Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia
Abstract Background/Aims: The present study attempted to identify the potential key genes and pathways of hyperlipidemia, and to investigate the possible mechanisms associated with them. Methods: The array data of GSE3059 were downloaded, including thirteen samples of hyperlipidemia from the Gene Ex...
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| Veröffentlicht in: | Cellular Physiology and Biochemistry 2018-08, Vol.48 (3), p.1151-1163 |
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| creator | Miao, Liu Yin, Rui-Xing Pan, Shang-Ling Yang, Shuo Yang, De-Zhai Lin, Wei-Xiong |
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Background/Aims: The present study attempted to identify the potential key genes and pathways of hyperlipidemia, and to investigate the possible mechanisms associated with them. Methods: The array data of GSE3059 were downloaded, including thirteen samples of hyperlipidemia from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was performed with WGCNA package, and the salmon and midnight blue modules were found as the highest correlation. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these two modules were performed by cluster Profiler and DOSE package. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection. Results: Five genes (histone deacetylase 4, HDAC4; F2R like trypsin receptor 1, F2RL1; abhydrolase domain containing 2, ABHD2; transmembrane 4 L six family member 1, TM4SF1; and family with sequence similarity 13-member A, FAM13A) were found with a significant meaning. When their expression levels were validated with RT-qPCR, the relative expression levels were lower (HDAC4) and higher (F2RL1, ABHD2, TM4SF1 and FAM13A) in hyperlipidemia than in normal controls (P < 0.05-0.01). Subgroup analysis showed that the relative expression levels of HDAC4 were lower, whereas those of F2RL1 and ABHD2 were higher in Maonan than in Han ethnic groups (P < 0.05). Conclusion: Except for genetic factors and environmental exposures, epigenetic influence was another mechanism of hyperlipidemia in our study populations, which needed to further confirm. |
| doi_str_mv | 10.1159/000491982 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_karge</sourceid><recordid>TN_cdi_karger_primary_491982</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A637527260</galeid><doaj_id>oai_doaj_org_article_24900f55ea3c4b8d9c2d0b9ccc9bb0a2</doaj_id><sourcerecordid>A637527260</sourcerecordid><originalsourceid>FETCH-LOGICAL-c502t-65be711d27b06d8404befdce5beb20c30102ac00874e89be86dfd3deb4babe6f3</originalsourceid><addsrcrecordid>eNpdkktv1DAUhSNERUthwR4hS2xgkXLtPGwvR6O2M1ILEg-xjPy4GTzNxMFOVObf42mGWSAvfH30nWP72ln2hsIVpZX8BAClpFKwZ9kFLRnNJefieaqBVrmQgp9nL2PcQlpyyV5k50UyVEDri2z4iW7za0RLbrFHsvT59Z8hYIzO9-Qzjo8-PJBFr7p9dJGsLfajax1G8m1AkypD7r2duiSo3pLVpJ9yIvmKnTqkjp6s9gOGzg3O4s6pV9lZq7qIr4_zZfbj5vr7cpXffbldLxd3uamAjXldaeSUWsY11FaUUGpsrcEkawamAApMGQDBSxRSo6htawuLutRKY90Wl9l6zrVebZshuJ0K-8Yr1zwJPmwaFUZnOmxYKQHaqkJVmFILKw2zoKUxRmoNiqWsD3PWEPzvCePY7Fw02HWqRz_FhgGvJXAqDuj7_9Ctn0LqX6Io5ZSzUhSJupqpjUr7u771Y1AmjUOLjO-xdUlf1AWvGGc1JMPH2WCCjzFge7oRhebwB5rTH0jsu-MRJr1DeyL_PXoC3s7AgwobDCfg6P8LKga0uw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2117172483</pqid></control><display><type>article</type><title>Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia</title><source>Karger_OA刊</source><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>EZB Electronic Journals Library</source><creator>Miao, Liu ; Yin, Rui-Xing ; Pan, Shang-Ling ; Yang, Shuo ; Yang, De-Zhai ; Lin, Wei-Xiong</creator><creatorcontrib>Miao, Liu ; Yin, Rui-Xing ; Pan, Shang-Ling ; Yang, Shuo ; Yang, De-Zhai ; Lin, Wei-Xiong</creatorcontrib><description>Abstract
