GBE1 Promotes Glioma Progression by Enhancing Aerobic Glycolysis through Inhibition of FBP1

Identification and validation of a novel Parkinson-Glioma feature gene signature in glioma and Parkinson's disease

Introduction

The prognosis for glioma is generally poor, and the 5-year survival rate for patients with this disease has not shown significant improvement over the past few decades. Parkinson's disease (PD) is a prevalent movement disorder, ranking as the second most common neurodegenerative disease after Alzheimer's disease. Although Parkinson's disease and glioma are distinct diseases, they may share certain underlying biological pathways that contribute to their development.

Objective

This study aims to investigate the involvement of genes associated with Parkinson's disease in the development and prognosis of glioma.

Methods

We obtained datasets from the TCGA, CGGA, and GEO databases, which included RNA sequencing data and clinical information of glioma and Parkinson's patients. Eight machine learning algorithms were used to identify Parkinson-Glioma feature genes (PGFGs). PGFGs associated with glioma prognosis were identified through univariate Cox analysis. A risk signature was constructed based on PGFGs using Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method. We subsequently validated its predictive ability using various methods, including ROC curves, calibration curves, KM survival analysis, C-index, DCA, independent prognostic analysis, and stratified analysis. To validate the reproducibility of the results, similar work was performed on three external test datasets. Additionally, a meta-analysis was employed to observe the heterogeneity and consistency of the signature across different datasets. We also compared the differences in genomic variations, functional enrichment, immune infiltration, and drug sensitivity analysis based on risk scores. This exploration aimed to uncover potential mechanisms of glioma occurrence and prognosis.

Results

We identified 30 PGFGs, of which 25 were found to be significantly associated with glioma survival. The prognostic signature, consisting of 19 genes, demonstrated excellent predictive performance for 1-, 2-, and 3-year overall survival (OS) of glioma. The signature emerged as an independent prognostic factor for glioma overall survival (OS), surpassing the predictive performance of traditional clinical variables. Notably, we observed differences in the tumor microenvironment (TME), levels of immune cell infiltration, immune gene expression, and drug resistance analysis among distinct risk groups. These findings may have significant implications for the clinical treatment of glioma patients.

Conclusion

The expression of genes related to Parkinson's disease is closely associated with the immune status and prognosis of glioma patients, potentially regulating glioma pathogenesis through multiple mechanisms. The interaction between genes associated with Parkinson's disease and the immune system during glioma development provides novel insights into the molecular mechanisms and targeted therapies for glioma.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Deciphering Glioblastoma: Fundamental and Novel Insights into the Biology and Therapeutic Strategies of Gliomas

Gliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.

FBP1 is a potential prognostic biomarker and correlated with tumor immunosuppressive microenvironment in glioblastoma

Hypoxia has been shown to contribute to tumor immunosuppressive microenvironment and is an effective prognostic indicator. This study aimed to screen prognostic hypoxia-related genes (HRGs) in glioblastoma and investigate the association between HRGs and tumor immunosuppressive microenvironment. The glioblastoma-related mRNA data were collected from TCGA, GEO, and CGGA databases. Totally 200 HRGs were obtained from the GSEA website. The prognostic HRGs were screened by univariate Cox regression analysis. Somatic mutation data of glioblastoma from TCGA was visualized using the "maftools" of R package. Immune cell infiltration proportions were calculated by CIBERSORT. The TISIDB online tool was applied to analyze the relationship between HRGs and immunoinhibitors as well as the HRG expression in different glioblastoma immune and molecular subtypes. Hub gene's mRNA and protein levels in cell lines were determined by qRT-PCR and western blot, respectively. The effects of hub gene knockdown on cell viability and migration ability were evaluated employing CCK8 and wound healing assays. The univariate Cox regression showed that high level of FBP1 (fructose-1,6-bisphosphatase 1) was a poor prognostic biomarker, and FBP1 was mainly expressed in lymphocyte depleted immune subtype of glioblastoma. High FBP1 mRNA and protein levels have been successfully validated in vitro. The somatic mutation analysis suggested that TP53 mutation rate was the highest in the high FBP1 glioblastoma group, while EGFR mutation rate was the highest in the low FBP1 glioblastoma group. In the high FBP1 group, the infiltration proportions and types of immune cells were less, dominated by macrophages M2, and the expression of CTLA4, LAG3, TIGIT, PDL1, and PDL2 was significantly upregulated. The expression of FBP1 was positively correlated with several immunoinhibitors, such as IL-10 and TGFβ-1. In conclusion, we demonstrated that FBP1 could serve as a prognostic biomarker for glioblastoma. The immune microenvironment in the high FBP1 group might be suppressed by up-regulating immune checkpoints and immunoinhibitors.

The Recent Research Progress of NF-κB Signaling on the Proliferation, Migration, Invasion, Immune Escape and Drug Resistance of Glioblastoma

Glioblastoma multiforme (GBM) is the most common and invasive primary central nervous system tumor in humans, accounting for approximately 45-50% of all primary brain tumors. How to conduct early diagnosis, targeted intervention, and prognostic evaluation of GBM, in order to improve the survival rate of glioblastoma patients, has always been an urgent clinical problem to be solved. Therefore, a deeper understanding of the molecular mechanisms underlying the occurrence and development of GBM is also needed. Like many other cancers, NF-κB signaling plays a crucial role in tumor growth and therapeutic resistance in GBM. However, the molecular mechanism underlying the high activity of NF-κB in GBM remains to be elucidated. This review aims to identify and summarize the NF-κB signaling involved in the recent pathogenesis of GBM, as well as basic therapy for GBM via NF-κB signaling.