221. https://doi.org/10.1042/CS20160786 doi: 10.1042/CS20160786
    [2] H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al., Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: Cancer J. Clin., 71 (2021), 209–249. https://doi.org/10.3322/caac.21660 doi: 10.3322/caac.21660
    [3] G. M. Clifford, J. S. Smith, M. Plumme, N. Muñoz, S. Franceschi, Human papillomavirus types in invasive cervical cancer worldwide: a meta-analysis, Brit. J. Cancer, 88 (2003), 63–69. https://doi.org/10.1038/sj.bjc.6600688 doi: 10.1038/sj.bjc.6600688
    [4] T. Sasagawa, H. Takagi, S. Makinoda, Immune responses against human papillomavirus (HPV) infection and evasion of host defense in cervical cancer, J. Infect. Chemother., 18 (2012), 807–815. https://doi.org/10.1007/s10156-012-0485-5 doi: 10.1007/s10156-012-0485-5
    [5] E. M. Burd, Human papillomavirus and cervical cancer, Clin. Microbiol. Rev., 16 (2003), 1–17. https://doi.org/10.1128/CMR.16.1.1-17.2003 doi: 10.1128/CMR.16.1.1-17.2003
    [6] C. A. Moody, L. A. Laimins, Human papillomavirus oncoproteins: pathways to transformation, Nat. Rev. Cancer, 10 (2010), 550–560. https://doi.org/10.1038/nrc2886 doi: 10.1038/nrc2886
    [7] T. S. N. Asih, S. Lenhart, S. Wise, L. Aryati, F. Adi-Kusumo, M. S. Hardianti, et al., The dynamics of HPV infection and cervical cancer cells, Bull. Math. Biol., 78 (2016), 4–20. https://doi.org/10.1007/s11538-015-0124-2 doi: 10.1007/s11538-015-0124-2
    [8] T. S. N. Asih, M. Masrukan, The analysis and interpretation of the all exist unstable equilibrium points of cervical cancer mathematical modeling, Proc. ICMSE, 4 (2017), 127–129.
    [9] L. Aryati, T. S. Noor-Asih, F. Adi-Kusumo, M. S. Hardianti, Global stability of the disease free equilibrium in a cervical cancer model: a chance to recover, Far East J. Math. Sci., 103 (2018), 1535–1546. https://doi.org/10.17654/MS103101535 doi: 10.17654/MS103101535
    [10] V. V. Akimenko, F. Adi-Kusumo, Stability analysis of an age-structured model of cervical cancer cells and HPV dynamics, Math. Biosci. Eng., 18 (2021), 6155–6177. https://doi.org/10.3934/mbe.2021308 doi: 10.3934/mbe.2021308
    [11] K. Allali, Stability analysis and optimal control of HPV infection model with early-stage cervical cancer, Biosystems, 199 (2021), 104321. https://doi.org/10.1016/j.biosystems.2020.104321 doi: 10.1016/j.biosystems.2020.104321
    [12] T. Malik, A. Gumel, E. Elbasha, Qualitative analysis of an age and sex structured vaccination model for human papillomavirus, Discrete Contin. Dynam. Syst. Ser. B, 18 (2013), 2151–2174. https://doi.org/10.3934/dcdsb.2013.18.2151 doi: 10.3934/dcdsb.2013.18.2151
    [13] M. Al-Arydah, R. Smith, An age-structured model of human papillomavirus vaccination, Math. Comput. Simul., 82 (2011), 629–652. https://doi.org/10.1016/j.matcom.2011.10.006 doi: 10.1016/j.matcom.2011.10.006
    [14] M. Al-Arydah, T. Malik, An age-structured model of the human papillomavirus dynamics and optimal vaccine control, Int. J. Biomath., 10 (2017), 1750083. https://doi.org/10.1142/S1793524517500838 doi: 10.1142/S1793524517500838
