한빛사 논문
Abstract
Soo Mi Kim1,13,†, Sun-Hee Leem2,†, In-Sun Chu3,†, Yun-Yong Park1, Sang-Cheol Kim3, Sang-Bae Kim1, Eun-Sung Park4, Jae Yun Lim5, Jeonghoon Heo6, Yoon Jun Kim7, Dae-Ghon Kim8, Ahmed Kaseb9, Young Nyun Park10, Xin Wei Wang11, Snorri S. Thorgeirsson12, Ju-Seog Lee1,*
1Department of Systems Biology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230
2Department of Biological Science, Dong-A University, Busan, Korea
3Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea
4Institute for Medical Convergence, Yonsei University College of Medicine, Seoul, Korea
5Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
6Kosin University College of Medicine, Busan, Korea
7Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea
8Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chonbuk National University Medical School and Hospital, Jeonju, Korea
9Department of GI Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230
10Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
11Lab of Human Carcinogenesis, National Cancer Institute, National Institute of Health, Bethesda, MD
12Lab of Experimental Carcinogenesis, National Cancer Institute, National Institute of Health, Bethesda, MD
13Department of Physiology, Chonbuk National University Medical School and Hospital, Jeonju, Korea
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 current study, we aim to develop an easy-to-use risk score with a limited number of genes that can robustly predict prognosis of patients with HCC. The risk score was developed by 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 significant risk factor among clinical variables examined together (hazard ratio [HR], 1.36; 95% confidential 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 stage of the disease in a simple and highly reproducible manner across multiple data sets. (HEPATOLOGY 2011.)
Keywords:Hepatocellular Carcinoma;Gene expression signature;Microarrays;Prognostic biomarkers
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