Eukaryotic initiation factor 4 A-3 promotes glioblastoma growth and invasion through the Notch1-dependent pathway.

BACKGROUND: As an adult tumor with the most invasion and the highest mortality rate, the inherent heterogeneity of glioblastoma (GBM) is the main factor that causes treatment failure. Therefore, it is important to have a deeper understanding of the pathology of GBM. Some studies have shown that Eukaryotic Initiation Factor 4A-3 (EIF4A3) can promote the growth of many people's tumors, and the role of specific molecules in GBM remains unclear. METHODS: The correlation between the expression of EIF4A3 gene and its prognosis was studied in 94 GBM patients using survival analysis. Further in vitro and in vivo experiments, the effect of EIF4A3 on GBM cells proliferation, migration, and the mechanism of EIF4A3 on GBM was explored. In addition, combined with bioinformatics analysis, we further confirmed that EIF4A3 contributes to the progress of GBM. RESULTS: The expression of EIF4A3 was upregulated in GBM tissues, and high expression of EIF4A3 is associated with poor prognosis in GBM. In vitro, knockdown of EIF4A3 significantly reduced the proliferation, migration, and invasion abilities of GBM cells, whereas overexpression of EIF4A3 led to the opposite effect. The analysis of differentially expressed genes related to EIF4A3 indicates that it is involved in many cancer-related pathways, such as Notch and JAK-STAT3 signal pathway. In Besides, we demonstrated the interaction between EIF4A3 and Notch1 by RNA immunoprecipitation. Finally, the biological function of EIF4A3-promoted GBM was confirmed in living organisms. CONCLUSION: The results of this study suggest that EIF4A3 may be a potential prognostic factor, and Notch1 participates in the proliferation and metastasis of GBM cells mediated by EIF4A3.

Screening seven hub genes associated with prognosis and immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.

Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma.

Background: Glioblastoma (GBM) is the most invasive brain tumors, and it is associated with high rates of recurrence and mortality. The purpose of this study was to investigate the expression of RBM8A in GBM and the potential influence of its expression on the disease. Methods: Levels of RBM8A mRNA in GBM patients and controls were examined in The Cancer Genome Atlas (TCGA), GSE16011 and GSE90604 databases. GBM samples in TCGA were divided into RBM8Ahigh and RBM8Alow groups. Differentially expressed genes (DEGs) between GBM patients and controls were identified, as were DEGs between RBM8Ahigh and RBM8Alow groups. DEGs common to both of these comparisons were analyzed for coexpression and regression analyses. In addition, we identified potential effects of RBM8A on competing endogenous RNAs, immune cell infiltration, methylation modifications, and somatic mutations. Results: RBM8A is expressed at significantly higher levels in GBM than control samples, and its level correlates with tumor purity. We identified a total of 488 mRNAs that differed between GBM and controls as well as between RBM8Ahigh and RBM8Alow groups, which enrichment analysis revealed to be associated mainly with neuroblast proliferation, and T cell immune responses. We identified 174 mRNAs that gave areas under the receiver operating characteristic curve >0.7 among coexpression module genes, of which 13 were significantly associated with overall survival of GBM patients. We integrated 11 candidate mRNAs through LASSO algorithm, then nomogram, risk score, and decision curve analyses were analyzed. We found that RBM8A may compete with DLEU1 for binding to miR-128-1-5p, and aberrant RBM8A expression was associations with tumor infiltration by immune cells. Some mRNAs associated with GBM prognosis also appear to be methylated or mutated. Conclusions: Our study strongly links RBM8A expression to GBM pathobiology and patient prognosis. The candidate mRNAs identified here may lead to therapeutic targets against the disease.

RBM8A Promotes Glioblastoma Growth and Invasion Through the Notch/STAT3 Pathway.

BACKGROUND: Glioblastoma (GBM) is a prevalent brain malignancy with an extremely poor prognosis, which is attributable to its invasive biological behavior. The RNA-binding motif protein 8A (RBM8A) has different effects on various human cancers. However, the role of RBM8A in GBM progression remains unclear. METHODS: We investigated the expression levels of RBM8A in 94 GBM patients and explored the correlation between RBM8A expression and patient prognosis. Using in vitro and in vivo assays, combined with GBM sequencing data from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we examined whether and how RBM8A contributes to GBM progression. RESULTS: RBM8A was up-regulated in GBM tissues, and its higher expression correlated with worse prognosis. Knockdown of RBM8A inhibited GBM progression and invasion ability both in vitro and in vivo. On the contrary, overexpression of RBM8A promoted GBM progression and invasion ability. Enrichment analysis of differentially expressed genes in GBM data identified the Notch1/STAT3 network as a potential downstream target of RBM8A, and this was supported by molecular docking studies. Furthermore, we demonstrated that RBM8A regulates the transcriptional activity of CBF1. The γ-secretase inhibitor DAPT significantly reversed RBM8A-enhanced GBM cell proliferation and invasion, and was associated with down-regulation of p-STAT3 and Notch1 protein. Finally, the gene set variance analysis score of genes involved in regulation of the Notch1/STAT3 network by RBM8A showed good diagnostic and prognostic value for GBM. CONCLUSIONS: RBM8A may promote GBM cell proliferation and migration by activating the Notch/STAT3 pathway in GBM cells, suggesting that RBM8A may serve as a potential therapeutic target for the treatment of GBM.

