Radiation Therapy in Patients With Brain Cancer: Post-proteomics Interpretation
Journal of Lasers in Medical Sciences,
Vol. 10 No. Supplement (2019),
1 December 2019
,
Page S59-S63
Abstract
Introduction: Radiation Therapy (RT) as a common method for cancer treatment could conclude in some side effects. Molecular investigation is one of the approaches that could assist in decrypting the molecular mechanisms of this incident. For this aim, protein-protein interaction network analysis as a complementary study of proteome is applied to explore the RT effect on brain cancer effect after the early stage of exposure prior to skin lesion appears.
Methods: Cytoscape 3.7.2 and its plug-ins analyzed the network of DEPs in the treatment condition and the centrality and pathway enrichments were conducted by the use of NetworkAnalyzer and ClueGO+CluePedia.
Results: A network of 15 DEPs indicated that six nodes are key players in the network stability and SERPINC1 and F5 are from the query proteins. Pathways of post-translational protein phosphorylation, Platelet degranulation, and Complement and coagulation cascades are the most highlighted ones for the central nodes that could be affected in radiation therapy.
Conclusion: The central proteins of the network of early stage treatments could have additional importance in the mechanisms of radiotherapy response prior to skin lesions. These candidates worth precise attention for this type of therapy after approving by validation studies.
- Radiation Therapy
- Protein-protein interaction network Analysis
- Gene ontology
How to Cite
References
Kim M-H, Jung S-Y, Ahn J, Hwang S-G, Woo H-J, An S, et al. Quantitative proteomic analysis of single or fractionated radiation-induced proteins in human breast cancer MDA-MB-231 cells. Cell & bioscience. 2015;5(1):2.
Lacombe J, Azria D, Mange A, Solassol J. Proteomic approaches to identify biomarkers predictive of radiotherapy outcomes. Expert review of proteomics. 2013;10(1):33-42.
Darby SC, Ewertz M, McGale P, Bennet AM, Blom-Goldman U, Brønnum D, et al. Risk of ischemic heart disease in women after radiotherapy for breast cancer. N Engl J Med. 2013;368(11):987-98. doi: 10.1056/NEJMoa1209825.
Ouerhani A, Chiappetta G, Souiai O, Mahjoubi H, Vinh J. Investigation of serum proteome homeostasis during radiation therapy by a quantitative proteomics approach. Biosci Rep. 2019;39(7):BSR20182319. doi: 10.1042/BSR20182319.
Zamanian-Azodi M, Rezaei-Tavirani M, Mahboubi M, Hamidpour M, Tavirani MR, Hamdieh M, et al. Serum proteomic study of women with obsessive-compulsive disorder, washing subtype. Basic Clin Neurosci. 2018;9(5):337-46. doi: 10.32598/bcn.9.5.337.
Amiri-Dashatan N, Koushki M, Abbaszadeh HA, Rostami-Nejad M, Rezaei-Tavirani M. Proteomics Applications in Health: Biomarker and Drug Discovery and Food Industry. Iran J Pharm Res. 2018;17(4):1523-36. doi: 10.22037/ijpr.2018.2306.
Rezaei-Tavirani M, Zamanian Azodi M, Bashash D, Ahmadi N, Rostaim-Nejad M. Breast Cancer Interaction Network Concept from Mostly Related Components. Galen Medical Journal. 2019;8:e1298. doi: 10.31661/gmj.v8i0.1298.
Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014;7(1):17-31.
Rezaei-Tavirani M, Rezaei Tavirani M, Zamanian Azodi M, Akbari Z, Hajimehdipoor H. Prediction of coffee effects in rats with healthy and NAFLD conditions based on protein-protein interaction network analysis. Res J Pharmacogn. 2019;6(4):7-15. doi: 10.22127/rjp.2019.93500
Rezaei-Tavirani M, Tavirani MR, Azodi MZ, Razzaghi M, Farshi HM. Evaluation of skin response after erbium: yttrium–aluminum–garnet laser irradiation: a network analysis approach. J Lasers Med Sci. 2019;10(3):194-199.
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431-2. doi: 10.1093/bioinformatics/btq675.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2016;45(D1): D362-68. doi: 10.1093/nar/gkw937.
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24(2):282-4. doi: 10.1093/bioinformatics/btm554.
Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661-3. doi: 10.1093/bioinformatics/btt019.
Xu J, Ying Y, Xiong G, Lai L, Wang Q, Yang Y. Knockdown of serpin peptidase inhibitor clade C member 1 inhibits the growth of nasopharyngeal carcinoma cells. Mol Med Rep. 2019;19(5):3658-66. doi: 10.3892/mmr.2019.10021.
Lu Z, Wang F, Liang M. SerpinC1/Antithrombin III in kidney-related diseases. Clin Sci (Lond). 2017;131(9):823-31. doi: 10.1042/CS20160669.
Hadden JW, Willoughby D. Advances in Immunopharmacology: Proceedings of the Second International Conference on Immunopharmacology. July 1982, Washington, USA: Elsevier; 2013.
Petrik V, Saadoun S, Loosemore A, Hobbs J, Opstad KS, Sheldon J, et al. Serum α2-HS glycoprotein predicts survival in patients with glioblastoma. Clin Chem. 2008;54(4):713-22. doi: 10.1373/clinchem.2007.096792.
Farshchian M, Kivisaari A, Ala-aho R, Riihilä P, Kallajoki M, Grénman R, et al. Serpin peptidase inhibitor clade A member 1 (SerpinA1) is a novel biomarker for progression of cutaneous squamous cell carcinoma. Am J Pathol. 2011;179(3):1110-9. doi: 10.1016/j.ajpath.2011.05.012.
Zhou L, Chen HM, Qu S, Li L, Zhao W, Liang ZG, et al. Reduced QSOX1 enhances radioresistance in nasopharyngeal carcinoma. Oncotarget. 2017;9(3):3230-41. doi: 10.18632/oncotarget.23227.
- Abstract Viewed: 483 times
- PDF Downloaded: 280 times