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Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis
Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781723/ https://www.ncbi.nlm.nih.gov/pubmed/31611952 http://dx.doi.org/10.3892/ol.2019.10796 |
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author | Li, Zhenhua Sang, Meixiang Tian, Ziqiang Liu, Zhao Lv, Jian Zhang, Fan Shan, Baoen |
author_facet | Li, Zhenhua Sang, Meixiang Tian, Ziqiang Liu, Zhao Lv, Jian Zhang, Fan Shan, Baoen |
author_sort | Li, Zhenhua |
collection | PubMed |
description | Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in ‘cell proliferation’, ‘cell growth’, ‘cell division’, ‘angiogenesis’ and ‘mitotic nuclear division’. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer. |
format | Online Article Text |
id | pubmed-6781723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-67817232019-10-14 Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis Li, Zhenhua Sang, Meixiang Tian, Ziqiang Liu, Zhao Lv, Jian Zhang, Fan Shan, Baoen Oncol Lett Articles Lung cancer is one of the most widespread neoplasms worldwide. To identify the key biomarkers in its carcinogenesis and development, the mRNA microarray datasets GSE102287, GSE89047, GSE67061 and GSE74706 were obtained from the Gene Expression Omnibus database. GEO2R was used to identify the differentially expressed genes (DEGs) in lung cancer. The Database for Annotation, Visualization and Integrated Discovery was used to analyze the functions and pathways of the DEGs, while the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape were used to obtain the protein-protein interaction (PPI) network. Kaplan Meier curves were used to analyze the effect of the hub genes on overall survival (OS). Module analysis was completed using Molecular Complex Detection in Cytoscape, and one co-expression network of these significant genes was obtained with cBioPortal. A total of 552 DEGs were identified among the four microarray datasets, which were mainly enriched in ‘cell proliferation’, ‘cell growth’, ‘cell division’, ‘angiogenesis’ and ‘mitotic nuclear division’. A PPI network, composed of 44 nodes and 886 edges, was constructed, and its significant module had 16 hub genes in the whole network: Opa interacting protein 5, exonuclease 1, PCNA clamp-associated factor, checkpoint kinase 1, hyaluronan-mediated motility receptor, maternal embryonic leucine zipper kinase, non-SMC condensin I complex subunit G, centromere protein F, BUB1 mitotic checkpoint serine/threonine kinase, cyclin A2, thyroid hormone receptor interactor 13, TPX2 microtubule nucleation factor, nucleolar and spindle associated protein 1, kinesin family member 20A, aurora kinase A and centrosomal protein 55. Survival analysis of these hub genes revealed that they were markedly associated with poor OS in patients with lung cancer. In summary, the hub genes and DEGs delineated in the research may aid the identification of potential targets for diagnostic and therapeutic strategies in lung cancer. D.A. Spandidos 2019-11 2019-09-04 /pmc/articles/PMC6781723/ /pubmed/31611952 http://dx.doi.org/10.3892/ol.2019.10796 Text en Copyright: © Li 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 Li, Zhenhua Sang, Meixiang Tian, Ziqiang Liu, Zhao Lv, Jian Zhang, Fan Shan, Baoen Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title | Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title_full | Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title_fullStr | Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title_full_unstemmed | Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title_short | Identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
title_sort | identification of key biomarkers and potential molecular mechanisms in lung cancer by bioinformatics analysis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781723/ https://www.ncbi.nlm.nih.gov/pubmed/31611952 http://dx.doi.org/10.3892/ol.2019.10796 |
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