Integrated analysis of gene expression changes associated with coronary artery disease

Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD s...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Miao, Liu [verfasserIn] Yin, Rui-Xing Huang, Feng Yang, Shuo Chen, Wu-Xian Wu, Jin-Zhen

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Array data Gene ontology annotation Kyoto encyclopedia of genes and genomes (KEGG) pathway Database for annotation Visualization and integrated discovery Protein-protein interaction (PPI) network Gene expression and cox proportional hazards regression

Anmerkung:	© The Author(s). 2019

Übergeordnetes Werk:	Enthalten in: Lipids in health and disease - London : Biomed Central, 2002, 18(2019), 1 vom: 09. Apr.
Übergeordnetes Werk:	volume:18 ; year:2019 ; number:1 ; day:09 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s12944-019-1032-5

Katalog-ID:	SPR02936048X

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520			\|a Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction.
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allfields	10.1186/s12944-019-1032-5 doi (DE-627)SPR02936048X (SPR)s12944-019-1032-5-e DE-627 ger DE-627 rakwb eng Miao, Liu verfasserin (orcid)0000-0001-6642-7005 aut Integrated analysis of gene expression changes associated with coronary artery disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213 Yin, Rui-Xing (orcid)0000-0001-7883-4310 aut Huang, Feng aut Yang, Shuo aut Chen, Wu-Xian aut Wu, Jin-Zhen aut Enthalten in Lipids in health and disease London : Biomed Central, 2002 18(2019), 1 vom: 09. Apr. (DE-627)355987694 (DE-600)2091381-3 1476-511X nnns volume:18 year:2019 number:1 day:09 month:04 https://dx.doi.org/10.1186/s12944-019-1032-5 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 18 2019 1 09 04
spelling	10.1186/s12944-019-1032-5 doi (DE-627)SPR02936048X (SPR)s12944-019-1032-5-e DE-627 ger DE-627 rakwb eng Miao, Liu verfasserin (orcid)0000-0001-6642-7005 aut Integrated analysis of gene expression changes associated with coronary artery disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213 Yin, Rui-Xing (orcid)0000-0001-7883-4310 aut Huang, Feng aut Yang, Shuo aut Chen, Wu-Xian aut Wu, Jin-Zhen aut Enthalten in Lipids in health and disease London : Biomed Central, 2002 18(2019), 1 vom: 09. Apr. (DE-627)355987694 (DE-600)2091381-3 1476-511X nnns volume:18 year:2019 number:1 day:09 month:04 https://dx.doi.org/10.1186/s12944-019-1032-5 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 18 2019 1 09 04
allfields_unstemmed	10.1186/s12944-019-1032-5 doi (DE-627)SPR02936048X (SPR)s12944-019-1032-5-e DE-627 ger DE-627 rakwb eng Miao, Liu verfasserin (orcid)0000-0001-6642-7005 aut Integrated analysis of gene expression changes associated with coronary artery disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213 Yin, Rui-Xing (orcid)0000-0001-7883-4310 aut Huang, Feng aut Yang, Shuo aut Chen, Wu-Xian aut Wu, Jin-Zhen aut Enthalten in Lipids in health and disease London : Biomed Central, 2002 18(2019), 1 vom: 09. Apr. (DE-627)355987694 (DE-600)2091381-3 1476-511X nnns volume:18 year:2019 number:1 day:09 month:04 https://dx.doi.org/10.1186/s12944-019-1032-5 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 18 2019 1 09 04
allfieldsGer	10.1186/s12944-019-1032-5 doi (DE-627)SPR02936048X (SPR)s12944-019-1032-5-e DE-627 ger DE-627 rakwb eng Miao, Liu verfasserin (orcid)0000-0001-6642-7005 aut Integrated analysis of gene expression changes associated with coronary artery disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213 Yin, Rui-Xing (orcid)0000-0001-7883-4310 aut Huang, Feng aut Yang, Shuo aut Chen, Wu-Xian aut Wu, Jin-Zhen aut Enthalten in Lipids in health and disease London : Biomed Central, 2002 18(2019), 1 vom: 09. Apr. (DE-627)355987694 (DE-600)2091381-3 1476-511X nnns volume:18 year:2019 number:1 day:09 month:04 https://dx.doi.org/10.1186/s12944-019-1032-5 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 18 2019 1 09 04
allfieldsSound	10.1186/s12944-019-1032-5 doi (DE-627)SPR02936048X (SPR)s12944-019-1032-5-e DE-627 ger DE-627 rakwb eng Miao, Liu verfasserin (orcid)0000-0001-6642-7005 aut Integrated analysis of gene expression changes associated with coronary artery disease 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213 Yin, Rui-Xing (orcid)0000-0001-7883-4310 aut Huang, Feng aut Yang, Shuo aut Chen, Wu-Xian aut Wu, Jin-Zhen aut Enthalten in Lipids in health and disease London : Biomed Central, 2002 18(2019), 1 vom: 09. Apr. (DE-627)355987694 (DE-600)2091381-3 1476-511X nnns volume:18 year:2019 number:1 day:09 month:04 https://dx.doi.org/10.1186/s12944-019-1032-5 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 18 2019 1 09 04
language	English
source	Enthalten in Lipids in health and disease 18(2019), 1 vom: 09. Apr. volume:18 year:2019 number:1 day:09 month:04
sourceStr	Enthalten in Lipids in health and disease 18(2019), 1 vom: 09. Apr. volume:18 year:2019 number:1 day:09 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Array data Gene ontology annotation Kyoto encyclopedia of genes and genomes (KEGG) pathway Database for annotation Visualization and integrated discovery Protein-protein interaction (PPI) network Gene expression and cox proportional hazards regression
isfreeaccess_bool	true
container_title	Lipids in health and disease
authorswithroles_txt_mv	Miao, Liu @@aut@@ Yin, Rui-Xing @@aut@@ Huang, Feng @@aut@@ Yang, Shuo @@aut@@ Chen, Wu-Xian @@aut@@ Wu, Jin-Zhen @@aut@@
publishDateDaySort_date	2019-04-09T00:00:00Z
hierarchy_top_id	355987694
id	SPR02936048X
language_de	englisch
