Comprehensive Characterization of Pyroptosis Patterns with Implications in Prognosis and Immunotherapy in Low-Grade Gliomas

Pyroptosis in glioma: Current management and future application

Glioma, the predominant form of central nervous system (CNS) malignancies, presents a significant challenge due to its high prevalence and low 5-year survival rate. The efficacy of current treatment methods is limited by the presence of the blood-brain barrier, the immunosuppressive microenvironment, and other factors. Immunotherapy has emerged as a promising approach, as it can overcome the blood-brain barrier. A tumor's immune privilege, which is induced by an immunosuppressive environment, constricts immunotherapy's clinical impact in glioma. Pyroptosis, a programmed cell death mechanism facilitated by gasdermins, plays a significant role in the management of glioma. Its ability to initiate and regulate tumor occurrence, progression, and metastasis is well-established. However, it is crucial to note that uncontrolled or excessive cell death can result in tissue damage, acute inflammation, and cytokine release syndrome, thereby potentially promoting tumor advancement or recurrence. This paper aims to elucidate the molecular pathways involved in pyroptosis and subsequently discuss its induction in cancer therapy. In addition, the current treatment methods of glioma and the use of pyroptosis in these treatments are introduced. It is hoped to provide more ideas for the treatment of glioma.

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma

Background

Pyroptosis, a lytic form of programmed cell death initiated by inflammasomes, has been reported to be closely associated with tumor proliferation, invasion and metastasis. However, the roles of pyroptosis genes (PGs) in low-grade glioma (LGG) remain unclear.

Methods

We obtained information for 1,681 samples, including the mRNA expression profiles of LGGs and normal brain tissues and the relevant corresponding clinical information from two public datasets, TCGA and GTEx, and identified 45 differentially expressed pyroptosis genes (DEPGs). Among these DEPGs, nine hub pyroptosis genes (HPGs) were identified and used to construct a genetic risk scoring model. A total of 476 patients, selected as the training group, were divided into low-risk and high-risk groups according to the risk score. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves verified the accuracy of the model, and a nomogram combining the risk score and clinicopathological characteristics was used to predict the overall survival (OS) of LGG patients. In addition, a cohort from the Gene Expression Omnibus (GEO) database was selected as a validation group to verify the stability of the model. qRT-PCR was used to analyze the gene expression levels of nine HPGs in paracancerous and tumor tissues from 10 LGG patients.

Results

Survival analysis showed that, compared with patients in the low-risk group, patients in the high-risk group had a poorer prognosis. A risk score model combining PG expression levels with clinical features was considered an independent risk factor. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched among the DEPGs and that immune activity was increased in the high-risk group.

Conclusion

In summary, we successfully constructed a model to predict the prognosis of LGG patients, which will help to promote individualized treatment and provide potential new targets for immunotherapy.

A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas

Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the "limma" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the "Seurat" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.

Pancancer transcriptomic profiling identifies key PANoptosis markers as therapeutic targets for oncology

Resistance to programmed cell death (PCD) is a hallmark of cancer. While some PCD components are prognostic in cancer, the roles of many molecules can be masked by redundancies and crosstalks between PCD pathways, impeding the development of targeted therapeutics. Recent studies characterizing these redundancies have identified PANoptosis, a unique innate immune-mediated inflammatory PCD pathway that integrates components from other PCD pathways. Here, we designed a systematic computational framework to determine the pancancer clinical significance of PANoptosis and identify targetable biomarkers. We found that high expression of PANoptosis genes was detrimental in low grade glioma (LGG) and kidney renal cell carcinoma (KIRC). ZBP1, ADAR, CASP2, CASP3, CASP4, CASP8 and GSDMD expression consistently had negative effects on prognosis in LGG across multiple survival models, while AIM2, CASP3, CASP4 and TNFRSF10 expression had negative effects for KIRC. Conversely, high expression of PANoptosis genes was beneficial in skin cutaneous melanoma (SKCM), with ZBP1, NLRP1, CASP8 and GSDMD expression consistently having positive prognostic effects. As a therapeutic proof-of-concept, we treated melanoma cells with combination therapy that activates ZBP1 and showed that this treatment induced PANoptosis. Overall, through our systematic framework, we identified and validated key innate immune biomarkers from PANoptosis which can be targeted to improve patient outcomes in cancers.

M7G-related LncRNAs: A comprehensive analysis of the prognosis and immunity in glioma

Today, numerous international researchers have demonstrated that N7-methylguanosine (m7G) related long non-coding RNAs (m7G-related lncRNAs) are closely linked to the happenings and developments of various human beings’ cancers. However, the connection between m7G-related lncRNAs and glioma prognosis has not been investigated. We did this study to look for new potential biomarkers and construct an m7G-related lncRNA prognostic signature for glioma. We identified those lncRNAs associated with DEGs from glioma tissue sequences as m7G-related lncRNAs. First, we used Pearson’s correlation analysis to identify 28 DEGs by glioma and normal brain tissue gene sequences and predicated 657 m7G-related lncRNAs. Then, eight lncRNAs associated with prognosis were obtained and used to construct the m7G risk score model by lasso and Cox regression analysis methods. Furthermore, we used Kaplan-Meier analysis, time-dependent ROC, principal component analysis, clinical variables, independent prognostic analysis, nomograms, calibration curves, and expression levels of lncRNAs to determine the model’s accuracy. Importantly, we validated the model with external and internal validation methods and found it has strong predictive power. Finally, we performed functional enrichment analysis (GSEA, aaGSEA enrichment analyses) and analyzed immune checkpoints, associated pathways, and drug sensitivity based on predictors. In conclusion, we successfully constructed the formula of m7G-related lncRNAs with powerful predictive functions. Our study provides instructional value for analyzing glioma pathogenesis and offers potential research targets for glioma treatment and scientific research.

