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Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma
Osteosarcoma (OS) is the most common histological type of primary bone cancer. The present study was designed to identify the key genes and signaling pathways involved in the metastasis of OS. Microarray data of GSE39055 were downloaded from the Gene Expression Omnibus database, which included 19 OS...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819903/ https://www.ncbi.nlm.nih.gov/pubmed/29328361 http://dx.doi.org/10.3892/ijmm.2018.3360 |
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author | Liu, Yang Sun, Wei Ma, Xiaojun Hao, Yuedong Liu, Gang Hu, Xiaohui Shang, Houlai Wu, Pengfei Zhao, Zexue Liu, Weidong |
author_facet | Liu, Yang Sun, Wei Ma, Xiaojun Hao, Yuedong Liu, Gang Hu, Xiaohui Shang, Houlai Wu, Pengfei Zhao, Zexue Liu, Weidong |
author_sort | Liu, Yang |
collection | PubMed |
description | Osteosarcoma (OS) is the most common histological type of primary bone cancer. The present study was designed to identify the key genes and signaling pathways involved in the metastasis of OS. Microarray data of GSE39055 were downloaded from the Gene Expression Omnibus database, which included 19 OS biopsy specimens before metastasis (control group) and 18 OS biopsy specimens after metastasis (case group). After the differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Analysis package, hierarchical clustering analysis and unsupervised clustering analysis were performed separately, using orange software and the self-organization map method. Based upon the Database for Annotation, Visualization and Integrated Discovery tool and Cytoscape software, enrichment analysis and protein-protein interaction (PPI) network analysis were conducted, respectively. After function deviation scores were calculated for the significantly enriched terms, hierarchical clustering analysis was performed using Cluster 3.0 software. Furthermore, logistic regression analysis was used to identify the terms that were significantly different. Those terms that were significantly different were validated using other independent datasets. There were 840 DEGs in the case group. There were various interactions in the PPI network [including intercellular adhesion molecule-1 (ICAM1), transforming growth factor β1 (TGFB1), TGFB1-platelet-derived growth factor subunit B (PDGFB) and PDGFB-platelet-derived growth factor receptor-β (PDGFRB)]. Regulation of cell migration, nucleotide excision repair, the Wnt signaling pathway and cell migration were identified as the terms that were significantly different. ICAM1, PDGFB, PDGFRB and TGFB1 were identified to be enriched in cell migration and regulation of cell migration. Nucleotide excision repair and the Wnt signaling pathway were the metastasis-associated pathways of OS. In addition, ICAM1, PDGFB, PDGFRB and TGFB1, which were involved in cell migration and regulation of cell migration may affect the metastasis of OS. |
format | Online Article Text |
id | pubmed-5819903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-58199032018-03-02 Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma Liu, Yang Sun, Wei Ma, Xiaojun Hao, Yuedong Liu, Gang Hu, Xiaohui Shang, Houlai Wu, Pengfei Zhao, Zexue Liu, Weidong Int J Mol Med Articles Osteosarcoma (OS) is the most common histological type of primary bone cancer. The present study was designed to identify the key genes and signaling pathways involved in the metastasis of OS. Microarray data of GSE39055 were downloaded from the Gene Expression Omnibus database, which included 19 OS biopsy specimens before metastasis (control group) and 18 OS biopsy specimens after metastasis (case group). After the differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Analysis package, hierarchical clustering analysis and unsupervised clustering analysis were performed separately, using orange software and the self-organization map method. Based upon the Database for Annotation, Visualization and Integrated Discovery tool and Cytoscape software, enrichment analysis and protein-protein interaction (PPI) network analysis were conducted, respectively. After function deviation scores were calculated for the significantly enriched terms, hierarchical clustering analysis was performed using Cluster 3.0 software. Furthermore, logistic regression analysis was used to identify the terms that were significantly different. Those terms that were significantly different were validated using other independent datasets. There were 840 DEGs in the case group. There were various interactions in the PPI network [including intercellular adhesion molecule-1 (ICAM1), transforming growth factor β1 (TGFB1), TGFB1-platelet-derived growth factor subunit B (PDGFB) and PDGFB-platelet-derived growth factor receptor-β (PDGFRB)]. Regulation of cell migration, nucleotide excision repair, the Wnt signaling pathway and cell migration were identified as the terms that were significantly different. ICAM1, PDGFB, PDGFRB and TGFB1 were identified to be enriched in cell migration and regulation of cell migration. Nucleotide excision repair and the Wnt signaling pathway were the metastasis-associated pathways of OS. In addition, ICAM1, PDGFB, PDGFRB and TGFB1, which were involved in cell migration and regulation of cell migration may affect the metastasis of OS. D.A. Spandidos 2018-03 2018-01-02 /pmc/articles/PMC5819903/ /pubmed/29328361 http://dx.doi.org/10.3892/ijmm.2018.3360 Text en Copyright: © Liu 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 Liu, Yang Sun, Wei Ma, Xiaojun Hao, Yuedong Liu, Gang Hu, Xiaohui Shang, Houlai Wu, Pengfei Zhao, Zexue Liu, Weidong Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title | Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title_full | Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title_fullStr | Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title_full_unstemmed | Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title_short | Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
title_sort | logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819903/ https://www.ncbi.nlm.nih.gov/pubmed/29328361 http://dx.doi.org/10.3892/ijmm.2018.3360 |
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