Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation
Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO...
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description | Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment. |
doi_str_mv | 10.1155/2021/4530180 |
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fullrecord | <record><control><sourceid>pubmed_cross</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8741379</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>35003319</sourcerecordid><originalsourceid>FETCH-LOGICAL-c387t-7e1bfa3316c51f6cc2923be3416e49e3d38107d8476bdddb3bbc2dcaab20d93f3</originalsourceid><addsrcrecordid>eNp9kE1Lw0AQhhdRbK3ePMveNXYnm2STi1CKrULFHhS8LfsVs5Juym7a0n9vSmvRi6cZmHeeGR6EroHcA6TpMCYxDJOUEsjJCeoDS_IoY5CfHnvy0UMXIXwRkgJL4Rz1aEoIpVD00cu4WSy9qYwLdm3wyIl6G2zATYnnxrfWGdfiqXEmYOE0nou22ohtwNbhUeutqPHESm_rWrS2cZforBR1MFeHOkDvk8e38VM0e50-j0ezSNGctREzIEvRPZCpFMpMqbiIqTQ0gcwkhaGa5kCYzhOWSa21pFKqWCshZEx0QUs6QA977nIlF0ar7kkvar70diH8ljfC8r8TZyv-2ax5zhKgrOgAd3uA8k0I3pTHXSB8p5XvtPKD1i5-8_veMfzjsQvc7gOVdVps7P-4b2lEgd0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central Open Access</source><source>Wiley Online Library Open Access</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Wang, Yanzhe ; Cai, Wenjuan ; Gu, Liya ; Ji, Xuefeng ; Shen, Qiusheng</creator><contributor>Shi, Jianxin</contributor><creatorcontrib>Wang, Yanzhe ; Cai, Wenjuan ; Gu, Liya ; Ji, Xuefeng ; Shen, Qiusheng ; Shi, Jianxin</creatorcontrib><description>Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.</description><identifier>ISSN: 1748-670X</identifier><identifier>EISSN: 1748-6718</identifier><identifier>DOI: 10.1155/2021/4530180</identifier><identifier>PMID: 35003319</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Adaptor Proteins, Signal Transducing - antagonists & inhibitors ; Adaptor Proteins, Signal Transducing - genetics ; Atrial Fibrillation - genetics ; Atrial Fibrillation - metabolism ; Atrial Fibrillation - pathology ; Cell Cycle Proteins - antagonists & inhibitors ; Cell Cycle Proteins - genetics ; Cell Line ; Cell Proliferation - genetics ; Computational Biology ; Down-Regulation ; Gene Knockdown Techniques ; Gene Ontology ; Gene Regulatory Networks ; Humans ; Protein Interaction Maps - genetics ; Signal Transduction - genetics ; Software ; Up-Regulation</subject><ispartof>Computational and mathematical methods in medicine, 2021-12, Vol.2021, p.4530180-20</ispartof><rights>Copyright © 2021 Yanzhe Wang et al.</rights><rights>Copyright © 2021 Yanzhe Wang et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-7e1bfa3316c51f6cc2923be3416e49e3d38107d8476bdddb3bbc2dcaab20d93f3</citedby><cites>FETCH-LOGICAL-c387t-7e1bfa3316c51f6cc2923be3416e49e3d38107d8476bdddb3bbc2dcaab20d93f3</cites><orcidid>0000-0001-9999-4874 ; 0000-0002-4873-4578 ; 0000-0002-8041-3651</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741379/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741379/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35003319$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Shi, Jianxin</contributor><creatorcontrib>Wang, Yanzhe</creatorcontrib><creatorcontrib>Cai, Wenjuan</creatorcontrib><creatorcontrib>Gu, Liya</creatorcontrib><creatorcontrib>Ji, Xuefeng</creatorcontrib><creatorcontrib>Shen, Qiusheng</creatorcontrib><title>Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation</title><title>Computational and mathematical methods in medicine</title><addtitle>Comput Math Methods Med</addtitle><description>Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.</description><subject>Adaptor Proteins, Signal Transducing - antagonists & inhibitors</subject><subject>Adaptor Proteins, Signal Transducing - genetics</subject><subject>Atrial Fibrillation - genetics</subject><subject>Atrial Fibrillation - metabolism</subject><subject>Atrial Fibrillation - pathology</subject><subject>Cell Cycle Proteins - antagonists & inhibitors</subject><subject>Cell Cycle Proteins - genetics</subject><subject>Cell Line</subject><subject>Cell Proliferation - genetics</subject><subject>Computational Biology</subject><subject>Down-Regulation</subject><subject>Gene Knockdown Techniques</subject><subject>Gene