Yue Zhong1,2,3, Yong Yang2,4, Lei He2,4, Yang Zhou2, Niangmei Cheng2, Geng Chen2,4, Bixing Zhao2,4, Yingchao Wang2,4, Gaoxiong Wang5,6, Xiaolong Liu2,3,4. 1. College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, 350002, People's Republic of China. 2. The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, 350025, People's Republic of China. 3. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian, 350002, People's Republic of China. 4. College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, Fujian, 350116, People's Republic of China. 5. Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, 362001, People's Republic of China. 6. Quanzhou Maternal and Child Health Hospital, Children's Hospital, Quanzhou, Fujian, 362017, People's Republic of China.
Abstract
BACKGROUND: The aberrant expressions of lncRNAs have been frequently demonstrated to be closely associated with the prognosis of patients in many cancer types including hepatocellular carcinoma (HCC). Integration of these lncRNAs might provide accurate evaluation of HCC. Therefore, this study aims to develop a novel prognostic evaluation model based on the expression of lncRNAs to predict the survival of HCC patients, postoperatively. PATIENTS AND METHODS: RNA sequencing (RNA-seq) analysis was performed for 61 HCC patients (training cohort) to screen prognosis-associated lncRNAs with univariate Cox regression and Log rank test analyses. Multivariate Cox regression analysis was then applied to establish the final model, which was further verified in a validation cohort (n=191). Moreover, performance of the mode was assessed with time-dependent receiver operating characteristic curve (tdROC), Harrell's c-index, and Gönen & Heller's K. RESULTS: After a serial statistical computation, a novel risk scoring model consisting of four lncRNAs and TNM staging was established, which could successfully divide the HCC patients into low-risk and high-risk groups with significantly different OS and RFS in both training and validation cohorts. tdROC analysis showed that this model achieved a high performance in predicting OS and 2-year RFS in both cohorts. Gene Set Enrichment Analysis revealed that HCC tumor tissues with high-risk score have stronger capacities in immune escape and resistance to treatment. CONCLUSION: We successfully established a novel prognostic evaluation model, which exhibited reliable capacity in predicting the OS and early recurrence of HCC patients with relatively higher accuracy.
BACKGROUND: The aberrant expressions of lncRNAs have been frequently demonstrated to be closely associated with the prognosis of patients in many cancer types including hepatocellular carcinoma (HCC). Integration of these lncRNAs might provide accurate evaluation of HCC. Therefore, this study aims to develop a novel prognostic evaluation model based on the expression of lncRNAs to predict the survival of HCC patients, postoperatively. PATIENTS AND METHODS: RNA sequencing (RNA-seq) analysis was performed for 61 HCC patients (training cohort) to screen prognosis-associated lncRNAs with univariate Cox regression and Log rank test analyses. Multivariate Cox regression analysis was then applied to establish the final model, which was further verified in a validation cohort (n=191). Moreover, performance of the mode was assessed with time-dependent receiver operating characteristic curve (tdROC), Harrell's c-index, and Gönen & Heller's K. RESULTS: After a serial statistical computation, a novel risk scoring model consisting of four lncRNAs and TNM staging was established, which could successfully divide the HCC patients into low-risk and high-risk groups with significantly different OS and RFS in both training and validation cohorts. tdROC analysis showed that this model achieved a high performance in predicting OS and 2-year RFS in both cohorts. Gene Set Enrichment Analysis revealed that HCC tumor tissues with high-risk score have stronger capacities in immune escape and resistance to treatment. CONCLUSION: We successfully established a novel prognostic evaluation model, which exhibited reliable capacity in predicting the OS and early recurrence of HCC patients with relatively higher accuracy.
Authors: Anthony W H Chan; Jianhong Zhong; Sarah Berhane; Hidenori Toyoda; Alessandro Cucchetti; KeQing Shi; Toshifumi Tada; Charing C N Chong; Bang-De Xiang; Le-Qun Li; Paul B S Lai; Vincenzo Mazzaferro; Marta García-Fiñana; Masatoshi Kudo; Takashi Kumada; Sasan Roayaie; Philip J Johnson Journal: J Hepatol Date: 2018-09-18 Impact factor: 25.083
Authors: Alessandro Cucchetti; Fabio Piscaglia; Eugenio Caturelli; Luisa Benvegnù; Marco Vivarelli; Giorgio Ercolani; Matteo Cescon; Matteo Ravaioli; Gian Luca Grazi; Luigi Bolondi; Antonio Daniele Pinna Journal: Ann Surg Oncol Date: 2008-11-26 Impact factor: 5.344