Cited 55 times in
A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김상우 | - |
dc.date.accessioned | 2015-01-06T17:31:45Z | - |
dc.date.available | 2015-01-06T17:31:45Z | - |
dc.date.issued | 2014 | - |
dc.identifier.issn | 1553-7390 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/100148 | - |
dc.description.abstract | Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers. | - |
dc.description.statementOfResponsibility | open | - |
dc.format.extent | e1003837 | - |
dc.relation.isPartOf | PLOS GENETICS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Cell Line, Tumor | - |
dc.subject.MESH | Cluster Analysis | - |
dc.subject.MESH | Computer Simulation | - |
dc.subject.MESH | Gene Expression Profiling | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Metabolic Networks and Pathways/genetics* | - |
dc.subject.MESH | Metabolome/genetics* | - |
dc.subject.MESH | Models, Biological | - |
dc.subject.MESH | Mutation/genetics | - |
dc.subject.MESH | Neoplasms/genetics* | - |
dc.subject.MESH | Neoplasms/metabolism* | - |
dc.subject.MESH | Systems Biology/methods* | - |
dc.title | A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Life Science (의생명과학부) | - |
dc.contributor.googleauthor | Hojung Nam | - |
dc.contributor.googleauthor | Miguel Campodonico | - |
dc.contributor.googleauthor | Aarash Bordbar | - |
dc.contributor.googleauthor | Daniel R. Hyduke | - |
dc.contributor.googleauthor | Sangwoo Kim | - |
dc.contributor.googleauthor | Daniel C. Zielinski | - |
dc.contributor.googleauthor | Bernhard O. Palsson | - |
dc.identifier.doi | 10.1371/journal.pcbi.1003837 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A00524 | - |
dc.relation.journalcode | J02538 | - |
dc.identifier.eissn | 1553-7404 | - |
dc.identifier.pmid | 25232952 | - |
dc.contributor.alternativeName | Kim, Sang Woo | - |
dc.contributor.affiliatedAuthor | Kim, Sang Woo | - |
dc.citation.volume | 10 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | e1003837 | - |
dc.identifier.bibliographicCitation | PLOS GENETICS, Vol.10(9) : e1003837, 2014 | - |
dc.identifier.rimsid | 51708 | - |
dc.type.rims | ART | - |
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