Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis
To discover novel prognostic biomarkers in ovarian serous carcinomas. A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using St...
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| Veröffentlicht in: | PloS one 2016-02, Vol.11 (2), p.e0149183-e0149183 |
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| creator | Willis, Scooter Villalobos, Victor M Gevaert, Olivier Abramovitz, Mark Williams, Casey Sikic, Branimir I Leyland-Jones, Brian |
| description | To discover novel prognostic biomarkers in ovarian serous carcinomas.
A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) |
| doi_str_mv | 10.1371/journal.pone.0149183 |
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A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.
Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).
A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0149183</identifier><identifier>PMID: 26886260</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adenomatous polyposis coli ; Antagonists ; Aquaporin 1 ; Axl protein ; Bioinformatics ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Biomarkers, Tumor - genetics ; Cancer ; Care and treatment ; Chemotherapy ; Complications and side effects ; Continuity (mathematics) ; E-cadherin ; False Positive Reactions ; Female ; Gene expression ; Gene Expression Regulation, Neoplastic ; Genes ; Genes, Neoplasm ; Genetic aspects ; Genetic Predisposition to Disease ; Health aspects ; Humans ; Kinases ; Medicine ; Medicine and Health Sciences ; Meta-analysis ; Metadata ; Ovarian cancer ; Ovarian carcinoma ; Ovarian Neoplasms - genetics ; People and Places ; Physical Sciences ; PTEN-induced putative kinase ; Regression analysis ; Regulators ; Research and Analysis Methods ; Statistical analysis ; Survival analysis</subject><ispartof>PloS one, 2016-02, Vol.11 (2), p.e0149183-e0149183</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Willis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Willis et al 2016 Willis et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-fee4cdd8b52bc1188f298044a842b8926bdede9cf275dc1f746591ccf2e4569e3</citedby><cites>FETCH-LOGICAL-c758t-fee4cdd8b52bc1188f298044a842b8926bdede9cf275dc1f746591ccf2e4569e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757072/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757072/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26886260$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Coleman, William B.</contributor><creatorcontrib>Willis, Scooter</creatorcontrib><creatorcontrib>Villalobos, Victor M</creatorcontrib><creatorcontrib>Gevaert, Olivier</creatorcontrib><creatorcontrib>Abramovitz, Mark</creatorcontrib><creatorcontrib>Williams, Casey</creatorcontrib><creatorcontrib>Sikic, Branimir I</creatorcontrib><creatorcontrib>Leyland-Jones, Brian</creatorcontrib><title>Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To discover novel prognostic biomarkers in ovarian serous carcinomas.
A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.
Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).
A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.</description><subject>Adenomatous polyposis coli</subject><subject>Antagonists</subject><subject>Aquaporin 1</subject><subject>Axl protein</subject><subject>Bioinformatics</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Cancer</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Complications and side effects</subject><subject>Continuity (mathematics)</subject><subject>E-cadherin</subject><subject>False Positive Reactions</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Genes, Neoplasm</subject><subject>Genetic aspects</subject><subject>Genetic Predisposition to Disease</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Kinases</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Meta-analysis</subject><subject>Metadata</subject><subject>Ovarian cancer</subject><subject>Ovarian carcinoma</subject><subject>Ovarian Neoplasms - genetics</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>PTEN-induced putative kinase</subject><subject>Regression analysis</subject><subject>Regulators</subject><subject>Research and Analysis Methods</subject><subject>Statistical analysis</subject><subject>Survival analysis</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl1v0zAUhiMEYh_wDxBEQkJw0WI7duxwgdQVGJWGihhwaznOSeqS2sVOJvbvcddsatAukC_89Zz3-By_SfIMoynOOH67dr23qp1unYUpwrTAInuQHOMiI5OcoOzhwfooOQlhjRDLRJ4_To5ILkROcnScfLg0tmkhPQcL6VfvGutCZ3R6ZtxG-V_gQ2psurxS3iibzpXV4N-ls_QLdGoyi_mvgwlPkke1agM8HebT5Menj9_nnycXy_PFfHYx0ZyJblIDUF1VomSk1BgLUZNCIEqVoKQUBcnLCioodE04qzSuOc1ZgXXcA2V5Adlp8mKvu21dkEMDgsR8x2UUZZFY7InKqbXcehOLuJZOGXlz4HwjlY_1tSArzrEGwrgWihLNVMxd1YQKXQJRJYpa74dsfbmBSoPtvGpHouMba1aycVeScsYRJ1Hg9SDg3e8eQic3JmhoW2XB9Tfv3nGcFxF9-Q96f3UD1ahYgLG1i3n1TlTOKM0YRgzlkZreQ8VRwcbo6JbaxPNRwJtRQGQ6-NM1qg9BLi6__T-7_DlmXx2wK1Bttwqu7TvjbBiDdA9q70LwUN81GSO5M_ttN-TO7HIwewx7fvhBd0G37s7-Agaw-NU</recordid><startdate>20160217</startdate><enddate>20160217</enddate><creator>Willis, Scooter</creator><creator>Villalobos, Victor