001     147458
005     20240229123024.0
024 7 _ |a 10.1002/1878-0261.12594
|2 doi
024 7 _ |a pmid:31677238
|2 pmid
024 7 _ |a 1574-7891
|2 ISSN
024 7 _ |a 1878-0261
|2 ISSN
024 7 _ |a altmetric:73290991
|2 altmetric
037 _ _ |a DKFZ-2019-02540
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Guan, Zhong
|0 P:(DE-He78)e2927c4f5c050e0ad98ebb65eebe0d56
|b 0
|e First author
|u dkfz
245 _ _ |a Individual and joint performance of DNA methylation profiles, genetic risk scores and environmental risk scores for predicting breast cancer risk.
260 _ _ |a Hoboken, NJ
|c 2020
|b John Wiley & Sons, Inc.
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1601377384_28064
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2020 Jan;14(1):42-53#EA:C070#LA:C070#
520 _ _ |a DNA methylation patterns in the blood, genetic risk scores (GRSs) and environmental risk factors can potentially improve breast cancer (BC) risk prediction. We assessed the individual and joint predictive performance of methylation, GRS and environmental risk factors for BC incidence in a prospective cohort study. In a cohort of 5462 women aged 50-75 from Germany, 101 BC cases were identified during 14 years of follow-up and were compared to 263 BC-free controls in a nested case-control design. Three previously suggested methylation risk scores (MRSs) based on methylation of 423, 248 and 131 cytosine-phosphate-guanine (CpG) loci, and a GRS based on the risk alleles from 269 recently identified single-nucleotide polymorphisms were constructed. Additionally, multiple previously proposed environmental risk scores (ERSs) were built based on environmental variables. Areas under the receiver operating characteristic curves (AUCs) were estimated for evaluating BC risk prediction performance. MRS and ERS showed limited accuracy in predicting BC incidence, with AUCs ranging from 0.52 to 0.56 and from 0.52 to 0.59, respectively. The GRS predicted BC incidence with a higher accuracy (AUC=0.61). Adjusted odds ratios per standard deviation increase (95% confidence interval) were 1.07 (0.84-1.36) and 1.40 (1.09-1.80) for the best performing MRS and ERS, respectively, and 1.48 (1.16-1.90) for the GRS. A full risk model combining the MRS, GRS and ERS predicted BC incidence with the highest accuracy (AUC=0.64), and might be useful for identifying high-risk populations for BC screening.
536 _ _ |a 313 - Cancer risk factors and prevention (POF3-313)
|0 G:(DE-HGF)POF3-313
|c POF3-313
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Raut, Janhavi R
|0 P:(DE-He78)43ea0369702f56d45fa4a32df9f49aca
|b 1
|u dkfz
700 1 _ |a Weigl, Korbinian
|0 P:(DE-He78)f4e98340e600f7411886c21c7b778d36
|b 2
|u dkfz
700 1 _ |a Schöttker, Ben
|0 P:(DE-He78)c67a12496b8aac150c0eef888d808d46
|b 3
|u dkfz
700 1 _ |a Holleczek, Bernd
|b 4
700 1 _ |a Zhang, Yan
|0 P:(DE-He78)6a8f87626cb610618a60d742677284cd
|b 5
|u dkfz
700 1 _ |a Brenner, Hermann
|0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
|b 6
|e Last author
|u dkfz
773 _ _ |a 10.1002/1878-0261.12594
|g p. 1878-0261.12594
|0 PERI:(DE-600)2322586-5
|n 1
|p 42-53
|t Molecular oncology
|v 14
|y 2020
|x 1878-0261
909 C O |p VDB
|o oai:inrepo02.dkfz.de:147458
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 0
|6 P:(DE-He78)e2927c4f5c050e0ad98ebb65eebe0d56
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 1
|6 P:(DE-He78)43ea0369702f56d45fa4a32df9f49aca
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-He78)f4e98340e600f7411886c21c7b778d36
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 3
|6 P:(DE-He78)c67a12496b8aac150c0eef888d808d46
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 5
|6 P:(DE-He78)6a8f87626cb610618a60d742677284cd
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 6
|6 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-313
|2 G:(DE-HGF)POF3-300
|v Cancer risk factors and prevention
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2020
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MOL ONCOL : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b MOL ONCOL : 2017
920 2 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
920 0 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
920 1 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
920 1 _ |0 I:(DE-He78)C120-20160331
|k C120
|l Präventive Onkologie
|x 1
920 1 _ |0 I:(DE-He78)HD01-20160331
|k HD01
|l DKTK HD zentral
|x 2
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C070-20160331
980 _ _ |a I:(DE-He78)C120-20160331
980 _ _ |a I:(DE-He78)HD01-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21