Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools
Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used...
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| Veröffentlicht in: | Oxidative medicine and cellular longevity 2021, Vol.2021 (1), p.8846951-8846951 |
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| creator | Liu, Heyu Li, Lirong Fan, Yuan Lu, Yaping Zhu, Changhong Xia, Wei |
| description | Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer. |
| doi_str_mv | 10.1155/2021/8846951 |
| format | Article |
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To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.</description><identifier>ISSN: 1942-0900</identifier><identifier>EISSN: 1942-0994</identifier><identifier>DOI: 10.1155/2021/8846951</identifier><identifier>PMID: 34512870</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Bioinformatics ; Biomarkers ; Cancer therapies ; Chemotherapy ; Computational Biology - methods ; Datasets ; DNA methylation ; Gene expression ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Genomes ; Humans ; Male ; Medical prognosis ; Ontology ; Prostate cancer ; Prostatic Neoplasms - genetics ; Proteins ; Signal transduction ; Software ; Tumors</subject><ispartof>Oxidative medicine and cellular longevity, 2021, Vol.2021 (1), p.8846951-8846951</ispartof><rights>Copyright © 2021 Heyu Liu et al.</rights><rights>Copyright © 2021 Heyu Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Heyu Liu et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-b8eacea9dfd7bbbc7ad10dd8f864d850fabcb6faa9a0a6030865ee5b9d5a7a883</citedby><cites>FETCH-LOGICAL-c448t-b8eacea9dfd7bbbc7ad10dd8f864d850fabcb6faa9a0a6030865ee5b9d5a7a883</cites><orcidid>0000-0002-7462-2342 ; 0000-0003-1832-5033</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/PMC8426106/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8426106/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4024,27923,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34512870$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>LV, Xiangmin</contributor><contributor>Xiangmin LV</contributor><creatorcontrib>Liu, Heyu</creatorcontrib><creatorcontrib>Li, Lirong</creatorcontrib><creatorcontrib>Fan, Yuan</creatorcontrib><creatorcontrib>Lu, Yaping</creatorcontrib><creatorcontrib>Zhu, Changhong</creatorcontrib><creatorcontrib>Xia, Wei</creatorcontrib><title>Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools</title><title>Oxidative medicine and cellular longevity</title><addtitle>Oxid Med Cell Longev</addtitle><description>Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.</description><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Cancer therapies</subject><subject>Chemotherapy</subject><subject>Computational Biology - methods</subject><subject>Datasets</subject><subject>DNA methylation</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Regulatory Networks</subject><subject>Genomes</subject><subject>Humans</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Ontology</subject><subject>Prostate cancer</subject><subject>Prostatic Neoplasms - genetics</subject><subject>Proteins</subject><subject>Signal transduction</subject><subject>Software</subject><subject>Tumors</subject><issn>1942-0900</issn><issn>1942-0994</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kUFvEzEQhS1ERUvgxhlZ4oIEobZj73ovSBCVtlIFFWrP1qw9m7ps7NT2Uvj37DYhAg6cZqT55mnePEJecPaOc6WOBRP8WGtZNYo_Ike8kWLOmkY-3veMHZKnOd8yVi2E5E_I4UIqLnTNjkhcxpBLGmzxMdDY0ctYMBQPPT3FgPTkxyZhztMQgqNfcTX08MB-xnIf07dMfaCXKeYCBekSgsVEr7MPK_rRRx-6mNbjgs30KsY-PyMHHfQZn-_qjFx_Orlans0vvpyeLz9czK2UusxbjWARGte5um1bW4PjzDnd6Uo6rVgHrW2rDqABBhVbMF0pRNU2TkENWi9m5P1WdzO0a3R29JSgN5vk15B-mgje_D0J_sas4nejpaj4-KgZeb0TSPFuwFzM2meLfQ8B45CNULUQnOtGjeirf9DbOKQw2psoXquKqemit1vKjs_KCbv9MZyZKUkzJWl2SY74yz8N7OHf0Y3Amy1w44ODe_9_uV-_z6mj</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Liu, Heyu</creator><creator>Li, Lirong</creator><creator>Fan, Yuan</creator><creator>Lu, Yaping</creator><creator>Zhu, Changhong</creator><creator>Xia, Wei</creator><general>Hindawi</general><general>Hindawi Limited</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7462-2342</orcidid><orcidid>https://orcid.org/0000-0003-1832-5033</orcidid></search><sort><creationdate>2021</creationdate><title>Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools</title><author>Liu, Heyu ; Li, Lirong ; Fan, Yuan ; Lu, Yaping ; Zhu, Changhong ; Xia, Wei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-b8eacea9dfd7bbbc7ad10dd8f864d850fabcb6faa9a0a6030865ee5b9d5a7a883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bioinformatics</topic><topic>Biomarkers</topic><topic>Cancer therapies</topic><topic>Chemotherapy</topic><topic>Computational Biology - methods</topic><topic>Datasets</topic><topic>DNA methylation</topic><topic>Gene expression</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Gene Regulatory Networks</topic><topic>Genomes</topic><topic>Humans</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Ontology</topic><topic>Prostate cancer</topic><topic>Prostatic Neoplasms - genetics</topic><topic>Proteins</topic><topic>Signal transduction</topic><topic>Software</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Heyu</creatorcontrib><creatorcontrib>Li, Lirong</creatorcontrib><creatorcontrib>Fan, Yuan</creatorcontrib><creatorcontrib>Lu, Yaping</creatorcontrib><creatorcontrib>Zhu, Changhong</creatorcontrib><creatorcontrib>Xia, Wei</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>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>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Publicly Available Content Database</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 Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Oxidative medicine and cellular longevity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Heyu</au><au>Li, Lirong</au><au>Fan, Yuan</au><au>Lu, Yaping</au><au>Zhu, Changhong</au><au>Xia, Wei</au><au>LV, Xiangmin</au><au>Xiangmin LV</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools</atitle><jtitle>Oxidative medicine and cellular longevity</jtitle><addtitle>Oxid Med Cell Longev</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><issue>1</issue><spage>8846951</spage><epage>8846951</epage><pages>8846951-8846951</pages><issn>1942-0900</issn><eissn>1942-0994</eissn><abstract>Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34512870</pmid><doi>10.1155/2021/8846951</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-7462-2342</orcidid><orcidid>https://orcid.org/0000-0003-1832-5033</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Bioinformatics Biomarkers Cancer therapies Chemotherapy Computational Biology - methods Datasets DNA methylation Gene expression Gene Expression Regulation, Neoplastic Gene Regulatory Networks Genomes Humans Male Medical prognosis Ontology Prostate cancer Prostatic Neoplasms - genetics Proteins Signal transduction Software Tumors |
| title | Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools |
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