{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:40:38Z","timestamp":1740159638112,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SYSCOL","award":["UE7-SYSCOL-258236"],"award-info":[{"award-number":["UE7-SYSCOL-258236"]}]},{"DOI":"10.13039\/501100009708","name":"Novo Nordisk Fonden","doi-asserted-by":"publisher","award":["NNF16OC0023182"],"award-info":[{"award-number":["NNF16OC0023182"]}],"id":[{"id":"10.13039\/501100009708","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008617","name":"H\u00f8jteknologifonden","doi-asserted-by":"publisher","award":["056-2010-1"],"award-info":[{"award-number":["056-2010-1"]}],"id":[{"id":"10.13039\/100008617","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004836","name":"Det Frie Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["0602-02128B","4183-00619","7016-00332B"],"award-info":[{"award-number":["0602-02128B","4183-00619","7016-00332B"]}],"id":[{"id":"10.13039\/501100004836","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The John and Birthe Meyer Foundation"},{"name":"The Danish Council for Strategic Research","award":["1309-00006B"],"award-info":[{"award-number":["1309-00006B"]}]},{"DOI":"10.13039\/100008363","name":"Kr\u00e6ftens Bek\u00e6mpelse","doi-asserted-by":"publisher","award":["R40-A1965_11_S2","R56-A3110-12-S2","R107-A7035","R133-A8520"],"award-info":[{"award-number":["R40-A1965_11_S2","R56-A3110-12-S2","R107-A7035","R133-A8520"]}],"id":[{"id":"10.13039\/100008363","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National cancer institute","doi-asserted-by":"crossref","award":["R01 CA207467"],"award-info":[{"award-number":["R01 CA207467"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100010344","name":"Aage og Johanne Louis-Hansens Fond","doi-asserted-by":"publisher","award":["17-2-0457"],"award-info":[{"award-number":["17-2-0457"]}],"id":[{"id":"10.13039\/501100010344","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005940","name":"Dansk Kr\u00e6ftforsknings Fond","doi-asserted-by":"publisher","award":["DKF-2017-26 - (26)"],"award-info":[{"award-number":["DKF-2017-26 - (26)"]}],"id":[{"id":"10.13039\/501100005940","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Knud og Edith Eriksen's mindefond"},{"name":"Neye fonden"},{"DOI":"10.13039\/501100010347","name":"Fabrikant Einar Willumsens Mindelegat","doi-asserted-by":"publisher","award":["6000073"],"award-info":[{"award-number":["6000073"]}],"id":[{"id":"10.13039\/501100010347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Clin Epigenet"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Transcriptional analysis is widely used to study the molecular biology of cancer and hold great biomarker potential for clinical patient stratification. Yet, accurate transcriptional profiling requires RNA of a high quality, which often cannot be retrieved from formalin-fixed, paraffin-embedded (FFPE) tumor tissue that is routinely collected and archived in clinical departments. To overcome this roadblock to clinical testing, we previously developed MethCORR, a method that infers gene expression from DNA methylation data, which is robustly retrieved from FFPE tissue. MethCORR was originally developed for colorectal cancer and with this study, we aim to: (1) extend the MethCORR method to 10 additional cancer types and (2) to illustrate that the inferred gene expression is accurate and clinically informative.