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Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis
The underlying causes of esophageal cancer (EC) are unknown. To explore the molecular mechanisms that lead to EC, gene expression profiles of large cohorts of patients with EC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases (GSE5364, GSE20347 and GSE23400)....
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491038/ https://www.ncbi.nlm.nih.gov/pubmed/32963620 http://dx.doi.org/10.3892/ol.2020.12077 |
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author | Li, Jiangfen Xie, Yufang Wang, Xueli Jiang, Chenhao Yuan, Xin Zhang, Anzhi Yang, Lan Liu, Chunxia Zou, Hong Li, Feng Hu, Jianming |
author_facet | Li, Jiangfen Xie, Yufang Wang, Xueli Jiang, Chenhao Yuan, Xin Zhang, Anzhi Yang, Lan Liu, Chunxia Zou, Hong Li, Feng Hu, Jianming |
author_sort | Li, Jiangfen |
collection | PubMed |
description | The underlying causes of esophageal cancer (EC) are unknown. To explore the molecular mechanisms that lead to EC, gene expression profiles of large cohorts of patients with EC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases (GSE5364, GSE20347 and GSE23400). The present study identified 83 upregulated and 22 downregulated genes between EC and normal tissue using R statistical software and the GEO2R web tool. The Database for Annotation, Visualization and Integrated Discovery was used to identify the associated pathways, and for functional annotation of the differentially expressed genes (DEGs). Protein-protein interactions of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database, and hub genes were visualized using Cytoscape software. An online Kaplan-Meier plotter survival analysis tool was utilized to evaluate the prognostic value of hub gene expression in patients with EC. Further analysis of an additional dataset from GEO (GSE21293) revealed that these genes were associated with infiltration and metastasis in EC. In addition, the Gene Expression Profiling Interactive Analysis tool was used to evaluate expression levels of hub genes in patients with EC for different pathological stages. The Ualcan analysis tool was used to evaluate the expression levels of hub genes for different histological types. Overall, ubiquitin conjugating enzyme E2 C, cyclin dependent kinase inhibitor 3, CDC28 protein kinase regulatory subunit 2, kinesin family member 20A (KIF20A) and RAD51 associated protein 1 (RAD51AP1) were upregulated in EC tissues compared with normal tissues, and upregulation of these genes was a poor prognostic factor for patients with EC, indicating that these genes may mediate EC cell infiltration and metastasis. Among the hub genes, KIF-20A had potential value for predicting the pathological stage of EC. KIF20A and RAD51AP1 were more informative biomarkers of esophageal squamous cell carcinoma. Further studies are required to explore the value of these genes in the treatment of EC. |
format | Online Article Text |
id | pubmed-7491038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-74910382020-09-21 Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis Li, Jiangfen Xie, Yufang Wang, Xueli Jiang, Chenhao Yuan, Xin Zhang, Anzhi Yang, Lan Liu, Chunxia Zou, Hong Li, Feng Hu, Jianming Oncol Lett Articles The underlying causes of esophageal cancer (EC) are unknown. To explore the molecular mechanisms that lead to EC, gene expression profiles of large cohorts of patients with EC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus (GEO) databases (GSE5364, GSE20347 and GSE23400). The present study identified 83 upregulated and 22 downregulated genes between EC and normal tissue using R statistical software and the GEO2R web tool. The Database for Annotation, Visualization and Integrated Discovery was used to identify the associated pathways, and for functional annotation of the differentially expressed genes (DEGs). Protein-protein interactions of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database, and hub genes were visualized using Cytoscape software. An online Kaplan-Meier plotter survival analysis tool was utilized to evaluate the prognostic value of hub gene expression in patients with EC. Further analysis of an additional dataset from GEO (GSE21293) revealed that these genes were associated with infiltration and metastasis in EC. In addition, the Gene Expression Profiling Interactive Analysis tool was used to evaluate expression levels of hub genes in patients with EC for different pathological stages. The Ualcan analysis tool was used to evaluate the expression levels of hub genes for different histological types. Overall, ubiquitin conjugating enzyme E2 C, cyclin dependent kinase inhibitor 3, CDC28 protein kinase regulatory subunit 2, kinesin family member 20A (KIF20A) and RAD51 associated protein 1 (RAD51AP1) were upregulated in EC tissues compared with normal tissues, and upregulation of these genes was a poor prognostic factor for patients with EC, indicating that these genes may mediate EC cell infiltration and metastasis. Among the hub genes, KIF-20A had potential value for predicting the pathological stage of EC. KIF20A and RAD51AP1 were more informative biomarkers of esophageal squamous cell carcinoma. Further studies are required to explore the value of these genes in the treatment of EC. D.A. Spandidos 2020-11 2020-09-09 /pmc/articles/PMC7491038/ /pubmed/32963620 http://dx.doi.org/10.3892/ol.2020.12077 Text en Copyright: © Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Articles Li, Jiangfen Xie, Yufang Wang, Xueli Jiang, Chenhao Yuan, Xin Zhang, Anzhi Yang, Lan Liu, Chunxia Zou, Hong Li, Feng Hu, Jianming Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title | Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title_full | Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title_fullStr | Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title_full_unstemmed | Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title_short | Identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
title_sort | identification of hub genes associated with esophageal cancer progression using bioinformatics analysis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491038/ https://www.ncbi.nlm.nih.gov/pubmed/32963620 http://dx.doi.org/10.3892/ol.2020.12077 |
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