Zihao Wang1,2, Xin Ji3, Lu Gao1,2, Xiaopeng Guo1,2, Wei Lian1,2, Kan Deng1,2, Bing Xing1,2. 1. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2. China Pituitary Disease Registry Center, Chinese Pituitary Adenoma Cooperative Group, Beijing, China. 3. Department of Nuclear Medicine, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Abstract
PURPOSE: Glioblastoma (GBM) is one of the most aggressive brain tumors with high mortality, and tumor-derived exosomes provide new insight into the mechanisms of GBM tumorigenesis, metastasis and therapeutic resistance. We aimed to establish an exosome-derived competitive endogenous RNA (ceRNA) network for constructing a prognostic model for GBM. METHODS: We obtained the expression profiles of long noncoding RNAs (lncRNAs), miRNAs, and mRNAs from the GEO and TCGA databases and identified differentially expressed RNAs in GBM to construct a ceRNA network. By performing lasso and multivariate Cox regression analyses, we identified optimal prognosis-related differentially expressed lncRNAs (DElncRNAs) and generated a risk score model termed the exosomal lncRNA (exo-lncRNA) signature. The exo-lncRNA signature was subsequently validated in the CGGA GBM cohort. Finally, a novel prognostic nomogram was constructed based on the exo-lncRNA signature and clinicopathological parameters and validated in the CGGA external cohort. Based on the ceRNA hypothesis, oncocers were identified based on highly positive correlations between lncRNAs and mRNAs mediated by the same miRNAs. Furthermore, regression analyses were performed to assess correlations between the expression abundances of lncRNAs in tumors and exosomes. RESULTS: A total of 45 DElncRNAs, six DEmiRNAs, and 38 DEmRNAs were identified, and an exosome-derived ceRNA network was built. Three optimal prognostic-related DElncRNAs, HOTAIR (HR=0.341, P<0.001), SOX21-AS1 (HR=0.30, P<0.001), and STEAP3-AS1 (HR=2.47, P<0.001), were included to construct the exo-lncRNA signature, which was further proven to be an independent prognostic factor. The novel prognostic nomogram was constructed based on the exo-lncRNA signature, patient age, pharmacotherapy, radiotherapy, IDH mutation status, and MGMT promoter status, with a concordance index of 0.878. ROC and calibration plots both suggested that the nomogram had beneficial discrimination and predictive abilities. A total of 11 pairs of prognostic oncocers were identified. Regression analysis suggested excellent consistency of the expression abundance of the three exosomal lncRNAs between exosomes and tumor tissues. CONCLUSIONS: Exosomal lncRNAs may serve as promising prognostic predictors and therapeutic targets. The prognostic nomogram based on the exo-lncRNA signature might provide an intuitive method for individualized survival prediction and facilitate better treatment strategies.
PURPOSE: Glioblastoma (GBM) is one of the most aggressive brain tumors with high mortality, and tumor-derived exosomes provide new insight into the mechanisms of GBM tumorigenesis, metastasis and therapeutic resistance. We aimed to establish an exosome-derived competitive endogenous RNA (ceRNA) network for constructing a prognostic model for GBM. METHODS: We obtained the expression profiles of long noncoding RNAs (lncRNAs), miRNAs, and mRNAs from the GEO and TCGA databases and identified differentially expressed RNAs in GBM to construct a ceRNA network. By performing lasso and multivariate Cox regression analyses, we identified optimal prognosis-related differentially expressed lncRNAs (DElncRNAs) and generated a risk score model termed the exosomal lncRNA (exo-lncRNA) signature. The exo-lncRNA signature was subsequently validated in the CGGA GBM cohort. Finally, a novel prognostic nomogram was constructed based on the exo-lncRNA signature and clinicopathological parameters and validated in the CGGA external cohort. Based on the ceRNA hypothesis, oncocers were identified based on highly positive correlations between lncRNAs and mRNAs mediated by the same miRNAs. Furthermore, regression analyses were performed to assess correlations between the expression abundances of lncRNAs in tumors and exosomes. RESULTS: A total of 45 DElncRNAs, six DEmiRNAs, and 38 DEmRNAs were identified, and an exosome-derived ceRNA network was built. Three optimal prognostic-related DElncRNAs, HOTAIR (HR=0.341, P<0.001), SOX21-AS1 (HR=0.30, P<0.001), and STEAP3-AS1 (HR=2.47, P<0.001), were included to construct the exo-lncRNA signature, which was further proven to be an independent prognostic factor. The novel prognostic nomogram was constructed based on the exo-lncRNA signature, patient age, pharmacotherapy, radiotherapy, IDH mutation status, and MGMT promoter status, with a concordance index of 0.878. ROC and calibration plots both suggested that the nomogram had beneficial discrimination and predictive abilities. A total of 11 pairs of prognostic oncocers were identified. Regression analysis suggested excellent consistency of the expression abundance of the three exosomal lncRNAs between exosomes and tumor tissues. CONCLUSIONS: Exosomal lncRNAs may serve as promising prognostic predictors and therapeutic targets. The prognostic nomogram based on the exo-lncRNA signature might provide an intuitive method for individualized survival prediction and facilitate better treatment strategies.
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