Predicting the prognosis of glioma by pyroptosis-related signature

Pyroptosis-Related Genes as Diagnostic Markers in Chronic Obstructive Pulmonary Disease and Its Correlation with Immune Infiltration

Background

Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between pyroptosis-related genes (PRGs) and COPD diagnosis in the context of immune infiltration, ultimately proposing a PRG-based diagnostic model for predicting COPD outcomes.

Methods

Clinical data and PRGs of COPD patients were sourced from the GEO database. The "ConsensusClusterPlus" package was employed to generate molecular subtypes derived from PRGs that were identified through differential expression analysis and LASSO Cox analysis. A diagnostic signature including eight genes (CASP4, CASP5, ELANE, GPX4, NLRP1, GSDME, NOD1and IL18) was also constructed. Immune cell infiltration calculated by the ESTIMATE score, Stroma scores and Immune scores were also compared on the basis of pyroptosis-related molecular subtypes and the risk signature. We finally used qRT - PCR to detect the expression levels of eight genes in COPD patient and normal.

Results

The diagnostic model, anchored on eight PRGs, underwent validation with an independent experimental cohort. The area under the receiver operating characteristic (ROC) curves (AUC) for the diagnostic model showcased values of 0.809, 0.765, and 0.956 for the GSE76925, GSE8545, and GSE5058 datasets, respectively. Distinct expression patterns and clinical attributes of PRGs were observed between the comparative groups, with functional analysis underscoring a disparity in immune-related functions between them.

Conclusion

In this study, we developed a potential as diagnostic biomarkers for COPD and have a significant role in modulating the immune response. Such insights pave the way for novel diagnostic and therapeutic strategies for COPD.

Identification of PANoptosis-related genes as prognostic indicators of thyroid cancer

Background

Thyroid cancer (THCA) has become a common malignancy in recent years, with the mortality rate steadily increasing. PANoptosis is a unique kind of programmed cell death (PCD), including pyroptosis, necroptosis, and apoptosis, and is involved in the proliferation and prognosis of numerous cancers. This paper demonstrated the connection between PANoptosis-related genes and THCA based on the analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, which have not been evaluated yet.

Methods

We identified PANoptosis-related differentially expressed genes (PRDEGs) by multi-analyzing the TCGA-THCA and GEO datasets. To identify the significant PRDEGs, a prognostic model was constructed using least absolute shrinkage and selection operator regression (LASSO). The predictive values of the significant PRDEGs for THCA outcomes were determined using Cox regression analysis and nomograms. Gene enrichment analyses were performed. Finally, immunohistochemistry was carried out using the human protein atlas.

Results

A LASSO regression model based on nine PRDEGs was constructed, and the prognostic value of key PRDEGs was explored via risk score. Univariate and multivariate Cox regression were implemented to identify further three significant PRDEGs closely related to distant metastasis, lymph node metastasis, and tumor stage. Then, a nomogram was constructed, which presented high predictive accuracy for 5 years survival of THCA patients. Gene enrichment analyses in THCA were strongly associated with PCD pathways. CASP6 presented significantly differential expression during clinical T stage, N stage, and PFI events (P < 0.05 for all) and demonstrated the highest degree of diagnostic efficacy in PRDEGs (HR: 2.060, 95 % CI: 1.170-3.628, P < 0.05). Immunohistochemistry showed CASP6 was more abundant in THCA tumor tissue.

Conclusion

A potential prognostic role for PRDEGs in THCA was identified, providing a new direction for treatment. CASP6 may be a potential therapeutic target and a novel prognostic biomarker for THCA.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma

BACKGROUND

Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown. Therefore, we comprehensively discussed the relationship between different models of PCD and the prognosis of glioma and provided new ideas for the optimal targeted therapy of glioma.

MATERIALS AND METHODS

We compared and analyzed the role of 14 PCD patterns on the prognosis from different levels. We constructed the cell death risk score (CDRS) index and conducted a comprehensive analysis of CDRS and TME characteristics, clinical characteristics, and drug response.

