Clinical and Biological Significance of a Necroptosis-Related Gene Signature in Glioma

Regulated cell death in glioma: promising targets for natural small-molecule compounds

Gliomas are prevalent malignant tumors in adults, which can be categorized as either localized or diffuse gliomas. Glioblastoma is the most aggressive and deadliest form of glioma. Currently, there is no complete cure, and the median survival time is less than one year. The main mechanism of regulated cell death involves organisms coordinating the elimination of damaged cells at risk of tumor transformation or cells hijacked by microorganisms for pathogen replication. This process includes apoptosis, necroptosis, autophagy, ferroptosis, pyroptosis, necrosis, parthanayosis, entosis, lysosome-dependent death, NETosis, oxiptosis, alkaliptosis, and disulfidaptosis. The main goal of clinical oncology is to develop therapies that promote the effective elimination of cancer cells by regulating cell death are the main goal of clinical oncology. Recently, scientists have utilized pertinent regulatory factors and natural small-molecule compounds to induce regulated cell death for the treatment of gliomas. By analyzing the PubMed and Web of Science databases, this paper reviews the research progress on the regulation of cell death and the role of natural small-molecule compounds in glioma. The aim is to provide help for the treatment of glioblastoma.

A necroptosis-related lncRNA signature was identified to predict the prognosis and immune microenvironment of IDH-wild-type GBM

Introduction

Necroptosis-related genes are essential for the advancement of IDH-wild-type GBM. However, the putative effects of necroptosis-related lncRNAs (nrlncRNAs) in IDH-wild-type GBM remain unknown.

Methods

By using the TCGA and GTEx databases, a nrlncRNA prognostic signature was created using LASSO Cox regression. The median risk score was used to categorize the patients into low and high-risk groups. To confirm the validity, univariate, multivariate Cox regression and ROC curves were used. Furthermore, by enrichment analysis, immune correlation analysis, and drug sensitivity analysis, the targeted lncRNAs were selected for further verification. As the highest upregulated expression in tumor than peritumor specimens, RP11-131L12.4 was selected for phenotype and functional experiments in primary GBM cells.

Results

Six lncRNAs were proved to be closely related to necroptosis in IDH-1-wild-type GBM, which were used to create a new signature. For 1-, 2-, and 3-year OS, the AUCs were 0.709, 0.645 and 0.694, respectively. Patients in the low-risk group had a better prognosis, stronger immune function activity, and more immune cell infiltration. In contrast, enrichment analysis revealed that the malignant phenotype was more prevalent in the high-risk group. In vitro experiments indicated that RP11-131L12.4 increased the tumor proliferation, migration and invasion, but decreased the necroptosis. Moreover, this nrlncRNA was also proved to be negatively associated with patient prognosis.

Conclusion

The signature of nrlncRNAs may aid in the formulation of tailored and precise treatment for individuals with IDH-wild-type GBM. RP11-131L12.4 may play indispensable role in necroptosis suppression.

Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.

A novel necroptosis-related gene signature for predict prognosis of glioma based on single-cell and bulk RNA sequencing

Background: Glioma is the most fatal neoplasm among the primary intracranial cancers. Necroptosis, a form of programmed cell death, is correlated with tumor progression and immune response. But, the role of necroptosis-related genes (NRGs) in glioma has not been well-uncovered.

Methods: Single-cell and bulk RNA sequencing data, obtained from publicly accessed databases, were used to establish a necroptosis-related gene signature for predicting the prognosis of glioma patients. Multiple bioinformatics algorithms were conducted to evaluate the efficacy of the signature. The relative mRNA level of each signature gene was validated by quantitative real-time reverse transcription PCR (qRT-PCR) in glioma cell lines compared to human astrocytes.

Results: In this predicted prognosis model, patients with a high risk score showed a shorter overall survival, which was verified in the testing cohorts. The signature risk score was positively related with immune cell infiltration and some immune check points, such as CD276 (B7-H3), CD152 (CTLA-4), CD223 (LAG-3), and CD274 (PD-L1). Single-cell RNA sequencing analysis confirmed that the glioma microenvironment consists of various immune cells with different markers. The eight NRGs of the signature were detected to be expressed in several immune cells. QRT-PCR results verified that all the eight signature genes were differentially expressed between human astrocytes and glioma cells.

Conclusion: The eight NRGs correlate with the immune microenvironment of glioma according to our bioinformatics analysis. This necroptosis-related gene signature may evaluate the precise methodology of predicting prognosis of glioma and provide a novel thought in glioma investigation.

A Novel Necroptosis-Related Prognostic Signature of Glioblastoma Based on Transcriptomics Analysis and Single Cell Sequencing Analysis

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.

Accumulation of RIPK1 into mitochondria is requisite for oxidative stress-mediated necroptosis and proliferation in Rat Schwann cells

The injury of Schwann cells is an important pathological feature of peripheral neuropathy. However, the explicit molecular mechanism and blocking method remains to be explored. In this study, we identified an pivotal executor of necroptosis-RIPK1, performed an unique function in response to oxidative stress-induced injury in Rat Schwann cells. We found that after oxidative stress-simulation by H₂O₂, RIPK1 was activated independent of genetic up-regulation, but through the post-translational modification, including its protein levels, phosphorylation of Serine 166 and Serine 321 sites and its general ubiquitination levels. Under a confocal microscopy, we found that RIPK1 was significantly accumulated into the mitochondria. And the phosphorylation, ubiquitination levels were also elevated in mitochondrial RIPK1, as indicated by immunoprecipitation. Through the administration of N-Acetyl-L-cysteine (NAC), a ROS inhibitor, we found that the phosphorylation, ubiquitination and mitochondrial location of RIPK1 was significantly suppressed. While administration of Necrostatin-1 (Nec-1) failed to influence the levels of ROS and mitochondrial membrane potential, revealing that RIPK1 served as the down-stream regulators of ROS. Lastly, pharmacological inhibition of RIPK1 by Nec-1 attenuated the levels of necroptosis, increased proliferation, as indicated by Annexin V/PI evaluation, CCK-8 detection, TEM scanning and EdU staining. Our results indicate a previous un-recognized post-translational change of RIPK1 in response to oxidative stress in Schwann cells.