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dc.contributor.authorSato, Fumiakija
dc.contributor.authorHatano, Etsuroja
dc.contributor.authorKitamura, Kojija
dc.contributor.authorMyomoto, Akiraja
dc.contributor.authorFujiwara, Takeshija
dc.contributor.authorTakizawa, Satokoja
dc.contributor.authorTsuchiya, Sokenja
dc.contributor.authorTsujimoto, Gozohja
dc.contributor.authorUemoto, Shinjija
dc.contributor.authorShimizu, Kazuharuja
dc.contributor.alternative佐藤, 史顕ja
dc.date.accessioned2011-03-04T05:32:52Z-
dc.date.available2011-03-04T05:32:52Z-
dc.date.issued2011-01-27-
dc.identifier.issn1932-6203ja
dc.identifier.urihttp://hdl.handle.net/2433/138084-
dc.description.abstractObjective: Hepatocellular carcinoma (HCC) is difficult to manage due to the high frequency of post-surgical recurrence. Early detection of the HCC recurrence after liver resection is important in making further therapeutic options, such as salvage liver transplantation. In this study, we utilized microRNA expression profiling to assess the risk of HCC recurrence after liver resection. Methods: We examined microRNA expression profiling in paired tumor and non-tumor liver tissues from 73 HCC patients who satisfied the Milan Criteria. We constructed prediction models of recurrence-free survival using the Cox proportional hazard model and principal component analysis. The prediction efficiency was assessed by the leave-one-out crossvalidation method, and the time-averaged area under the ROC curve (ta-AUROC). Results: The univariate Cox analysis identified 13 and 56 recurrence-related microRNAs in the tumor and non-tumor tissues, such as miR-96. The number of recurrence-related microRNAs was significantly larger in the non-tumor-derived microRNAs (N-miRs) than in the tumor-derived microRNAs (T-miRs, P,0.0001). The best ta-AUROC using the whole dataset, T-miRs, NmiRs, and clinicopathological dataset were 0.8281, 0.7530, 0.7152, and 0.6835, respectively. The recurrence-free survival curve of the low-risk group stratified by the best model was significantly better than that of the high-risk group (Log-rank: P = 0.00029). The T-miRs tend to predict early recurrence better than late recurrence, whereas N-miRs tend to predict late recurrence better (P,0.0001). This finding supports the concept of early recurrence by the dissemination of primary tumor cells and multicentric late recurrence by the ‘field effect’. Conclusion: microRNA profiling can predict HCC recurrence in Milan criteria cases.ja
dc.format.mimetypeapplication/pdfja
dc.language.isoengja
dc.publisherPublic Library of Science(PLoS)ja
dc.rights© 2011 Sato et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ja
dc.titleMicroRNA Profile Predicts Recurrence after Resection in Patients with Hepatocellular Carcinoma within the Milan Criteriaja
dc.type.niitypeJournal Articleja
dc.identifier.jtitlePLoS ONEja
dc.identifier.volume6ja
dc.identifier.issue1ja
dc.relation.doi10.1371/journal.pone.0016435ja
dc.textversionpublisherja
dc.identifier.artnume16435ja
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