RESULTS

Effects of different PCDs at the genomic, functional, and immune microenvironment levels were discussed. CDRS index containing 6 gene signatures and a nomogram were established. High CDRS is associated with a worse prognosis. Through transcriptome and single-cell data, we found that patients with high CDRS showed stronger immunosuppressive characteristics. Moreover, the high-CDRS group was resistant to the traditional glioma chemotherapy drug Vincristine, but more sensitive to the Temozolomide and the clinical experimental drug Bortezomib. In addition, we identified 19 key potential therapeutic targets during malignant differentiation of tumor cells.

CONCLUSION

Overall, we provide the first systematic description of the role of 14 PCDs in glioma. A new CDRS model was built to predict the prognosis and to provide a new idea for the targeted therapy of glioma.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci

Backgrounds

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Methods

A genome-wide association study (GWAS) was conducted to identify risk loci in 484 adult patients with glioma. Cox regression analysis was performed to assess the association between GWAS-risk loci and overall survival as well as progression-free survival in glioma. The prognostic model was constructed using LASSO Cox regression analysis and multivariate Cox regression analysis. The nomogram model was constructed based on the single nucleotide polymorphism (SNP) classifier and clinical indicators, enabling the prediction of survival rates at 1-year, 2-year, and 3-year intervals. Additionally, the receiver operator characteristic (ROC) curve was employed to evaluate the prediction value of the nomogram. Finally, functional enrichment and tumor-infiltrating immune analyses were conducted to examine the biological functions of the associated genes.

Results

Our study found suggestive evidence that a total of 57 SNPs were correlated with glioma prognosis (p < 5 × 10-5). Subsequently, we identified 25 SNPs with the most significant impact on glioma prognosis and developed a prognostic model based on these SNPs. The 25 SNP-based classifier and clinical factors (including age, gender, surgery, and chemotherapy) were identified as independent prognostic risk factors. Subsequently, we constructed a prognostic nomogram based on independent prognostic factors to predict individualized survival. ROC analyses further showed that the prediction accuracy of the nomogram (AUC = 0.956) comprising the 25 SNP-based classifier and clinical factors was significantly superior to that of each individual variable.

Conclusion

We identified a SNP classifier and clinical indicators that can predict the prognosis of glioma patients and established a prognostic prediction model in the Chinese Han population. This study offers valuable insights for clinical practice, enabling improved evaluation of patients' prognosis and informing treatment options.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Background

Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma.

Methods

We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining.

Results

Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses.

Conclusion

The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

TGIF2 is a potential biomarker for diagnosis and prognosis of glioma

Background

TGFB-induced factor homeobox 2 (TGIF2), a member of the Three-Amino-acid-Loop-Extension (TALE) superfamily, has been implicated in various malignant tumors. However, its prognostic significance in glioma, impact on tumor immune infiltration, and underlying mechanisms in glioma development remain elusive.

Methods

The expression of TGIF2 in various human normal tissues, normal brain tissues, and gliomas was investigated using HPA, TCGA, GTEx, and GEO databases. The study employed several approaches, including Kaplan-Meier analysis, ROC analysis, logistic regression, Cox regression, GO analysis, KEGG analysis, and GSEA, to explore the relationship between TGIF2 expression and clinicopathologic features, prognostic value, and potential biological functions in glioma patients. The impact of TGIF2 on tumor immune infiltration was assessed through Estimate, ssGSEA, and Spearman analysis. Genes coexpressed with TGIF2 were identified, and the protein-protein interaction (PPI) network of these coexpressed genes were constructed using the STRING database and Cytoscape software. Hub genes were identified using CytoHubba plugin, and their clinical predictive value was explored. Furthermore, in vitro experiments were performed by knocking down and knocking out TGIF2 using siRNA and CRISPR/Cas9 gene editing, and the role of TGIF2 in glioma cell invasion and migration was analyzed using transwell assay, scratch wound-healing assay, RT-qPCR, and Western blot.

