Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model

Author(s): Zhi Li, Tianyue Zhang, Haojie Lei, Liyan Wei, Yuanning Liu, Yadi Shi, Shuyi Li, Bowen Shen, Hao Guo, Zhangqian Chen, Xiaorong Yi, Hao Zhang*

Journal Name: Current Bioinformatics

Volume 15 , Issue 3 , 2020

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Abstract:

Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric cancer were analyzed, screened and mined through modeling and network modeling to find valuable data associated with multi-drug resistance of gastric cancer.

Methods: First, data sets were preprocessed from three aspects: data processing, data annotation and classification, and functional clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory network was constructed by mining interactions among the genes. This paper computed the values of each node in each classified primary gene regulatory network and ranked these nodes according to their scores. On the basis of this, the appropriate core node was selected and the corresponding core network was developed.

Results and Conclusion: Finally, core network modules were analyzed, which were mined. After the correlation analysis, the result showed that the constructed network module had 20 core genes. This module contained valuable data associated with multi-drug resistance in gastric cancer.

Keywords: Gastric cancer, multi-drug resistance, differentially expressed genes, gene regulatory networks, data annotation, clustering.

[1]
Wang R, Chen XZ. High mortality from hepatic, gastric and esophageal cancers in mainland China: 40 years of experience and development. Clin Res Hepatol Gastroenterol 2014; 38(6): 751-6.
[http://dx.doi.org/10.1016/j.clinre.2014.04.014] [PMID: 24994519]
[2]
Alberts SR, Cervantes A, van de Velde CJH. Gastric cancer: epidemiology, pathology and treatment. Ann Oncol 2003; 14(Suppl. 2): ii31-6.
[http://dx.doi.org/10.1093/annonc/mdg726] [PMID: 12810455]
[3]
Beck WT. The cell biology of multiple drug resistance. Biochem Pharmacol 1987; 36(18): 2879-87.
[http://dx.doi.org/10.1016/0006-2952(87)90198-5] [PMID: 2888464]
[4]
Hendrickson DG, Hogan DJ, Herschlag D, Ferrell JE, Brown PO. Systematic identification of mRNAs recruited to argonaute 2 by specific microRNAs and corresponding changes in transcript abundance. PLoS One 2008; 3(5): e2126-6.
[http://dx.doi.org/10.1371/journal.pone.0002126] [PMID: 18461144]
[5]
Suphavilai C, Zhu L, Chen JY. A method for developing regulatory gene set networks to characterize complex biological systems. BMC Genomics 2015; 16(Suppl. 11): S4.
[http://dx.doi.org/10.1186/1471-2164-16-S11-S4] [PMID: 26576648]
[6]
Kim Y, Hao J, Gautam Y, Mersha TB, Kang M. DiffGRN: differential gene regulatory network analysis. Int J Data Min Bioinform 2018; 20(4): 362-79.
[http://dx.doi.org/10.1504/IJDMB.2018.094891] [PMID: 31114627]
[7]
Kanehisa M. Post-genome informatics[M]Oxford University Press (OUP) 2000.
[8]
de Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 2002; 9(1): 67-103.
[http://dx.doi.org/10.1089/10665270252833208] [PMID: 11911796]
[9]
Chen Z, Zhang L, Xia L, et al. Genomic analysis of drug resistant gastric cancer cell lines by combining mRNA and microRNA expression profiling. Cancer Lett 2014; 350(1-2): 43-51.
[http://dx.doi.org/10.1016/j.canlet.2014.04.010] [PMID: 24759738]
[10]
Huang DW, Sherman BT, Tan Q, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007; 35(Suppl. 2): W169-75.
[http://dx.doi.org/10.1093/nar/gkm415]
[11]
Liao Q, Liu C, Yuan X, et al. Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network. Nucleic Acids Res 2011; 39(9): 3864-78.
[http://dx.doi.org/10.1093/nar/gkq1348] [PMID: 21247874]
[12]
Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 2007; 8(9): R183.
[http://dx.doi.org/10.1186/gb-2007-8-9-r183] [PMID: 17784955]
[13]
Voevodski K, Teng SH, Xia Y. Spectral affinity in protein networks. BMC Syst Biol 2009; 3(1): 112.
[http://dx.doi.org/10.1186/1752-0509-3-112] [PMID: 19943959]
[14]
Grolmusz V. A note on the pagerank of undirected graphs. Inf Process Lett 2015; 115(6): 633-4.
[http://dx.doi.org/10.1016/j.ipl.2015.02.015]


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Article Details

VOLUME: 15
ISSUE: 3
Year: 2020
Page: [225 - 234]
Pages: 10
DOI: 10.2174/1574893614666190722102557
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