Identification of genes and biological pathways in glioma via integrated bioinformatics analysis

Identification of genes and biological pathways

Keywords 

Glioma, hub genes, robust rank aggregation, bioinformatics, biomarker, differential expression

Abstract 

Objective: Glioma is the most common intracranial primary malignancy, but its pathogenesis remains unclear. 

Methods: We integrated four eligible glioma microarray datasets from the gene expression omnibus database using the robust rank aggregation method to identify a group of significantly differently expressed genes (DEGs) between glioma and normal samples. We used these DEGs to explore key genes closely associated with glioma survival through weighted gene co-expression network analysis. We then constructed validations of prognosis and survival analyses for the key genes via multiple databases. We also explored their potential biological functions using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). 

Results: We selected DLGAP5, CDCA8, NCAPH, and CCNB2, as four genes that were abnormally up-regulated in glioma samples, for verification. They showed high levels of isocitrate dehydrogenase gene mutation and tumor grades, as well as good prognostic and diagnostic values for glioma. Their methylation levels were generally lower in glioma samples. GSEA and GSVA analyses suggested the genes were closely involved with glioma proliferation. 

Conclusion: These findings provide new insights into the pathogenesis of glioma. The hub genes have the potential to be used as diagnostic and therapeutic markers. 

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Introduction 


Glioma is a refractory malignant tumor prone to relapse, which is associated with poor prognosis and whose pathogenesis remains unclear. The 2016 update of the World Health Organisation classification of central nervous system tumors integrated molecular characteristics such as mutations in isocitrate dehydrogenase genes (IDH), the 1p/19q codeletion,3, and the H3 K27M mutant. 

The use of these novel molecular biological markers offers promising future diagnostic and therapeutic targets. For example, the IDH mutation status provides important information for the accurate diagnosis and prognosis of glioma.

With the recent application of microarrays and high-throughput sequencing technology, molecular targeted therapy enables the possibility of individualized treatments following the accurate identification and inhibition of tumor gene mutations.7 Although the use of different technology platforms or small sample sizes is associated with limitations and inconsistencies, these can be overcome by applying robust regression analysis (RRA) as an integrated bioinformatics method, particularly in cancer research.

Materials and methods Identification of glioma GEO datasets Four eligible glioma datasets (GSE4290, GSE7696, GSE50161, and GSE68848) were downloaded from the GEO database. 

The selection criteria were as follows: 

  1. The collection of gliomas and corresponding adjacent or normal tissues; 
  2. Including more than 60 samples; and 
  3. Microarray datasets on the same platform. In addition, microarray datasets from the Oncomine database were used to analyze hub gene expression differences between glioma samples and normal tissues. 

A total of 693 glioma samples were downloaded from the Chinese Glioma Genome Atlas (CGGA http://www.cgga.org.cn) database, and 702 glioma samples were obtained from The Cancer Genome Atlas (TCGA) database. GEO dataset information and comprehensive demographic information of patients are shown in Table 1 and Table 2, respectively. The study protocol was approved by the Clinical Medical Research Ethics Committee of the First Affiliated Hospital of Bengbu Medical College. 

Screening of robust DEGs Each GEO dataset was normalized to identify DEGs through the R package Limma. N RRA was used to integrate the results to obtain the most significant DEGs.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis GO annotation and KEGG pathway analyses were conducted for identified DEGs using the R package cluster profile. 

GO terms or KEGG pathways with adjusted P < 0.05 were considered statistically significant. Weighted gene co-expression network analysis (WGCNA) The top 4000 up-regulated DEGs (according to P-value) from RRA analysis

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