DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Kwang-Hyun | ko |
dc.contributor.author | Lee, Soobeom | ko |
dc.contributor.author | Kim, Dongsan | ko |
dc.contributor.author | Shin, Dongkwan | ko |
dc.contributor.author | Joo, Jae Il | ko |
dc.contributor.author | Park, Sang-Min | ko |
dc.date.accessioned | 2017-10-23T01:31:22Z | - |
dc.date.available | 2017-10-23T01:31:22Z | - |
dc.date.created | 2017-06-21 | - |
dc.date.created | 2017-06-21 | - |
dc.date.created | 2017-06-21 | - |
dc.date.issued | 2017-04 | - |
dc.identifier.citation | Current Opinion in Systems Biology, v.2, pp.49 - 58 | - |
dc.identifier.issn | 2452-3100 | - |
dc.identifier.uri | http://hdl.handle.net/10203/226326 | - |
dc.description.abstract | Cancer is a complex disease for which conventional therapeutic approaches often encounter a fundamental limitation. As an alternative approach, there is a renewed challenge in systems biology for cancer reversion by converting cancer cells into normal cells. Historically, such reversion has been observed sporadically, but no systems analysis has been attempted so far. We review the phenomenal observations of cancer reversion in history and introduce two relevant systems biological approaches based on molecular network modeling. We further introduce the recent development of network control strategies that can be used to identify useful molecular targets for cancer reversion and then discuss future challenges in systems biology. | - |
dc.language | English | - |
dc.publisher | Elsevier Ltd | - |
dc.subject | APC protein | - |
dc.subject | BCR ABL protein | - |
dc.subject | CCAAT enhancer binding protein beta | - |
dc.subject | cell protein | - |
dc.subject | DNA | - |
dc.subject | epidermal growth factor | - |
dc.subject | floxuridine | - |
dc.subject | histone deacetylase | - |
dc.subject | histone deacetylase inhibitor | - |
dc.subject | Myc protein | - |
dc.subject | octamer transcription factor 4 | - |
dc.subject | peroxisome proliferator activated receptor gamma | - |
dc.subject | protein Krev 1 | - |
dc.subject | protein MDM2 | - |
dc.subject | protein p53 | - |
dc.subject | protein SIAH1 | - |
dc.subject | protein TCTP | - |
dc.subject | protein tpt1 | - |
dc.subject | protein Wip1 | - |
dc.subject | retinoic acid | - |
dc.subject | STAT3 protein | - |
dc.subject | transcription factor Sox2 | - |
dc.subject | transforming growth factor beta | - |
dc.subject | unclassified drug | - |
dc.subject | Wnt protein | - |
dc.subject | aneuploidy | - |
dc.subject | antineoplastic activity | - |
dc.subject | apoptosis | - |
dc.subject | B cell leukemia | - |
dc.subject | blastocyst | - |
dc.subject | breast cancer | - |
dc.subject | cancer reversion | - |
dc.subject | cancer therapy | - |
dc.subject | carcinogenesis | - |
dc.subject | carcinogenicity | - |
dc.subject | cell cycle arrest | - |
dc.subject | cell differentiation | - |
dc.subject | cell nucleus transplantation | - |
dc.subject | cell proliferation | - |
dc.subject | cell self-renewal | - |
dc.subject | data-driven statistical network model | - |
dc.subject | disease course | - |
dc.subject | DNA damage | - |
dc.subject | embryonal carcinoma | - |
dc.subject | embryonic microenvironment | - |
dc.subject | epigenetics | - |
dc.subject | extracellular matrix | - |
dc.subject | gene amplification | - |
dc.subject | gene expression | - |
dc.subject | gene regulatory network | - |
dc.subject | gene targeting | - |
dc.subject | germ cell | - |
dc.subject | glioblastoma | - |
dc.subject | H-1 parvovirus | - |
dc.subject | human | - |
dc.subject | intestine carcinoma | - |
dc.subject | leukemia remission | - |
dc.subject | mechanism-based logical network model | - |
dc.subject | metabolome | - |
dc.subject | molecular regulatory network | - |
dc.subject | negative feedback | - |
dc.subject | nonhuman | - |
dc.subject | nuclear reprogramming | - |
dc.subject | oncogene | - |
dc.subject | oncogene addiction | - |
dc.subject | ovary teratoma | - |
dc.subject | phenotype | - |
dc.subject | plant tumor | - |
dc.subject | pluripotent stem cell | - |
dc.subject | positive feedback | - |
dc.subject | promyelocytic leukemia | - |
dc.subject | protein expression | - |
dc.subject | protein function | - |
dc.subject | revertant | - |
dc.subject | Review | - |
dc.subject | Rous sarcoma virus | - |
dc.subject | sarcoma | - |
dc.subject | signal transduction | - |
dc.subject | somatic mutation | - |
dc.subject | statistical model | - |
dc.subject | systems biology | - |
dc.subject | teratocarcinoma | - |
dc.subject | tumor microenvironment | - |
dc.title | Cancer reversion, a renewed challenge in systems biology | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85017159635 | - |
dc.type.rims | ART | - |
dc.citation.volume | 2 | - |
dc.citation.beginningpage | 49 | - |
dc.citation.endingpage | 58 | - |
dc.citation.publicationname | Current Opinion in Systems Biology | - |
dc.identifier.doi | 10.1016/j.coisb.2017.01.005 | - |
dc.contributor.localauthor | Cho, Kwang-Hyun | - |
dc.contributor.nonIdAuthor | Lee, Soobeom | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Review | - |
dc.subject.keywordAuthor | Cancer reversion | - |
dc.subject.keywordAuthor | Data-driven network modeling | - |
dc.subject.keywordAuthor | Mechanism-based network modeling | - |
dc.subject.keywordAuthor | Network control | - |
dc.subject.keywordAuthor | Systems biology | - |
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