Background/Aims: The present study attempted to identify the potential key genes and pathways of hyperlipidemia, and to investigate the possible mechanisms associated with them. Methods: The array data of GSE3059 were downloaded, including thirteen samples of hyperlipidemia from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was performed with WGCNA package, and the salmon and midnight blue modules were found as the highest correlation. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these two modules were performed by cluster Profiler and DOSE package. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection. Results: Five genes (histone deacetylase 4, HDAC4; F2R like trypsin receptor 1, F2RL1; abhydrolase domain containing 2, ABHD2; transmembrane 4 L six family member 1, TM4SF1; and family with sequence similarity 13-member A, FAM13A) were found with a significant meaning. When their expression levels were validated with RT-qPCR, the relative expression levels were lower (HDAC4) and higher (F2RL1, ABHD2, TM4SF1 and FAM13A) in hyperlipidemia than in normal controls (P < 0.05-0.01). Subgroup analysis showed that the relative expression levels of HDAC4 were lower, whereas those of F2RL1 and ABHD2 were higher in Maonan than in Han ethnic groups (P < 0.05). Conclusion: Except for genetic factors and environmental exposures, epigenetic influence was another mechanism of hyperlipidemia in our study populations, which needed to further confirm.</description><identifier>ISSN: 1015-8987</identifier><identifier>EISSN: 1421-9778</identifier><identifier>DOI: 10.1159/000491982</identifier><identifier>PMID: 30045016</identifier><language>eng</language><publisher>Basel, Switzerland: S. Karger AG</publisher><subject>Adult ; Age ; Aged ; Analysis ; Apolipoproteins ; Array data ; Cardiovascular disease ; Cholesterol ; Chronic illnesses ; Clustering ; Coronary vessels ; Databases, Genetic ; Diabetes ; Down-Regulation ; Epigenetic influence ; Epigenetic inheritance ; Family medical history ; Female ; Gene expression ; Gene Expression Profiling ; Gene Ontology ; Gene Ontology annotation ; Gene Regulatory Networks ; Genomes ; Humans ; Hyperlipidemia ; Hyperlipidemias - genetics ; Hyperlipidemias - metabolism ; Identification and classification ; Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ; Lipids ; Low density lipoprotein ; Male ; Males ; Methods ; Middle Aged ; Minority & ethnic groups ; Original Paper ; Protein Interaction Maps ; Protein-protein interaction (PPI) network ; Protein-protein interactions ; Up-Regulation ; Weighted gene co-expression networks analyzed</subject><ispartof>Cellular Physiology and Biochemistry, 2018-08, Vol.48 (3), p.1151-1163</ispartof><rights>2018 The Author(s). Published by S. Karger AG, Basel</rights><rights>2018 The Author(s). Published by S. Karger AG, Basel.</rights><rights>COPYRIGHT 2018 S. Karger AG</rights><rights>2018 The Author(s). Published by S. Karger AG, Basel . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at: https://uk.sagepub.com/en-gb/eur/reusing-open-access-and-sage-choice-content</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-65be711d27b06d8404befdce5beb20c30102ac00874e89be86dfd3deb4babe6f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,2102,27635,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30045016$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Miao, Liu</creatorcontrib><creatorcontrib>Yin, Rui-Xing </creatorcontrib><creatorcontrib>Pan, Shang-Ling</creatorcontrib><creatorcontrib>Yang, Shuo</creatorcontrib><creatorcontrib>Yang, De-Zhai</creatorcontrib><creatorcontrib>Lin, Wei-Xiong</creatorcontrib><title>Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia</title><title>Cellular Physiology and Biochemistry</title><addtitle>Cell Physiol Biochem</addtitle><description>Abstract
Background/Aims: The present study attempted to identify the potential key genes and pathways of hyperlipidemia, and to investigate the possible mechanisms associated with them. Methods: The array data of GSE3059 were downloaded, including thirteen samples of hyperlipidemia from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was performed with WGCNA package, and the salmon and midnight blue modules were found as the highest correlation. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these two modules were performed by cluster Profiler and DOSE package. