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Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis

BACKGROUND: Ovarian cancer is a leading cause of the death from gynecologic malignancies. Hypoxia is closely related to the malignant growth of cells. However, the molecular mechanism of hypoxia-regulated ovarian cancer cells remains unclear. Thus, this study was conducted to identify the key genes...

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Autores principales: Zhang, Ke, Kong, Xiangjun, Feng, Guangde, Xiang, Wei, Chen, Long, Yang, Fang, Cao, Chunyu, Ding, Yifei, Chen, Hang, Chu, Mingxing, Wang, Pingqing, Zhang, Baoyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828062/
https://www.ncbi.nlm.nih.gov/pubmed/29482638
http://dx.doi.org/10.1186/s13048-018-0388-x
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author Zhang, Ke
Kong, Xiangjun
Feng, Guangde
Xiang, Wei
Chen, Long
Yang, Fang
Cao, Chunyu
Ding, Yifei
Chen, Hang
Chu, Mingxing
Wang, Pingqing
Zhang, Baoyun
author_facet Zhang, Ke
Kong, Xiangjun
Feng, Guangde
Xiang, Wei
Chen, Long
Yang, Fang
Cao, Chunyu
Ding, Yifei
Chen, Hang
Chu, Mingxing
Wang, Pingqing
Zhang, Baoyun
author_sort Zhang, Ke
collection PubMed
description BACKGROUND: Ovarian cancer is a leading cause of the death from gynecologic malignancies. Hypoxia is closely related to the malignant growth of cells. However, the molecular mechanism of hypoxia-regulated ovarian cancer cells remains unclear. Thus, this study was conducted to identify the key genes and pathways implicated in the regulation of hypoxia by bioinformatics analysis. METHODS: Using the datasets of GSE53012 downloaded from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were screened by comparing the RNA expression from cycling hypoxia group, chronic hypoxia group, and control group. Subsequently, cluster analysis was performed followed by the construction of the protein-protein interaction (PPI) network of the overlapping DEGs between the cycling hypoxia and chronic hypoxia using ClusterONE. In addition, gene ontology (GO) functional and pathway enrichment analyses of the DEGs in the most remarkable module were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. Ultimately, the signaling pathways associated with hypoxia were verified by RT-PCR, WB, and MTT assays. RESULTS: A total of 931 overlapping DEGs were identified. Nine hub genes and seven node genes were screened by analyzing the PPI and pathway integration networks, including ESR1, MMP2, ErbB2, MYC, VIM, CYBB, EDN1, SERPINE1, and PDK. Additionally, 11 key pathways closely associated with hypoxia were identified, including focal adhesion, ErbB signaling, and proteoglycans in cancer, among which the ErbB signaling pathway was verified by RT-PCR, WB, and MTT assays. Furthermore, functional enrichment analysis revealed that these genes were mainly involved in the proliferation of ovarian cancer cells, such as regulation of cell proliferation, cell adhesion, positive regulation of cell migration, focal adhesion, and extracellular matrix binding. CONCLUSION: The results show that hypoxia can promote the proliferation of ovarian cancer cells by affecting the invasion and adhesion functions through the dysregulation of ErbB signaling, which may be governed by the HIF-1α-TGFA-EGFR-ErbB2-MYC axis. These findings will contribute to the identification of new targets for the diagnosis and treatment of ovarian cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13048-018-0388-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-58280622018-02-28 Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis Zhang, Ke Kong, Xiangjun Feng, Guangde Xiang, Wei Chen, Long Yang, Fang Cao, Chunyu Ding, Yifei Chen, Hang Chu, Mingxing Wang, Pingqing Zhang, Baoyun J Ovarian Res Research BACKGROUND: Ovarian cancer is a leading cause of the death from gynecologic malignancies. Hypoxia is closely related to the malignant growth of cells. However, the molecular mechanism of hypoxia-regulated ovarian cancer cells remains unclear. Thus, this study was conducted to identify the key genes and pathways implicated in the regulation of hypoxia by bioinformatics analysis. METHODS: Using the datasets of GSE53012 downloaded from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were screened by comparing the RNA expression from cycling hypoxia group, chronic hypoxia group, and control group. Subsequently, cluster analysis was performed followed by the construction of the protein-protein interaction (PPI) network of the overlapping DEGs between the cycling hypoxia and chronic hypoxia using ClusterONE. In addition, gene ontology (GO) functional and pathway enrichment analyses of the DEGs in the most remarkable module were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. Ultimately, the signaling pathways associated with hypoxia were verified by RT-PCR, WB, and MTT assays. RESULTS: A total of 931 overlapping DEGs were identified. Nine hub genes and seven node genes were screened by analyzing the PPI and pathway integration networks, including ESR1, MMP2, ErbB2, MYC, VIM, CYBB, EDN1, SERPINE1, and PDK. Additionally, 11 key pathways closely associated with hypoxia were identified, including focal adhesion, ErbB signaling, and proteoglycans in cancer, among which the ErbB signaling pathway was verified by RT-PCR, WB, and MTT assays. Furthermore, functional enrichment analysis revealed that these genes were mainly involved in the proliferation of ovarian cancer cells, such as regulation of cell proliferation, cell adhesion, positive regulation of cell migration, focal adhesion, and extracellular matrix binding. CONCLUSION: The results show that hypoxia can promote the proliferation of ovarian cancer cells by affecting the invasion and adhesion functions through the dysregulation of ErbB signaling, which may be governed by the HIF-1α-TGFA-EGFR-ErbB2-MYC axis. These findings will contribute to the identification of new targets for the diagnosis and treatment of ovarian cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13048-018-0388-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-26 /pmc/articles/PMC5828062/ /pubmed/29482638 http://dx.doi.org/10.1186/s13048-018-0388-x Text en © The Author(s). 2018 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 Research
Zhang, Ke
Kong, Xiangjun
Feng, Guangde
Xiang, Wei
Chen, Long
Yang, Fang
Cao, Chunyu
Ding, Yifei
Chen, Hang
Chu, Mingxing
Wang, Pingqing
Zhang, Baoyun
Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_full Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_fullStr Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_full_unstemmed Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_short Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_sort investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828062/
https://www.ncbi.nlm.nih.gov/pubmed/29482638
http://dx.doi.org/10.1186/s13048-018-0388-x
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