Chunyu Zhang1, Haitao Liu2, Pengfei Xu3, Yinqiu Tan1, Yang Xu1, Long Wang1, Baohui Liu1, Qianxue Chen1, Daofeng Tian4. 1. Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, People's Republic of China. 2. Department of Cardiothoracic Surgery, The First Affiliated Hospital of Jiaxing University, Jiaxing, 314001, Zhejiang Province, People's Republic of China. 3. Sun Yat-sen University, The Seventh Affiliated Hospital, Shenzhen, 518000, Guangdong Province, People's Republic of China. 4. Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, People's Republic of China. tiandaofeng@hotmail.com.
Abstract
BACKGROUND: To accurately predict the prognosis of glioma patients. METHODS: A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. RESULTS: By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. CONCLUSION: We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.
BACKGROUND: To accurately predict the prognosis of gliomapatients. METHODS: A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. RESULTS: By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of gliomapatients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of gliomapatients. CONCLUSION: We established a five-lncRNA signature as a potentially reliable tool for survival prediction of gliomapatients.
Authors: Marc Sultan; Marcel H Schulz; Hugues Richard; Alon Magen; Andreas Klingenhoff; Matthias Scherf; Martin Seifert; Tatjana Borodina; Aleksey Soldatov; Dmitri Parkhomchuk; Dominic Schmidt; Sean O'Keeffe; Stefan Haas; Martin Vingron; Hans Lehrach; Marie-Laure Yaspo Journal: Science Date: 2008-07-03 Impact factor: 47.728
Authors: Michael Weller; Wolfgang Wick; Ken Aldape; Michael Brada; Mitchell Berger; Stefan M Pfister; Ryo Nishikawa; Mark Rosenthal; Patrick Y Wen; Roger Stupp; Guido Reifenberger Journal: Nat Rev Dis Primers Date: 2015-07-16 Impact factor: 52.329