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Recurrence-associated pathways in hepatitis B virus-positive hepatocellular carcinoma

Authors
 Bu-Yeo Kim  ;  Dong Wook Choi  ;  Seon Rang Woo  ;  Eun-Ran Park  ;  Je-Geun Lee  ;  Su-Hyeon Kim  ;  Imhoi Koo  ;  Sun-Hoo Park  ;  Chul Ju Han  ;  Sang Bum Kim  ;  Young Il Yeom  ;  Suk-Jin Yang  ;  Ami Yu  ;  Jae Won Lee  ;  Ja June Jang  ;  Myung-Haing Cho  ;  Won Kyung Jeon  ;  Young Nyun Park  ;  Kyung-Suk Suh  ;  Kee-Ho Lee 
Citation
 BMC GENOMICS, Vol.16 : 279, 2015 
Journal Title
 BMC GENOMICS 
Issue Date
2015
MeSH
Adult ; Algorithms ; Carcinoma, Hepatocellular/complications ; Carcinoma, Hepatocellular/diagnosis* ; Carcinoma, Hepatocellular/epidemiology ; Cluster Analysis ; Databases, Factual ; Disease-Free Survival ; Female ; Hepacivirus/isolation & purification ; Hepatitis B/complications ; Hepatitis B/diagnosis* ; Hepatitis B/virology ; Hepatitis B virus/isolation & purification ; Humans ; Liver Neoplasms/complications ; Liver Neoplasms/diagnosis* ; Liver Neoplasms/epidemiology ; Male ; Middle Aged ; Neoplasm Recurrence, Local ; Principal Component Analysis ; Prognosis ; Risk
Keywords
Recurrence-associated pathway ; Hepatocellular carcinoma ; Principal component analysis ; Prognosis ; Risk ; Small tumor
Abstract
BACKGROUND: Despite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma (HCC) recurrence for application in clinical purposes. To overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of HCC recurrence. RESULTS: By implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis B virus (HBV)-positive HCC recurrence (p < 0.01), from our microarray dataset composed of 142 HBV-positive HCCs. In relation to the public HBV- and public hepatitis C virus (HCV)-positive HCC datasets, we detected 46 (71.9%) and 18 (28.1%) common recurrence-associated pathways, respectively. However, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different HCC datasets. Non-supervised clustering of the 64 recurrence-associated pathways facilitated the classification of HCC patients into high- and low-risk subgroups, based on risk of recurrence (p < 0.0001). The pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public HCC datasets. Through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor (p < 0.0001) along with tumor number, tumor size and Edmondson's grade. Moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup (N = 12) among patients (N = 26) with small HCC (<3 cm). CONCLUSIONS: Using pathway-based analysis, we successfully identified the pathways involved in recurrence of HBV-positive HCC that may be effectively used as prognostic markers.
Files in This Item:
T201503330.pdf Download
DOI
10.1186/s12864-015-1472-x
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Park, Young Nyun(박영년) ORCID logo https://orcid.org/0000-0003-0357-7967
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/141038
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