Comprehensive Analysis of REST/NRSF Gene in Glioma and Its ceRNA Network Identification

Targeting of REST with rationally-designed small molecule compounds exhibits synergetic therapeutic potential in human glioblastoma cells

BACKGROUND

Glioblastoma (GBM) is an aggressive brain cancer associated with poor prognosis, intrinsic heterogeneity, plasticity, and therapy resistance. In some GBMs, cell proliferation is fueled by a transcriptional regulator, repressor element-1 silencing transcription factor (REST).

RESULTS

Using CRISPR/Cas9, we identified GBM cell lines dependent on REST activity. We developed new small molecule inhibitory compounds targeting small C-terminal domain phosphatase 1 (SCP1) to reduce REST protein level and transcriptional activity in glioblastoma cells. Top leads of the series like GR-28 exhibit potent cytotoxicity, reduce REST protein level, and suppress its transcriptional activity. Upon the loss of REST protein, GBM cells can potentially compensate by rewiring fatty acid metabolism, enabling continued proliferation. Combining REST inhibition with the blockade of this compensatory adaptation using long-chain acyl-CoA synthetase inhibitor Triacsin C demonstrated substantial synergetic potential without inducing hepatotoxicity.

CONCLUSIONS

Our results highlight the efficacy and selectivity of targeting REST alone or in combination as a therapeutic strategy to combat high-REST GBM.

Systematic analysis identifies REST as an oncogenic and immunological biomarker in glioma

The repressor element 1 silencing transcription factor (REST) has been proposed to function as a transcription factor to silence gene transcription by binding to repressor element 1 (RE1), a highly conserved DNA motif. The functions of REST in various tumors have been studied, but its role and correlation with immune cell infiltration remains uncertain in gliomas. REST expression was analyzed in datasets of The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) and validated by the Gene Expression Omnibus and Human Protein Atlas databases. The clinical prognosis of REST was evaluated by clinical survival data of TCGA cohort and validated by Chinese Glioma Genome Atlas cohort. MicroRNAs (miRNAs) contributing to REST overexpression in glioma were identified by a combination of a series of in silico analyses, including expression analysis, correlation analysis, and survival analysis. The correlations between immune cell infiltration level and REST expression were analyzed by TIMER2 and GEPIA2 tools. Enrichment analysis of REST was performed using STRING and Metascape tools. The expression and function of predicted upstream miRNAs at REST and their association with glioma malignancy and migration were also confirmed in glioma cell lines. REST was highly expressed and associated with poorer overall survival and disease-specific survival in glioma and some other tumors. MiR-105-5p and miR-9-5p were identified as the most potential upstream miRNAs of REST in glioma patient cohort and experiments in vitro. REST expression was positively correlated with infiltration of immune cells and the expression of immune checkpoints such as PD1/PD-L1 and CTLA-4 in glioma. Furthermore, histone deacetylase 1 (HDAC1) was a potential REST-related gene in glioma. Enrichment analysis of REST found chromatin organization and histone modification were the most significant enriched terms, and Hedgehog-Gli pathway might be involved in the effect of REST on the pathogenesis of glioma. Our study suggests REST to be an oncogenic gene and the biomarker of poor prognosis in glioma. High REST expression might affect the tumor microenvironment of glioma. More basic experiments and large clinical trials aimed at the carcinogenetic study of REST in glioma will be needed in the future.

Pyroptosis-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Glioma

Objective: Gliomas are the most common primary tumors in the central nervous system with a bad prognosis. Pyroptosis, an inflammatory form of regulated cell death, plays a vital role in the progression and occurrence of tumors. However, the value of pyroptosis related genes (PRGs) in glioma remains poorly understood. This study aims to construct a PRGs signature risk model and explore the correlation with clinical characteristics, prognosis, tumor microenviroment (TME), and immune checkpoints.

Methods: RNA sequencing profiles and the relevant clinical data were obtained from the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA), the Repository of Molecular Brain Neoplasia Data (REMBRANDT), and the Genotype-Tissue Expression Project (GTEx-Brain). Then, the differentially expressed pyroptosis related genes (PRGs) were identified, and the least absolute shrinkage and selection operator (LASSO) and mutiCox regression model was generated using the TCGA-train dataset. Then the expression of mRNA and protein levels of PRGs signature was detected through qPCR and human protein atlas (HPA). Further, the predictive ability of the PRGs-signature, prognostic analysis, and stratification analysis were utilized and validated using TCGA-test, CGGA, and REMBRANDT datasets. Subsequently, we constructed the nomogram by combining the PRGs signature and other key clinical features. Moreover, we used gene set enrichment analysis (GSEA), GO, KEGG, the tumor immune dysfunction and exclusion (TIDE) single-sample GSEA (ssGSEA), and Immunophenoscore (IPS) to determine the relationship between PRGs and TME, immune infiltration, and predict the response of immune therapy in glioma.

Results: A four-gene PRGs signature (CASP4, CASP9, GSDMC, IL1A) was identified and stratified patients into low- or high-risk group. Survival analysis, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group than in the low-risk group. Correlation analysis showed that the risk score was correlated with poor disease features. Furthermore, GSEA and immune infiltrating and IPS analysis showed that the PRGs signature could potentially predict the TME, immune infiltration, and immune response in glioma.

Conclusion: The newly identified four-gene PRGs signature is effective in diagnosis and could robustly predict the prognosis of glioma, and its impact on the TME and immune cell infiltrations may provide further guidance for immunotherapy.