Abstract
Background: Although the diagnosis and treatment of glioblastoma (GBM) is significantly improved with recent progresses, there is still a large heterogeneity in therapeutic effects and overall survival. The aim of this study is to analyze gene expressions of transcription factors (TFs) in GBM so as to discover new tumor markers. Methods: Differentially expressed TFs are identified by data mining using public databases. The GBM transcriptome profile is downloaded from The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) method is used to cluster the differentially expressed genes to discover hub genes and signal pathways. The TFs affecting the prognosis of GBM are screened by univariate and multivariate COX regression analysis, and the receiver operating characteristic (ROC) curve is determined. The GBM hazard model and nomogram map are constructed by integrating the clinical data. Finally, the TFs involving potential signaling pathways in GBM are screened by Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results: There are 68 differentially expressed TFs in GBM, of which 43 genes are upregulated and 25 genes are downregulated. NMF clustering analysis suggested that GBM patients are divided into three groups: Clusters A, B, and C. LHX2, MEOX2, SNAI2, and ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis. The risk score of those four genes are calculated based on the beta coefficient of each gene, and we found that the predictive ability of the risk score gradually increased with the prolonged predicted termination time by time-dependent ROC curve analysis. The nomogram results have showed that the integration of risk score, age, gender, chemotherapy, radiotherapy, and 1p/19q can further improve predictive ability towards the survival of GBM. The pathways in cancer, phosphoinositide 3-kinases (PI3K)–Akt signaling, Hippo signaling, and proteoglycans, are highly enriched in high-risk groups by GSEA. These genes are mainly involved in cell migration, cell adhesion, epithelial–mesenchymal transition (EMT), cell cycle, and other signaling pathways by GO and KEGG analysis. Conclusion: The four-factor combined scoring model of LHX2, MEOX2, SNAI2, and ZNF22 can precisely predict the prognosis of patients with GBM.
Highlights
Glioblastomas (GBMs) are the most common malignant tumors in the central nervous system (CNS), which accounts for 14.9% of primary CNS and 47.1% of primary brain tumors
LHX2, MEOX2, SNAI2, and ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis
The risk score of those four genes are calculated based on the beta coefficient of each gene, and we found that the predictive ability of the risk score gradually increased with the prolonged predicted termination time by time-dependent receiver operating characteristic (ROC) curve analysis
Summary
Glioblastomas (GBMs) are the most common malignant tumors in the central nervous system (CNS), which accounts for 14.9% of primary CNS and 47.1% of primary brain tumors. The incidence of GBM increases with age, being most common during 75–84 years of age It is generally associated with a poor prognosis, in which median overall survival (OS) is 15 months and 5-year survival is only about 5.5% (Ostrom et al, 2017). For the past 10 years, rapid advancement in bioinformatics has provided better tools to explore the molecular characteristics of cancer. This way, many molecular markers and molecular characterizing systems of GBM have been identified, which offers novel insights into the better understanding of progression mechanisms, diagnosis, and treatment of GBM (Lee et al, 2018). The aim of this study is to analyze gene expressions of transcription factors (TFs) in GBM so as to discover new tumor markers
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