MicroRNA signatures predict prognosis of patients with glioblastoma multiforme through the Cancer Genome Atlas

// Ying Yuan 1, 2, * , Hua Zhang 2, 3 , Xuexia Liu 4, * , Zhongming Lu 5 , Guojun Li 2, 6 , Meixia Lu 7 and Xiaofeng Tao 1 1 Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China 2 Department of Head and Neck Surgery, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA 3 Department of Otolaryngology-Head and Neck Surgery, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China 4 Department of Central Laboratory, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China 5 Department of Otolaryngology-Head and Neck Surgery, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangzhou, China 6 Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX, USA 7 Department of Epidemiology and Biostatistics and The Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China * These authors have contributed equally to this work and are considered co-first authors Correspondence to: Xiaofeng Tao, email: cjr.taoxiaofeng@vip.163.com Meixia Lu, email: mlu@hust.edu.cn Keywords: glioblastoma multiforme, microRNA, prognosis, overall survival, TCGA Received: February 07, 2017 Accepted: March 14, 2017 Published: April 06, 2017 ABSTRACT MicroRNAs (miRNAs) play major roles in various biological processes and have been implicated in the pathogenesis and malignant progression of glioblastoma multiforme (GBM). The aim of this study was to assess the predictive values of miRNAs for overall survival (OS) of patients with GBM. MiRNA expression profiles and clinical information of 563 GBM patients were obtained from the Cancer Genome Atlas. The most significantly altered miRNAs were identified and miRNA expression profiles were performed, through principal component analysis, the least absolute shrinkage and selection operator method. The survival analysis was performed using the Cox regression models. Additionally, receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. We used the bioinformatics tools to establish the miRNA signature for biological relevance assessment. A linear prognostic model of three miRNAs was developed and the patients were divided into high risk and low risk groups based this model. The area under the ROC curve (AUC) for the three miRNA signature predicting 5-year survival was 0.894 (95%CI, 0.789-1.000) in the testing set and0.841 (95%CI, 0.689-0.993) in all GBM patients. High risk patients had significantly shorter OS than patients with low risk ( P < 0.001). The results from this study support a three miRNA signature for outcome prediction of GBM. These results provided a new prospect for prognostic biomarker of GBM.