3D And 2D RNA Structure Prediction Of The BRCA2 Gene And Its Silencing RNA In The Breast Cancer
DOI:
https://doi.org/10.21580/wjc.v3i1.6019Keywords:
BRCA, Bioinformatics, Breast Cancer, Molecular Simulation, Gene NetworkingAbstract
Breast cancer is one of the most threatening diseases for women. It is found that BRCA2 gene plays a significant role in breast cancer, provided that mutations occurred. The objective of this study is to determine whether the bioinformatics approach could provide the gene networking, molecular simulation, and computational metabolomics information to shed the relation between BRCA2 gene mutation with breast cancer progression. The methods are utilizing molecular simulation tools to comprehend the biochemical interaction of BRCA2 gene with other oncogenic genes. Lastly, the molecular docking tool is devised to provide the molecular interactions information. It could be implied that the Computer-Aided Drug Design (CADD)-based in silico transcriptomics tools could provide the fine-grained information on the exact role of BRCA2 gene in the progression of breast cancer. The clinical impact of this study could only be measured after the wet laboratory experiment is conducted to validate the computational approach results
Downloads
References
Anurogo, D., Parikesit, A.A., Ikrar, T., 2019. LncRNAs in CONDBITs Perspectives, From Genetics towards Theranostics. J. Sains Kesihat. Malaysia 17, 1–16. https://doi.org/10.17576/jskm-2019-1702-01
Arifin, M.Z., Agustriawan, D., Parikesit, A.A.P., 2020. Molecular simulation oF MDM2 and E6AP proteins as P53 regulator in cervical cancer. Biointerface Res. Appl. Chem. 10, 5875–5879. https://doi.org/10.33263/BRIAC104.875879
Bonofiglio, D., Giordano, C., De Amicis, F., Lanzino, M., Andò, S., 2016. Natural Products as Promising Antitumoral Agents in Breast Cancer: Mechanisms of Action and Molecular Targets. Mini-Reviews Med. Chem. 16, 596–604. https://doi.org/10.2174/1389557515666150709110959
Burnett, J.C., Rossi, J.J., 2012. RNA-based therapeutics: current progress and future prospects. Chem. Biol. 19, 60–71. https://doi.org/10.1016/j.chembiol.2011.12.008
Cherigo, L., Lopez, D., Martinez-Luis, S., 2015. Marine natural products as breast cancer resistance protein inhibitors. Mar. Drugs. https://doi.org/10.3390/md13042010
Cleator, S., Heller, W., Coombes, R.C., 2007. Triple-negative breast cancer: therapeutic options. Lancet Oncol. 8, 235–244. https://doi.org/10.1016/S1470-2045(07)70074-8
Davis, B.K., 1998. The forces driving molecular evolution. Prog. Biophys. Mol. Biol. 69, 83–150. https://doi.org/10.1016/S0079-6107(97)00034-5
Eigen, M., McCaskill, J., Schuster, P., 1988. Molecular quasi-species. J. Phys. Chem. 92, 6881–6891. https://doi.org/10.1021/j100335a010
Flamm, C., Hofacker, I.L., Stadler, P.F., Wolfinger, T., Wolfinger, M.T., 2002. Barrier Trees of Degenerate Landscapes. Zeitschrift für Phys. Chemie 216, 155. https://doi.org/10.1524/zpch.2002.216.2.155
Goncalves, R., Warner, W.A., Luo, J., Ellis, M.J., 2014. New concepts in breast cancer genomics and genetics. Breast Cancer Res. https://doi.org/10.1186/s13058-014-0460-4
Gruber, A.R., Lorenz, R., Bernhart, S.H., Neuböck, R., Hofacker, I.L., 2008. The Vienna RNA websuite. Nucleic Acids Res. 36. https://doi.org/10.1093/nar/gkn188
Hashem, Y., Auffinger, P., 2009. A short guide for molecular dynamics simulations of RNA systems. Methods 47, 187–197. https://doi.org/10.1016/j.ymeth.2008.09.020
Hedau, S., Batra, M., Singh, U., Bharti, A., Ray, A., Das, B., 2015. Expression of BRCA1 and BRCA2 proteins and their correlation with clinical staging in breast cancer. J. Cancer Res. Ther. https://doi.org/10.4103/0973-1482.140985
Kozakov, D., Hall, D.R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D., Vajda, S., 2017. The ClusPro web server for protein-protein docking. Nat. Protoc. https://doi.org/10.1038/nprot.2016.169
Lorenz, R., Bernhart, S.H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P.F., Hofacker, I.L., 2011. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26. https://doi.org/10.1186/1748-7188-6-26
McDowell, S.E., Spacková, N., Sponer, J., Walter, N.G., 2007. Molecular dynamics simulations of RNA: an in silico single molecule approach. Biopolymers 85, 169–84. https://doi.org/10.1002/bip.20620
Parikesit, A.A., 2018. The Construction of Two and Three Dimensional Molecular Models for the miR-31 and Its Silencer as the Triple Negative Breast Cancer Biomarkers. Online J. Biol. Sci. 18, 424–431. https://doi.org/10.3844/ojbsci.2018.424.431
Parikesit, A.A., Agustriawan, D., Nurdiansyah, R., 2020. Protein Annotation of Breast-cancer-related Proteins with Machine-learning Tools. Makara J. Sci. 24, 6. https://doi.org/10.7454/mss.v24i1.12106
Parikesit, A.A., Agustriawan, D., Nurdiansyah, R., 2018a. Telaah Sistematis Diagnosis dan Pengobatan Kanker Payudara Berbasis Transkriptomik, in: PROSIDING SEMINAR NASIONAL BIOLOGI 2018. FMIPA UNESA, Surabaya, pp. 438–443.
