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Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis

Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well u...

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Autores principales: Zhang, Nan, Zhang, Shao-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6732948/
https://www.ncbi.nlm.nih.gov/pubmed/31516588
http://dx.doi.org/10.3892/ol.2019.10723
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author Zhang, Nan
Zhang, Shao-Wei
author_facet Zhang, Nan
Zhang, Shao-Wei
author_sort Zhang, Nan
collection PubMed
description Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein-protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan-Meier plotter was used to analyze overall survival (OS). Additionally, one co-expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC.
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spelling pubmed-67329482019-09-12 Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis Zhang, Nan Zhang, Shao-Wei Oncol Lett Articles Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein-protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan-Meier plotter was used to analyze overall survival (OS). Additionally, one co-expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC. D.A. Spandidos 2019-10 2019-08-06 /pmc/articles/PMC6732948/ /pubmed/31516588 http://dx.doi.org/10.3892/ol.2019.10723 Text en Copyright: © Zhang 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
Zhang, Nan
Zhang, Shao-Wei
Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title_full Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title_fullStr Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title_full_unstemmed Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title_short Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
title_sort identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6732948/
https://www.ncbi.nlm.nih.gov/pubmed/31516588
http://dx.doi.org/10.3892/ol.2019.10723
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