Results

TGIF2 mRNA was found to be upregulated in 21 cancers, including glioma. High expression of TGIF2 was associated with malignant phenotypes and poor prognosis in glioma patients, indicating its potential as an independent prognostic factor. Furthermore, elevated TGIF2 expression positively correlated with cell cycle regulation, DNA synthesis and repair, extracellular matrix (ECM) components, immune response, and several signaling pathways that promote tumor progression. TGIF2 showed correlations with Th2 cells, macrophages, and various immunoregulatory genes. The hub genes coexpressed with TGIF2 demonstrated significant predictive value. Additionally, in vitro experiments revealed that knockdown and knockout of TGIF2 inhibited glioma cell invasion, migration and suppressed the epithelial-mesenchymal transition (EMT) phenotype.

Conclusion

TGIF2 emerges as a potential biomarker for glioma, possibly linked to tumor immune infiltration and EMT.

Gasdermins: a dual role in pyroptosis and tumor immunity

The gasdermin (GSDM) protein family plays a pivotal role in pyroptosis, a process critical to the body's immune response, particularly in combatting bacterial infections, impeding tumor invasion, and contributing to the pathogenesis of various inflammatory diseases. These proteins are adept at activating inflammasome signaling pathways, recruiting immune effector cells, creating an inflammatory immune microenvironment, and initiating pyroptosis. This article serves as an introduction to the GSDM protein-mediated pyroptosis signaling pathways, providing an overview of GSDMs' involvement in tumor immunity. Additionally, we explore the potential applications of GSDMs in both innovative and established antitumor strategies.

Study of Therapeutic Mechanisms of Bupi Yichang Formula against Colon Cancer Based on Network Pharmacology, Machine Learning, and Experimental Verification

Bupi Yichang formula (BPYCF) has shown the anti-cancer potential; however, its effects on colon cancer and the mechanisms remain unknown. This study intended to explore the effects of BPYC on colon cancer and its underlying mechanisms. BPYCF-related and colon cancer-related targets were acquired from public databases, followed by differentially expressed genes (DEG) identification. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using clusterProfiler. A protein-protein interaction (PPI) network was constructed using STRING database. CytoHubba and MCODE to screen the hub targets. A diagnostic model was built using random forest algorithm. Molecular docking was conducted using PyMOL and AutoDock. High-performance liquid chromatograph-mass spectrometry (HPLC-MS) analysis and in vitro validation were performed. Forty-six overlapping targets of BPYCF-related, colon cancer-related targets, and DEGs were obtained. GO and KEGG analyses showed that the targets were mainly enriched in response to lipopolysaccharide, neuronal cell body, protein serine/threonine/tyrosine, as well as C-type lectin receptor, NOD-like receptor, and TNF signaling pathways. Five targets were identified as the pivotal targets, among which, NOS3, CASP8, RIPK3, and TNFRSF10B were stably docked with the core active component, naringenin. Naringenin was also identified from the BPYCF sample through HPLC-MS analysis. In vitro experiments showed that BPYCF inhibited cell viability, reduced NOS3 expression, and elevated CASP8, RIPK3, and TNFRSF10B expression in colon cancer cells. BPYCF might treat colon cancer mainly by regulating NOS3, CASP8, RIPK3, and TN-FRSF10B. This study first revealed the therapeutic effects and mechanisms of BPYCF against colon cancer, paving the path for the development of targeted therapeutic strategies for this cancer in the clinic.

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.

GSDMA at the crossroads between pyroptosis and tumor immune evasion in glioma

Pyroptosis, an inflammatory and programmed cell death process, has been controversial in its role in tumor immunity. However, as the first molecule in the gasdermin family, the mechanism of GSDMA in glioma growth is not well understood. We identified the differentially expressed gene GSDMA from Treg cells-related genes using the TCGA database. The biological functions of GSDMA and the relationship between GSDMA expression and tumor immune cell infiltration and cancer patient survival were investigated using open-source databases and platforms. Additionally, flow cytometry analysis was used to examine the effect of GSDMA on tumor immune cell infiltration. Our study showed that GSDMA expression played an important role in immune evasion in glioma. Patients with high GSDMA expression had a worse prognosis. In vivo studies demonstrated that GSDMA knockdown could enhance the infiltration level of CD8+ T cells. High GSDMA expression was also positively correlated with poor anti-PD-L1 treatment outcomes in GBM patients, suggesting that GSDMA may be a potential biomarker that should be considered in combination with anti-PD-L1 therapy for glioma patients. In conclusion, our study demonstrates that high GSDMA expression in gliomas is associated with immune-infiltrating cells CD8+ T cells and Treg cells, and indicates a worse prognosis in glioma. Therefore, GSDMA may serve as a therapeutic target for glioma progression and should be applied in immunotherapy for glioma patients.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction