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Regression models to infer gene expression information from DNA methylation were developed for ten common cancer types using matched RNA sequencing and DNA methylation profiles (HumanMethylation450 BeadChip) from The Cancer Genome Atlas Project. Robust and accurate gene expression profiles were inferred for all cancer types: on average, the expression of 11,000 genes was modeled with good accuracy and an intra-sample correlation of<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>\u2009=\u20090.90 between inferred and measured gene expression was observed. Molecular pathway analysis and transcriptional subtyping were performed for breast, prostate, and lung cancer samples to illustrate the general usability of the inferred gene expression profiles: overall, a high correlation of<jats:italic>r<\/jats:italic>\u2009=\u20090.96 (Pearson) in pathway enrichment scores and a 76% correspondence in molecular subtype calls were observed when using measured and inferred gene expression as input. Finally, inferred expression from FFPE tissue correlated better with RNA sequencing data from matched fresh-frozen tissue than did RNA sequencing data from FFPE tissue (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.0001; Wilcoxon rank-sum test).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In all cancers investigated, MethCORR enabled DNA methylation-based transcriptional analysis, thus enabling future analysis of cancer in situations where high-quality DNA, but not RNA, is available. Here, we provide the framework and resources for MethCORR modeling of ten common cancer types, thereby widely expanding the possibilities for transcriptional studies of archival FFPE material.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s13148-021-01000-0","type":"journal-article","created":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T09:36:17Z","timestamp":1611826577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MethCORR infers gene expression from DNA methylation and allows molecular analysis of ten common cancer types using fresh-frozen and formalin-fixed paraffin-embedded tumor samples"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8608-8589","authenticated-orcid":false,"given":"Trine B.","family":"Mattesen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7406-2103","authenticated-orcid":false,"given":"Claus L.","family":"Andersen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2084-0336","authenticated-orcid":false,"given":"Jesper B.","family":"Bramsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"issue":"6","key":"1000_CR1","doi-asserted-by":"crossref","first-page":"837","DOI":"10.5858\/134.6.837","volume":"134","author":"G Puppa","year":"2010","unstructured":"Puppa G, Sonzogni A, Colombari R, Pelosi G. TNM staging system of colorectal carcinoma: a critical appraisal of challenging issues. Arch Pathol Lab Med. 2010;134(6):837\u201352.","journal-title":"Arch Pathol Lab Med"},{"issue":"11","key":"1000_CR2","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1038\/nm.3967","volume":"21","author":"J Guinney","year":"2015","unstructured":"Guinney J, Dienstmann R, Wang X, de Reynies A, Schlicker A, Soneson C, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015;21(11):1350\u20136.","journal-title":"Nat Med"},{"key":"1000_CR3","doi-asserted-by":"crossref","unstructured":"TCGA. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61\u201370.","DOI":"10.1038\/nature11412"},{"issue":"7592","key":"1000_CR4","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/nature16965","volume":"531","author":"P Bailey","year":"2016","unstructured":"Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47\u201352.","journal-title":"Nature"},{"issue":"6","key":"1000_CR5","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1016\/j.celrep.2017.04.045","volume":"19","author":"JB Bramsen","year":"2017","unstructured":"Bramsen JB, Rasmussen MH, Ongen H, Mattesen TB, Orntoft MW, Arnadottir SS, et al. Molecular-subtype-specific biomarkers improve prediction of prognosis in colorectal cancer. Cell Rep. 2017;19(6):1268\u201380.","journal-title":"Cell Rep"},{"key":"1000_CR6","doi-asserted-by":"crossref","unstructured":"TCGA. