{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(1)"],"submitter":["Jurmeister P"],"pubmed_abstract":["The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive "],"journal":["Nature communications"],"pagination":["7148"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9705411"],"repository":["biostudies-literature"],"pubmed_title":["DNA methylation-based classification of sinonasal tumors."],"pmcid":["PMC9705411"],"pubmed_authors":["Ribbat-Idel J","Bremmer F","Idel C","Stadelmann C","Klauschen F","Koch A","Harter P","Bockmayr M","Hench J","Leitheiser M","Hummel M","Lehmann A","Haji M","Mochmann LH","Denkert C","Frank S","von Deimling A","Keber U","Jones DTW","Capper D","Della Monica R","Paya Capilla E","Lorenzo-Guerra SL","Schmid S","Thieme A","Chiariotti L","Hoffmann I","Hasselblatt M","Schuller U","Mertins P","Keyl P","Dittmayer C","Sill M","Liu J","Jurmeister P","Gloß S","Friedrich C","Perner S","Seegerer P","Dohmen H","Forster M","Pfister SM","Fritz R","Johann PD","Hermsen M","Snuderl M","Jank P","Paulus W","Roller R","Richter A","Muller KR","Vollbrecht C","Hartmann W","Blaker H","Wefers A","Howitt BE","Forgo E","Jarosch A","Heppner F","Lund VJ","Lechner M","Agaimy A","Marinelli A","Schallenberg S","Heim D","Englert B"],"additional_accession":[]},"is_claimable":false,"name":"DNA methylation-based classification of sinonasal tumors.","description":"The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2026-06-05T05:58:38.726Z","creation":"2025-04-19T13:21:38.854Z"},"accession":"S-EPMC9705411","cross_references":{"pubmed":["36443295"],"doi":["10.1038/s41467-022-34815-3"]}}