Background

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.

Methods

Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.

Results

Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.

Conclusion

The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

Xihuang pill facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis

Glioma is the most common malignancy in the central nervous system. This study aims to disclose the impacts of Xihuang pill (XHP), a traditional Chinese formula, on glioma cell pyroptosis and relevant molecular mechanism. U251 and SHG-44 cells were treated with XHP alone or together with oe-POU4F1 and sh-STAT3. CCK8 assay detected the viability, flow cytometry evaluated pyroptosis, and microscopy observed cell morphology. LDH release was determined by the LDH kit and the levels of IL-1β and IL-18 were detected by ELISA. Immunofluorescence showed NLRP3 expression in glioma cells and western blotting measured the levels of POU4F1, STAT3, NLRP3, ASC, cleaved caspase-1, and IL-1β. The binding of POU4F1 to STAT3 was verified. Primary glioma model was established to observe tumor change by in vivo imaging, determine the levels of Ki67 and NLRP3 by immunochemistry, and detect relevant protein levels by western blotting. XHP treatment alone downregulated POU4F1 and STAT3 levels, aroused pyroptotic appearance in glioma cells such as ballooning swelling, reduced cell viability and number of pyroptotic cells, increased LDH release and IL-1β and IL-18 levels, formed NLRP3 sports in cells, and elevated the levels of pyroptosis-related proteins. However, POU4F1 overexpression or STAT3 silencing suppressed XHP-promoted pyroptosis. Mechanistically, POU4F1 acted as a transcription factor of STAT3 and regulated its transcription. In primary glioma models, XHP enhanced glioma cell pyroptosis and blocked glioma growth. XHP facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis.

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.

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Objective

To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS.

Methods

We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes.

Results

We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores.

Conclusion

The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Integrated analysis to identify the prognostic and immunotherapeutic roles of coagulation-associated gene signature in clear cell renal cell carcinoma

The coagulation system is closely related to the physiological status and immune response of the body. Recent years, studies focusing on the association between coagulation system abnormalities and tumor progression have been widely reported. In clear cell renal cell carcinoma (ccRCC), poor prognosis often occurs in patients with venous tumor thrombosis and coagulation system abnormalities, and there is a lack of research in related fields. Significant differences in coagulation function were also demonstrated in our clinical sample of patients with high ccRCC stage or grade. Therefore, in this study, we analyzed the biological functions of coagulation-related genes (CRGs) in ccRCC patients using single-cell sequencing and TCGA data to establish the 5-CRGs based diagnostic signature and predictive signature for ccRCC. Univariate and multivariate Cox analyses suggested that prognostic signature could be an independent risk factor. Meanwhile, we applied CRGs for consistent clustering of ccRCC patients, and the two classes showed significant survival and genotype differences. The differences in individualized treatment between the two different subtypes were revealed by pathway enrichment analysis and immune cell infiltration analysis. In summary, we present the first systematic analysis of the significance of CRGs in the diagnosis, prognosis, and individualized treatment of ccRCC patients.

Identification of a robust scoring system based on metabolic genes followed by in-depth validation of ATP1A3 in glioma

AIMS

In the past few decades, the prognosis of glioma patients has not significantly improved. Therefore, to provide more precise medical services for glioma patients, it is urgent to identify more clinically meaningful subtypes, establish more robust clinical prediction models, and find more effective therapeutic targets.

