Identification of novel prognostic markers of survival time in high-risk neuroblastoma using gene expression profiles.
differential gene expression
gene regulatory networks
machine learning
neuroblastoma
prognostic markers
Journal
Oncotarget
ISSN: 1949-2553
Titre abrégé: Oncotarget
Pays: United States
ID NLM: 101532965
Informations de publication
Date de publication:
17 Nov 2020
17 Nov 2020
Historique:
received:
02
07
2020
accepted:
27
10
2020
entrez:
27
11
2020
pubmed:
28
11
2020
medline:
28
11
2020
Statut:
epublish
Résumé
Neuroblastoma is the most common extracranial solid tumor in childhood. Patients in high-risk group often have poor outcomes with low survival rates despite several treatment options. This study aimed to identify a genetic signature from gene expression profiles that can serve as prognostic indicators of survival time in patients of high-risk neuroblastoma, and that could be potential therapeutic targets. RNA-seq count data was downloaded from UCSC Xena browser and samples grouped into Short Survival (SS) and Long Survival (LS) groups. Differential gene expression (DGE) analysis, enrichment analyses, regulatory network analysis and machine learning (ML) prediction of survival group were performed. Forty differentially expressed genes (DEGs) were identified including genes involved in molecular function activities essential for tumor proliferation. DEGs used as features for prediction of survival groups included
Identifiants
pubmed: 33245713
doi: 10.18632/oncotarget.27808
pii: 27808
pmc: PMC7679032
doi:
Types de publication
Journal Article
Langues
eng
Pagination
4293-4305Informations de copyright
Copyright: © 2020 Giwa et al.
Déclaration de conflit d'intérêts
CONFLICTS OF INTEREST Authors have no conflicts of interest to declare.
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