Ontology</subject><subject>Gene Regulatory Networks</subject><subject>Humans</subject><subject>Protein Interaction Maps - genetics</subject><subject>Signal Transduction - genetics</subject><subject>Software</subject><subject>Up-Regulation</subject><issn>1748-670X</issn><issn>1748-6718</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kE1Lw0AQhhdRbK3ePMveNXYnm2STi1CKrULFHhS8LfsVs5Juym7a0n9vSmvRi6cZmHeeGR6EroHcA6TpMCYxDJOUEsjJCeoDS_IoY5CfHnvy0UMXIXwRkgJL4Rz1aEoIpVD00cu4WSy9qYwLdm3wyIl6G2zATYnnxrfWGdfiqXEmYOE0nou22ohtwNbhUeutqPHESm_rWrS2cZforBR1MFeHOkDvk8e38VM0e50-j0ezSNGctREzIEvRPZCpFMpMqbiIqTQ0gcwkhaGa5kCYzhOWSa21pFKqWCshZEx0QUs6QA977nIlF0ar7kkvar70diH8ljfC8r8TZyv-2ax5zhKgrOgAd3uA8k0I3pTHXSB8p5XvtPKD1i5-8_veMfzjsQvc7gOVdVps7P-4b2lEgd0</recordid><startdate>20211231</startdate><enddate>20211231</enddate><creator>Wang, Yanzhe</creator><creator>Cai, Wenjuan</creator><creator>Gu, Liya</creator><creator>Ji, Xuefeng</creator><creator>Shen, Qiusheng</creator><general>Hindawi</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</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>5PM</scope><orcidid>https://orcid.org/0000-0001-9999-4874</orcidid><orcidid>https://orcid.org/0000-0002-4873-4578</orcidid><orcidid>https://orcid.org/0000-0002-8041-3651</orcidid></search><sort><creationdate>20211231</creationdate><title>Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation</title><author>Wang, Yanzhe ; Cai, Wenjuan ; Gu, Liya ; Ji, Xuefeng ; Shen, Qiusheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-7e1bfa3316c51f6cc2923be3416e49e3d38107d8476bdddb3bbc2dcaab20d93f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptor Proteins, Signal Transducing - antagonists & inhibitors</topic><topic>Adaptor Proteins, Signal Transducing - genetics</topic><topic>Atrial Fibrillation - genetics</topic><topic>Atrial Fibrillation - metabolism</topic><topic>Atrial Fibrillation - pathology</topic><topic>Cell Cycle Proteins - antagonists & inhibitors</topic><topic>Cell Cycle Proteins - genetics</topic><topic>Cell Line</topic><topic>Cell Proliferation - genetics</topic><topic>Computational Biology</topic><topic>Down-Regulation</topic><topic>Gene Knockdown Techniques</topic><topic>Gene Ontology</topic><topic>Gene Regulatory Networks</topic><topic>Humans</topic><topic>Protein Interaction Maps - genetics</topic><topic>Signal Transduction - genetics</topic><topic>Software</topic><topic>Up-Regulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Yanzhe</creatorcontrib><creatorcontrib>Cai, Wenjuan</creatorcontrib><creatorcontrib>Gu, Liya</creatorcontrib><creatorcontrib>Ji, Xuefeng</creatorcontrib><creatorcontrib>Shen, Qiusheng</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Computational and mathematical methods in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yanzhe</au><au>Cai, Wenjuan</au><au>Gu, Liya</au><au>Ji, Xuefeng</au><au>Shen, Qiusheng</au><au>Shi, Jianxin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation</atitle><jtitle>Computational and mathematical methods in medicine</jtitle><addtitle>Comput Math Methods Med</addtitle><date>2021-12-31</date><risdate>2021</risdate><volume>2021</volume><spage>4530180</spage><epage>20</epage><pages>4530180-20</pages><issn>1748-670X</issn><eissn>1748-6718</eissn><abstract>Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>35003319</pmid><doi>10.1155/2021/4530180</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0001-9999-4874</orcidid><orcidid>https://orcid.org/0000-0002-4873-4578</orcidid><orcidid>https://orcid.org/0000-0002-8041-3651</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptor Proteins, Signal Transducing - antagonists & inhibitors Adaptor Proteins, Signal Transducing - genetics Atrial Fibrillation - genetics Atrial Fibrillation - metabolism Atrial Fibrillation - pathology Cell Cycle Proteins - antagonists & inhibitors Cell Cycle Proteins - genetics Cell Line Cell Proliferation - genetics Computational Biology Down-Regulation Gene Knockdown Techniques Gene Ontology Gene Regulatory Networks Humans Protein Interaction Maps - genetics Signal Transduction - genetics Software Up-Regulation |
title | Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation |
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