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511(7511):543\u201350.","DOI":"10.1038\/nature13385"},{"issue":"1","key":"1000_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.ccell.2016.05.004","volume":"30","author":"J Hedegaard","year":"2016","unstructured":"Hedegaard J, Lamy P, Nordentoft I, Algaba F, Hoyer S, Ulhoi BP, et al. Comprehensive Transcriptional Analysis of Early-Stage Urothelial Carcinoma. Cancer Cell. 2016;30(1):27\u201342.","journal-title":"Cancer Cell"},{"issue":"11","key":"1000_CR8","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1369\/jhc.2008.951863","volume":"56","author":"JY Chung","year":"2008","unstructured":"Chung JY, Braunschweig T, Williams R, Guerrero N, Hoffmann KM, Kwon M, et al. Factors in tissue handling and processing that impact RNA obtained from formalin-fixed, paraffin-embedded tissue. J Histochem Cytochem. 2008;56(11):1033\u201342.","journal-title":"J Histochem Cytochem"},{"issue":"1","key":"1000_CR9","doi-asserted-by":"publisher","first-page":"e0170632","DOI":"10.1371\/journal.pone.0170632","volume":"12","author":"A Esteve-Codina","year":"2017","unstructured":"Esteve-Codina A, Arpi O, Martinez-Garcia M, Pineda E, Mallo M, Gut M, et al. A comparison of RNA-Seq results from paired formalin-fixed paraffin-embedded and fresh-frozen glioblastoma tissue samples. PLoS ONE. 2017;12(1):e0170632.","journal-title":"PLoS ONE"},{"issue":"11","key":"1000_CR10","doi-asserted-by":"publisher","first-page":"e81925","DOI":"10.1371\/journal.pone.0081925","volume":"8","author":"N Norton","year":"2013","unstructured":"Norton N, Sun Z, Asmann YW, Serie DJ, Necela BM, Bhagwate A, et al. Gene expression, single nucleotide variant and fusion transcript discovery in archival material from breast tumors. PLoS ONE. 2013;8(11):e81925.","journal-title":"PLoS ONE"},{"issue":"5","key":"1000_CR11","doi-asserted-by":"publisher","first-page":"e0216050","DOI":"10.1371\/journal.pone.0216050","volume":"14","author":"Y Zhao","year":"2019","unstructured":"Zhao Y, Mehta M, Walton A, Talsania K, Levin Y, Shetty J, et al. Robustness of RNA sequencing on older formalin-fixed paraffin-embedded tissue from high-grade ovarian serous adenocarcinomas. PLoS ONE. 2019;14(5):e0216050.","journal-title":"PLoS ONE"},{"issue":"1","key":"1000_CR12","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1038\/s41467-020-16000-6","volume":"11","author":"TB Mattesen","year":"2020","unstructured":"Mattesen TB, Rasmussen MH, Sandoval J, Ongen H, Arnadottir SS, Gladov J, et al. MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer. Nature Commun. 2020;11(1):2025.","journal-title":"Nature Commun"},{"issue":"6","key":"1000_CR13","doi-asserted-by":"publisher","first-page":"829","DOI":"10.4161\/epi.28790","volume":"9","author":"S Moran","year":"2014","unstructured":"Moran S, Vizoso M, Martinez-Cardus A, Gomez A, Matias-Guiu X, Chiavenna SM, et al. Validation of DNA methylation profiling in formalin-fixed paraffin-embedded samples using the Infinium HumanMethylation450 Microarray. Epigenetics. 2014;9(6):829\u201333.","journal-title":"Epigenetics"},{"issue":"3","key":"1000_CR14","doi-asserted-by":"publisher","first-page":"389","DOI":"10.2217\/epi.15.114","volume":"8","author":"S Moran","year":"2016","unstructured":"Moran S, Arribas C, Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8(3):389\u201399.","journal-title":"Epigenomics"},{"issue":"7","key":"1000_CR15","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1038\/labinvest.2015.53","volume":"95","author":"TC de Ruijter","year":"2015","unstructured":"de Ruijter TC, de Hoon JP, Slaats J, de Vries B, Janssen MJ, van Wezel T, et al. Formalin-fixed, paraffin-embedded (FFPE) tissue epigenomics using Infinium HumanMethylation450 BeadChip assays. Lab Invest J Tech Methods Pathol. 2015;95(7):833\u201342.","journal-title":"Lab Invest J Tech Methods Pathol."