Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Abstract

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein–protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM–receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial–mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

EFNA4 as a potential prognostic biomarker and therapeutic target for GBMLGG

“…Differentially expressed genes were signi cantly enriched in signaling pathways that promoted tumorigenesis in GBMLGG, including the tyrosine kinase, Ras, PI3K-Akt, and MAPK signaling pathways (23,24). The enrichment of the genes in synaptic and axon guidance pathways suggested that the tumorigenic effects of EFNA4 were related to the promotion of cell proliferation and remodeling of neural networks in GBMLGG (25). In addition, we found an enrichment of the differential genes in the neuroactive ligand-receptor interaction pathway, in which the increased activity was related to enhanced invasiveness in GBM(26).…”

Identification of Key Differentially Expressed mRNAs, miRNAs, lncRNAs, and circRNAs for Xp11 Translocation Renal Cell Carcinoma (RCC) Based on Whole-Transcriptome Sequencing

“…The pathway enrichment analysis approach intends to annotate signaling pathways of the previously identified differential genes using the KEGG database and to retrieve all signal transduction pathways in which the genes participate. The significance level ( p value) and misjudgment rate (FDR) of each signal route then were calculated using Fisher’s exact test and multiple comparison tests [ 21 ]. The enriched gene count ≥ 2 and the level p < 0.05 were judged meaningful for GO terms and KEGG pathways in this study.…”

Searching Prognostic Hub Genes for the Management of Gliomagenesis through Transcriptome Profiling

“…The higher expression of screened hub genes namely TP53 (HR = 1.9; log-rank P = 3.1e-07), CDK1(HR = 4.8; log-rank P = 0), UBA52(HR = 3; log-rank P = 2.2e-16) and UBB (HR = 2.8; logrank P = 1.8e-15) mRNAs indicates the worst survival in the LGG and GBM patients (Fig. 3) [12]. This nding suggests that increased expression levels of these four genes at the time of diagnosis may signal a poor prognosis, potentially leading to a reduced overall survival rate among glioma patients except SRC (HR = 0.53; log-rank P = 0.00024).…”

“…The PPI network analysis aids in correlating the relationship between biological modules and the signi cance of proteins [12,17]. The STRING module in Cytoscape v3.9.1 software was utilized to construct the PPI network.…”

“…Notably, a study by Hsu et al, in 2019 identi ed DLGAP5, CDCA8, NCAPH, and CCNB2 through integrated analysis of four glioma microarray datasets [11]. Another study in 2022 by Nayak and Singh, ascertained the use of CTNNB1, ITGB1, TNC, EGFR, and SHOX2 as potential hub genes through integrated transcriptome pro ling and bioinformatics analysis [12].…”