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection. Results: Five genes (histone deacetylase 4, HDAC4; F2R like trypsin receptor 1, F2RL1; abhydrolase domain containing 2, ABHD2; transmembrane 4 L six family member 1, TM4SF1; and family with sequence similarity 13-member A, FAM13A) were found with a significant meaning. When their expression levels were validated with RT-qPCR, the relative expression levels were lower (HDAC4) and higher (F2RL1, ABHD2, TM4SF1 and FAM13A) in hyperlipidemia than in normal controls (P < 0.05-0.01). Subgroup analysis showed that the relative expression levels of HDAC4 were lower, whereas those of F2RL1 and ABHD2 were higher in Maonan than in Han ethnic groups (P < 0.05). Conclusion: Except for genetic factors and environmental exposures, epigenetic influence was another mechanism of hyperlipidemia in our study populations, which needed to further confirm.</description><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Analysis</subject><subject>Apolipoproteins</subject><subject>Array data</subject><subject>Cardiovascular disease</subject><subject>Cholesterol</subject><subject>Chronic illnesses</subject><subject>Clustering</subject><subject>Coronary vessels</subject><subject>Databases, Genetic</subject><subject>Diabetes</subject><subject>Down-Regulation</subject><subject>Epigenetic influence</subject><subject>Epigenetic inheritance</subject><subject>Family medical history</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Ontology</subject><subject>Gene Ontology annotation</subject><subject>Gene Regulatory Networks</subject><subject>Genomes</subject><subject>Humans</subject><subject>Hyperlipidemia</subject><subject>Hyperlipidemias - genetics</subject><subject>Hyperlipidemias - metabolism</subject><subject>Identification and classification</subject><subject>Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway</subject><subject>Lipids</subject><subject>Low density lipoprotein</subject><subject>Male</subject><subject>Males</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Minority & ethnic groups</subject><subject>Original Paper</subject><subject>Protein Interaction Maps</subject><subject>Protein-protein interaction (PPI) network</subject><subject>Protein-protein interactions</subject><subject>Up-Regulation</subject><subject>Weighted gene co-expression networks analyzed</subject><issn>1015-8987</issn><issn>1421-9778</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>M--</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DOA</sourceid><recordid>eNpdkktv1DAUhSNERUthwR4hS2xgkXLtPGwvR6O2M1ILEg-xjPy4GTzNxMFOVObf42mGWSAvfH30nWP72ln2hsIVpZX8BAClpFKwZ9kFLRnNJefieaqBVrmQgp9nL2PcQlpyyV5k50UyVEDri2z4iW7za0RLbrFHsvT59Z8hYIzO9-Qzjo8-PJBFr7p9dJGsLfajax1G8m1AkypD7r2duiSo3pLVpJ9yIvmKnTqkjp6s9gOGzg3O4s6pV9lZq7qIr4_zZfbj5vr7cpXffbldLxd3uamAjXldaeSUWsY11FaUUGpsrcEkawamAApMGQDBSxRSo6htawuLutRKY90Wl9l6zrVebZshuJ0K-8Yr1zwJPmwaFUZnOmxYKQHaqkJVmFILKw2zoKUxRmoNiqWsD3PWEPzvCePY7Fw02HWqRz_FhgGvJXAqDuj7_9Ctn0LqX6Io5ZSzUhSJupqpjUr7u771Y1AmjUOLjO-xdUlf1AWvGGc1JMPH2WCCjzFge7oRhebwB5rTH0jsu-MRJr1DeyL_PXoC3s7AgwobDCfg6P8LKga0uw</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Miao, Liu</creator><creator>Yin, Rui-Xing </creator><creator>Pan, Shang-Ling</creator><creator>Yang, Shuo</creator><creator>Yang, De-Zhai</creator><creator>Lin, Wei-Xiong</creator><general>S. Karger AG</general><general>Cell Physiol Biochem Press GmbH & Co KG</general><scope>M--</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20180801</creationdate><title>Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia</title><author>Miao, Liu ; Yin, Rui-Xing ; Pan, Shang-Ling ; Yang, Shuo ; Yang, De-Zhai ; Lin, Wei-Xiong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-65be711d27b06d8404befdce5beb20c30102ac00874e89be86dfd3deb4babe6f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Analysis</topic><topic>Apolipoproteins</topic><topic>Array data</topic><topic>Cardiovascular disease</topic><topic>Cholesterol</topic><topic>Chronic illnesses</topic><topic>Clustering</topic><topic>Coronary vessels</topic><topic>Databases, Genetic</topic><topic>Diabetes</topic><topic>Down-Regulation</topic><topic>Epigenetic influence</topic><topic>Epigenetic inheritance</topic><topic>Family medical history</topic><topic>Female</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Ontology</topic><topic>Gene Ontology annotation</topic><topic>Gene Regulatory Networks</topic><topic>Genomes</topic><topic>Humans</topic><topic>Hyperlipidemia</topic><topic>Hyperlipidemias - genetics</topic><topic>Hyperlipidemias - metabolism</topic><topic>Identification and classification</topic><topic>Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway</topic><topic>Lipids</topic><topic>Low density lipoprotein</topic><topic>Male</topic><topic>Males</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Minority & ethnic groups</topic><topic>Original Paper</topic><topic>Protein Interaction Maps</topic><topic>Protein-protein interaction (PPI) network</topic><topic>Protein-protein interactions</topic><topic>Up-Regulation</topic><topic>Weighted gene co-expression networks analyzed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Miao, Liu</creatorcontrib><creatorcontrib>Yin, Rui-Xing </creatorcontrib><creatorcontrib>Pan, Shang-Ling</creatorcontrib><creatorcontrib>Yang, Shuo</creatorcontrib><creatorcontrib>Yang, De-Zhai</creatorcontrib><creatorcontrib>Lin, Wei-Xiong</creatorcontrib><collection>Karger_OA刊</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile</collection><collection>ProQuest Central (Corporate)</collection><collection>Health Medical collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Cellular Physiology and Biochemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Miao, Liu</au><au>Yin, Rui-Xing </au><au>Pan, Shang-Ling</au><au>Yang, Shuo</au><au>Yang, De-Zhai</au><au>Lin, Wei-Xiong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia</atitle><jtitle>Cellular Physiology and Biochemistry</jtitle><addtitle>Cell Physiol Biochem</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>48</volume><issue>3</issue><spage>1151</spage><epage>1163</epage><pages>1151-1163</pages><issn>1015-8987</issn><eissn>1421-9778</eissn><abstract>Abstract
Background/Aims: The present study attempted to identify the potential key genes and pathways of hyperlipidemia, and to investigate the possible mechanisms associated with them. Methods: The array data of GSE3059 were downloaded, including thirteen samples of hyperlipidemia from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was performed with WGCNA package, and the salmon and midnight blue modules were found as the highest correlation. Gene Ontology annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for these two modules were performed by cluster Profiler and DOSE package. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection. Results: Five genes (histone deacetylase 4, HDAC4; F2R like trypsin receptor 1, F2RL1; abhydrolase domain containing 2, ABHD2; transmembrane 4 L six family member 1, TM4SF1; and family with sequence similarity 13-member A, FAM13A) were found with a significant meaning. When their expression levels were validated with RT-qPCR, the relative expression levels were lower (HDAC4) and higher (F2RL1, ABHD2, TM4SF1 and FAM13A) in hyperlipidemia than in normal controls (P < 0.05-0.01). Subgroup analysis showed that the relative expression levels of HDAC4 were lower, whereas those of F2RL1 and ABHD2 were higher in Maonan than in Han ethnic groups (P < 0.05). Conclusion: Except for genetic factors and environmental exposures, epigenetic influence was another mechanism of hyperlipidemia in our study populations, which needed to further confirm.</abstract><cop>Basel, Switzerland</cop><pub>S. Karger AG</pub><pmid>30045016</pmid><doi>10.1159/000491982</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Age Aged Analysis Apolipoproteins Array data Cardiovascular disease Cholesterol Chronic illnesses Clustering Coronary vessels Databases, Genetic Diabetes Down-Regulation Epigenetic influence Epigenetic inheritance Family medical history Female Gene expression Gene Expression Profiling Gene Ontology Gene Ontology annotation Gene Regulatory Networks Genomes Humans Hyperlipidemia Hyperlipidemias - genetics Hyperlipidemias - metabolism Identification and classification Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway Lipids Low density lipoprotein Male Males Methods Middle Aged Minority & ethnic groups Original Paper Protein Interaction Maps Protein-protein interaction (PPI) network Protein-protein interactions Up-Regulation Weighted gene co-expression networks analyzed |
| title | Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia |
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