Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma

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dc.citation.firstpage15015en_US
dc.citation.issueNumber19en_US
dc.citation.journalTitleOncotargeten_US
dc.citation.lastpage15026en_US
dc.citation.volumeNumber9en_US
dc.contributor.authorYe, Fengdanen_US
dc.contributor.authorJia, Dongyaen_US
dc.contributor.authorLu, Mingyangen_US
dc.contributor.authorLevine, Herberten_US
dc.contributor.authorDeem, Michael W.en_US
dc.contributor.orgBioengineeringen_US
dc.contributor.orgBiosciencesen_US
dc.contributor.orgPhysics and Astronomyen_US
dc.date.accessioned2018-07-16T21:54:26Zen_US
dc.date.available2018-07-16T21:54:26Zen_US
dc.date.issued2018en_US
dc.description.abstractAbnormal metabolism is an emerging hallmark of cancer. Cancer cells utilize both aerobic glycolysis and oxidative phosphorylation (OXPHOS) for energy production and biomass synthesis. Understanding the metabolic reprogramming in cancer can help design therapies to target metabolism and thereby to improve prognosis. We have previously argued that more malignant tumors are usually characterized by a more modular expression pattern of cancer-associated genes. In this work, we analyzed the expression patterns of metabolism genes in terms of modularity for 371 hepatocellular carcinoma (HCC) samples from the Cancer Genome Atlas (TCGA). We found that higher modularity significantly correlated with glycolytic phenotype, later tumor stages, higher metastatic potential, and cancer recurrence, all of which contributed to poorer prognosis. Among patients with recurred tumors, we found the correlation of higher modularity with worse prognosis during early to mid-progression. Furthermore, we developed metrics to calculate individual modularity, which was shown to be predictive of cancer recurrence and patientsﾒ survival and therefore may serve as a prognostic biomarker. Our overall conclusion is that more aggressive HCC tumors, as judged by decreased host survival probability, had more modular expression patterns of metabolic genes. These results may be used to identify cancer driver genes and for drug design.en_US
dc.identifier.citationYe, Fengdan, Jia, Dongya, Lu, Mingyang, et al.. "Modularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinoma." <i>Oncotarget,</i> 9, no. 19 (2018) Impact Journals: 15015-15026. https://doi.org/10.18632/oncotarget.24551.en_US
dc.identifier.doihttps://doi.org/10.18632/oncotarget.24551en_US
dc.identifier.urihttps://hdl.handle.net/1911/102446en_US
dc.language.isoengen_US
dc.publisherImpact Journalsen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en_US
dc.subject.keywordmodularityen_US
dc.subject.keywordmetabolismen_US
dc.subject.keywordhepatocellular carcinomaen_US
dc.subject.keywordHCCen_US
dc.subject.keywordprognosisen_US
dc.titleModularity of the metabolic gene network as a prognostic biomarker for hepatocellular carcinomaen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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