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Sixty-five gene-based risk score classifier predicts overall survival in hepatocellular carcinoma

Authors
 Soo Mi Kim  ;  Sun-Hee Leem  ;  In-Sun Chu  ;  Yun-Yong Park  ;  Sang-Cheol Kim  ;  Sang-Bae Kim  ;  Eun-Sung Park  ;  Jae Yun Lim  ;  Jeonghoon Heo  ;  Yoon Jun Kim  ;  Dae-Ghon Kim  ;  Ahmed Kaseb  ;  Young Nyun Park  ;  Xin Wei Wang  ;  Snorri S. Thorgeirsson  ;  Ju-Seog Lee 
Citation
 Hepatology, Vol.55(5) : 1443-1452, 2012 
Journal Title
 Hepatology 
ISSN
 0270-9139 
Issue Date
2012
MeSH
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Area Under Curve ; Carcinoma, Hepatocellular/genetics* ; Carcinoma, Hepatocellular/mortality* ; Carcinoma, Hepatocellular/pathology ; Cohort Studies ; Databases, Factual ; Disease-Free Survival ; Female ; Gene Expression Profiling/classification* ; Gene Expression Regulation, Neoplastic ; Genetic Predisposition to Disease/epidemiology* ; Humans ; Kaplan-Meier Estimate ; Liver Neoplasms/genetics* ; Liver Neoplasms/mortality* ; Liver Neoplasms/pathology ; Male ; Middle Aged ; Multivariate Analysis ; Predictive Value of Tests ; Proportional Hazards Models ; ROC Curve ; Risk Assessment ; Survival Analysis ; Young Adult
Keywords
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Area Under Curve ; Carcinoma, Hepatocellular/genetics* ; Carcinoma, Hepatocellular/mortality* ; Carcinoma, Hepatocellular/pathology ; Cohort Studies ; Databases, Factual ; Disease-Free Survival ; Female ; Gene Expression Profiling/classification* ; Gene Expression Regulation, Neoplastic ; Genetic Predisposition to Disease/epidemiology* ; Humans ; Kaplan-Meier Estimate ; Liver Neoplasms/genetics* ; Liver Neoplasms/mortality* ; Liver Neoplasms/pathology ; Male ; Middle Aged ; Multivariate Analysis ; Predictive Value of Tests ; Proportional Hazards Models ; ROC Curve ; Risk Assessment ; Survival Analysis ; Young Adult
Abstract
Clinical application of the prognostic gene expression signature has been delayed due to the large number of genes and complexity of prediction algorithms. In the current study we aimed to develop an easy-to-use risk score with a limited number of genes that can robustly predict prognosis of patients with hepatocellular carcinoma (HCC). The risk score was developed using Cox coefficient values of 65 genes in the training set (n = 139) and its robustness was validated in test sets (n = 292). The risk score was a highly significant predictor of overall survival (OS) in the first test cohort (P = 5.6 × 10(-5), n = 100) and the second test cohort (P = 5.0 × 10(-5) , n = 192). In multivariate analysis, the risk score was a significant risk factor among clinical variables examined together (hazard ratio [HR], 1.36; 95% confidence interval [CI], 1.13-1.64; P = 0.001 for OS). CONCLUSION: The risk score classifier we have developed can identify two clinically distinct HCC subtypes at early and late stages of the disease in a simple and highly reproducible manner across multiple datasets.
Files in This Item:
T201205983.pdf Download
DOI
22105560
Appears in Collections:
5. Research Institutes (연구소) > Institute for Medical Convergence (연의-생공연 메디컬융합연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Park, Young Nyun(박영년) ORCID logo https://orcid.org/0000-0003-0357-7967
Park, Eun Sung(박은성)
Lim, Jae Yun(임재윤)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/90366
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