    [15] L. Spinelli, A. Torricelli, P. Ubezio, B. Basse, Modelling the balance between quiescence and cell death in normal and tumour cell populations, Math. Biosci., 202 (2006), 349–370. https://doi.org/10.1016/j.mbs.2006.03.016 doi: 10.1016/j.mbs.2006.03.016
    [16] Z. Liu, J. Chen, J. Pang, P. Bi, S. Ruan, Modeling and analysis of a nonlinear age-structured model for tumor cell populations with quiescence, J. Nonlinear Sci., 28 (2018), 1763–1791. https://doi.org/10.1007/s00332-018-9463-0 doi: 10.1007/s00332-018-9463-0
    [17] M. Gyllenberg, G. F. Webb, A nonlinear structured population model of tumor growth with quiescence, J. Math. Biol., 28 (1990), 671–694. https://doi.org/10.1007/BF00160231 doi: 10.1007/BF00160231
    [18] B. Basse, P. Ubezio, A generalised age-and phase-structured model of human tumour cell populations both unperturbed and exposed to a range of cancer therapies, Bull. Math. Biol., 69 (2007), 1673–1690. https://doi.org/10.1007/s11538-006-9185-6 doi: 10.1007/s11538-006-9185-6
    [19] G. S. Chaffey, D. J. Lloyd, A. C. Skeldon, N. F. Kirkby, The effect of the $G_1$-S transition checkpoint on an age structured cell cycle model, PloS One, 9 (2014), e83477. https://doi.org/10.1371/journal.pone.0083477 doi: 10.1371/journal.pone.0083477
    [20] M. Nowak, R. M. May, Virus Dynamics: Mathematical Principles of Immunology and Virology, Oxford University Press, UK, 2000.
    [21] S. Patil, R. S. Rao, N. Amrutha, D. S. Sanketh, Analysis of human papilloma virus in oral squamous cell carcinoma using p16: An immunohistochemical study, J. Int. Soc. Prev. Community Dent., 4 (2014), 61–66. https://doi.org/10.4103/2231-0762.131269 doi: 10.4103/2231-0762.131269
    [22] J. Yang, Z. Qiu, X. Li, Global stability of an age-structured cholera model, Math. Biosci. Eng., 11 (2014), 641–665. https://doi.org/10.3934/mbe.2014.11.641 doi: 10.3934/mbe.2014.11.641
    [23] Q. Richard, Global stability in a competitive infection-age structured model, Math. Model. Nat. Phenom., 15 (2020), 54. https://doi.org/10.1051/mmnp/2020007 doi: 10.1051/mmnp/2020007
    [24] X. Rui, X. Tian, F. Zhang, Global dynamics of a tuberculosis transmission model with age of infection and incomplete treatment, Adv. Differ. Equations, 242 (2017), 1–34. https://doi.org/10.1186/s13662-017-1294-z doi: 10.1186/s13662-017-1294-z
    [25] X. Tian, R. Xu, N. Bai, J. Lin, Bifurcation analysis of an age-structured SIRI epidemic model, Math. Biosci. Eng., 17 (2020), 7130–7150. https://doi.org/10.3934/mbe.2020366 doi: 10.3934/mbe.2020366
    [26] C. M. Martin, J. J. O'Leary, Histology of cervical intraepithelial neoplasia and the role of biomarkers, Best Pract. Res. Clin. Obstet. Gynaecol., 25 (2011), 605–615. https://doi.org/10.1016/j.bpobgyn.2011.04.005 doi: 10.1016/j.bpobgyn.2011.04.005
    [27] X. Li, J. Liu, M. Martcheva, An age-structured two-strain epidemic model with super-infection, Math. Biosci. Eng., 7 (2010), 123. https://doi.org/10.3934/mbe.2010.7.123 doi: 10.3934/mbe.2010.7.123
    [28] A. Khan, G. Zaman, Global analysis of an age-structured SEIR endemic model, Chaos Solitons Fract., 108 (2018), 154–165. https://doi.org/10.1016/j.chaos.2018.01.037 doi: 10.1016/j.chaos.2018.01.037
    [29] X. Li, J. Yang, M. Martcheva, Age Structured Epidemic Modeling, Springer Nature, Switzerland, 2020.