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Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Array data</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gene ontology annotation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kyoto encyclopedia of genes and genomes (KEGG) pathway</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Database for annotation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Visualization and integrated discovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Protein-protein interaction (PPI) network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gene expression and cox proportional hazards regression</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Rui-Xing</subfield><subfield code="0">(orcid)0000-0001-7883-4310</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Feng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Shuo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Wu-Xian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Jin-Zhen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Lipids in health and disease</subfield><subfield code="d">London : Biomed Central, 2002</subfield><subfield code="g">18(2019), 1 vom: 09. 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author	Miao, Liu
spellingShingle	Miao, Liu misc Array data misc Gene ontology annotation misc Kyoto encyclopedia of genes and genomes (KEGG) pathway misc Database for annotation misc Visualization and integrated discovery misc Protein-protein interaction (PPI) network misc Gene expression and cox proportional hazards regression Integrated analysis of gene expression changes associated with coronary artery disease
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topic_title	Integrated analysis of gene expression changes associated with coronary artery disease Array data (dpeaa)DE-He213 Gene ontology annotation (dpeaa)DE-He213 Kyoto encyclopedia of genes and genomes (KEGG) pathway (dpeaa)DE-He213 Database for annotation (dpeaa)DE-He213 Visualization and integrated discovery (dpeaa)DE-He213 Protein-protein interaction (PPI) network (dpeaa)DE-He213 Gene expression and cox proportional hazards regression (dpeaa)DE-He213
topic	misc Array data misc Gene ontology annotation misc Kyoto encyclopedia of genes and genomes (KEGG) pathway misc Database for annotation misc Visualization and integrated discovery misc Protein-protein interaction (PPI) network misc Gene expression and cox proportional hazards regression
topic_unstemmed	misc Array data misc Gene ontology annotation misc Kyoto encyclopedia of genes and genomes (KEGG) pathway misc Database for annotation misc Visualization and integrated discovery misc Protein-protein interaction (PPI) network misc Gene expression and cox proportional hazards regression
topic_browse	misc Array data misc Gene ontology annotation misc Kyoto encyclopedia of genes and genomes (KEGG) pathway misc Database for annotation misc Visualization and integrated discovery misc Protein-protein interaction (PPI) network misc Gene expression and cox proportional hazards regression
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title	Integrated analysis of gene expression changes associated with coronary artery disease
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title_full	Integrated analysis of gene expression changes associated with coronary artery disease
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author_browse	Miao, Liu Yin, Rui-Xing Huang, Feng Yang, Shuo Chen, Wu-Xian Wu, Jin-Zhen
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title_sort	integrated analysis of gene expression changes associated with coronary artery disease
title_auth	Integrated analysis of gene expression changes associated with coronary artery disease
abstract	Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. © The Author(s). 2019
abstractGer	Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. © The Author(s). 2019
abstract_unstemmed	Background This study investigated the pathways and genes involved in coronary artery disease (CAD) and the associated mechanisms. Methods Two array data sets of GSE19339 and GSE56885 were downloaded. The limma package was used to analyze the differentially expressed genes (DEGs) in normal and CAD specimens. Examination of DEGs through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology annotation was achieved by Database for Annotation, Visualization and Integrated Discovery (DAVID). The Cytoscape software facilitated the establishment of the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) was performed for the significant modules. Results We identified 413 DEGs (291 up-regulated and 122 down-regulated). Approximately 256 biological processes, only 1 cellular component, and 21 molecular functions were identified by GO analysis and 10 pathways were enriched by KEGG. Moreover, 264 protein pairs and 64 nodes were visualized by the PPI network. After the MCODE analysis, the top 4 high degree genes, including interleukin 1 beta (IL1B, degree = 29), intercellular adhesion molecule 1 (ICAM1, degree = 25), Jun proto-oncogene (JUN, degree = 23) and C-C motif chemokine ligand 2 (CCL2, degree = 20) had been identified to validate in RT-PCR and Cox proportional hazards regression between CAD and normals. Conclusions The relative expression of IL1B, ICAM1 and CCL2 was higher in CAD than in normal controls (P < 0.05–0.001), but only IL1B and CCL2 genes were confirmed after testing the gene expression in blood and/or analyzing in Cox proportional hazards regression (P < 0.05–0.001), and the proper mechanism may involve in the AGE-RAGE signaling pathway, fluid shear stress, the tumor necrosis factor (TNF) and cytokine-cytokine receptor interaction. © The Author(s). 2019
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title_short	Integrated analysis of gene expression changes associated with coronary artery disease
url	https://dx.doi.org/10.1186/s12944-019-1032-5
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author2	Yin, Rui-Xing Huang, Feng Yang, Shuo Chen, Wu-Xian Wu, Jin-Zhen
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Integrated analysis of gene expression changes associated with coronary artery disease

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