Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics

Abstract

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Under various treatments, some patients still have a poor prognosis. Hence, it is necessary to find new valid targets for NB therapy. In this study, a comprehensive bioinformatic analysis was used to identify differentially expressed genes (DEGs) between NB and control cells, and to select hub genes associated with NB. GSE66586 and GSE78061 datasets were downloaded from the Gene Expression Omnibus (GEO) database and DEGs were selected. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the selected DEGs. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks and perform modular analysis of the DEGs. The R2 database was used for prognostic analysis. We identified a total of 238 DEGs from two microarray databases. GO enrichment analysis shows that these DEGs are mainly concentrated in the regulation of cell growth, cell migration, cell fate determination, and cell maturation. KEGG pathway analysis showed that these DEGs are mainly involved in focal adhesion, the TNF signaling pathway, cancer-related pathways, and signaling pathways regulating stem cell pluripotency. We identified the 15 most closely related DEGs from the PPI network, and performed R2 database prognostic analysis to select five hub genes - CTGF, EDN1, GATA2, LOX, and SERPINE1. This study distinguished hub genes and related signaling pathways that can potentially serve as diagnostic indicators and therapeutic biomarkers for NB, thereby improving understanding of the molecular mechanisms involved in NB.

Keywords: Bioinformatics analysis; Biomarkers; Differentially expressed genes; Neuroblastoma; Prognostic.