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Cited 3 times in

1H NMR based urinary metabolites profiling dataset of canine mammary tumors

DC Field Value Language
dc.contributor.author김상우-
dc.contributor.author정재호-
dc.contributor.author양인석-
dc.date.accessioned2022-12-22T01:34:19Z-
dc.date.available2022-12-22T01:34:19Z-
dc.date.issued2022-03-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/191261-
dc.description.abstractThe identification of efficient and sensitive biomarkers for non-invasive tests is one of the major challenges in cancer diagnosis. To address this challenge, metabolomics is widely applied for identifying biomarkers that detect abnormal changes in cancer patients. Canine mammary tumors exhibit physiological characteristics identical to those in human breast cancer and serve as a useful animal model to conduct breast cancer research. Here, we aimed to provide a reliable large-scale metabolite dataset collected from dogs with mammary tumors, using proton nuclear magnetic resonance spectroscopy. We identified 55 metabolites in urine samples from 20 benign, 87 malignant, and 49 healthy control subjects. This dataset provides details of mammary tumor-specific metabolites in dogs and insights into cancer-specific metabolic alterations that share similar molecular characteristics.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC DATA-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAnimals-
dc.subject.MESHDogs*-
dc.subject.MESHFemale-
dc.subject.MESHMammary Neoplasms, Animal* / urine-
dc.subject.MESHMetabolomics-
dc.subject.MESHProton Magnetic Resonance Spectroscopy-
dc.title1H NMR based urinary metabolites profiling dataset of canine mammary tumors-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthorSongyeon Lee-
dc.contributor.googleauthorByung-Joon Seung-
dc.contributor.googleauthorIn Seok Yang-
dc.contributor.googleauthorJueun Lee-
dc.contributor.googleauthorTaewoong Ha-
dc.contributor.googleauthorHee-Myung Park-
dc.contributor.googleauthorJae-Ho Cheong-
dc.contributor.googleauthorSangwoo Kim-
dc.contributor.googleauthorJung-Hyang Sur-
dc.contributor.googleauthorGeum-Sook Hwang-
dc.contributor.googleauthorHojung Nam-
dc.identifier.doi10.1038/s41597-022-01229-1-
dc.contributor.localIdA00524-
dc.contributor.localIdA03717-
dc.relation.journalcodeJ03673-
dc.identifier.eissn2052-4463-
dc.identifier.pmid35361774-
dc.contributor.alternativeNameKim, Sang Woo-
dc.contributor.affiliatedAuthor김상우-
dc.contributor.affiliatedAuthor정재호-
dc.citation.volume9-
dc.citation.number1-
dc.citation.startPage132-
dc.identifier.bibliographicCitationSCIENTIFIC DATA, Vol.9(1) : 132, 2022-03-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers

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