SYSTEMIC ANALYSIS OF microRNAs ACTIVITY IN TUMOR GROWTH
DOI:
https://doi.org/10.11603/mie.1996-1960.2019.4.11018Keywords:
non-coding RNAs, microRNAs, transposons, genomic instability, damage of genes oncosuppressor, oncogenic activation, mutations, mathematical modeling, epigeneticAbstract
Background. Some data on the role of miRNAs are conceptualized. When analyzing possible strategies for restoring the normal level of p53 and p53-dependent microRNAs in order to prevent malignant neoplasms, it was suggested that the detected veils changed the understanding of gene expression and set a precedent for the development of new methods for the diagnosis and treatment of cancer. It is important to identify additional potential microRNAs targets and develop safe microRNA-based treatment methods so that microRNAs modulation becomes a critical method for the treatment and treatment of cancer. In this regard, the studied variants of anticancer therapy associated with the simultaneous hyperactivation of two apoptosis regulators, p53 and microRNAs, are of interest.
Materials and methods. Results. Within the framework of the accepted mathematical modeling, a potentially high anti-blastoma therapy is shown, the target of which is the p53 inhibitor protein as the main link of the p53 positive feedback loop-miRNA, as well as the initiation of tumor metastasis. Conclusions are drawn: 1. MicroRNAs are the most important regulators of cell differentiation, proliferation, and survival. Changes in miRNA expression are clearly associated with the progression of numerous human diseases, in particular cancer. 2. MicroRNAs play a key role in the genesis of tumors as important modulators/demodulators in cell pathways, regulating target gene expression through repression or mRNA degradation.
Conclusions. MicroRNAs are attractive candidates for the role of prognostic biomarkers and therapeutic targets in cancer.
References
Lee, R. C., Feinbaum, R. L., Ambros, V. (1993). The c. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 75, 843-54.
Mattick, J. S., Gagen, M. J. (2001). The evolution of controlled multitasked gene networks: The role of introns and other noncoding RNAs in the development of complex organisms. Mol Biol Evol. 18, 1611-30.
Azam, M. R., Fazal, S., Ullah, M., Bhatti, A. I. (2018). Systems-based strategies for p53 recovery. IET Syst boil, 12(3), 101-7.
Tao, G., Martin, J. F. (2013). MicroRNAs get to the heart of development. Elife, 2, 01710.
Menghini, R., Stohr, R, Federici, M. (2014). MicroRNAs in vascular aging and atherosclerosis. Ageing Res Rev., 17, 68-78.
Timoneda, O., Nunez-Hernandez, F., Balcells, I. et al. (2014). The role of viral and host microRNAs in the Aujeszky's disease virus during the infection process. PLoS One, 9, 86965.
Calin, G. A., Sevignani, C., Dumitru, C. D. et al. (2004). Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci USA, 101 (9), 2999-3004.
Rodic, N., Burns, K. H. (2013). Long interested element (LINE-1): passenger of driver in human neoplasms? Plos Genetics, 9 (3), 1003402.
Scarola, M., Schoeftner, S., Schneider, C., Benetti, R. (2010). MIR-335 directly targets Rb 1 (PRb/p105) in a proximal connection to p53 — dependent stress response. Cancer Res., 70, 6925-33.
Khavinson, V. K. (2014). Peptides, genome, aging Adv. Gerontol., 27 (2), 257-64.
Tansizichaiya, S., Rahman, M. A., Roberts, A. P. (2019). The transposon register. Mol. DNA, 10, 40.
Blandino, G., Valenti, Sacconi, A., Di Agostino, S. (2019). Wild type-and mutant p53 proteins in mitohondrial dysfunction: emerging insights in cancer disease. Semin Cell Dev Biol., 1084.
Bisio, A., De Sanctis, V., Del Vescovo, V., Denti, M. A., Jegga, A. G., Inga, A., Ciribilli, Y. (2013). Identification of new p53 target microRNAs by bioinformatics and functional analysis. BMC Cancer.
Ren, Z. J., Nong, X, Y., Lv., Y. R. et al. (2014). MIR-509-5 joins the Mdm2/p53 feedback loop and regulates cancer cell growth. Cell Death Dis., 5, 1387.
Xie, C., Chen, W., Zhang, M., Cai, Q., Xu, W., Li, X., Jiang, S. (2015). MDMN4 regulation by the let 7 miRNA family in the DNA damage response of glioma cells. FEBC left, 589 (15), 1958-65.
Neault, M., Couteau, F., Bonnean, E., De Guire, V., Mallette, F, A. (2017). Molecular regulation of cellular senescence by MicroRNAs: implications in cancer and age-related diseases. Int. Rev. Cell. Mol. Biol., 334, 27-98.
Issler, M. V. C., Mombach, J. C. M. (2017). Micro-RNA-16 feedback loop with p53 and Wip 1 can regulate cell fate determination between apoptosis and senescence in DNA damage response. PLoS ONE, 12, 18574.
Wang B., Li D., Sidler C. et al. (2015). A suppressive role of ionizing — responsive MIR-29.c in the development of liver carcinoma via targeting VIP 1 Oncotarget., 6, 9937-50.