M</creator><creator>Gevaert, Olivier</creator><creator>Abramovitz, Mark</creator><creator>Williams, Casey</creator><creator>Sikic, Branimir I</creator><creator>Leyland-Jones, Brian</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20160217</creationdate><title>Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis</title><author>Willis, Scooter ; Villalobos, Victor M ; Gevaert, Olivier ; Abramovitz, Mark ; Williams, Casey ; Sikic, Branimir I ; Leyland-Jones, Brian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-fee4cdd8b52bc1188f298044a842b8926bdede9cf275dc1f746591ccf2e4569e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adenomatous polyposis coli</topic><topic>Antagonists</topic><topic>Aquaporin 1</topic><topic>Axl protein</topic><topic>Bioinformatics</topic><topic>Biological markers</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Cancer</topic><topic>Care and treatment</topic><topic>Chemotherapy</topic><topic>Complications and side effects</topic><topic>Continuity (mathematics)</topic><topic>E-cadherin</topic><topic>False Positive Reactions</topic><topic>Female</topic><topic>Gene expression</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Genes</topic><topic>Genes, Neoplasm</topic><topic>Genetic aspects</topic><topic>Genetic Predisposition to Disease</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Kinases</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Meta-analysis</topic><topic>Metadata</topic><topic>Ovarian cancer</topic><topic>Ovarian carcinoma</topic><topic>Ovarian Neoplasms - genetics</topic><topic>People and Places</topic><topic>Physical Sciences</topic><topic>PTEN-induced putative kinase</topic><topic>Regression analysis</topic><topic>Regulators</topic><topic>Research and Analysis Methods</topic><topic>Statistical analysis</topic><topic>Survival analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Willis, Scooter</creatorcontrib><creatorcontrib>Villalobos, Victor M</creatorcontrib><creatorcontrib>Gevaert, Olivier</creatorcontrib><creatorcontrib>Abramovitz, Mark</creatorcontrib><creatorcontrib>Williams, Casey</creatorcontrib><creatorcontrib>Sikic, Branimir I</creatorcontrib><creatorcontrib>Leyland-Jones, Brian</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Willis, Scooter</au><au>Villalobos, Victor M</au><au>Gevaert, Olivier</au><au>Abramovitz, Mark</au><au>Williams, Casey</au><au>Sikic, Branimir I</au><au>Leyland-Jones, Brian</au><au>Coleman, William B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-02-17</date><risdate>2016</risdate><volume>11</volume><issue>2</issue><spage>e0149183</spage><epage>e0149183</epage><pages>e0149183-e0149183</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To discover novel prognostic biomarkers in ovarian serous carcinomas.
A meta-analysis of all single genes probes in the TCGA and HAS ovarian cohorts was performed to identify possible biomarkers using Cox regression as a continuous variable for overall survival. Genes were ranked by p-value using Stouffer's method and selected for statistical significance with a false discovery rate (FDR) <.05 using the Benjamini-Hochberg method.
Twelve genes with high mRNA expression were prognostic of poor outcome with an FDR <.05 (AXL, APC, RAB11FIP5, C19orf2, CYBRD1, PINK1, LRRN3, AQP1, DES, XRCC4, BCHE, and ASAP3). Twenty genes with low mRNA expression were prognostic of poor outcome with an FDR <.05 (LRIG1, SLC33A1, NUCB2, POLD3, ESR2, GOLPH3, XBP1, PAXIP1, CYB561, POLA2, CDH1, GMNN, SLC37A4, FAM174B, AGR2, SDR39U1, MAGT1, GJB1, SDF2L1, and C9orf82).
A meta-analysis of all single genes identified thirty-two candidate biomarkers for their possible role in ovarian serous carcinoma. These genes can provide insight into the drivers or regulators of ovarian cancer and should be evaluated in future studies. Genes with high expression indicating poor outcome are possible therapeutic targets with known antagonists or inhibitors. Additionally, the genes could be combined into a prognostic multi-gene signature and tested in future ovarian cohorts.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26886260</pmid><doi>10.1371/journal.pone.0149183</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Adenomatous polyposis coli Antagonists Aquaporin 1 Axl protein Bioinformatics Biological markers Biology and Life Sciences Biomarkers Biomarkers, Tumor - genetics Cancer Care and treatment Chemotherapy Complications and side effects Continuity (mathematics) E-cadherin False Positive Reactions Female Gene expression Gene Expression Regulation, Neoplastic Genes Genes, Neoplasm Genetic aspects Genetic Predisposition to Disease Health aspects Humans Kinases Medicine Medicine and Health Sciences Meta-analysis Metadata Ovarian cancer Ovarian carcinoma Ovarian Neoplasms - genetics People and Places Physical Sciences PTEN-induced putative kinase Regression analysis Regulators Research and Analysis Methods Statistical analysis Survival analysis |
| title | Single Gene Prognostic Biomarkers in Ovarian Cancer: A Meta-Analysis |
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