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Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods

BACKGROUND: Melanoma is a highly invasive malignant skin tumor. While melanoma may share some similarities with that of melanocytic nevi, there also exist a number of distinct differences between these conditions. An analysis of these differences may provide a means to more effectively evaluate the...

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Autores principales: He, Congcong, Zhang, Yujing, Jiang, Hanghang, Niu, Xueli, Qi, Ruiqun, Gao, Xinghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275674/
https://www.ncbi.nlm.nih.gov/pubmed/32547879
http://dx.doi.org/10.7717/peerj.9273
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author He, Congcong
Zhang, Yujing
Jiang, Hanghang
Niu, Xueli
Qi, Ruiqun
Gao, Xinghua
author_facet He, Congcong
Zhang, Yujing
Jiang, Hanghang
Niu, Xueli
Qi, Ruiqun
Gao, Xinghua
author_sort He, Congcong
collection PubMed
description BACKGROUND: Melanoma is a highly invasive malignant skin tumor. While melanoma may share some similarities with that of melanocytic nevi, there also exist a number of distinct differences between these conditions. An analysis of these differences may provide a means to more effectively evaluate the etiology and pathogenesis of melanoma. In particular, differences in aberrant methylation expression may prove to represent a critical distinction. METHODS: Data from gene expression datasets (GSE3189 and GSE46517) and gene methylation datasets (GSE86355 and GSE120878) were downloaded from the GEO database. GEO2R was used to obtain differentially expressed genes (DEGs) and differentially methylation genes (DMGs). Function and pathway enrichment of selected genes were performed using the DAVID database. A protein-protein interaction (PPI) network was constructed by STRING while its visualization was achieved with use of cytoscape. Primary melanoma samples from TCGA were used to identify significant survival genes. RESULTS: There was a total of 199 genes in the hypermethylation-low expression group, while 136 genes in the hypomethylation-high expression group were identified. The former were enriched in the biological processes of transcription regulation, RNA metabolism and regulation of cell proliferation. The later were highly involved in cell cycle regulation. 13 genes were screened out after survival analysis and included: ISG20, DTL, TRPV2, PLOD3, KIF3C, DLGAP4, PI4K2A, WIPI1, SHANK2, SLC16A10, GSTA4O, LFML2A and TMEM47. CONCLUSION: These findings reveal some of the methylated differentially expressed genes and pathways that exist between melonoma and melanocytic nevi. Moreover, we have identified some critical genes that may help to improve the diagnosis and treatment of melanoma.
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spelling pubmed-72756742020-06-15 Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods He, Congcong Zhang, Yujing Jiang, Hanghang Niu, Xueli Qi, Ruiqun Gao, Xinghua PeerJ Bioinformatics BACKGROUND: Melanoma is a highly invasive malignant skin tumor. While melanoma may share some similarities with that of melanocytic nevi, there also exist a number of distinct differences between these conditions. An analysis of these differences may provide a means to more effectively evaluate the etiology and pathogenesis of melanoma. In particular, differences in aberrant methylation expression may prove to represent a critical distinction. METHODS: Data from gene expression datasets (GSE3189 and GSE46517) and gene methylation datasets (GSE86355 and GSE120878) were downloaded from the GEO database. GEO2R was used to obtain differentially expressed genes (DEGs) and differentially methylation genes (DMGs). Function and pathway enrichment of selected genes were performed using the DAVID database. A protein-protein interaction (PPI) network was constructed by STRING while its visualization was achieved with use of cytoscape. Primary melanoma samples from TCGA were used to identify significant survival genes. RESULTS: There was a total of 199 genes in the hypermethylation-low expression group, while 136 genes in the hypomethylation-high expression group were identified. The former were enriched in the biological processes of transcription regulation, RNA metabolism and regulation of cell proliferation. The later were highly involved in cell cycle regulation. 13 genes were screened out after survival analysis and included: ISG20, DTL, TRPV2, PLOD3, KIF3C, DLGAP4, PI4K2A, WIPI1, SHANK2, SLC16A10, GSTA4O, LFML2A and TMEM47. CONCLUSION: These findings reveal some of the methylated differentially expressed genes and pathways that exist between melonoma and melanocytic nevi. Moreover, we have identified some critical genes that may help to improve the diagnosis and treatment of melanoma. PeerJ Inc. 2020-06-03 /pmc/articles/PMC7275674/ /pubmed/32547879 http://dx.doi.org/10.7717/peerj.9273 Text en ©2020 He et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
He, Congcong
Zhang, Yujing
Jiang, Hanghang
Niu, Xueli
Qi, Ruiqun
Gao, Xinghua
Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title_full Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title_fullStr Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title_full_unstemmed Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title_short Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
title_sort identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275674/
https://www.ncbi.nlm.nih.gov/pubmed/32547879
http://dx.doi.org/10.7717/peerj.9273
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