Wanli Yang1, Liaoran Niu1, Xinhui Zhao2, Lili Duan1, Yiding Li1, Xiaoqian Wang1, Yujie Zhang1, Wei Zhou1, Jinqiang Liu1, Qingchuan Zhao1, Yu Han3, Daiming Fan1, Liu Hong1. 1. State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical University Xi'an 710032, Shaanxi Province, China. 2. Department of Thyroid and Breast Surgery, Xi'an No. 3 Hospital, The Affiliated Hospital of Northwest University, Northwest University Xi'an 710018, Shaanxi Province, China. 3. Department of Otolaryngology, Xijing Hospital, Fourth Military Medical University Xi'an 710032, Shaanxi Province, China.
Abstract
OBJECTIVE: This study aimed to identify the novel prognostic gene signature based on autophagy-associated genes (ARGs) in hepatocellular carcinoma (HCC). METHODS: The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model. Kaplan-Meier and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the model. GSE14520 dataset (testing cohort) was used to validate the prognostic risk model in TCGA. A clinical nomogram was established to predict the survival rate of HCC patients. RESULTS: Totally 27 differentially expressed ARGs were identified. Three OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these three ARGs, a prognostic prediction model was constructed. HCC patients with high risk score present poorer prognosis than those with low risk score both in TCGA cohort (P=4.478e-04) and testing cohort (P=1.274e-03). Moreover, the risk score curve shows a well feasibility in predicting the patients' survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict survival rate in HCC patients. CONCLUSIONS: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients. AJTR
OBJECTIVE: This study aimed to identify the novel prognostic gene signature based on autophagy-associated genes (ARGs) in hepatocellular carcinoma (HCC). METHODS: The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model. Kaplan-Meier and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the model. GSE14520 dataset (testing cohort) was used to validate the prognostic risk model in TCGA. A clinical nomogram was established to predict the survival rate of HCC patients. RESULTS: Totally 27 differentially expressed ARGs were identified. Three OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these three ARGs, a prognostic prediction model was constructed. HCC patients with high risk score present poorer prognosis than those with low risk score both in TCGA cohort (P=4.478e-04) and testing cohort (P=1.274e-03). Moreover, the risk score curve shows a well feasibility in predicting the patients' survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict survival rate in HCC patients. CONCLUSIONS: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients. AJTR
Authors: Maria Cattoni; Eric Vallières; Lisa M Brown; Amir A Sarkeshik; Stefano Margaritora; Alessandra Siciliani; Pier Luigi Filosso; Francesco Guerrera; Andrea Imperatori; Nicola Rotolo; Farhood Farjah; Grace Wandell; Kimberly Costas; Catherine Mann; Michal Hubka; Stephen Kaplan; Alexander S Farivar; Ralph W Aye; Brian E Louie Journal: J Thorac Cardiovasc Surg Date: 2017-09-09 Impact factor: 5.209
Authors: Ethan Cerami; Jianjiong Gao; Ugur Dogrusoz; Benjamin E Gross; Selcuk Onur Sumer; Bülent Arman Aksoy; Anders Jacobsen; Caitlin J Byrne; Michael L Heuer; Erik Larsson; Yevgeniy Antipin; Boris Reva; Arthur P Goldberg; Chris Sander; Nikolaus Schultz Journal: Cancer Discov Date: 2012-05 Impact factor: 39.397