Midkine promotes glioblastoma progression via PI3K-Akt signaling.

BACKGROUND: Our previous bioinformatics-based study found that midkine (MDK) was associated with poor prognosis of glioblastoma (GBM). However, the mechanism of MDK in GBM remains elusive. METHODS: A public GBM-related dataset and GBM tissues from our center were used validate the aberrant expression of MDK in GBM at the RNA and protein levels. The relationship between MDK expression and survival of GBM patients was also explored through survival analysis. Subsequently, we identified MDK-related GBM-specific genes using differential expression analysis. Functional enrichment analyses were performed to reveal their potential biological functions. CCK-8, 5-ethynyl-2'-deoxyuridine, and Matrigel-transwell assays were performed in GBM cell lines in which MDK was knocked out or overexpressed in order assess the effects of MDK on proliferation, migration, and invasion of GBM cells. Western blotting was performed to detect candidate proteins. RESULTS: Our study showed MDK is a promising diagnostic and prognostic biomarker for GBM because it is highly expressed in the disease and it is associated with poor prognosis. MDK is involved in various cancer-related pathways, such as PI3K-Akt signaling, the cell cycle, and VEGF signaling. A comprehensive transcriptional regulatory network was constructed to show the potential pathways through which MDK may be involved in GBM. In vitro, Overexpression of MDK augmented proliferation, migration, and invasion of GBM cell lines, whereas suppression of MDK led to the opposite effects. Furthermore, our study confirmed that MDK promotes the progression of GBM by activating the PI3K-Akt signaling pathway. CONCLUSIONS: Our present study proposes that MDK promotes GBM by activating the PI3K-Akt signaling pathway, and it describes a potential regulatory network involved.

Identification of three glioblastoma subtypes and a six-gene prognostic risk index based on the expression of growth factors and cytokines.

Glioblastoma multiforme (GBM) is the most common and invasive tumor of the central nervous system. Growth factors and cytokines (GFCKs) play a crucial role in tumor invasion. In the present study, GFCK expression profiles from GBM patients in the Chinese Glioma Genome Atlas were used to perform sample clustering with nonnegative matrix factorization. Three GBM subtypes were identified based on differences in GFCK expression, and the subtypes differed in characteristics and prognosis. A prognostic risk index (RI) comprising six GFCKs (BMP2, CCN3, GKN1, LIF, MDK, and SEMA3G) was defined using univariate Cox hazard analysis and multivariate stepwise Cox regression. The RI was validated in two independent data sets and may be independent of some known prognostic factors. Our results suggest that GBM occurs as different subtypes expressing different patterns of GFCKs and that these expression patterns can be captured in an RI that can predict prognosis.

Identification of Key Differentially Expressed Transcription Factors in Glioblastoma.

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults. Our study focused on uncovering differentially expressed genes (DEGs) and their methylation in order to identify novel diagnostic biomarkers and potential treatment targets. Using GBM RNA-sequencing data from The Cancer Genome Atlas (TCGA) database, DEGs between GBM samples and paracancer tissue samples were analyzed. Enrichment analysis for DEGs and transcription factors (TFs) was performed. A total of 1029 upregulated genes and 1542 downregulated genes were identified, which were associated mainly with multiple tumor-related and immune-related pathways such as cell cycle, mitogen-activated protein kinase signaling pathway, leukocyte transendothelial migration, and autoimmune thyroid disease. These DEGs were enriched for 174 TFs, and six TFs were differentially expressed and identified as key TFs in GBM: HOXA3, EN1, ZIC1, and FOXD3 were upregulated, while HLF and EGR3 were downregulated. A total of 1978 DEGs were involved in the regulatory networks of the six key differentially expressed TFs. High expression of EN1 was associated with shorter overall survival, while high expression of EGR3 was associated with shorter recurrence-free survival. The six TFs were differentially methylated in GBM samples compared with paracancer tissues. Our study identifies numerous DEGs and their associated pathways as potential contributors to GBM, particularly the TFs EN1, EGR3, HOXA3, ZIC1, FOXD3, and HLF. The differential expression of these TFs may be unlikely driven by aberrant methylation. These TFs may be useful as diagnostic markers and treatment targets in GBM, and EN1 and EGR3 may have predictive prognostic value.