Optimization of cancer immunotherapy through pyroptosis: A pyroptosis-related signature predicts survival benefit and potential synergy for immunotherapy in glioma

Background

Pyroptosis is a critical type of programmed cell death that is strongly associated with the regulation of tumor and immune cell functions. However, the role of pyroptosis in tumor progression and remodeling of the tumor microenvironment in gliomas has not been extensively studied. Thus, in this study, we aimed to establish a comprehensive pyroptosis-related signature and uncover its potential clinical application in gliomas.

Methods

The TCGA glioma cohort was obtained and divided into training and internal validation cohorts, while the CGGA glioma cohort was used as an external validation cohort. Unsupervised consensus clustering was performed to identify pyroptosis-related expression patterns. A Cox regression analysis was performed to establish a pyroptosis-related risk signature. Real-time quantitative PCR was performed to analyze the expression of signature genes in glioma tissues. Immune infiltration was analyzed and validated by immunohistochemical staining. The expression patterns of signature genes in different cell types were analyzed using single-cell RNA sequencing data. Finally, therapeutic responses to chemotherapy, immunotherapy, and potential small-molecule inhibitors were investigated.

Results

Patients with glioma were stratified into clusters 1 and 2 based on the expression patterns of pyroptosis-related genes. Cluster 2 showed a longer overall (P<0.001) and progression-free survival time (P<0.001) than Cluster 1. CD8+ T cell enrichment was observed in Cluster 1. A pyroptosis-related risk signature (PRRS) was then established. The high PRRS group showed a significantly poorer prognosis than the low PRRS group in the training cohort (P<0.001), with validation in the internal and external validation cohorts. Immunohistochemical staining demonstrated that CD8+ T cells were enriched in high PRRS glioma tissues. PRRS genes also showed cell-specific expression in tumor and immune cells. Moreover, the high PRRS risk group showed higher temozolomide sensitivity and increased response to anti-PD1 treatment in a glioblastoma immunotherapy cohort. Finally, Bcl-2 inhibitors were screened as candidates for adjunct immunotherapy of gliomas.

Conclusion

The pyroptosis-related signature established in this study can be used to reliably predict clinical outcomes and immunotherapy responses in glioma patients. The correlation between the pyroptosis signature and the tumor immune microenvironment may be used to further guide the sensitization of glioma patients to immunotherapy.

Identification of pyroptosis related subtypes and tumor microenvironment infiltration characteristics in breast cancer

Understanding the association of pyroptosis with tumor progression, prognosis and effect on immunotherapeutic response in breast cancer (BC) is limited. This study analysed forty pyroptosis-related genes to construct the pyroptosis score. Association of the pyroptosis score with the overall survival, clinical features, tumor mutation load, immune cell infiltration, and treatment sensitivity of patients with BC was analysed. Out of 983 BC samples, 304 (30.93%) had genetic alterations with the highest TP53 frequency. We identified three separate subtypes associated with pyroptosis action. These subtypes correlate with the clinicopathological characteristics, TME immune cell infiltration, and disease prognosis. Based on the expression levels of the pyroptosis genes, we divided the pyroptosis score into a high group and a low group. The immune-activated pyroptosis subtype had a higher score with a better prognosis. We also observed that the pyroptosis score correlates with the tumor mutation burden. The pyroptosis score and disease prognosis were directly proportional. A higher pyroptosis score indicated a better prognosis. Results suggest that the pyroptosis-related gene prognosis model is closely related to the immune cell infiltration of BC. The three pyroptosis subtypes associated with BC assist in accurately identifying the tumor subtype, the prognosis of immunotherapy drugs and the patient’s therapeutic response.

A Novel CRISPR/Cas9 Screening Potential Index for Prognostic and Immunological Prediction in Low-Grade Glioma

Glioma is a malignancy with the highest mortality in central nervous system disorders. Here, we implemented the computational tools based on CRISPR/Cas9 to predict the clinical outcomes and biological characteristics of low-grade glioma (LGG). The transcriptional expression profiles and clinical phenotypes of LGG patients were retrieved from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. The CERES algorithm was used to screen for LGG-lethal genes. Cox regression and random survival forest were adopted for survival-related gene selection. Nonnegative matrix factorization distinguished patients into different clusters. Single-sample gene set enrichment analysis was employed to create a novel CRISPR/Cas9 screening potential index (CCSPI), and patients were stratified into low- and high-CCSPI groups. Survival analysis, area under the curve values (AUCs), nomogram, and tumor microenvironment exploration were included for the model validation. A total of 20 essential genes in LGG were used to classify patients into two clusters and construct the CCSPI system. High-CCSPI patients were associated with a worse prognosis of both training and validation set (p < 0.0001) and higher immune fractions than low-CCSPI individuals. The CCSPI system had a promising performance with 1-, 3-, and 5-year AUCs of 0.816, 0.779, 0.724, respectively, and the C-index of the nomogram model reached 0.743 (95% CI = 0.725–0.760). Immune-infiltrating cells and immune checkpoints such as PD-1/PD-L1 and POLD3 were positively associated with CCSPI. In conclusion, the CCSPI had prognostic value in LGG, and the model will deepen our cognition of the interaction between the CNS and immune system in different LGG subtypes.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes

Purpose

Ferroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.

Patients and Methods

The data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient's response to anti-PD-1/PD-L1 immunotherapy was also investigated.

Results

FRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.

Conclusion

A prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.