},{"issue":"8","key":"1000_CR16","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1093\/bioinformatics\/bty796","volume":"35","author":"X Dong","year":"2019","unstructured":"Dong X, Lin L, Zhang R, Zhao Y, Christiani DC, Wei Y, et al. TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach. Bioinformatics (Oxford, England). 2019;35(8):1278\u201383.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"19","key":"1000_CR17","doi-asserted-by":"publisher","first-page":"3635","DOI":"10.1093\/bioinformatics\/btz137","volume":"35","author":"Y Wang","year":"2019","unstructured":"Wang Y, Franks JM, Whitfield ML, Cheng C. BioMethyl: an R package for biological interpretation of DNA methylation data. Bioinformatics (Oxford, England). 2019;35(19):3635\u201341.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"8","key":"1000_CR18","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1200\/JCO.2008.18.1370","volume":"27","author":"JS Parker","year":"2009","unstructured":"Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160\u20137.","journal-title":"J Clin Oncol"},{"issue":"3","key":"1000_CR19","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10549-015-3572-3","volume":"153","author":"EC Inwald","year":"2015","unstructured":"Inwald EC, Koller M, Klinkhammer-Schalke M, Zeman F, Hofst\u00e4dter F, Gerstenhauer M, et al. 4-IHC classification of breast cancer subtypes in a large cohort of a clinical cancer registry: use in clinical routine for therapeutic decisions and its effect on survival. Breast Cancer Res Treat. 2015;153(3):647\u201358.","journal-title":"Breast Cancer Res Treat"},{"issue":"3","key":"1000_CR20","doi-asserted-by":"publisher","first-page":"E734","DOI":"10.9778\/cmajo.20170030","volume":"5","author":"S Fallahpour","year":"2017","unstructured":"Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer survival by molecular subtype: a population-based analysis of cancer registry data. CMAJ Open. 2017;5(3):E734\u20139.","journal-title":"CMAJ Open"},{"key":"1000_CR21","doi-asserted-by":"crossref","unstructured":"TCGA. The molecular taxonomy of primary prostate cancer. Cell. 2015;163(4):1011\u201325.","DOI":"10.1016\/j.cell.2015.10.025"},{"issue":"31","key":"1000_CR22","doi-asserted-by":"publisher","first-page":"5079","DOI":"10.1200\/JCO.2005.05.1748","volume":"24","author":"DN Hayes","year":"2006","unstructured":"Hayes DN, Monti S, Parmigiani G, Gilks CB, Naoki K, Bhattacharjee A, et al. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J Clin Oncol. 2006;24(31):5079\u201390.","journal-title":"J Clin Oncol"},{"issue":"9","key":"1000_CR23","doi-asserted-by":"publisher","first-page":"e0203608","DOI":"10.1371\/journal.pone.0203608","volume":"13","author":"D Groelz","year":"2018","unstructured":"Groelz D, Viertler C, Pabst D, Dettmann N, Zatloukal K. Impact of storage conditions on the quality of nucleic acids in paraffin embedded tissues. PLoS ONE. 2018;13(9):e0203608.","journal-title":"PLoS ONE"},{"issue":"7","key":"1000_CR24","doi-asserted-by":"publisher","first-page":"e0181756","DOI":"10.1371\/journal.pone.0181756","volume":"12","author":"A Yakovleva","year":"2017","unstructured":"Yakovleva A, Plieskatt JL, Jensen S, Humeida R, Lang J, Li G, et al. Fit for genomic and proteomic purposes: sampling the fitness of nucleic acid and protein derivatives from formalin fixed paraffin embedded tissue. PLoS ONE. 2017;12(7):e0181756.","journal-title":"PLoS ONE"},{"issue":"6","key":"1000_CR25","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1016\/j.eururo.2014.07.045","volume":"66","author":"Y Liu","year":"2014","unstructured":"Liu Y, Noon AP, Aguiar Cabeza E, Shen J, Kuk C, Ilczynski C, et al. Next-generation RNA sequencing of archival formalin-fixed paraffin-embedded urothelial bladder cancer. Eur Urol. 2014;66(6):982\u20136.","journal-title":"Eur Urol"},{"issue":"3","key":"1000_CR26","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.urolonc.2013.06.014","volume":"32","author":"X Sharron Lin","year":"2014","unstructured":"Sharron Lin X, Hu L, Sandy K, Correll M, Quackenbush J, Wu CL, et al. Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: applying RNA-sequencing analysis on archived specimens. Urol Oncol. 2014;32(3):327\u201336.","journal-title":"Urol Oncol"},{"issue":"1","key":"1000_CR27","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s12885-017-3237-1","volume":"17","author":"B Jovanovic","year":"2017","unstructured":"Jovanovic B, Sheng Q, Seitz RS, Lawrence KD, Morris SW, Thomas LR, et al. Comparison of triple-negative breast cancer molecular subtyping using RNA from matched fresh-frozen versus formalin-fixed paraffin-embedded tissue. BMC Cancer. 2017;17(1):241.","journal-title":"BMC Cancer"},{"key":"1000_CR28","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1186\/1471-2164-15-1087","volume":"15","author":"P Li","year":"2014","unstructured":"Li P, Conley A, Zhang H, Kim HL. Whole-Transcriptome profiling of formalin-fixed, paraffin-embedded renal cell carcinoma by RNA-seq. BMC Genomics. 2014;15:1087.","journal-title":"BMC Genomics"},{"issue":"1","key":"1000_CR29","doi-asserted-by":"publisher","first-page":"6980","DOI":"10.1038\/s41598-019-43282-8","volume":"9","author":"W Jones","year":"2019","unstructured":"Jones W, Greytak S, Odeh H, Guan P, Powers J, Bavarva J, et al. Deleterious effects of formalin-fixation and delays to fixation on RNA and miRNA-Seq profiles. Sci Rep. 2019;9(1):6980.","journal-title":"Sci Rep"},{"issue":"5","key":"1000_CR30","doi-asserted-by":"publisher","first-page":"e98187","DOI":"10.1371\/journal.pone.0098187","volume":"9","author":"J Hedegaard","year":"2014","unstructured":"Hedegaard J, Thorsen K, Lund MK, Hein AM, Hamilton-Dutoit SJ, Vang S, et al. Next-generation sequencing of RNA and DNA isolated from paired fresh-frozen and formalin-fixed paraffin-embedded samples of human cancer and normal tissue. PLoS ONE. 2014;9(5):e98187.","journal-title":"PLoS ONE"},{"key":"1000_CR31","doi-asserted-by":"crossref","unstructured":"Li J, Fu C, Speed TP, Wang W, Symmans WF. Accurate RNA sequencing from formalin-fixed cancer tissue to represent high-quality transcriptome from frozen tissue. JCO Precis Oncol. 2018.","DOI":"10.1200\/PO.17.00091"},{"issue":"1","key":"1000_CR32","doi-asserted-by":"publisher","first-page":"16618","DOI":"10.1038\/s41598-017-16747-x","volume":"7","author":"PW Eide","year":"2017","unstructured":"Eide PW, Bruun J, Lothe RA, Sveen A. CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Sci Rep. 2017;7(1):16618.","journal-title":"Sci Rep"},{"issue":"2","key":"1000_CR33","doi-asserted-by":"publisher","first-page":"R30","DOI":"10.1186\/gb-2014-15-2-r30","volume":"15","author":"A Feber","year":"2014","unstructured":"Feber A, Guilhamon P, Lechner M, Fenton T, Wilson GA, Thirlwell C, et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biol. 2014;15(2):R30.","journal-title":"Genome Biol"},{"issue":"6","key":"1000_CR34","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1038\/s41587-020-0546-8","volume":"38","author":"MJ Goldman","year":"2020","unstructured":"Goldman MJ, Craft B, Hastie M, Repe\u010dka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675\u20138.","journal-title":"Nat Biotechnol"},{"issue":"12","key":"1000_CR35","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1056\/NEJMp1607591","volume":"375","author":"RL Grossman","year":"2016","unstructured":"Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a shared vision for cancer genomic data. N Engl J Med. 2016;375(12):1109\u201312.","journal-title":"N Engl J Med"},{"key":"1000_CR36","doi-asserted-by":"crossref","unstructured":"Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013;41(Database issue):D991\u20135.","DOI":"10.1093\/nar\/gks1193"},{"key":"1000_CR37","doi-asserted-by":"crossref","unstructured":"Kuhn M. Building predictive models in R using the caret package. 2008;28(5):26.","DOI":"10.18637\/jss.v028.i05"},{"key":"1000_CR38","unstructured":"Dowle M, Srinivasan A. data.table: Extension of `data.frame`. R package version 1.12.8. https:\/\/CRAN.R-project.org\/package=data.table. 2019."},{"key":"1000_CR39","unstructured":"Hastie TTR, Narasimhan B, Chu G. Impute: impute: Imputation for microarray data. R package version 1.64.0. 2020."},{"issue":"2","key":"1000_CR40","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MCG.2014.1","volume":"34","author":"C Turkay","year":"2014","unstructured":"Turkay C, Lex A, Streit M, Pfister H, Hauser H. Characterizing cancer subtypes using dual analysis in Caleydo StratomeX. IEEE Comput Graph Appl. 2014;34(2):38\u201347.","journal-title":"IEEE Comput Graph Appl"},{"issue":"12","key":"1000_CR41","doi-asserted-by":"publisher","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","volume":"26","author":"MD Wilkerson","year":"2010","unstructured":"Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics (Oxford, England). 2010;26(12):1572\u20133.","journal-title":"Bioinformatics (Oxford, England)"},{"issue":"5","key":"1000_CR42","doi-asserted-by":"publisher","first-page":"e36530","DOI":"10.1371\/journal.pone.0036530","volume":"7","author":"MD Wilkerson","year":"2012","unstructured":"Wilkerson MD, Yin X, Walter V, Zhao N, Cabanski CR, Hayward MC, et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS ONE. 2012;7(5):e36530.","journal-title":"PLoS ONE"},{"issue":"43","key":"1000_CR43","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545.","journal-title":"Proc Natl Acad Sci"},{"issue":"7269","key":"1000_CR44","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/nature08460","volume":"462","author":"DA Barbie","year":"2009","unstructured":"Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462(7269):108\u201312.","journal-title":"Nature"},{"issue":"5","key":"1000_CR45","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/ng0506-500","volume":"38","author":"M Reich","year":"2006","unstructured":"Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat Genet. 2006;38(5):500\u20131.","journal-title":"Nat Genet."},{"key":"1000_CR46","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.1038\/ncomms3612","volume":"4","author":"K Yoshihara","year":"2013","unstructured":"Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.","journal-title":"Nat Commun"},{"issue":"2","key":"1000_CR47","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.cell.2018.02.052","volume":"173","author":"J Liu","year":"2018","unstructured":"Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173(2):400-16.e11.","journal-title":"Cell"},{"issue":"1","key":"1000_CR48","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/A:1023949509487","volume":"52","author":"S Monti","year":"2003","unstructured":"Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1):91\u2013118.","journal-title":"Mach Learn"}],"container-title":["Clinical Epigenetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13148-021-01000-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13148-021-01000-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13148-021-01000-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T16:22:09Z","timestamp":1619108529000},"score":1,"resource":{"primary":{"URL":"https:\/\/clinicalepigeneticsjournal.biomedcentral.com\/articles\/10.1186\/s13148-021-01000-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,28]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1000"],"URL":"https:\/\/doi.org\/10.1186\/s13148-021-01000-0","relation":{},"ISSN":["1868-7075","1868-7083"],"issn-type":[{"type":"print","value":"1868-7075"},{"type":"electronic","value":"1868-7083"}],"subject":[],"published":{"date-parts":[[2021,1,28]]},"assertion":[{"value":"18 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"20"}}