Comprehensive In Silico Analysis of a Novel Serum Exosome-Derived Competitive Endogenous RNA Network for Constructing a Prognostic Model for Glioblastoma - Wang, Zihao; Ji, Xin; Gao, Lu; Guo, Xiaopeng; Lian, Wei; Deng, Kan; Xing, Bing (Frontiers in Oncology, 2021)
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 the optimal prognosis-related differentially expressed lncRNAs (DElncRNAs) and constructed a risk score model called 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 the clinicopathological parameters and further validated in the CGGA external cohort. Based on the ceRNA hypothesis, the oncocers were identified by highly positive correlations between lncRNAs and mRNAs mediated by the same miRNAs. Furthermore, regression analyses were performed to assess the correlations between the expression abundances of lncRNAs in tumors and exosomes. Results: A total of 45 DElncRNAs, 6 DEmiRNAs, and 38 DEmRNAs were identified, and the exosome-derived ceRNA network was constructed. 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 and had a concordance index of 0.878. The ROC and calibration plots both suggested that the nomogram had beneficial discrimination and predictive abilities. Finally, a total of 11 pairs of prognostic oncocers were identified. Regression analysis suggested excellent consistency of the expression abundance of the 3 exosomal lncRNAs between exosomes and tumor tissues. Conclusions: The exosomal lncRNAs may serve as promising prognostic predictors and therapeutic targets. The prognostic nomogram based on the exo-lncRNA signature could provide an intuitive method for individualized survival prediction and facilitating better treatment strategies.