Morphological Changes Caused by Synthesized Zinc Oxide Nanoparticles in MDA-MB 231 Cells and Prediction with Multi-Linear Regression http://www.doi.org/10.26538/tjnpr/v7i12.36
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Abstract
Developing a novel approach to treat cancerous cells while sparing healthy cells is crucial in cancer research. Plant-synthesized using Zinc Oxide Nanoparticles nanoparticles are biocompatible and stable, making them ideal for biomedical, industrial, cell imaging, and biosensor applications. This study reported the morphological development caused by synthesized Zinc Oxide Nanoparticles (ZnO-NPs-AE) in MDA-MB 231 human breast cancer cells, and a multi-linear regression (MLR) model that predicts the morphological changes was also developed. Morphological parameters such as cell body diameter (CBD), process thickness (PT) and field diameter (FD) were analyzed after treatment with various ZnO-NPs-AE concentrations and incubation for 24 hours. Significant decrease in CBD with 25, 50, 100 and 200 μg/mL ZnO-NPs-AE (P > 0.05; n ≥ 10) and 2.5, 5 and 10 μg/mL of synthesized ZnO-NPs-AE did not show any significant effect on the CBD (P < 0.05; n ≥ 10). Similarly, a significant decrease in PT and FD was observed with increased concentration. Furthermore, the concentration of the nanoparticles and our morphometric results were considered in MLR modelling and performance efficiency was evaluated based on correlation coefficient (R), coefficient of determination (DC), mean square error (MSE) and root mean square error (RMSE). Overall, our performance efficiency results revealed the model's ability to predict morphological changes in MDA-MB 231 cells following treatment with synthesized ZnO-NPs-AE.
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References
McGuire S. World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. Adv Nutr. 2016; 15;7(2):418-9. https://doi.org/10.3945/an.116.012211.
Lebeau A, and Denkert C. Updated WHO classification of tumors of the breast: the most important changes. Pathologe. 2021; 2(3):270-280. https://doi.org/10.1007/s00292-021-00934-9.
Ubhenin AE, Ikebuiro JO, Idris RI, Anura F, Erharuyi O. Exploring Mechanisms of Tumorigenesis and Plant-Based Therapies: A Comprehensive Review of Cancer Pathogenesis and Treatment Strategies. Trop J Nat Prod Res. 2023; 7(11):5026-5033. http://www.doi.org/10.26538/tjnpr/v7i11.2.
Kooti W, Servatyari K, Behzadifar M, Asadi-Samani M, Sadeghi F, Nouri B, Zare Marzouni H. Effective Medicinal Plant in Cancer Treatment, Part 2: Review Study. J Evid Based Complementary Altern Med. 2017; (4):982-995. https://doi.org/10.1177/2156587217696927.
Shareef M, Ashraf MA, Sarfraz M. Natural cures for breast cancer treatment. Saudi Pharm J. 2016; 24(3):233-40. https://doi.org/10.1016/j.jsps.2016.04.018.
Sastrodihardjo S, Sasaki Y, Shiba Y, Kanno Y. Possible involvement of reorganization of actin filaments, induced by tumor-promoting phorbol esters, in changes in colony shape and enhancement of proliferation of cultured epithelial cells. J Cell Physiol. 1987; 132(1):49-56. https://doi.org/10.1002/jcp.1041320107.
Mohler JL, Bakewell WE, Sharief Y, Coleman WB, Chay CH, Silvers SM, Smith GJ. Detection of candidates for cancer cell motility inhibitory protein in the Dunning adenocarcinoma model. Clin Exp Metastasis. 1995; 13(6):474-80. https://doi.org/10.1007/BF00118186.
Kavaz D, Umar H, Aliyu MR. Green Synthesized Metallic Oxide Nanomaterials for Diverse Applications. J Nanoworld. 2022; 8(1):14-22. https://doi.org/10.17756/nwj.2022-096.
Halilu EM, Ngweh VA, Airemwen CO. Green Synthesis of Silver Nanoparticles from Parinari curatellifolia Methanol Stem Bark Extract and Evaluation of Antioxidant and Antimicrobial Activities. Trop J Nat Prod Res. 2023; 7(3):2498-2505 http://www.doi.org/10.26538/tjnpr/v7i3.5.
Kavaz D, Umar H, Shehu S. Synthesis, characterization, antimicrobial and antimetastatic activity of silver nanoparticles synthesized from Ficus ingens leaf, Artificial Cells, Nanomedicine and Biotechnology. 2018; 46(sup3). https://doi.org/10.1080/21691401.2018.1536060
Wang B, Zhang J, Chen C, Xu G, Qin X, Hong Y, Bose DD, Qiu F, Zou Z. The size of zinc oxide nanoparticles controls its toxicity through impairing autophagic flux in A549 lung epithelial cells. Toxicol Lett. 2018; 15;285:51-59. https://doi.org/10.1016/j.toxlet.2017.12.025.
Sundrarajan M, Ambika S, and Bharathi K. Plant-extract mediated synthesis of ZnO nanoparticles using Pongamia pinnata and their activity against pathogenic bacteria. Adv. Powder Tech. 2015; 26(5). https://doi.org/10.1016/j.apt.2015.07.001.