MATERIALS AND METHODS

Four distinct metabolic-associated subtypes were identified by the NMF algorithm based on metabolic genes (MEGs). A robust scoring system was constructed based on the differentially expressed genes (DEGs) screened from the four metabolic-associated subtypes with the LASSO regression algorithm and multivariate Cox regression analysis. Further analysis of scoring systems was done by different R packages. In addition, the ATP1A3 gene was screened and bioinformatics analysis of it was conducted on several public websites. GSEA software was utilized to search hallmark signaling pathways closely related to ATP1A3. Cytological experiments were used to investigate the role of ATP1A3 in the malignant progression of glioblastoma (GBM) cells.

KEY FINDINGS

Four metabolic-associated subtypes with significantly different clinicopathological characteristics were identified, and a robust scoring system with outstanding clinical application value was established. In addition, a tumor suppressor gene ATP1A3 was found, which is expected to be a potential therapeutic target for glioma.

SIGNIFICANCE

This study is of great significance in the diagnosis, prognosis, and prediction of the response to immune checkpoint blockers (ICBs) for glioma patients. More importantly, this study found a potential therapeutic target for glioma.

Cytohesin-4 Upregulation in Glioma-Associated M2 Macrophages Is Correlated with Pyroptosis and Poor Prognosis

Cytohesin-4 (CYTH4) is a member of the PSCD family. Members of this family appear to mediate the regulation of protein sorting and membrane trafficking. In previous studies, CYTH4 has been linked with multiple brain diseases, but not glioma, the most common type of brain tumor. We utilized multiple glioma single-cell RNA sequencing datasets and bulk data from the TCGA and CGGA and conducted GSEA and KEGG and GO analyses. Biomarker potential was tested via ROC curve analysis. Radar plots were used to study TMB and MSI correlations. Immune cell studies were conducted using CIBERSORT. All statistical analyses were performed in R software and GraphPad Prism 9. CYTH4 was overexpressed in the glioma macrophage population in several single-cell RNA sequencing datasets and was most correlated with M2 macrophages. CYTH4 expression was higher in tumor tissues and was correlated with survival and WHO grade. ROC curves suggested CYTH4 overexpression to be a potential glioma biomarker. GSEA results indicated a relationship between CYTH4 and apoptosis, and PPI analysis supported a pyroptosis correlation. KEGG and GO analysis results linked CYTH4 with antigen processing and presentation and neutrophil activities. In summary, the study identified a CYTH4/pyroptosis/M2 macrophage axis. CYTH4 was upregulated in M2 macrophages in glioma and affected pyroptosis. CYTH4 overexpression is a potential biomarker predicting a poor prognosis.

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.

Pyroptosis-Related Signature Predicts the Progression of Ulcerative Colitis and Colitis-Associated Colorectal Cancer as well as the Anti-TNF Therapeutic Response

Ulcerative colitis (UC) is a complex intestinal inflammation with an increasing risk of colitis-associated colorectal cancer (CAC). However, the pathogenesis is still unclear between active UC and inactive UC. Recently, it has been reported that pyroptosis-related genes (PRGs) are closely associated with inflammatory disease activity. Nevertheless, the specific roles of PRGs in the progression and treatment of UC and CAC remain unclear. In this study, we identified 30 differentially expressed PRGs based on the immune landscape of active and inactive UC samples. Meanwhile, weighted gene coexpression network analysis was applied to explore important genes associated with active UC. By intersecting with the differentially expressed PRGs, CASP5, GBP1, GZMB, IL1B, and IRF1 were selected as key PRGs to construct a pyroptosis-related signature (PR-signature). Then, logistic regression analysis was performed to validate the PR-signature and establish a pyroptosis-related score (PR-Score). We demonstrated that PR-Score had a powerful ability to distinguish active UC from inactive UC in multiple datasets. Besides, PR-Score was positively correlated with immune cell infiltration and inflammatory microenvironment in UC. Lower PR-Score was associated with a better response to anti-TNF therapy for patients with UC. Additionally, high-PR-Score was found to suppress CAC and improve the survival outcomes of patients with colorectal cancer. Finally, the levels of the PR-signature genes were validated both in vitro and in vivo. These findings can improve our understanding of PRGs in UC and provide new markers for predicting the occurrence of active UC or CAC and the treatment of UC.

