000156719 001__ 156719 000156719 005__ 20240229123113.0 000156719 0247_ $$2doi$$a10.1186/s13148-020-00872-y 000156719 0247_ $$2pmid$$apmid:32552915 000156719 0247_ $$2pmc$$apmc:PMC7301507 000156719 0247_ $$2ISSN$$a1868-7075 000156719 0247_ $$2ISSN$$a1868-7083 000156719 037__ $$aDKFZ-2020-01057 000156719 041__ $$aeng 000156719 082__ $$a610 000156719 1001_ $$0P:(DE-He78)6ea02cd0867abd195dedd893b73bb513$$aYu, Haixin$$b0$$eFirst author 000156719 245__ $$aIndividual and joint contributions of genetic and methylation risk scores for enhancing lung cancer risk stratification: data from a population-based cohort in Germany. 000156719 260__ $$a[S.l.]$$bBioMed Central$$c2020 000156719 3367_ $$2DRIVER$$aarticle 000156719 3367_ $$2DataCite$$aOutput Types/Journal article 000156719 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1600774514_15869 000156719 3367_ $$2BibTeX$$aARTICLE 000156719 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000156719 3367_ $$00$$2EndNote$$aJournal Article 000156719 500__ $$a#EA:C070#LA:C070# 000156719 520__ $$aRisk stratification for lung cancer (LC) screening is so far mostly based on smoking history. This study aimed to assess if and to what extent such risk stratification could be enhanced by additional consideration of genetic risk scores (GRSs) and epigenetic risk scores defined by DNA methylation.We conducted a nested case-control study of 143 incident LC cases and 1460 LC-free controls within a prospective cohort of 9949 participants aged 50-75 years with 14-year follow-up. Lifetime smoking history was obtained in detail at recruitment. We built a GRS based on 31 previously identified LC-associated single-nucleotide polymorphisms (SNPs) and a DNA methylation score (MRS) based on methylation of 151 previously identified smoking-associated cytosine-phosphate-guanine (CpG) loci. We evaluated associations of GRS and MRS with LC incidence by logistic regression models, controlling for age, sex, smoking status, and pack-years. We compared the predictive performance of models based on pack-years alone with models additionally including GRS and/or MRS using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).GRS and MRS showed moderate and strong associations with LC risk even after comprehensive adjustment for smoking history (adjusted odds ratio [95% CI] comparing highest with lowest quartile 1.93 [1.05-3.71] and 5.64 [2.13-17.03], respectively). Similar associations were also observed within the risk groups of ever and heavy smokers. Addition of GRS and MRS furthermore strongly enhanced LC prediction beyond prediction by pack-years (increase of optimism-corrected AUC among heavy smokers from 0.605 to 0.654, NRI 26.7%, p = 0.0106, IDI 3.35%, p = 0.0036), the increase being mostly attributable to the inclusion of MRS.Consideration of MRS, by itself or in combination with GRS, may strongly enhance LC risk stratification. 000156719 536__ $$0G:(DE-HGF)POF3-313$$a313 - Cancer risk factors and prevention (POF3-313)$$cPOF3-313$$fPOF III$$x0 000156719 588__ $$aDataset connected to CrossRef, PubMed, 000156719 7001_ $$0P:(DE-He78)43ea0369702f56d45fa4a32df9f49aca$$aRaut, Janhavi$$b1 000156719 7001_ $$0P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aSchöttker, Ben$$b2 000156719 7001_ $$0P:(DE-He78)53e1a2846c69064e27790dbf349ccaec$$aHolleczek, Bernd$$b3 000156719 7001_ $$0P:(DE-He78)6a8f87626cb610618a60d742677284cd$$aZhang, Yan$$b4 000156719 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b5$$eLast author 000156719 773__ $$0PERI:(DE-600)2553921-8$$a10.1186/s13148-020-00872-y$$gVol. 12, no. 1, p. 89$$n1$$p89$$tClinical epigenetics$$v12$$x1868-7083$$y2020 000156719 909CO $$ooai:inrepo02.dkfz.de:156719$$pVDB 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)6ea02cd0867abd195dedd893b73bb513$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)43ea0369702f56d45fa4a32df9f49aca$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c67a12496b8aac150c0eef888d808d46$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)53e1a2846c69064e27790dbf349ccaec$$aDeutsches Krebsforschungszentrum$$b3$$kDKFZ 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)6a8f87626cb610618a60d742677284cd$$aDeutsches Krebsforschungszentrum$$b4$$kDKFZ 000156719 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ 000156719 9131_ $$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$$x0 000156719 9141_ $$y2020 000156719 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCLIN EPIGENETICS : 2018$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2019-12-20 000156719 915__ $$0LIC:(DE-HGF)CCBYNV$$2V:(DE-HGF)$$aCreative Commons Attribution CC BY (No Version)$$bDOAJ$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bCLIN EPIGENETICS : 2018$$d2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$f2019-12-20 000156719 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2019-12-20 000156719 9202_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0 000156719 9200_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0 000156719 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0 000156719 9201_ $$0I:(DE-He78)C120-20160331$$kC120$$lPräventive Onkologie$$x1 000156719 9201_ $$0I:(DE-He78)HD01-20160331$$kHD01$$lDKTK HD zentral$$x2 000156719 980__ $$ajournal 000156719 980__ $$aVDB 000156719 980__ $$aI:(DE-He78)C070-20160331 000156719 980__ $$aI:(DE-He78)C120-20160331 000156719 980__ $$aI:(DE-He78)HD01-20160331 000156719 980__ $$aUNRESTRICTED