Parikesit, A.A., Utomo, D.H., Karimah, N., 2018b. Determination of secondary and tertiary structures of cervical cancer lncRNA diagnostic and siRNA therapeutic biomarkers. Indones. J. Biotechnol. 23, 1. https://doi.org/10.22146/ijbiotech.28508
Peshkin, B.N., Alabek, M.L., Isaacs, C., 2010. BRCA1/2 mutations and triple negative breast cancers. Breast Dis. 32, 25–33. https://doi.org/10.3233/BD-2010-0306
Petrovic, N., Davidovic, R., Bajic, V., Obradovic, M., Isenovic, R.E., 2017. MicroRNA in breast cancer: The association with BRCA1/2. Cancer Biomarkers. https://doi.org/10.3233/CBM-160319
Rother, M., Milanowska, K., Puton, T., Jeleniewicz, J., Rother, K., Bujnicki, J.M., 2011. ModeRNA server: an online tool for modeling RNA 3D structures. Bioinformatics 27, 2441–2442. https://doi.org/10.1093/bioinformatics/btr400
Spicer, J., 2005. Technology evaluation: nimotuzumab, the Center of Molecular Immunology/YM BioSciences/Oncoscience. Curr. Opin. Mol. Ther. 7, 182–91.
Šponer, J., Bussi, G., Krepl, M., Banáš, P., Bottaro, S., Cunha, R.A., Gil-Ley, A., Pinamonti, G., Poblete, S., Jurečka, P., Walter, N.G., Otyepka, M., 2018. RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview. Chem. Rev. 118, 4177–4338. https://doi.org/10.1021/acs.chemrev.7b00427
Surveillance Epidemiology and End Results Program, 2019. Cancer Stat Facts: Female Breast Cancer. Natl. Cancer Inst. 1–10.
Tafer, H., Ameres, S.L., Obernosterer, G., Gebeshuber, C.A., Schroeder, R., Martinez, J., Hofacker, I.L., 2008. The impact of target site accessibility on the design of effective siRNAs. Nat. Biotechnol. 26, 578–83. https://doi.org/10.1038/nbt1404
TBI, 2016. Vienna RNA Package Web Version 2.0 [WWW Document]. URL http://rna.tbi.univie.ac.at/#intro
Valeska, M.D., Adisurja, G.P., Bernard, S., Wijaya, R., Aldino, M., Parikesit, A.A., 2019. The Role of Bioinformatics in Personalized Medicine: Your Future Medical Treatment. Cermin Dunia Kedokt. 46, 785–788.
