Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
Abstract 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 ke...
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doaj-3e66268a15964b2c8af2f159a01c0f312020-11-24T20:45:04ZengBMCJournal of Ovarian Research1757-22152018-02-0111111110.1186/s13048-018-0388-xInvestigation of hypoxia networks in ovarian cancer via bioinformatics analysisKe Zhang0Xiangjun Kong1Guangde Feng2Wei Xiang3Long Chen4Fang Yang5Chunyu Cao6Yifei Ding7Hang Chen8Mingxing Chu9Pingqing Wang10Baoyun Zhang11Bioengineering Institute of Chongqing UniversityState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of MacauSichuan TQLS Animal Husbandry Science and Technology Co., LtdBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityKey Laboratory of Farm Animal Genetic Resources and Germplasm Innovation of Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural SciencesBioengineering Institute of Chongqing UniversityBioengineering Institute of Chongqing UniversityAbstract 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.http://link.springer.com/article/10.1186/s13048-018-0388-xOvarian cancerBioinformatics analysesErbB signaling pathwayMolecular mechanismHypoxia |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ke Zhang Xiangjun Kong Guangde Feng Wei Xiang Long Chen Fang Yang Chunyu Cao Yifei Ding Hang Chen Mingxing Chu Pingqing Wang Baoyun Zhang |
spellingShingle |
Ke Zhang Xiangjun Kong Guangde Feng Wei Xiang Long Chen Fang Yang Chunyu Cao Yifei Ding Hang Chen Mingxing Chu Pingqing Wang Baoyun Zhang Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis Journal of Ovarian Research Ovarian cancer Bioinformatics analyses ErbB signaling pathway Molecular mechanism Hypoxia |
author_facet |
Ke Zhang Xiangjun Kong Guangde Feng Wei Xiang Long Chen Fang Yang Chunyu Cao Yifei Ding Hang Chen Mingxing Chu Pingqing Wang Baoyun Zhang |
author_sort |
Ke Zhang |
title |
Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis |
title_short |
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_sort |
investigation of hypoxia networks in ovarian cancer via bioinformatics analysis |
publisher |
BMC |
series |
Journal of Ovarian Research |
issn |
1757-2215 |
publishDate |
2018-02-01 |
description |
Abstract 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. |
topic |
Ovarian cancer Bioinformatics analyses ErbB signaling pathway Molecular mechanism Hypoxia |
url |
http://link.springer.com/article/10.1186/s13048-018-0388-x |
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