Our data suggest that EGFL7 expression is a novel predictive factor for the clinical progression of malignant glioma, and may constitute a therapeutic target for anti-angiogenesis therapy in patients with the disease.

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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.

Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime-PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13-gene signature which named Combined Therapy Sensitivity Index (CTSI). Based on a cutoff point, we divided patients into high-risk group and low-risk group. Based on Kaplan-Meier analysis and multivariate Cox regression analysis, we found that patients in the high-risk group had a shorter overall survival time than patients in the low-risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65-3.42). By comparing the status of IDH mutation, TERT promoter mutation (TERTp-mut) and MGMT promoter methylation, CTSI was predictable in IDH wild-type (IDH-wt)/MGMT promoter unmethylated (MGMTp-unmeth) patients (p=0.037 in IDH-wt/TERTp-mut/MGMTp-unmeth subgroup, HR: 1.98; p=0.032 in IDH-wt/TERTp-wt/MGMTp-unmeth subgroup, HR: 2.09). Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high-risk and low-risk groups. Our results showed CTSI risk score can predict the prognosis of IDH-wt/MGMTp-unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.