Molecular Insights and Prognosis Associated With RBM8A in Glioblastoma

Old players and new insights: unraveling the role of RNA-binding proteins in brain tumors

The human genome harbors >1,600 evolutionarily conserved RNA-binding proteins (RBPs), with extensive multi-omics investigations documenting their pervasive dysregulation in malignancies ranging from glioblastoma to melanoma. These RBPs are integral to the complex regulatory networks governing hallmark cancer processes. Recent studies have investigated the multifaceted contributions of RBPs to tumorigenesis, tumor metabolism, the tumor-immune microenvironment, and resistance to therapy. This complexity is further compounded by the intricate regulation of RNA function at various levels by RBPs, as well as the post-translational modifications of RBPs, which improve their functional capacity. Moreover, numerous RBP-based therapeutics have emerged, each underpinned by distinct molecular mechanisms that extend from genomic analysis to the interference of RBPs' function. This review aims to provide a comprehensive overview of the recent progress in the meticulous roles of RBPs in brain tumors and to explore potential therapeutic interventions targeting these RBPs, complemented by a discussion of innovative techniques emerging in this research field. Advances in deciphering RNA-RBP interactomes and refining targeted therapeutic strategies are revealing the transformative potential of RBP-centric approaches in brain tumor treatment, establishing them as pivotal agents for overcoming current clinical challenges.

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses

BACKGROUND

Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM.

METHODS

A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023.

RESULTS

From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts.

CONCLUSION

We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Mechanism of Notch Signaling Pathway in Malignant Progression of Glioblastoma and Targeted Therapy

Glioblastoma multiforme (GBM) is the most aggressive form of glioma and the most common primary tumor of the central nervous system. Despite significant advances in clinical management strategies and diagnostic techniques for GBM in recent years, it remains a fatal disease. The current standard of care includes surgery, radiation, and chemotherapy, but the five-year survival rate for patients is less than 5%. The search for a more precise diagnosis and earlier intervention remains a critical and urgent challenge in clinical practice. The Notch signaling pathway is a critical signaling system that has been extensively studied in the malignant progression of glioblastoma. This highly conserved signaling cascade is central to a variety of biological processes, including growth, proliferation, self-renewal, migration, apoptosis, and metabolism. In GBM, accumulating data suggest that the Notch signaling pathway is hyperactive and contributes to GBM initiation, progression, and treatment resistance. This review summarizes the biological functions and molecular mechanisms of the Notch signaling pathway in GBM, as well as some clinical advances targeting the Notch signaling pathway in cancer and glioblastoma, highlighting its potential as a focus for novel therapeutic strategies.

RNA-sequencing to discover genes and signaling pathways associated with venous thromboembolism in glioblastoma patients: A case-control study

BACKGROUND

Glioblastoma patients are at high risk of developing venous thromboembolism (VTE). Tumor-intrinsic features are considered to play a role, but the underlying pathophysiological mechanisms remain incompletely understood.

OBJECTIVES

To identify tumor-expressed genes and signaling pathways that associate with glioblastoma-related VTE by using next generation RNA-sequencing (RNA-Seq).

METHODS

The tumor gene expression profile of 23 glioblastoma patients with VTE and 23 glioblastoma patients without VTE was compared using an unpaired analysis. Ingenuity Pathway Analysis (IPA) core analysis was performed on the top 50 differentially expressed genes to explore associated functions and pathways. Based on full RNA-Seq data, molecular glioblastoma subtypes were determined by performing cluster analysis.

RESULTS

Of the 19,327 genes, 1246 (6.4 %) were differentially expressed between glioblastoma patients with and without VTE (unadjusted P < 0.05). The most highly overexpressed gene was GLI1, a classical target gene in the Sonic Hedgehog (Shh) signaling pathway (log2 fold change: 3.7; unadjusted P < 0.0001, adjusted P = 0.219). In line, Shh signaling was among the top canonical pathways and processes associated with VTE. The proportion of patients with the proneural/neural glioblastoma subtype was higher among those with VTE than controls.

CONCLUSION

Shh signaling may be involved in the development of glioblastoma-related VTE.

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.

Identification of Candidate Genes Associated With Prognosis in Glioblastoma

Background

Glioblastoma (GBM) is the most common malignant primary brain tumor, which associated with extremely poor prognosis.

Methods

Data from datasets GSE16011, GSE7696, GSE50161, GSE90598 and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) between patients and controls. DEGs common to all five datasets were analyzed for functional enrichment and for association with overall survival using Cox regression. Candidate genes were further screened using least absolute shrinkage and selection operator (LASSO) and random forest algorithms, and the effects of candidate genes on prognosis were explored using a Gaussian mixed model, a risk model, and concordance cluster analysis. We also characterized the GBM landscape of immune cell infiltration, methylation, and somatic mutations.

Results

We identified 3,139 common DEGs, which were associated mainly with PI3K-Akt signaling, focal adhesion, and Hippo signaling. Cox regression identified 106 common DEGs that were significantly associated with overall survival. LASSO and random forest algorithms identified six candidate genes (AEBP1, ANXA2R, MAP1LC3A, TMEM60, PRRG3 and RPS4X) that predicted overall survival and GBM recurrence. AEBP1 showed the best prognostic performance. We found that GBM tissues were heavily infiltrated by T helper cells and macrophages, which correlated with higher AEBP1 expression. Stratifying patients based on the six candidate genes led to two groups with significantly different overall survival. Somatic mutations in AEBP1 and modified methylation of MAP1LC3A were associated with GBM.

Conclusion

We have identified candidate genes, particularly AEBP1, strongly associated with GBM prognosis, which may help in efforts to understand and treat the disease.