Wheeler, N.J., Agbedanu, P.N., Kimber, M.J., Ribeiro, Paula, Day, T.A., Zamanian, M., Werf, M., Vlas, S., Brooker, S., Looman, C., Nagelkerke, N., Habbema, J., Engels, D., Murray, C., Vos, T., Lozano, R., Naghavi, M., Flaxman, A., Michaud, C., Wang, W., Wang, L., Liang, Y.-S., Gilbert, I., Chen, B., Wen, J.-F., Scimone, M., Kravarik, K., Lapan, S., Reddien, P, Zhang, J., Yuan, Z., Zheng, M., Sun, Y., Wang, Y., Yang, S., Evans, D., Owlarn, S., Romero, B.T., Chen, C., Aboobaker, A., Reddien, PW, Bermange, A., Murfitt, K., Jennings, J., Alvarado, A.S., Collins, J., Hou, X., Romanova, E., Lambrus, B., Miller, C., Saberi, A., Sweedler, J., Newmark, P, Gilleard, J., Robb, S., Ross, E., Sa, A., Abril, J., Cebri’a, F., Rodrıguez-Esteban, G., Horn, T., Fraguas, S., Calvo, B., Bartscherer, K., Sal’o, E., Galloni, M., Resch, A., Palakodeti, D., Lu, Y.-C., Horowitz, M., Graveley, B., Solana, J., Kao, D., Mihaylova, Y., Jaber-Hijazi, F., Malla, S., Wilson, R., Aboobaker, A., Nishimura, O., Hirao, Y., Tarui, H., Agata, K., Garcia-Fernandez, J., Ram, J., Mar-any, G., Mun, A., Kreshchenko, N., Pag’an, O., Deats, S., Baker, D., Montgomery, E., Wilk, G., Tenaglia, M., Semon, J., Ramakrishnan, L., Amatya, C., DeSaer, C., Dalhoff, Z., Eggerichs, M., Magoč, T., Salzberg, S., Grabherr, M., Haas, B., Yassour, M., Zerbino, D., Birney, E., Schulz, M., Zerbino, D., Vingron, M., Birney, E., Langmead, B., Trapnell, C., Pop, M., Salzberg, S., Li, B., Dewey, C., Li, W., Godzik, A., Conesa, A., G’otz, S., Garc’ıa-G’omez, J., Terol, J., Tal’on, M., Robles, M., Lechner, M., Findeiss, S., Steiner, L., Marz, M., Stadler, P., Prohaska, S., Logan-Klumpler, F., Silva, N., Boehme, U., Rogers, M., Velarde, G., McQuillan, J., Carver, T., Aslett, M., Olsen, C., Subramanian, S., Phan, I., Farris, C., Mitra, S., Ramasamy, G., Wang, H., Tivey, A., Jackson, A., Houston, R., Parkhill, J., Holden, M., Harb, O., Brunk, B., Myler, P., Roos, D., Carrington, M., Smith, D., Hertz-Fowler, C., Berriman, M., Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S., Marra, M., Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., Madden, T., Pruitt, K., Brown, G., Hiatt, S., Thibaud-Nissen, F., Astashyn, A., Ermolaeva, O., Farrell, C., Hart, J., Landrum, M., McGarvey, K., Murphy, M., Rask-Andersen, M., Alm’en, M., Schïoth, H., Milligan, J., Jolly, E., Schneider, C., Rasband, W., Eliceiri, K., Noldus, L., Spink, A., Tegelenbosch, R., Koressaar, T., Remm, M., Rouhana, L., Weiss, J., Forsthoefel, D., Lee, H., King, R., Inoue, T., Shibata, N., Agata, K., Newmark, P, Kao, D., Felix, D., Aboobaker, A., Patocka, N., Ribeiro, P, Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., Pesseat, S., Quinn, A., Sangrador-Vegas, A., Scheremetjew, M., Yong, S.-Y., Lopez, R., Hunter, S., Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M., Hetrick, B., Han, M., Helgeson, L., Nolen, B., Nogi, T., Zhang, D., Chan, J., Marchant, J., Crowther, G., Shanmugam, D., Carmona, S., Doyle, M., Hertz-Fowler, C., Berriman, M., Nwaka, S., Wang, B., Collins, J., Newmark, PA, 2015. Functional analysis of Girardia tigrina transcriptome seeds pipeline for anthelmintic target discovery. Parasit. Vectors 8, 34. https://doi.org/10.1186/s13071-014-0622-3
WHO, 2015. WHO | Breast cancer: prevention and control [WWW Document]. WHO. URL https://www.who.int/cancer/detection/breastcancer/en/
Wolfinger, M.T., Svrcek-Seiler, W.A., Flamm, C., Hofacker, I.L., Stadler, P.F., 2004. Efficient computation of RNA folding dynamics. J. Phys. A. Math. Gen. 37, 4731–4741. https://doi.org/10.1088/0305-4470/37/17/005
Wu, D., Rice, C.M., Wang, X., 2012. Cancer bioinformatics: A new approach to systems clinical medicine. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-13-71
Zaleska, K., 2015. MiRNA - Therapeutic tool in breast cancer? Where are we now? Reports Pract. Oncol. Radiother. https://doi.org/10.1016/j.rpor.2014.10.009
Downloads
Additional Files
Published
Issue
Section
License
The copyright of the received article shall be assigned to the publisher of the journal. The intended copyright includes the right to publish the article in various forms (including reprints). The journal maintains the publishing rights to published articles. Therefore, the author must submit a statement of the Copyright Transfer Agreement.*)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In line with the license, authors and any users (readers and other researchers) are allowed to share and adapt the material. In addition, the material must be given appropriate credit, provided with a link to the license, and indicated if changes were made. If authors remix, transform or build upon the material, authors must distribute their contributions under the same license as the original.
*) Authors whose articles are accepted for publication will receive confirmation via email to send a Copyright Transfer Agreement.