Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods
BACKGROUND Cervical cancer is one of the common gynecological tumors that seriously harm women's health, so it is particularly important to accurately explore the underlying mechanism of its occurrence and clinical prognosis. MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze t...
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| Veröffentlicht in: | Medical science monitor 2022-04, Vol.28, p.e934799-e934799 |
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| description | BACKGROUND Cervical cancer is one of the common gynecological tumors that seriously harm women's health, so it is particularly important to accurately explore the underlying mechanism of its occurrence and clinical prognosis. MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze the differentially expressed genes from the 4 databases: GSE6791, GSE9750, GSE63514, and GSE67522. Then, the DAVID website was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These protein-protein interaction (PPI) networks of DEGS were visualized and analyzed using the STRING website and the hub genes were further screened using the Cytohubba plugin. Lastly, the functions of the hub genes were further analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) online tools, Human Protein Atlas (HPA) databases, and the QuartataWeb database. RESULTS In the 4 Profile datasets, 101 cancer tissues and 67 normal tissues were collected. Among the 78 differentially expressed genes in the 4 datasets, 51 genes were upregulated and 27 genes were downregulated. The PPIs of these differentially expressed genes were visualized using Cytoscape and the Interaction Gene Search Tool (STRING). Then, further analysis of hub genes using the GEPIA tool and Kaplan-Meier curves that showed upregulation of CDK1 and PRC1 is associated with better survival, while AURKA is associated with worse survival. Among these hub genes, only AURKA was closely related to the prognosis of cervical cancer, and 21 potential drugs were found. CONCLUSIONS These results suggest that AURKA and its drug candidates can improve the individualized diagnosis and treatment of cervical cancer in the future. |
| doi_str_mv | 10.12659/MSM.934799 |
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MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze the differentially expressed genes from the 4 databases: GSE6791, GSE9750, GSE63514, and GSE67522. Then, the DAVID website was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These protein-protein interaction (PPI) networks of DEGS were visualized and analyzed using the STRING website and the hub genes were further screened using the Cytohubba plugin. Lastly, the functions of the hub genes were further analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) online tools, Human Protein Atlas (HPA) databases, and the QuartataWeb database. RESULTS In the 4 Profile datasets, 101 cancer tissues and 67 normal tissues were collected. Among the 78 differentially expressed genes in the 4 datasets, 51 genes were upregulated and 27 genes were downregulated. The PPIs of these differentially expressed genes were visualized using Cytoscape and the Interaction Gene Search Tool (STRING). Then, further analysis of hub genes using the GEPIA tool and Kaplan-Meier curves that showed upregulation of CDK1 and PRC1 is associated with better survival, while AURKA is associated with worse survival. Among these hub genes, only AURKA was closely related to the prognosis of cervical cancer, and 21 potential drugs were found. CONCLUSIONS These results suggest that AURKA and its drug candidates can improve the individualized diagnosis and treatment of cervical cancer in the future.</description><identifier>ISSN: 1643-3750</identifier><identifier>ISSN: 1234-1010</identifier><identifier>EISSN: 1643-3750</identifier><identifier>DOI: 10.12659/MSM.934799</identifier><identifier>PMID: 35428744</identifier><language>eng</language><publisher>United States: International Scientific Literature, Inc</publisher><subject>Aurora Kinase A - genetics ; Aurora Kinase A - metabolism ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Computational Biology - methods ; Database Analysis ; Databases, Genetic ; Female ; Gene Expression Profiling - methods ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Humans ; Protein Interaction Maps - genetics ; Uterine Cervical Neoplasms - genetics</subject><ispartof>Medical science monitor, 2022-04, Vol.28, p.e934799-e934799</ispartof><rights>Med Sci Monit, 2022 2022</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c381t-e1618ab96ccca723dda5e1e5291e776f462abea1229d8532a90d63a4f931c3743</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020271/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020271/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35428744$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Dan</creatorcontrib><creatorcontrib>Liu, Yanling</creatorcontrib><creatorcontrib>Cheng, Shuyu</creatorcontrib><creatorcontrib>Liu, Guoyan</creatorcontrib><title>Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods</title><title>Medical science monitor</title><addtitle>Med Sci Monit</addtitle><description>BACKGROUND Cervical cancer is one of the common gynecological tumors that seriously harm women's health, so it is particularly important to accurately explore the underlying mechanism of its occurrence and clinical prognosis. MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze the differentially expressed genes from the 4 databases: GSE6791, GSE9750, GSE63514, and GSE67522. Then, the DAVID website was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These protein-protein interaction (PPI) networks of DEGS were visualized and analyzed using the STRING website and the hub genes were further screened using the Cytohubba plugin. Lastly, the functions of the hub genes were further analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) online tools, Human Protein Atlas (HPA) databases, and the QuartataWeb database. RESULTS In the 4 Profile datasets, 101 cancer tissues and 67 normal tissues were collected. Among the 78 differentially expressed genes in the 4 datasets, 51 genes were upregulated and 27 genes were downregulated. The PPIs of these differentially expressed genes were visualized using Cytoscape and the Interaction Gene Search Tool (STRING). Then, further analysis of hub genes using the GEPIA tool and Kaplan-Meier curves that showed upregulation of CDK1 and PRC1 is associated with better survival, while AURKA is associated with worse survival. Among these hub genes, only AURKA was closely related to the prognosis of cervical cancer, and 21 potential drugs were found. CONCLUSIONS These results suggest that AURKA and its drug candidates can improve the individualized diagnosis and treatment of cervical cancer in the future.