Neault, M., Couteau, F., Bonnean, E., De Guire, V., Mallette, F. A. (2017). Molecular regulation of cellular senescence by MicroRNAs: implications in cancer and age-related diseases. Int. Rev. Cell. Mol. Biol., 334, 27-98.
Rahman M., Lovat F., Romano G. et al. (2014). MIR 15b/16-2 regulates factors that promotes p53 phosphorylation and augments the DNA damage response following radiation in the lung. J. Biol. Chem., 289 (1), 26406-416.
Pichiorri, F., Snh, S. S., Rocci, A. et al (2010). Downregulation of p53 — inducible micro-RNAs 192, and 215 impairs the p53/ MDM2 autoregulatory loop in multiple myeloma evelopment. Cancer Cell, 18, 367-81.
Formari, F., Millaz, M., Calass, M., et al. (2014). p53/ mdm 2 feedback loop sustains miR-221 expression and distances the response to anticancer treatments in hepatocellular carcinoma. Mal. Cancer Res., 12, 203-16.
Scarola, M., Schoeftner, S., Schneider, C. et al (2010). MIR-335 directly targets Rb 1 (PRb/p105) in a proximal connection to p53 — dependent stress response. Cancer Res., 70, 6925-33.
Suzuki, H. I., Yamagata, K., Sngimoto, K. et al. (2009)/ Modulation of microRNA processing by p53. Nature, 460, 529-33.
Wee, E. J., Peters, K., Nair, S. S. et al. (2012). Mapping the regulatory sequences controlling 93 breast cancer-associated miRNA genes leads to the identification of two functional promoters of the Has-mir-200b cluster, methylation of which is associated with metastasis or hormone receptor status in advanced breast cancer. Oncogene, 31, 4182-95.
Rothe, F., Ignatiadis, M., Chaboteaux, C. et al. (2011). Global microRNA expression profiling identifies MiR-210 associated with tumor proliferation, invasion and poor clinical outcome in breast cancer. PLoS One. 6, 20980.
Yang, M., Shen, H., Qiu, C., Ni, Y, Wang, L., Dong, W., Liao, Y., Du, J. (2013). High expression of miR-21 and miR-155 predicts recurrence and unfavourable survival in non-small cell lung cancer. Eur J Cancer, 49, 604-15.
Zhu, J., Feng, Y., Ke, Z., Yang, Z., Zhou, J., Huang, X., Wang, L. (2012). Down-regulation of miR-183 promotes migration and invasion of osteosarcoma by targeting Ezrin. Am J Pathol., 180, 2440-51.
Toiyama, Y., Hur, K., Tanaka, K., Inoue, Y., Kusunoki, M., Boland, C. R., Goel, A. (2014). Serum miR-200c is a novel prognostic and metastasis-predictive biomarker in patients with colorectal cancer. . Ann Surg., 259, 735-743.
Zhao, C., Zhang, Y., Popel, A. S. (2019). Mechanistic computational models of microRNA — mediated signaling networks in human diseases Int J. Mol. Sci. 20(2), doi: 10. 3390 / ijms 20020421
Khamin, R., Vincio, V. (2008). Complitional Melling of Post-transcriptional gene regulation by microRNAs J. Complitional. Biol., 15 (3), 305-16.
Zinovyev, A., Morozova, N., Gorban, A. et al. (2013). Mathematical modeling of microRNA — mediated mechanisms of translocation repression. Adv., Exp. Med Biol., 774, 189-224.
Lai, X., Bhattacharya, A., Scwitz, V. et al. (2013). A systems biology approach to study microRNA — regulated gene regulatory networks. Bio med. Res. International Ast., 703849.
Lai, X., Wolkenhauer, O., Vera, Jn. (2016). Understanding microRNA — mediated gene regulatory networks through mathematical modellity. Nucleic Acides Rees., 44(13), 6019-35.
Luo, Z., Azencott, R., Zhao, Y. (2014). Modelling MicroRNA — MicroRNA interactions: tithing chemical kinetics equations to microarray data. BMC Systems Biol., 8 (19).
Ooi, H. K., Ma, L. (2015). Integral Control Feedback Circuit for the Reactivation of Malfunctioning p53 Pathway. G — bio. MN.
Azam, M. R., Fazal, S., Ulcah, M., et al. (2018). Systems-based strategies for p53 recovery. IET Syst boil, 12 (3), 101-7.
Moore, R., Ooi, H. K., Kang, T. et al. (2015). Mir-192 — weidated positive feedback loop controls the robnethess of stress — induced p53 oscillations breast cancer cells. Plos Computational Biol., 11 (12), 1004652.
Varopaeva, O. F., Lysachev, P. D., Senotrusova, S. D. et al. (2019). n/modelyrovanye varyantov protyvoopukholevoi terapyy. [Hyperactivation of the p53 signaling pathway and micro RNA: Mathematical modeling of antitumor therapy options.]. Matematycheskaia byolohyia y byoynformatyka, Vol. 14 (1), 355-72. [In Russian].
Chumakov, P. M. (2007). Belok r53 y eho unyversalnbie funktsyy v mnohokletochnom orhanyzme. [The p53 protein and its universal functions in a multicellular organism]. Usp. byol. fyzyky, 47, 3-52. [In Russian].
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