title: MicroRNA profile predicts recurrence after resection in patients with hepatocellular carcinoma within the Milan Criteria.
abstract: OBJECTIVE
  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 cross-validation 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, N-miRs, 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'.
  
  
  CONCLUSIONS
  MicroRNA profiling can predict HCC recurrence in Milan criteria cases.
authors.affiliation: Department of Nanobio Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan. fsato@pharm.kyoto-u.ac.jp
authors.forename: Fumiaki
authors.initials: F
authors.lastname: Sato
authors.forename: Etsuro
authors.initials: E
authors.lastname: Hatano
authors.forename: Koji
authors.initials: K
authors.lastname: Kitamura
authors.forename: Akira
authors.initials: A
authors.lastname: Myomoto
authors.forename: Takeshi
authors.initials: T
authors.lastname: Fujiwara
authors.forename: Satoko
authors.initials: S
authors.lastname: Takizawa
authors.forename: Soken
authors.initials: S
authors.lastname: Tsuchiya
authors.forename: Gozoh
authors.initials: G
authors.lastname: Tsujimoto
authors.forename: Shinji
authors.initials: S
authors.lastname: Uemoto
authors.forename: Kazuharu
authors.initials: K
authors.lastname: Shimizu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000401:mortality / Q000601:surgery; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000601:surgery; D008297:Male; D035683:MicroRNAs / Q000032:analysis*; D008875:Middle Aged; D011184:Postoperative Period; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012008:Recurrence; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D035683:MicroRNAs