A novel risk signature that combines 10 long noncoding RNAs to predict neuroblastoma prognosis. Issue 4 (14th October 2019) - Gao, Li; Lin, Peng; Chen, Peng; Gao, Rui‐Zhi; Yang, Hong; He, Yun; Chen, Jia‐Bo; Luo, Yi‐Ge; Xu, Qiong‐Qian; Liang, Song‐Wu; Gu, Jin‐Han; Huang, Zhi‐Guang; et al.
Abstract: Neuroblastoma (NBL) is the most frequently encountered extracranial solid neoplasm and impacts significantly on the survival of patients, especially in cases of advanced tumor stage or relapse. A long noncoding RNA (lncRNA) signature to predict the survival of patients with NBL is proposed in this paper. Differentially expressed lncRNA (DElncRNA) was selected using the Limma plus Voom package in R based on the RNA‐sequencing data downloaded from the Therapeutically Applicable Research To Generate Effective Treatments database and Genotype‐Tissue Expression database. Univariate cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate cox regression analysis were conducted to identify candidate DElncRNAs for the risk signature. Consequently, 10 DElncRNAs were designated as candidate DElncRNAs for the risk signature. Time‐dependent receiver operating characteristic curves and Kapan–Meier survival curves confirmed the efficacy of the risk signature in predicting the survival of patients with NBL (area under the curve = 0.941; p  ≤ .001). One of the DElncRNA constituent subparts (LINC01010) was significantly associated with the survival outcome of patients with NBL in GSE62564 ( p  = .004). Thus, a risk signature comprising 10 DElncRNAs was identified as effective for individual risk stratification and the survival prediction outcomes of patients with NBL. Abstract : In this study, we developed a 10‐differentially expressed lncRNA (DElncRNA) risk signature based on RNA‐sequencing data from Therapeutically Applicable Research To Generate Effective Treatments and Genotype‐Tissue Expression by applying combination methods of differential expression analysis, univariate cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate cox regression analysis. The 10‐DElncRNA risk signature could effectively predict the prognosis of patients with NBL independent of important clinical variables. Finally, a DElncRNA‐differentially expressed messenger RNA (DEmRNA) regulation network and functional annotations for DEmRNAs correlated with DElncRNA constituent subparts facilitated understandings of the mechanism of the 10‐DElncRNA risk signature in NBL.