Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis

Abstract

Glioblastoma (GBM), characterized by high morbidity and mortality, is one of the most common lethal diseases worldwide. To identify the molecular mechanisms that contribute to the development of GBM, three cohort profile datasets (GSE50161, GSE90598 and GSE104291) were integrated and thoroughly analyzed; these datasets included 57 GBM cases and 22 cases of normal brain tissue. The current study identified differentially expressed genes (DEGs), and analyzed potential candidate genes and pathways. Additionally, a DEGs-associated protein-protein interaction (PPI) network was established for further investigation. Then, the hub genes associated with prognosis were identified using a Kaplan-Meier analysis based on The Cancer Genome Atlas database. Firstly, the current study identified 378 consistent DEGs (240 upregulated and 138 downregulated). Secondly, a cluster analysis of the DEGs was performed based on functions of the DEGs and signaling pathways were analyzed using the enrichment analysis tool on DAVID. Thirdly, 245 DEGs were identified using PPI network analysis. Among them, two co-expression modules comprising of 30 and 27 genes, respectively, and 35 hub genes were identified using Cytoscape MCODE. Finally, Kaplan-Meier analysis of the hub genes revealed that the increased expression of calcium-binding protein 1 (CABP1) was negatively associated with relapse-free survival. To summarize, all enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways may participate in mechanisms underlying GBM occurrence and progression, however further studies are required. CABP1 may be a key gene associated with the biological process of GBM development and may be involved in a crucial mechanism of GBM progression.

Integrative Analysis of Dna Methylation and Gene Expression Profiles to Explore Potential Biomarkers of Glioblastoma

“…Other genes like C1QB and C1QC which are involved in complement cascade, KIAA0101 (or PCALF) in cell proliferation, and PARP9 performing in DNA damage repair have been known in literature to be up-regulated in GBM and promote tumorigenesis in consistent with our results (34-37) while no witness was found for NUP37 as an encoding gene for Nucleoporin 37; a member of nuclear pore complex (NPC) which also was shown to be upregulated in the signature. On the other side, CLDN10 encoding claudin 10 in Tight junction interactions and RAB11FIP4 located on chromosome 17q11.2 and encodes for Rab11 family-interacting protein 4 which plays role in the regulation of endocytic tra c, both were shown to be down-regulated in the signature in consistent with the past ndings (38)(39)(40), although a controversial result also has been reported for the latter (41).…”

Bioinformatics Analysis of Differentially Expressed Genes and miRNAs in Low-Grade Gliomas

“…Deregulation of miRNA function is associated with an increasing number of human diseases, in particular, glioma. [23][24][25] Each network contains MREs for a combination of different miR-NAs, and therefore, they can have an impact on several genes and target lncRNAs. 23,26 In this study, 16 miRNAs interact with 44 genes and 44 lncRNAs ( Figure 7).…”

A bioinformatics investigation into molecular mechanism of Yinzhihuang granules for treating hepatitis B by network pharmacology and molecular docking verification

“…Subsequently, (5) an herb-compound-target-pathway network was built by linking herbs, compounds, corresponding targets, and pathways. All of the above networks were constructed using Cytoscape 3.7.1 (https ://www.cytos cape.org/) [68][69][70] , which is a software package for visualizing and analyzing networks. To represent large biological datasets in an easily interpretable manner, biological information is frequently visualized as graphs, i.e., a set of nodes and edges.…”