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MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer
Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent...
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Published in: | BioMed research international 2020, Vol.2020 (2020), p.1-20 |
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description | Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC. |
doi_str_mv | 10.1155/2020/8604340 |
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Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2020/8604340</identifier><identifier>PMID: 32420375</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>1-Phosphatidylinositol 3-kinase ; AKT protein ; Biological markers ; Biomarkers ; Biomarkers, Tumor - genetics ; Biomarkers, Tumor - metabolism ; Cancer ; Chromatids ; Databases, Nucleic Acid ; Datasets ; Diagnosis ; Diagnostic systems ; Disease-Free Survival ; Encyclopedias ; Enrichment ; Extracellular matrix ; Female ; Gene expression ; Genes ; Genetic aspects ; Genetic diversity ; Genomes ; Genomics ; Humans ; Kinases ; Male ; Medical prognosis ; Microtubule-Associated Proteins - genetics ; Microtubule-Associated Proteins - metabolism ; Minichromosome Maintenance Complex Component 2 - genetics ; Minichromosome Maintenance Complex Component 2 - metabolism ; Molecular modelling ; Neoplasm Proteins - genetics ; Neoplasm Proteins - metabolism ; Pancreatic cancer ; Pancreatic Neoplasms - diagnosis ; Pancreatic Neoplasms - genetics ; Pancreatic Neoplasms - metabolism ; Pancreatic Neoplasms - mortality ; Pathogenesis ; Prognosis ; Protein interaction ; Protein-protein interactions ; Proteins ; RNA ; Sister chromatids ; Software ; Survival Rate ; Tumors</subject><ispartof>BioMed research international, 2020, Vol.2020 (2020), p.1-20</ispartof><rights>Copyright © 2020 Yajun Deng et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Yajun Deng 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. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2020 Yajun Deng et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c461t-b98a3bef2229004b8d789e4c1942969de30a77a45065c731e1b6f85f294315dc3</citedby><cites>FETCH-LOGICAL-c461t-b98a3bef2229004b8d789e4c1942969de30a77a45065c731e1b6f85f294315dc3</cites><orcidid>0000-0002-8689-7634 ; 0000-0003-4099-5287 ; 0000-0003-0953-6525 ; 0000-0003-2191-7910</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2400274371/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2400274371?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,4010,25731,27900,27901,27902,36989,36990,44566,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32420375$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Dasgupta, Bhaskar</contributor><contributor>Bhaskar Dasgupta</contributor><creatorcontrib>Xie, Qiqi</creatorcontrib><creatorcontrib>Hao, Jinyong</creatorcontrib><creatorcontrib>Ma, Hanyun</creatorcontrib><creatorcontrib>Deng, Yajun</creatorcontrib><creatorcontrib>Zhao, Ruochen</creatorcontrib><title>MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.</description><subject>1-Phosphatidylinositol 3-kinase</subject><subject>AKT protein</subject><subject>Biological markers</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Cancer</subject><subject>Chromatids</subject><subject>Databases, Nucleic Acid</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Disease-Free Survival</subject><subject>Encyclopedias</subject><subject>Enrichment</subject><subject>Extracellular matrix</subject><subject>Female</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genetic diversity</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Kinases</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Microtubule-Associated Proteins - 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genetics</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Cancer</topic><topic>Chromatids</topic><topic>Databases, Nucleic Acid</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Disease-Free Survival</topic><topic>Encyclopedias</topic><topic>Enrichment</topic><topic>Extracellular matrix</topic><topic>Female</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genetic diversity</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Humans</topic><topic>Kinases</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Microtubule-Associated Proteins - genetics</topic><topic>Microtubule-Associated Proteins - metabolism</topic><topic>Minichromosome Maintenance Complex Component 2 - genetics</topic><topic>Minichromosome Maintenance Complex Component 2 - metabolism</topic><topic>Molecular modelling</topic><topic>Neoplasm Proteins - genetics</topic><topic>Neoplasm Proteins - metabolism</topic><topic>Pancreatic cancer</topic><topic>Pancreatic Neoplasms - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xie, Qiqi</au><au>Hao, Jinyong</au><au>Ma, Hanyun</au><au>Deng, Yajun</au><au>Zhao, Ruochen</au><au>Dasgupta, Bhaskar</au><au>Bhaskar Dasgupta</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>20</epage><pages>1-20</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>32420375</pmid><doi>10.1155/2020/8604340</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-8689-7634</orcidid><orcidid>https://orcid.org/0000-0003-4099-5287</orcidid><orcidid>https://orcid.org/0000-0003-0953-6525</orcidid><orcidid>https://orcid.org/0000-0003-2191-7910</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 1-Phosphatidylinositol 3-kinase AKT protein Biological markers Biomarkers Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Cancer Chromatids Databases, Nucleic Acid Datasets Diagnosis Diagnostic systems Disease-Free Survival Encyclopedias Enrichment Extracellular matrix Female Gene expression Genes Genetic aspects Genetic diversity Genomes Genomics Humans Kinases Male Medical prognosis Microtubule-Associated Proteins - genetics Microtubule-Associated Proteins - metabolism Minichromosome Maintenance Complex Component 2 - genetics Minichromosome Maintenance Complex Component 2 - metabolism Molecular modelling Neoplasm Proteins - genetics Neoplasm Proteins - metabolism Pancreatic cancer Pancreatic Neoplasms - diagnosis Pancreatic Neoplasms - genetics Pancreatic Neoplasms - metabolism Pancreatic Neoplasms - mortality Pathogenesis Prognosis Protein interaction Protein-protein interactions Proteins RNA Sister chromatids Software Survival Rate Tumors |
title | MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer |
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