Iron Oxide Nanoparticles Decorated with Functional Peptides for a Targeted siRNA Delivery to Glioma Cells

Glioma is a deadly form of brain cancer, and the difficulty of treating glioma is exacerbated by the chemotherapeutic resistance developed in the tumor cells over the time of treatment. siRNA can be used to silence the gene responsible for the increased resistance, and sensitize the glioma cells to drugs. Here, iron oxide nanoparticles functionalized with peptides (NP-CTX-R₁₀) were used to deliver siRNA to silence O6-methylguanine-DNA methyltransferase (MGMT) to sensitize tumor cells to alkylating drug, Temozolomide (TMZ). The NP-CTX-R₁₀ could complex with siRNA through electrostatic interactions and was able to deliver the siRNA to different glioma cells. The targeting ligand chlorotoxin and cell penetrating peptide polyarginine (R₁₀) enhanced the transfection capability of siRNA to a level comparable to commercially available Lipofectamine. The NP-siRNA was able to achieve up to 90% gene silencing. Glioma cells transfected with NP-siRNA targeting MGMT showed significantly elevated sensitivity to TMZ treatment. This nanoparticle formulation demonstrates the ability to protect siRNA from degradation and to efficiently deliver the siRNA to induce therapeutic gene knockdown.

Pyroptosis-based risk score predicts prognosis and drug sensitivity in lung adenocarcinoma

Pyroptosis is a recently identified form of programmed cell death; however, its role in lung adenocarcinoma (LUAD) remains unclear. Therefore, we set out to explore the prognostic potential of pyroptosis-related genes in LUAD. The pyroptosis-related risk score (PRRS) was developed by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression. We found that PRRS was an independent prognostic factor for LUAD. LUAD patients in the high-PRRS group showed a significantly shorter overall survival (OS) and enriched in cell proliferation-related pathways. Then pathway enrichment analyses, mutation profile, tumor microenvironment, and drug sensitivity analysis were further studied in PRRS stratified LUAD patients. Tumor purity (TP) analyses revealed that L-PRRS LUAD patients had a lower TP, and patients in L-TP + L-PRRS subgroup had the most prolonged OS. Mutation analyses suggested that the L-PRRS LUAD patients had a lower tumor mutation burden (TMB), and patients in H-TMB + L-PRRS subgroup had the most prolonged OS. Drug sensitivity analyses showed that PRRS was significantly negatively correlated with the sensitivity of cisplatin, besarotene, etc., while it was significantly positively correlated with the sensitivity of kin001-135. Eventually, a nomogram was constructed based on PRRS and clinical characters of LUAD. Overall, the pyroptosis-related signature is helpful for prognostic prediction and in guiding treatment for LUAD patients.

Bulk and Single-Cell Transcriptome Analyses Revealed That the Pyroptosis of Glioma-Associated Macrophages Participates in Tumor Progression and Immunosuppression

Glioma is the most common of all central nervous system (CNS) malignancies and is associated with a poor prognosis. Pyroptosis has been proven to be associated with the progression of multiple tumors and CNS diseases. However, the relationships between pyroptosis and clinical prognosis and immune cell infiltration are unclear in glioma. In this study, we conducted a comprehensive exploration of pyroptosis in glioma. First, prognosis-related genes were screened at each key regulatory locus in the pyroptosis pathway, and the prognostic ability and coexpression relationships of GSDMD and its upstream pathway genes NLRC4/CASP1/CASP4 were identified and well validated in multiple datasets. Tissue microarray-based immunohistochemistry results showed higher levels of NLRC4 and N-terminal GSDMD in high-grade gliomas, providing conclusive evidence of pyroptosis in gliomas. The robustness of the prognostic model based on these four genes was well validated in TCGA and CGGA cohorts. Bulk RNA-seq-based analysis showed that the group defined as the high-risk group according to the model showed activation of multiple inflammatory response pathways and impaired synaptic gene expression and had a higher infiltration of bone marrow-derived macrophages (BMDMs) and a hypersuppressed immune microenvironment. More importantly, three independent single-cell RNA-seq (scRNA-seq) datasets demonstrated that tumor-infiltrating macrophages, particularly BMDMs but not tissue-resident microglia, showed significant coexpression of the GSDMD and CASP genes, and BMDMs from high-grade gliomas accounted for a higher proportion of immune infiltrating cells and had higher expression of pyroptosis genes. Finally, we revealed the activation of pathways in response to LPS/bacteria and oxidative stress during BMDM development toward the pyroptosis cell fate by pseudotime trajectory analysis, suggesting potential BMDM pyroptosis initiators. The above results provide not only novel insights into the pathological mechanisms of glioma but also novel therapeutic targets for glioma, suggesting the potential application of pyroptosis inhibitors (e.g., disulfiram).

