Cargando…

Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray

Osteosarcoma (OS) is one of the most malignant tumors in children and young adults. To better understand the underlying mechanism, five related datasets deposited in the Gene Expression Omnibus were included in the present study. The Bioconductor ‘limma’ package was used to identify differentially e...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Junkui, Xu, Hongen, Qi, Muge, Zhang, Chi, Shi, Jianxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755242/
https://www.ncbi.nlm.nih.gov/pubmed/31432118
http://dx.doi.org/10.3892/mmr.2019.10543
_version_ 1783453191365459968
author Sun, Junkui
Xu, Hongen
Qi, Muge
Zhang, Chi
Shi, Jianxiang
author_facet Sun, Junkui
Xu, Hongen
Qi, Muge
Zhang, Chi
Shi, Jianxiang
author_sort Sun, Junkui
collection PubMed
description Osteosarcoma (OS) is one of the most malignant tumors in children and young adults. To better understand the underlying mechanism, five related datasets deposited in the Gene Expression Omnibus were included in the present study. The Bioconductor ‘limma’ package was used to identify differentially expressed genes (DEGs) and the ‘Weighted Gene Co-expression Network Analysis’ package was used to construct a weighted gene co-expression network to identify key modules and hub genes, associated with OS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes overrepresentation analyses were used for functional annotation. The results indicated that 1,405 genes were dysregulated in OS, including 927 upregulated and 478 downregulated genes, when the cut off value was set at a ≥2 fold-change and an adjusted P-value of P<0.01 was used. Functional annotation of DEGs indicated that these genes were involved in the extracellular matrix (ECM) and that they function in several processes, including biological adhesion, ECM organization, cell migration and leukocyte migration. These findings suggested that dysregulation of the ECM shaped the tumor microenvironment and modulated the OS hallmark. Genes assigned to the yellow module were positively associated with OS and could contribute to the development of OS. In conclusion, the present study has identified several key genes that are potentially druggable genes or therapeutics targets in OS. Functional annotations revealed that the dysregulation of the ECM may contribute to OS development and, therefore, provided new insights to improve our understanding of the mechanisms underlying OS.
format Online
Article
Text
id pubmed-6755242
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher D.A. Spandidos
record_format MEDLINE/PubMed
spelling pubmed-67552422019-09-25 Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray Sun, Junkui Xu, Hongen Qi, Muge Zhang, Chi Shi, Jianxiang Mol Med Rep Articles Osteosarcoma (OS) is one of the most malignant tumors in children and young adults. To better understand the underlying mechanism, five related datasets deposited in the Gene Expression Omnibus were included in the present study. The Bioconductor ‘limma’ package was used to identify differentially expressed genes (DEGs) and the ‘Weighted Gene Co-expression Network Analysis’ package was used to construct a weighted gene co-expression network to identify key modules and hub genes, associated with OS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes overrepresentation analyses were used for functional annotation. The results indicated that 1,405 genes were dysregulated in OS, including 927 upregulated and 478 downregulated genes, when the cut off value was set at a ≥2 fold-change and an adjusted P-value of P<0.01 was used. Functional annotation of DEGs indicated that these genes were involved in the extracellular matrix (ECM) and that they function in several processes, including biological adhesion, ECM organization, cell migration and leukocyte migration. These findings suggested that dysregulation of the ECM shaped the tumor microenvironment and modulated the OS hallmark. Genes assigned to the yellow module were positively associated with OS and could contribute to the development of OS. In conclusion, the present study has identified several key genes that are potentially druggable genes or therapeutics targets in OS. Functional annotations revealed that the dysregulation of the ECM may contribute to OS development and, therefore, provided new insights to improve our understanding of the mechanisms underlying OS. D.A. Spandidos 2019-10 2019-07-31 /pmc/articles/PMC6755242/ /pubmed/31432118 http://dx.doi.org/10.3892/mmr.2019.10543 Text en Copyright: © Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Sun, Junkui
Xu, Hongen
Qi, Muge
Zhang, Chi
Shi, Jianxiang
Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title_full Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title_fullStr Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title_full_unstemmed Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title_short Identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
title_sort identification of key genes in osteosarcoma by meta-analysis of gene expression microarray
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755242/
https://www.ncbi.nlm.nih.gov/pubmed/31432118
http://dx.doi.org/10.3892/mmr.2019.10543
work_keys_str_mv AT sunjunkui identificationofkeygenesinosteosarcomabymetaanalysisofgeneexpressionmicroarray
AT xuhongen identificationofkeygenesinosteosarcomabymetaanalysisofgeneexpressionmicroarray
AT qimuge identificationofkeygenesinosteosarcomabymetaanalysisofgeneexpressionmicroarray
AT zhangchi identificationofkeygenesinosteosarcomabymetaanalysisofgeneexpressionmicroarray
AT shijianxiang identificationofkeygenesinosteosarcomabymetaanalysisofgeneexpressionmicroarray