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Identification and analysis of key genes in osteosarcoma using bioinformatics

Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, wa...

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Autores principales: Diao, Chunyu, Xi, Yong, Xiao, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778824/
https://www.ncbi.nlm.nih.gov/pubmed/29435005
http://dx.doi.org/10.3892/ol.2017.7649
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author Diao, Chunyu
Xi, Yong
Xiao, Tao
author_facet Diao, Chunyu
Xi, Yong
Xiao, Tao
author_sort Diao, Chunyu
collection PubMed
description Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t-test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein-protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non-metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor-κB complex subunits, peroxisome proliferator activated receptor α, signal transducer and activator of transcription 3 and MYC proto-oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS.
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spelling pubmed-57788242018-02-12 Identification and analysis of key genes in osteosarcoma using bioinformatics Diao, Chunyu Xi, Yong Xiao, Tao Oncol Lett Articles Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t-test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein-protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non-metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor-κB complex subunits, peroxisome proliferator activated receptor α, signal transducer and activator of transcription 3 and MYC proto-oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS. D.A. Spandidos 2018-03 2017-12-19 /pmc/articles/PMC5778824/ /pubmed/29435005 http://dx.doi.org/10.3892/ol.2017.7649 Text en Copyright: © Diao 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
Diao, Chunyu
Xi, Yong
Xiao, Tao
Identification and analysis of key genes in osteosarcoma using bioinformatics
title Identification and analysis of key genes in osteosarcoma using bioinformatics
title_full Identification and analysis of key genes in osteosarcoma using bioinformatics
title_fullStr Identification and analysis of key genes in osteosarcoma using bioinformatics
title_full_unstemmed Identification and analysis of key genes in osteosarcoma using bioinformatics
title_short Identification and analysis of key genes in osteosarcoma using bioinformatics
title_sort identification and analysis of key genes in osteosarcoma using bioinformatics
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5778824/
https://www.ncbi.nlm.nih.gov/pubmed/29435005
http://dx.doi.org/10.3892/ol.2017.7649
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