The identification of lung cancer gene-drug module based on multiplex networks algorithm_Journal of Biomedical Engineering

Authors：

ZHOU Gang ,  GAO Jie

School of Science, Jiangnan University, Wuxi, Jiangsu 214122, P. R. China;

Corresponding author：

GAO Jie, Email: gaojie@jiangnan.edu.cn

Keywords：

gene expression; drug response; similarity network; module identification

DOI：

10.7507/1001-5515.202102006

Video：

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Abstract Full text Figures/Tables Video References Cited by

Using modular identification methods in gene-drug multiplex networks to infer new gene-drug associations can identify new therapeutic target genes for known drugs. In this paper, based on the gene expression data and drug response data of lung cancer in the genomics of drug sensitivity in cancer (GDSC) database, a multiple network algorithm is proposed. First, a heterogeneous network of genes of lung cancer and drugs in different cell lines is constructed, and then a network module identification method based on graph entropy is used. In this heterogeneous network, network modules are identified, and five lung cancer gene-drug association modules are identified through iterative convergence. Compared with other methods, the algorithm has better results in terms of running time, accuracy and robustness, and the identified modules have obvious biological significance. The research results in this article have guiding significance for the medication and treatment of lung cancer, and can provide references for the treatment of other diseases with the same targeted genes.

Citation： ZHOU Gang, GAO Jie. The identification of lung cancer gene-drug module based on multiplex networks algorithm. Journal of Biomedical Engineering, 2021, 38(6): 1111-1117. doi: 10.7507/1001-5515.202102006 Copy

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1. Dugger S, Platt A, Goldstein D B. Drug development in the era of precision medicine. Nat Rev Drug Discov, 2018, 17(3): 183-196.
2. Roses A D. Pharmacogenetics in drug discovery and development: a translational perspective. Nat Rev Drug Discov, 2008, 7(10): 807-817.
3. Vilar S, Harpaz R, Uriarte E, et al. Drug-drug interaction through molecular structure similarity analysis. J Am Med Inform Assoc, 2012, 19(6): 1066-1074.
4. Vilar S, Uriarte E, Santana L, et al. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat Protoc, 2014, 9(9): 2147-2163.
5. Wang Y L, Xiao J W, Suzek T O, et al. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res, 2009, 37: W623-W633.
6. Yuan L, Guo L H, Yuan C A, et al. Integration of multi-omics data for gene regulatory network inference and application to breast cancer. IEEE/ACM Trans Comput Biol Bioinform, 2019, 16(3): 782-791.
7. Valdeolivas A, Tichit L, Navarro C, et al. Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics, 2019, 35(3): 497-505.
8. Shang H X, Liu Z P. Network-based prioritization of cancer genes by integrative ranks from multi-omics data. Comput Biol and Med, 2020, 119: 103692.
9. Ma X K, Liu Z Y, Zhang Z Y, et al. Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data. BMC Bioinformatics, 2017, 18(1): 72.
10. Chen J Y, Zhang S H. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics, 2016, 32(11): 1724-1732.
11. Zhou D Y, Bousquet O, Lal T N, et al. Learning with local and global consistency. Adv Neural Inf Proc Syst, 2003, 16(3): 321-328.
12. Page L, Brin S, Motwani R, et al. The PageRank citation ranking: bringing order to the Web. Stanford Digital Libraries Working Paper, 1998, 9(1): 1-14.
13. Cho M S, Lee J M, Lee K M. Reweighted random walks for graph matching. Lect Notes Comput Sc, 2010, 6315: 492-505.
14. Chen J Z, Peng H, Han G Q, et al. HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification. Bioinformatics, 2019, 35(4): 602-610.
15. Hirohashi S, Kanai Y. Cell adhesion system and human cancer morphogenesis. Cancer Sci, 2003, 94(7): 575-581.
16. Liang Zhongxing, Brooks J, Willard M, et al. CXCR4/CXCL12 axis promotes VEGF-mediated tumor angiogenesis through Akt signaling pathway. Biochem Biophys Res Commun, 2007, 359(3): 716-722.
17. Lin L P, Asthana S, Chan E, et al. Mapping the molecular determinants of BRAF oncogene dependence in human lung cancer. Proc Natl Acad Sci U S A, 2014, 111(7): E748-E757.
18. Robert C, Karaszewska B, Schachter J, et al. Improved overall survival in melanoma with combined dabrafenib and trametinib. N Engl J Med, 2015, 372(1): 30-39.
19. Kim K B, Kefford R, Pavlick A C, et al. Phase II study of the MEK1/MEK2 inhibitor Trametinib in patients with metastatic BRAF-mutant cutaneous melanoma previously treated with or without a BRAF inhibitor. J Clin Oncol, 2013, 31(4): 482-489.

Journal of Biomedical Engineering

The identification of lung cancer gene-drug module based on multiplex networks algorithm

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