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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhang, Ke [verfasserIn] Kong, Xiangjun Feng, Guangde Xiang, Wei Chen, Long Yang, Fang Cao, Chunyu Ding, Yifei Chen, Hang Chu, Mingxing Wang, Pingqing Zhang, Baoyun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Ovarian cancer Bioinformatics analyses ErbB signaling pathway Molecular mechanism Hypoxia

Anmerkung:	© The Author(s). 2018

Übergeordnetes Werk:	Enthalten in: Journal of ovarian research - London : BioMed Central, 2008, 11(2018), 1 vom: 26. Feb.
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:1 ; day:26 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s13048-018-0388-x

Katalog-ID:	SPR029670675

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520			\|a 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.
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allfields	10.1186/s13048-018-0388-x doi (DE-627)SPR029670675 (SPR)s13048-018-0388-x-e DE-627 ger DE-627 rakwb eng Zhang, Ke verfasserin aut Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 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. Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Kong, Xiangjun aut Feng, Guangde aut Xiang, Wei aut Chen, Long aut Yang, Fang aut Cao, Chunyu aut Ding, Yifei aut Chen, Hang aut Chu, Mingxing aut Wang, Pingqing aut Zhang, Baoyun aut Enthalten in Journal of ovarian research London : BioMed Central, 2008 11(2018), 1 vom: 26. Feb. (DE-627)581041070 (DE-600)2455679-8 1757-2215 nnns volume:11 year:2018 number:1 day:26 month:02 https://dx.doi.org/10.1186/s13048-018-0388-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1 26 02
spelling	10.1186/s13048-018-0388-x doi (DE-627)SPR029670675 (SPR)s13048-018-0388-x-e DE-627 ger DE-627 rakwb eng Zhang, Ke verfasserin aut Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 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. Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Kong, Xiangjun aut Feng, Guangde aut Xiang, Wei aut Chen, Long aut Yang, Fang aut Cao, Chunyu aut Ding, Yifei aut Chen, Hang aut Chu, Mingxing aut Wang, Pingqing aut Zhang, Baoyun aut Enthalten in Journal of ovarian research London : BioMed Central, 2008 11(2018), 1 vom: 26. Feb. (DE-627)581041070 (DE-600)2455679-8 1757-2215 nnns volume:11 year:2018 number:1 day:26 month:02 https://dx.doi.org/10.1186/s13048-018-0388-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1 26 02
allfields_unstemmed	10.1186/s13048-018-0388-x doi (DE-627)SPR029670675 (SPR)s13048-018-0388-x-e DE-627 ger DE-627 rakwb eng Zhang, Ke verfasserin aut Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 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. Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Kong, Xiangjun aut Feng, Guangde aut Xiang, Wei aut Chen, Long aut Yang, Fang aut Cao, Chunyu aut Ding, Yifei aut Chen, Hang aut Chu, Mingxing aut Wang, Pingqing aut Zhang, Baoyun aut Enthalten in Journal of ovarian research London : BioMed Central, 2008 11(2018), 1 vom: 26. Feb. (DE-627)581041070 (DE-600)2455679-8 1757-2215 nnns volume:11 year:2018 number:1 day:26 month:02 https://dx.doi.org/10.1186/s13048-018-0388-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1 26 02
allfieldsGer	10.1186/s13048-018-0388-x doi (DE-627)SPR029670675 (SPR)s13048-018-0388-x-e DE-627 ger DE-627 rakwb eng Zhang, Ke verfasserin aut Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 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. Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Kong, Xiangjun aut Feng, Guangde aut Xiang, Wei aut Chen, Long aut Yang, Fang aut Cao, Chunyu aut Ding, Yifei aut Chen, Hang aut Chu, Mingxing aut Wang, Pingqing aut Zhang, Baoyun aut Enthalten in Journal of ovarian research London : BioMed Central, 2008 11(2018), 1 vom: 26. Feb. (DE-627)581041070 (DE-600)2455679-8 1757-2215 nnns volume:11 year:2018 number:1 day:26 month:02 https://dx.doi.org/10.1186/s13048-018-0388-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1 26 02
allfieldsSound	10.1186/s13048-018-0388-x doi (DE-627)SPR029670675 (SPR)s13048-018-0388-x-e DE-627 ger DE-627 rakwb eng Zhang, Ke verfasserin aut Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 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. Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213 Kong, Xiangjun aut Feng, Guangde aut Xiang, Wei aut Chen, Long aut Yang, Fang aut Cao, Chunyu aut Ding, Yifei aut Chen, Hang aut Chu, Mingxing aut Wang, Pingqing aut Zhang, Baoyun aut Enthalten in Journal of ovarian research London : BioMed Central, 2008 11(2018), 1 vom: 26. Feb. (DE-627)581041070 (DE-600)2455679-8 1757-2215 nnns volume:11 year:2018 number:1 day:26 month:02 https://dx.doi.org/10.1186/s13048-018-0388-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 1 26 02
language	English
source	Enthalten in Journal of ovarian research 11(2018), 1 vom: 26. Feb. volume:11 year:2018 number:1 day:26 month:02
sourceStr	Enthalten in Journal of ovarian research 11(2018), 1 vom: 26. Feb. volume:11 year:2018 number:1 day:26 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Ovarian cancer Bioinformatics analyses ErbB signaling pathway Molecular mechanism Hypoxia
isfreeaccess_bool	true
container_title	Journal of ovarian research
authorswithroles_txt_mv	Zhang, Ke @@aut@@ Kong, Xiangjun @@aut@@ Feng, Guangde @@aut@@ Xiang, Wei @@aut@@ Chen, Long @@aut@@ Yang, Fang @@aut@@ Cao, Chunyu @@aut@@ Ding, Yifei @@aut@@ Chen, Hang @@aut@@ Chu, Mingxing @@aut@@ Wang, Pingqing @@aut@@ Zhang, Baoyun @@aut@@
publishDateDaySort_date	2018-02-26T00:00:00Z
hierarchy_top_id	581041070
id	SPR029670675
language_de	englisch
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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. 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author	Zhang, Ke
spellingShingle	Zhang, Ke misc Ovarian cancer misc Bioinformatics analyses misc ErbB signaling pathway misc Molecular mechanism misc Hypoxia Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
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topic_title	Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis Ovarian cancer (dpeaa)DE-He213 Bioinformatics analyses (dpeaa)DE-He213 ErbB signaling pathway (dpeaa)DE-He213 Molecular mechanism (dpeaa)DE-He213 Hypoxia (dpeaa)DE-He213
topic	misc Ovarian cancer misc Bioinformatics analyses misc ErbB signaling pathway misc Molecular mechanism misc Hypoxia
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title	Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
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title_full	Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
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author_browse	Zhang, Ke Kong, Xiangjun Feng, Guangde Xiang, Wei Chen, Long Yang, Fang Cao, Chunyu Ding, Yifei Chen, Hang Chu, Mingxing Wang, Pingqing Zhang, Baoyun
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title_sort	investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
title_auth	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 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. © The Author(s). 2018
abstractGer	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. © The Author(s). 2018
abstract_unstemmed	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. © The Author(s). 2018
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title_short	Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis
url	https://dx.doi.org/10.1186/s13048-018-0388-x
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author2	Kong, Xiangjun Feng, Guangde Xiang, Wei Chen, Long Yang, Fang Cao, Chunyu Ding, Yifei Chen, Hang Chu, Mingxing Wang, Pingqing Zhang, Baoyun
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