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∗CKB) + (−0.152376975∗expDST) + (0.032032815∗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.
Highlights
Neuroblastoma is the most common extracranial tumor in children and is the most common tumor in infants and young children [1]
Construction of Coexpression Modules. e raw data of neuroblastoma were downloaded from the TARGET database and contained expression values of 20,098 genes from 147 patients. e details of clinical/pathological features are listed in Table 1. e raw data are preprocessed by using R for background correction and normalization
Gene annotations are performed to match probes and gene symbols, and probes that match several genes are removed, and for genes matched by multiple probes, the median is considered the final expression value. e SD of each gene was calculated and ranged from large to small, and 5,025 genes were selected for weighted gene coexpression network analysis (WGCNA) analysis
Summary
Neuroblastoma is the most common extracranial tumor in children and is the most common tumor in infants and young children [1]. Amplification mutations in the N-myc gene are common in neuroblastoma. 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. 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. We constructed a three-gene prognostic model: risk score (0.003812659 ∗ CKB) + (−0.152376975 ∗ expDST) + (0.032032815 ∗ expDUT). Our study constructed a neuroblastoma coexpressing gene module and identified a prognostic potential risk model for prognosis in neuroblastoma
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