The differential usage of molecular machinery in brain cancer patients with iron-enriched glioma environments

Gliomas are neuroepithelial tumors in the brain or spinal cord that arise from glial or precursor cells and include astrocytomas, oligodendrogliomas, and ependymomas. They are the most common malignant primary central nervous system tumors, representing 75% of cases in adults and 24% of all cases of primary brain and CNS tumors [1,2].

Pyroptosis: a novel signature to predict prognosis and immunotherapy response in gliomas

Gliomas are the most common primary brain tumors and are highly malignant with a poor prognosis. Pyroptosis, an inflammatory form of programmed cell death, promotes the inflammatory cell death of cancer. Studies have demonstrated that pyroptosis can promote the inflammatory cell death (ICD) of cancer, thus affecting the prognosis of cancer patients. Therefore, genes that control pyroptosis could be a promising candidate bio-indicator in tumor therapy. The function of pyroptosis-related genes (PRGs) in gliomas was investigated based on the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA) and the Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. In this study, using the non-negative matrix factorization (NMF) clustering method, 26 PRGs from the RNA sequencing data were divided into two subgroups. The LASSO and Cox regression was used to develop a 4-gene (BAX, Caspase-4, Caspase-8, PLCG1) risk signature, and all glioma patients in the CGGA, TCGA and Rembrandt cohorts were divided into low- and high-risk groups. The results demonstrate that the gene risk signature related to clinical features can be used as an independent prognostic indicator in glioma patients. Moreover, the high-risk subtype had rich immune infiltration and high expression of immune checkpoint genes in the tumor immune microenvironment (TIME). The analysis of the Submap algorithm shows that patients in the high-risk group could benefit more from anti-PD1 treatment. The risk characteristics associated with pyroptosis proposed in this study play an essential role in TIME and can potentially predict the prognosis and immunotherapeutic response of glioma patients.

Pyroptosis related genes signature predicts prognosis and immune infiltration of tumor microenvironment in hepatocellular carcinoma

Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan–Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P &lt; 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Background: Glioma is the most prevalent malignant intracranial tumor. Many studies have shown that angiogenesis plays a crucial role in glioma tumorigenesis, metastasis, and prognosis. In this study, we conducted a comprehensive analysis of angiogenesis-related genes (ARGs) in glioma.

Methods: RNA-sequencing data of glioma patients were obtained from TCGA and CGGA databases. Via consensus clustering analysis, ARGs in the sequencing data were distinctly classified into two subgroups. We performed univariate Cox regression analysis to determine prognostic differentially expressed ARGs and least absolute shrinkage and selection operator Cox regression to construct a 14-ARG risk signature. The CIBERSORT algorithm was used to explore immune cell infiltration, and the ESTIMATE algorithm was applied to calculate immune and stromal scores.

Results: We found that the 14-ARG signature reflected the infiltration characteristics of different immune cells in the tumor immune microenvironment. Additionally, total tumor mutational burden increased significantly in the high-risk group. We combined the 14-ARG signature with patient clinicopathological data to construct a nomogram for predicting 1-, 3-, and 5-year overall survival with good accuracy. The predictive value of the prognostic model was verified in the CGGA cohort. SPP1 was a potential biomarker of glioma risk and was involved in the proliferation, invasion, and angiogenesis of glioma cells.

