Oncotarget

Research Papers:

Identification of novel prognostic markers of survival time in high-risk neuroblastoma using gene expression profiles

Abdulazeez Giwa, Azeez Fatai, Junaid Gamieldien, Alan Christoffels and Hocine Bendou _

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Oncotarget. 2020; 11:4293-4305. https://doi.org/10.18632/oncotarget.27808

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Abstract

Abdulazeez Giwa1, Azeez Fatai2, Junaid Gamieldien1, Alan Christoffels1 and Hocine Bendou1

1 SAMRC Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa

2 Department of Biochemistry, Lagos State University, Lagos, Nigeria

Correspondence to:

Hocine Bendou,email: hocine@sanbi.ac.za

Keywords: neuroblastoma; differential gene expression; prognostic markers; machine learning; gene regulatory networks

Received: July 02, 2020     Accepted: October 27, 2020     Published: November 17, 2020

Copyright: © 2020 Giwa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

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 EVX2, NHLH2, PRSS12, POU6F2, HOXD10, MAPK15, RTL1, LGR5, CYP17A1, OR10AB1P, MYH14, LRRTM3, GRIN3A, HS3ST5, CRYAB and NXPH3. An accuracy score of 82% was obtained by the ML classification models. SMIM28 was revealed to possibly have a role in tumor proliferation and aggressiveness. Our results indicate that these DEGs can serve as prognostic indicators of survival in high-risk neuroblastoma patients and will assist clinicians in making better therapeutic and patient management decisions.


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