</description><subject>Aurora Kinase A - genetics</subject><subject>Aurora Kinase A - metabolism</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Computational Biology - methods</subject><subject>Database Analysis</subject><subject>Databases, Genetic</subject><subject>Female</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Gene Regulatory Networks</subject><subject>Humans</subject><subject>Protein Interaction Maps - genetics</subject><subject>Uterine Cervical Neoplasms - genetics</subject><issn>1643-3750</issn><issn>1234-1010</issn><issn>1643-3750</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkctLAzEQxoMo1tfJu-QoSDWvzW4ugtYnWD2o55Ams21km2iyLfjfG6wWPc3AfPPN44fQISWnlMlKnY2fx6eKi1qpDbRDpeBDXldk808-QLs5vxHCGkmqbTTglWBNLcQO0vcOQu9bb03vY8CxxY9xCR2-hQAZm-DwRc7RetODw1dpMc3YBzyCtCwtHR6ZYCHhySe-9NGHNqZ5MbIZj6GfRZf30VZrugwHP3EPvd5cv4zuhg9Pt_eji4eh5Q3th0AlbcxESWutqRl3zlRAoWKKQl3LVkhmJmAoY8o1FWdGESe5Ea3i1PJa8D10vvJ9X0zm4Gw5KplOvyc_N-lTR-P1_0rwMz2NS60II6ymxeD4xyDFjwXkXs99ttB1JkBcZF0-TWUjyuOK9GQltSnmnKBdj6FEfyPRBYleISnqo7-brbW_DPgXZ_OIFQ</recordid><startdate>20220416</startdate><enddate>20220416</enddate><creator>Wang, Dan</creator><creator>Liu, Yanling</creator><creator>Cheng, Shuyu</creator><creator>Liu, Guoyan</creator><general>International Scientific Literature, Inc</general><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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220416</creationdate><title>Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods</title><author>Wang, Dan ; Liu, Yanling ; Cheng, Shuyu ; Liu, Guoyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c381t-e1618ab96ccca723dda5e1e5291e776f462abea1229d8532a90d63a4f931c3743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aurora Kinase A - genetics</topic><topic>Aurora Kinase A - metabolism</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Computational Biology - methods</topic><topic>Database Analysis</topic><topic>Databases, Genetic</topic><topic>Female</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Gene Regulatory Networks</topic><topic>Humans</topic><topic>Protein Interaction Maps - genetics</topic><topic>Uterine Cervical Neoplasms - genetics</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Dan</creatorcontrib><creatorcontrib>Liu, Yanling</creatorcontrib><creatorcontrib>Cheng, Shuyu</creatorcontrib><creatorcontrib>Liu, Guoyan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medical science monitor</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Dan</au><au>Liu, Yanling</au><au>Cheng, Shuyu</au><au>Liu, Guoyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods</atitle><jtitle>Medical science monitor</jtitle><addtitle>Med Sci Monit</addtitle><date>2022-04-16</date><risdate>2022</risdate><volume>28</volume><spage>e934799</spage><epage>e934799</epage><pages>e934799-e934799</pages><issn>1643-3750</issn><issn>1234-1010</issn><eissn>1643-3750</eissn><abstract>BACKGROUND Cervical cancer is one of the common gynecological tumors that seriously harm women's health, so it is particularly important to accurately explore the underlying mechanism of its occurrence and clinical prognosis. MATERIAL AND METHODS In the GEO database, GEO2R was used to analyze the differentially expressed genes from the 4 databases: GSE6791, GSE9750, GSE63514, and GSE67522. Then, the DAVID website was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. These protein-protein interaction (PPI) networks of DEGS were visualized and analyzed using the STRING website and the hub genes were further screened using the Cytohubba plugin. Lastly, the functions of the hub genes were further analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) online tools, Human Protein Atlas (HPA) databases, and the QuartataWeb database. RESULTS In the 4 Profile datasets, 101 cancer tissues and 67 normal tissues were collected. Among the 78 differentially expressed genes in the 4 datasets, 51 genes were upregulated and 27 genes were downregulated. The PPIs of these differentially expressed genes were visualized using Cytoscape and the Interaction Gene Search Tool (STRING). Then, further analysis of hub genes using the GEPIA tool and Kaplan-Meier curves that showed upregulation of CDK1 and PRC1 is associated with better survival, while AURKA is associated with worse survival. Among these hub genes, only AURKA was closely related to the prognosis of cervical cancer, and 21 potential drugs were found. CONCLUSIONS These results suggest that AURKA and its drug candidates can improve the individualized diagnosis and treatment of cervical cancer in the future.</abstract><cop>United States</cop><pub>International Scientific Literature, Inc</pub><pmid>35428744</pmid><doi>10.12659/MSM.934799</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Aurora Kinase A - genetics Aurora Kinase A - metabolism Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Computational Biology - methods Database Analysis Databases, Genetic Female Gene Expression Profiling - methods Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Protein Interaction Maps - genetics Uterine Cervical Neoplasms - genetics |
| title | Identification of Novel Genes and Associated Drugs in Cervical Cancer by Bioinformatics Methods |
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