Identification of Prognostic Genes in Neuroblastoma in Children by Weighted Gene Coexpression Network Analysis

Abstract

Background. Neuroblastoma is a malignant neuroendocrine tumor from the sympathetic nervous system, the most common extracranial tumor in children. Identifying potential prognostic markers of neuroblastoma can provide clues for early diagnosis, recurrence, and treatment. Methods. RNA sequence data and clinical features of 147 neuroblastomas were obtained from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments project) database. Application weighted gene coexpression network analysis (WGCNA) was used to construct a free-scale gene coexpression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. We performed Lasso regression and Cox regression analyses to identify the three most important genes and develop a new prognostic model. Data from the GSE85047 cohort verified the predictive accuracy of the prognostic model. Results. 14 coexpression modules were constructed using WGCNA. Brown coexpression modules were found to be significantly associated with disease survival status. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analyses using the Cox proportional hazards regression model. Finally, we constructed a three-gene prognostic model: risk score = (0.003812659 $\ast$ CKB) + (−0.152376975 $\ast$ expDST) + (0.032032815 $\ast$ expDUT). The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group ( $P=1.225e-06$ ). The risk model was also regarded as an independent predictor of prognosis (HR = 1.632; 95% CI = 1.391–1.934; $P<0.001$ ). Conclusion. Our study constructed a neuroblastoma coexpressing gene module and identified a prognostic potential risk model for prognosis in neuroblastoma.