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Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis
BACKGROUND: Lung cancer has been a common malignant tumor with a leading cause of morbidity and mortality, current molecular targets are woefully lacking comparing to the highly progressive cancer. The study is designed to identify new prognostic predictors and potential gene targets based on bioinf...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737627/ https://www.ncbi.nlm.nih.gov/pubmed/31528121 http://dx.doi.org/10.1186/s12935-019-0956-1 |
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author | Ma, Wenxia Wang, Bin Zhang, Yaping Wang, Ziyue Niu, Dan Chen, Siyu Zhang, Zhirong Shen, Ningning Han, Weixia Zhang, Xiaoqin Wei, Rong Wang, Chen |
author_facet | Ma, Wenxia Wang, Bin Zhang, Yaping Wang, Ziyue Niu, Dan Chen, Siyu Zhang, Zhirong Shen, Ningning Han, Weixia Zhang, Xiaoqin Wei, Rong Wang, Chen |
author_sort | Ma, Wenxia |
collection | PubMed |
description | BACKGROUND: Lung cancer has been a common malignant tumor with a leading cause of morbidity and mortality, current molecular targets are woefully lacking comparing to the highly progressive cancer. The study is designed to identify new prognostic predictors and potential gene targets based on bioinformatic analysis of Gene Expression Omnibus (GEO) database. METHODS: Four cDNA expression profiles GSE19188, GSE101929, GSE18842 and GSE33532 were chosen from GEO database to analyze the differently expressed genes (DEGs) between non-small cell lung cancer (NSCLC) and normal lung tissues. After the DEGs functions were analyzed, the protein–protein interaction network (PPI) of DEGs were constructed, and the core gene in the network which has high connectivity degree with other genes was identified. We analyzed the association of the gene with the development of NSCLC as well as its prognosis. Lastly we explored the conceivable signaling mechanism of the gene regulation during the development of NSCLC. RESULTS: A total of 92 up regulated and 214 down regulated DEGs were shared in four cDNA expression profiles. Based on their PPI network, TOP2A was connected with most of other genes and was selected for further analysis. Kaplan–Meier overall survival analysis (OS) revealed that TOP2A was associated with worse NSCLC patients survival. And both GEPIA analysis and immunohistochemistry experiment (IHC) confirmed that TOP2A was aberrant gain of expression in cancer comparing to normal tissues. The clinical significance of TOP2A and probable signaling pathways it involved in were further explored, and a positive correlation between TOP2A and TPX2 expression was found in lung cancer tissues. CONCLUSION: Using bioinformatic analysis, we revealed that TOP2A could be adopted as a prognostic indicator of NSCLC and it potentially regulate cancer development through co-work with TPX2. However, more detailed experiments are needed to clarify its drug target role in clinical medical use. |
format | Online Article Text |
id | pubmed-6737627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-67376272019-09-16 Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis Ma, Wenxia Wang, Bin Zhang, Yaping Wang, Ziyue Niu, Dan Chen, Siyu Zhang, Zhirong Shen, Ningning Han, Weixia Zhang, Xiaoqin Wei, Rong Wang, Chen Cancer Cell Int Primary Research BACKGROUND: Lung cancer has been a common malignant tumor with a leading cause of morbidity and mortality, current molecular targets are woefully lacking comparing to the highly progressive cancer. The study is designed to identify new prognostic predictors and potential gene targets based on bioinformatic analysis of Gene Expression Omnibus (GEO) database. METHODS: Four cDNA expression profiles GSE19188, GSE101929, GSE18842 and GSE33532 were chosen from GEO database to analyze the differently expressed genes (DEGs) between non-small cell lung cancer (NSCLC) and normal lung tissues. After the DEGs functions were analyzed, the protein–protein interaction network (PPI) of DEGs were constructed, and the core gene in the network which has high connectivity degree with other genes was identified. We analyzed the association of the gene with the development of NSCLC as well as its prognosis. Lastly we explored the conceivable signaling mechanism of the gene regulation during the development of NSCLC. RESULTS: A total of 92 up regulated and 214 down regulated DEGs were shared in four cDNA expression profiles. Based on their PPI network, TOP2A was connected with most of other genes and was selected for further analysis. Kaplan–Meier overall survival analysis (OS) revealed that TOP2A was associated with worse NSCLC patients survival. And both GEPIA analysis and immunohistochemistry experiment (IHC) confirmed that TOP2A was aberrant gain of expression in cancer comparing to normal tissues. The clinical significance of TOP2A and probable signaling pathways it involved in were further explored, and a positive correlation between TOP2A and TPX2 expression was found in lung cancer tissues. CONCLUSION: Using bioinformatic analysis, we revealed that TOP2A could be adopted as a prognostic indicator of NSCLC and it potentially regulate cancer development through co-work with TPX2. However, more detailed experiments are needed to clarify its drug target role in clinical medical use. BioMed Central 2019-09-11 /pmc/articles/PMC6737627/ /pubmed/31528121 http://dx.doi.org/10.1186/s12935-019-0956-1 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Primary Research Ma, Wenxia Wang, Bin Zhang, Yaping Wang, Ziyue Niu, Dan Chen, Siyu Zhang, Zhirong Shen, Ningning Han, Weixia Zhang, Xiaoqin Wei, Rong Wang, Chen Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title | Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title_full | Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title_fullStr | Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title_full_unstemmed | Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title_short | Prognostic significance of TOP2A in non-small cell lung cancer revealed by bioinformatic analysis |
title_sort | prognostic significance of top2a in non-small cell lung cancer revealed by bioinformatic analysis |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737627/ https://www.ncbi.nlm.nih.gov/pubmed/31528121 http://dx.doi.org/10.1186/s12935-019-0956-1 |
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