Padalia H, Moteriya P. Chanda S. Synergistic Antimicrobial and Cytotoxic Potential of Zinc Oxide Nanoparticles Synthesized Using Cassia auriculata Leaf Extract”, BioNanoScience, 2018; 8(1). https://doi.org/10.1007/s12668-017-0463-6.
Suresh J, Ganeshan P, Vincent A, Mahalingam S, Sun I H. Green synthesis and characterization of zinc oxide nanoparticle using insulin plant (Costus pictus D. Don) and investigation of its antimicrobial as well as anticancer activities. Advances in Natural Sciences: Nanoscience and Nanotechnology. 2018; 9(1). https://doi.org/10.1088/2043-6254/aaa6f1.
Umar H, Kavaz D, Rizaner N. Biosynthesis of zinc oxide nanoparticles using Albizia lebbeck stem bark, and evaluation of its antimicrobial, antioxidant, and cytotoxic activities on human breast cancer cell lines. Int J Nanomedicine. 2018; 20;14:87-100. https://doi.org/10.2147/IJN.S186888.
Kusumoto D, Yuasa S. The application of convolutional neural network to stem cell biology. Inflamm Regener. 2019; 39, 14. https://doi.org/10.1186/s41232-019-0103-3.
Sivaramakrishnan N, Subramaniyaswamy V, and Indragandhi V. Multi-Linear Regression Technique for Predicting the Liver Disease. Innov. in Power and Adv. Computing Tech. (i-PACT). 2021. pp. 1-7. https://doi.org/10.1109/i-PACT52855.2021.9696792.
Steiner, D. F. et al. (2018) “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer”, American J Surgical Pathol. 2021; 42(12). https://doi.org/10.1097/PAS.0000000000001151.
Zheng F, Zhan M, Huang X, Abdul Hameed MD, Zhan CG. Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches. Bioorg Med Chem. 2014; 1;22(1):538-49. https://doi.org/10.1016/j.bmc.2013.10.053.
Hadi S, Setiawan D, Viogenta P, SunardiS, Nastiti K, Nisa K, Andiarsa D.Molecular Docking and Dynamics Study of Compounds from Combretum indicumvar. B Seeds as Alcohol Dehydrogenase Inhibitors. Trop J Nat Prod Res. 2023; 7(11):5087-5096.http://www.doi.org/10.26538/tjnpr/v7i11.11.
Meghdadi A, and Javar N. Evaluation of nitrate sources and the percent contribution of bacterial denitrification in hyporheic zone using isotope fractionation technique and multi-linear regression analysis. J Environ. Manag. 2018; 222: 54-65. https://doi.org/10.1016/j.jenvman.2018.05.022.
Huzaifa U, Abdullahi GU, Maryam. RA, Dilber UO. (2023) “Phytofabrication of Zinc Oxide Nanoparticles Using Cyprus Mentha Piperita and Evaluation of its Anticancer And Antimicrobial Activity. European Chemical Bulletin. 2023; 12(6),7632–7638. htttps://doi.org/10.48047/ecb/2023.12.si6.678.
Fraser SP, Ding Y, Liu A, Foster CS, Djamgoz MB. Tetrodotoxin suppresses morphological enhancement of the metastatic MAT-LyLu rat prostate cancer cell line. Cell Tissue Res. 1999; 295(3):505-12. doi: 10.1007/s004410051256. PMID: 10022970.
Ghali UM, Usman AG, Chellube ZM, et al. Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN Appl. Sci. 2020; 2, 1871. https://doi.org/10.1007/s42452-020-03690-2.
Sinharay S. An Overview of Statistics in Education. Editor(s): Penelope Peterson, Eva Baker, Barry McGaw. International Encyclopedia of Education (Third Edition). Elsevier. 2010; 1-11. ISBN 9780080448947. https://doi.org/10.1016/B978-0-08-044894-7.01719-X.
Umar H, Rizaner N, Usman AG, Aliyu MR, Adun H, Ghali UM, Uzun Ozsahin D, Abba SI. Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models. Pharmaceuticals (Basel). 2023; 8;16(6):858. https://doi.org/10.3390/ph16060858.
Klarsfeld A, Laufer R, Fontaine B, Devillers Thiéry A, Dubreuil C, Changeux JP. Regulation of muscle AChR α subunit gene expression by electrical activity: Involvement of protein kinase C and Ca 2+. Neuron. 1989; 2(3). https://doi.org/10.1016/0896-6273(89)90307-3.
Hahn CG, Covault J. Neural regulation of N-cadherin gene expression in developing and adult skeletal muscle. Journal of Neuroscience. 1992; 12(12). https://doi.org/10.1523/jneurosci.12-12-04677.1992.
Ling W, Chao C, Lijie G, Qin L, Hongyan D, Hongsheng B,Dadong G. Zinc oxide nanoparticles induce murine photoreceptor cell death via mitochondria-related signaling pathway, Artificial Cells, Nanomedicine, and Biotechnology. 2018; 46:sup1, 1102-1113. https://doi.org/10.1080/21691401.2018.1446018.
Adun H, Doga K, Mustafa D, Huzaifa U, Ifeoluwa W. An experimental investigation of thermal conductivity and dynamic viscosity of Al2O3-ZnO-Fe3O4 ternary hybrid nanofluid and development of machine learning model. Powder Technology, 2021; 394. https://doi.org/10.1016/j.powtec.2021.09.039.