Identification of Potential Prognostic Genes for Neuroblastoma

Background and Objective: Neuroblastoma (NB), the most common pediatric solid tumor apart from brain tumor, is associated with dismal long-term survival. The aim of this study was to identify a gene signature to predict the prognosis of NB patients.Materials and Methods: GSE49710 dataset from the Ge...

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Main Authors: Xiaodan Zhong, Yuanning Liu, Haiming Liu, Yutong Zhang, Linyu Wang, Hao Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Genetics
Subjects:
GEO
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00589/full
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spelling doaj-ed7128a85a7a486eb2f5dfb2855e42732020-11-25T00:09:20ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-11-01910.3389/fgene.2018.00589429455Identification of Potential Prognostic Genes for NeuroblastomaXiaodan Zhong0Xiaodan Zhong1Xiaodan Zhong2Yuanning Liu3Yuanning Liu4Haiming Liu5Haiming Liu6Yutong Zhang7Linyu Wang8Linyu Wang9Hao Zhang10Hao Zhang11College of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, ChinaDepartment of Pediatric Oncology, The First Hospital of Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, ChinaDepartment of Pediatric Oncology, The First Hospital of Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaKey Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, ChinaBackground and Objective: Neuroblastoma (NB), the most common pediatric solid tumor apart from brain tumor, is associated with dismal long-term survival. The aim of this study was to identify a gene signature to predict the prognosis of NB patients.Materials and Methods: GSE49710 dataset from the Gene Expression Omnibus (GEO) database was downloaded and differentially expressed genes (DEGs) were analyzed using R package “limma” and SPSS software. The gene ontology (GO) and pathway enrichment analysis were established via DAVID database. Random forest (RF) and risk score model were used to pick out the gene signature in predicting the prognosis of NB patients. Simultaneously, the receiving operating characteristic (ROC) and Kaplan-Meier curve were plotted. GSE45480 and GSE16476 datasets were employed to validate the robustness of the gene signature.Results: A total of 131 DEGs were identified, which were mainly enriched in cancer-related pathways. Four genes (ERCC6L, AHCY, STK33, and NCAN) were selected as a gene signature, which was included in the top six important features in RF model, to predict the prognosis in NB patients, its area under the curve (AUC) could reach 0.86, and Cox regression analysis revealed that the 4-gene signature was an independent prognostic factor of overall survival and event-free survival. As well as in GSE16476. Additionally, the robustness of discriminating different groups of the 4-gene signature was verified to have a commendable performance in GSE45480 and GSE49710.Conclusion: The present study identified a gene-signature in predicting the prognosis in NB, which may provide novel prognostic markers, and some of the genes may be as treatment targets according to biological experiments in the future.https://www.frontiersin.org/article/10.3389/fgene.2018.00589/fullneuroblastomadifferentially expressed genesgene signaturesprognosisGEOERCC6L
collection DOAJ
language English
format Article
sources DOAJ
author Xiaodan Zhong
Xiaodan Zhong
Xiaodan Zhong
Yuanning Liu
Yuanning Liu
Haiming Liu
Haiming Liu
Yutong Zhang
Linyu Wang
Linyu Wang
Hao Zhang
Hao Zhang
spellingShingle Xiaodan Zhong
Xiaodan Zhong
Xiaodan Zhong
Yuanning Liu
Yuanning Liu
Haiming Liu
Haiming Liu
Yutong Zhang
Linyu Wang
Linyu Wang
Hao Zhang
Hao Zhang
Identification of Potential Prognostic Genes for Neuroblastoma
Frontiers in Genetics
neuroblastoma
differentially expressed genes
gene signatures
prognosis
GEO
ERCC6L
author_facet Xiaodan Zhong
Xiaodan Zhong
Xiaodan Zhong
Yuanning Liu
Yuanning Liu
Haiming Liu
Haiming Liu
Yutong Zhang
Linyu Wang
Linyu Wang
Hao Zhang
Hao Zhang
author_sort Xiaodan Zhong
title Identification of Potential Prognostic Genes for Neuroblastoma
title_short Identification of Potential Prognostic Genes for Neuroblastoma
title_full Identification of Potential Prognostic Genes for Neuroblastoma
title_fullStr Identification of Potential Prognostic Genes for Neuroblastoma
title_full_unstemmed Identification of Potential Prognostic Genes for Neuroblastoma
title_sort identification of potential prognostic genes for neuroblastoma
publisher Frontiers Media S.A.
series Frontiers in Genetics
issn 1664-8021
publishDate 2018-11-01
description Background and Objective: Neuroblastoma (NB), the most common pediatric solid tumor apart from brain tumor, is associated with dismal long-term survival. The aim of this study was to identify a gene signature to predict the prognosis of NB patients.Materials and Methods: GSE49710 dataset from the Gene Expression Omnibus (GEO) database was downloaded and differentially expressed genes (DEGs) were analyzed using R package “limma” and SPSS software. The gene ontology (GO) and pathway enrichment analysis were established via DAVID database. Random forest (RF) and risk score model were used to pick out the gene signature in predicting the prognosis of NB patients. Simultaneously, the receiving operating characteristic (ROC) and Kaplan-Meier curve were plotted. GSE45480 and GSE16476 datasets were employed to validate the robustness of the gene signature.Results: A total of 131 DEGs were identified, which were mainly enriched in cancer-related pathways. Four genes (ERCC6L, AHCY, STK33, and NCAN) were selected as a gene signature, which was included in the top six important features in RF model, to predict the prognosis in NB patients, its area under the curve (AUC) could reach 0.86, and Cox regression analysis revealed that the 4-gene signature was an independent prognostic factor of overall survival and event-free survival. As well as in GSE16476. Additionally, the robustness of discriminating different groups of the 4-gene signature was verified to have a commendable performance in GSE45480 and GSE49710.Conclusion: The present study identified a gene-signature in predicting the prognosis in NB, which may provide novel prognostic markers, and some of the genes may be as treatment targets according to biological experiments in the future.
topic neuroblastoma
differentially expressed genes
gene signatures
prognosis
GEO
ERCC6L
url https://www.frontiersin.org/article/10.3389/fgene.2018.00589/full
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