    [30] H. Inaba, Threshold and stability results for an age-structured epidemic model, J. Math. Biol., 28 (1990), 411–34. https://doi.org/10.1007/BF00178326 doi: 10.1007/BF00178326
    [31] A. K. Miller, K. Munger, F. R. Adler, A mathematical model of cell cycle dysregulation due to Human Papillomavirus infection, Bull. Math. Biol., 79 (2017), 1564–1585. https://doi.org/10.1007/s11538-017-0299-9 doi: 10.1007/s11538-017-0299-9
    [32] S. Park, S. Chung, K. M. Kim, K. C. Jung, C. Park, E. R. Hahm, et al., Determination of binding constant of transcription factor myc–max/max–max and E-box DNA: the effect of inhibitors on the binding, Biochim. Biophys. Acta, Gen. Subj., 1670 (2004), 217–228. https://doi.org/ 10.1016/j.bbagen.2003.12.007 doi: 10.1016/j.bbagen.2003.12.007
    [33] F. Ansarizadeh, M. Singh, D. Richards, Modelling of tumor cells regression in response to chemotherapeutic treatment, Appl. Math. Modell., 48 (2017), 96–112. https://doi.org/10.1016/j.apm.2017.03.045 doi: 10.1016/j.apm.2017.03.045
    [34] F. J. Solis, S. E. Delgadillo, Evolution of a mathematical model of an aggressive–invasive cancer under chemotherapy, Comput. Math. Appl., 69 (2015), 545–558. https://doi.org/10.1016/j.camwa.2015.01.013 doi: 10.1016/j.camwa.2015.01.013
    [35] E. R. Sari, D. Lestari, E. Yulianti, R. Subekti, Stability analysis of a mathematical model of tumor with chemotherapy, J. Phys. Conf. Ser., 1321 (2019), 022072. https://doi.org/10.1088/1742-6596/1321/2/022072 doi: 10.1088/1742-6596/1321/2/022072
    [36] R. Eskander, K. S. Tewari, Immunotherapy: an evolving paradigm in the treatment of advanced cervical cancer, Clin. Ther., 37 (2015), 20–38. https://doi.org/10.1016/j.clinthera.2014.11.010 doi: 10.1016/j.clinthera.2014.11.010
    [37] P. K. Roy, A. K. Roy, E. N. Khailov, F. Al Basir, E. V. Grigorieva, Model of the optimal immunotherapy of psoriasis by introducing IL-10 and IL-22 inhibitor, J. Biol. Syst., 28 (2020), 609–639. https://doi.org/10.1142/S0218339020500084 doi: 10.1142/S0218339020500084
    [38] A. K. Roy, F. Al Basir, P. K. Roy, A vivid cytokines interaction model on psoriasis with the effect of impulse biologic (TNF-$\alpha$ inhibitor) therapy, J. Theor. Biol., 474 (2019), 63–77. https://doi.org/10.1016/j.jtbi.2019.04.007 doi: 10.1016/j.jtbi.2019.04.007
    [39] A. Khan, G. Zaman, R. Ullah, N. Naveed, Optimal control strategies for a heroin epidemic model with age-dependent susceptibility and recovery-age, AIMS Math., 6 (2021), 1377–1394. https://doi.org/10.3934/math.2021086 doi: 10.3934/math.2021086
    [40] A. K. Roy, M. Nelson, P. K. Roy, A control-based mathematical study on psoriasis dynamics with special emphasis on IL-21 and IFN-$\gamma$ interaction network, Math. Methods Appl. Sci., 44 (2021), 13403–13420. https://doi.org/10.1002/mma.7635 doi: 10.1002/mma.7635
    [41] A. K. Roy, P. K. Roy, E. Grigorieva, Mathematical insights on psoriasis regulation: Role of $\text {Th}_1$ and $\text {Th}_2$ cells, Math. Biosci. Eng., 15 (2018), 717–738. https://doi.org/10.3934/mbe.2018032 doi: 10.3934/mbe.2018032
  • Reader Comments
  • © 2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2173) PDF downloads(181) Cited by(1)

Article outline

Figures and Tables

Figures(3)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog