{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T19:10:27Z","timestamp":1775157027148,"version":"3.50.1"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["515991405"],"award-info":[{"award-number":["515991405"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["515571394"],"award-info":[{"award-number":["515571394"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["458890590"],"award-info":[{"award-number":["458890590"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene\u2013miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/bioconductor.org\/packages\/devel\/bioc\/html\/SPONGE.html.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad276","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T14:54:23Z","timestamp":1682088863000},"source":"Crossref","is-referenced-by-count":15,"title":["spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape"],"prefix":"10.1093","volume":"39","author":[{"given":"Fabio","family":"Boniolo","sequence":"first","affiliation":[{"name":"Present\u2002address:\u2002Department of Pediatric Oncology, Dana-Farber Cancer Institute , Boston, MA, USA"},{"name":"Broad Institute of MIT and Harvard Present\u2002address:\u2002 , Cambridge, MA, USA"},{"name":"Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich , Freising 85354, Germany"},{"name":"Chair of Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich , Munich 81675, Germany"},{"name":"Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280 , Heidelberg 69120, Germany"},{"name":"Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Ismaninger Str. 22 , Munich 81675, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1920-288X","authenticated-orcid":false,"given":"Markus","family":"Hoffmann","sequence":"additional","affiliation":[{"name":"Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich , Freising 85354, Germany"},{"name":"Institute for Advanced Study, Technical University of Munich , Garching 85748, Germany"},{"name":"Present\u00a0address:\u00a0National Institute of Diabetes, Digestive, and Kidney Diseases , Bethesda, MD 20892, USA"}]},{"given":"Norman","family":"Roggendorf","sequence":"additional","affiliation":[{"name":"Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich , Freising 85354, Germany"}]},{"given":"Bahar","family":"Tercan","sequence":"additional","affiliation":[{"name":"Institute for Systems Biology , Seattle, WA 98109, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0282-0462","authenticated-orcid":false,"given":"Jan","family":"Baumbach","sequence":"additional","affiliation":[{"name":"Chair of Computational Systems Biology, University of Hamburg , Hamburg, Germany"},{"name":"Computational BioMedicine Lab, University of Southern Denmark , Odense, Denmark"}]},{"given":"Mauro A A","family":"Castro","sequence":"additional","affiliation":[{"name":"Bioinformatics and Systems Biology Laboratory, Universidade Federal do Paran\u00e1 , Curitiba, Brazil"}]},{"given":"A Gordon","family":"Robertson","sequence":"additional","affiliation":[{"name":"BC Cancer Genome Sciences Centre , Vancouver, BC V5Z 4S6, Canada"},{"name":"Present\u00a0address:\u00a0Dxige Research Inc. , Courtenay, BC V9N 1C2, Canada"}]},{"given":"Dieter","family":"Saur","sequence":"additional","affiliation":[{"name":"Chair of Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich , Munich 81675, Germany"},{"name":"Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Im Neuenheimer Feld 280 , Heidelberg 69120, Germany"},{"name":"Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Ismaninger Str. 22 , Munich 81675, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0941-4168","authenticated-orcid":false,"given":"Markus","family":"List","sequence":"additional","affiliation":[{"name":"Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich , Freising 85354, Germany"}]}],"member":"286","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"2023052707230172600_btad276-B1","doi-asserted-by":"crossref","first-page":"e05005","DOI":"10.7554\/eLife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"Elife"},{"key":"2023052707230172600_btad276-B2","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1038\/s41467-022-28524-0","article-title":"Proteomic analysis 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