000164288 001__ 164288
000164288 005__ 20240229123210.0
000164288 0247_ $$2doi$$a10.3390/ijms21218150
000164288 0247_ $$2pmid$$apmid:33142733
000164288 0247_ $$2ISSN$$a1422-0067
000164288 0247_ $$2ISSN$$a1661-6596
000164288 0247_ $$2altmetric$$aaltmetric:93759307
000164288 037__ $$aDKFZ-2020-02371
000164288 041__ $$aeng
000164288 082__ $$a540
000164288 1001_ $$0P:(DE-He78)9e2b4e6534d883b8808221c71e206367$$aDeutelmoser, Heike$$b0$$eFirst author
000164288 245__ $$aGenotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients.
000164288 260__ $$aBasel$$bMolecular Diversity Preservation International$$c2020
000164288 3367_ $$2DRIVER$$aarticle
000164288 3367_ $$2DataCite$$aOutput Types/Journal article
000164288 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1614097942_21102
000164288 3367_ $$2BibTeX$$aARTICLE
000164288 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000164288 3367_ $$00$$2EndNote$$aJournal Article
000164288 500__ $$a#EA:C120#LA:C120#
000164288 520__ $$aColorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes-so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m2 (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data.
000164288 536__ $$0G:(DE-HGF)POF3-313$$a313 - Cancer risk factors and prevention (POF3-313)$$cPOF3-313$$fPOF III$$x0
000164288 588__ $$aDataset connected to CrossRef, PubMed,
000164288 7001_ $$aLorenzo Bermejo, Justo$$b1
000164288 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b2
000164288 7001_ $$0P:(DE-He78)f4e98340e600f7411886c21c7b778d36$$aWeigl, Korbinian$$b3
000164288 7001_ $$0P:(DE-HGF)0$$aPark, Hanla A$$b4
000164288 7001_ $$0P:(DE-HGF)0$$aHaffa, Mariam$$b5
000164288 7001_ $$aHerpel, Esther$$b6
000164288 7001_ $$aSchneider, Martin$$b7
000164288 7001_ $$00000-0001-7641-059X$$aUlrich, Cornelia M$$b8
000164288 7001_ $$0P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aHoffmeister, Michael$$b9
000164288 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b10
000164288 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b11
000164288 7001_ $$0P:(DE-He78)0e36ebe046be3cabb1c2e282725180b9$$aScherer, Dominique$$b12$$eLast author
000164288 773__ $$0PERI:(DE-600)2019364-6$$a10.3390/ijms21218150$$gVol. 21, no. 21, p. 8150 -$$n21$$p8150$$tInternational journal of molecular sciences$$v21$$x1422-0067$$y2020
000164288 909CO $$ooai:inrepo02.dkfz.de:164288$$pVDB
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)9e2b4e6534d883b8808221c71e206367$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)f4e98340e600f7411886c21c7b778d36$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-HGF)0$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-HGF)0$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$60000-0001-7641-059X$$aDeutsches Krebsforschungszentrum$$b8$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f$$aDeutsches Krebsforschungszentrum$$b9$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aDeutsches Krebsforschungszentrum$$b10$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aDeutsches Krebsforschungszentrum$$b11$$kDKFZ
000164288 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)0e36ebe046be3cabb1c2e282725180b9$$aDeutsches Krebsforschungszentrum$$b12$$kDKFZ
000164288 9131_ $$0G:(DE-HGF)POF3-313$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vCancer risk factors and prevention$$x0
000164288 9141_ $$y2020
000164288 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bINT J MOL SCI : 2018$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2020-01-02
000164288 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$f2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$f2020-01-02
000164288 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-01-02
000164288 9201_ $$0I:(DE-He78)C120-20160331$$kC120$$lPräventive Onkologie$$x0
000164288 9201_ $$0I:(DE-He78)C060-20160331$$kC060$$lC060 Biostatistik$$x1
000164288 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x2
000164288 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lC020 Epidemiologie von Krebs$$x3
000164288 9201_ $$0I:(DE-He78)B280-20160331$$kB280$$lB280 Translationale funktionelle Krebsgenomik$$x4
000164288 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x5
000164288 980__ $$ajournal
000164288 980__ $$aVDB
000164288 980__ $$aI:(DE-He78)C120-20160331
000164288 980__ $$aI:(DE-He78)C060-20160331
000164288 980__ $$aI:(DE-He78)C070-20160331
000164288 980__ $$aI:(DE-He78)C020-20160331
000164288 980__ $$aI:(DE-He78)B280-20160331
000164288 980__ $$aI:(DE-He78)HD01-20160331
000164288 980__ $$aUNRESTRICTED