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Identification of epigenetically altered genes and potential gene targets in melanoma using bioinformatic methods

Authors Duan HH, Jiang K, Wei DK, Zhang LJ, Cheng DL, Lv M, Xu YB, He AM

Received 18 July 2017

Accepted for publication 4 October 2017

Published 20 December 2017 Volume 2018:11 Pages 9—15

DOI https://doi.org/10.2147/OTT.S146663

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Manfred Beleut

Peer reviewer comments 3

Editor who approved publication: Dr Jianmin Xu


Honghao Duan, Ke Jiang, Dengke Wei, Lijun Zhang, Deliang Cheng, Min Lv, Yuben Xu, Aimin He

Department of Hand Surgery, Honghui Hospital, Xi’an Jiaotong University Health Science Center, Xi’an, Shaanxi, People’s Republic of China

Abstract: This study aimed to analyze epigenetically and genetically altered genes in melanoma to get a better understanding of the molecular circuitry of melanoma and identify potential gene targets for the treatment of melanoma. The microarray data of GSE31879, including mRNA expression profiles (seven melanoma and four melanocyte samples) and DNA methylation profiles (seven melanoma and five melanocyte samples), were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and differentially methylated positions (DMPs) were screened using the linear models for microarray data (limma) package in melanoma compared with melanocyte samples. Gene ontology (GO) and pathway enrichment analysis of the DEGs were carried out using the Database for Annotation, Visualization, and Integrated Discovery. Moreover, differentially methylated genes (DMGs) were identified, and a transcriptional regulatory network was constructed using the University of California Santa Cruz genome browser database. A total of 1,215 DEGs (199 upregulated and 1,016 downregulated) and 14,094 DMPs (10,450 upregulated and 3,644 downregulated) were identified in melanoma compared with melanocyte samples. Additionally, the upregulated and downregulated DEGs were significantly associated with different GO terms and pathways, such as pigment cell differentiation, biosynthesis, and metabolism. Furthermore, the transcriptional regulatory network showed that DMGs such as Aristaless-related homeobox (ARX), damage-specific DNA binding protein 2 (DDB2), and myelin basic protein (MBP) had higher node degrees. Our results showed that several methylated genes (ARX, DDB2, and MBP) may be involved in melanoma progression.

Keywords: melanoma, DNA methylation, differentially expressed genes, gene ontology, pathway enrichment analysis, transcriptional regulatory network

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