Conclusion: In conclusion, we established and validated a novel ARG risk signature that independently predicted the clinical outcomes of glioma patients and was associated with the tumor immune microenvironment.

Pan-Cancer Pyroptosis Analyses Identified Novel Immunology and Chemotherapy-Related Prognostic Signatures in Cancer Subtypes

Despite mounting evidence linking pyroptotic cell death to tumor growth, the clinical significance and disease mechanism of pyroptosis in cancer remain uncertain. In this study, we established a unique gene signature (π signature) that can be used as a predictive and prognostic tool in pyroptosis-related cancer subtypes. We found that the 13 core pyroptosis genes exerted opposite prognostic effects in different cancer types, which were subgrouped as pyroptosis positively related cancer and pyroptosis negatively related cancer. Subsequently, π signature was identified separately from the hub genes in pyroptosis positively related cancer and pyroptosis negatively related cancer subtypes. It was shown that π signature was well correlated with patient survival, pathological stages, tumor lymphocyte infiltration, and immunotherapy response. π signature was also applied as a predictive tool for chemotherapy drug responses and used as an independent factor for patient overall survival prediction. In short, this elaborated genetic signature could help us understand the oncogenic mechanism and pave the way for further therapeutic strategies based on pyroptosis.

The Identification of Necroptosis-Related Subtypes, the Construction of a Prognostic Model, and the Characterization of the Tumor Microenvironment in Gliomas

Necroptosis is a recently discovered form of cell death that plays a vital role in the progression of cancer, the spread of metastases, and the immunologic response to tumors. Due to the dual role of necrotic apoptotic processes in tumor pathogenesis and the heterogeneity of gliomas, the function of necroptosis in the glioma microenvironment is still poorly understood. We characterized the expression of necroptosis-related genes (NRGs) within glioma samples at both the genetic and transcriptional levels, identifying three distinct subtypes. Additionally, we constructed a risk score, which is capable of accurately predicting patient prognosis, correlates with tumor mutation burden (TMB), tumor stem cell index (CSC), immune checkpoints, and predicts tumor drug sensitivity. To facilitate its application in the clinic, we developed a nomogram and demonstrated that it predicts the prognosis of glioma patients with good accuracy and reliability using multiple datasets. We examined the function of necroptosis in the tumor microenvironment (TME) and the prognosis of gliomas, which may be useful for guiding individualized treatment plans for gliomas targeting necroptosis.

Identification of a Pyroptosis-Related Gene Signature for Predicting the Immune Status and Prognosis in Lung Adenocarcinoma

Background: Pyroptosis is a form of programmed cell death triggered by the rupture of cell membranes and the release of inflammatory substances; it is essential in the occurrence and development of cancer. A considerable number of studies have revealed that pyroptosis is closely associated to the biological process of several cancers. However, the role of pyroptosis in lung adenocarcinoma (LUAD) remains elusive. The purpose of this study was to explore the prognostic role of pyroptosis-related genes (PRGs) and their relationship with the tumor immune microenvironment (TIME) in LUAD.

Methods: Gene expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic PRG signature was established in the training set and verified in the validation sets. Functional enrichment and immune microenvironment analyses related to PRGs were performed and a nomogram based on the risk score and clinical characteristics was established. What is more, quantitative real-time PCR (qRT-PCR) analysis was applied in order to verify the potential biomarkers for LUAD.

Results: A prognostic signature based on five PRGs was constructed to separate LUAD patients into two risk groups. Patients in the high-risk group had worse prognoses than those in the low-risk group. The signature was identified as independent via Cox regression analyses and obtained the largest area under the curve (AUC = 0.677) in the receiver operating characteristic (ROC). Functional enrichment and immune microenvironment analyses demonstrated that the immune status was significantly different in the two subgroups and that immunotherapy may be effective for the high-risk group. Furthermore, qRT-PCR analysis verified that serum PRKACA and GPX4 could serve as diagnostic biomarkers for LUAD.

Conclusion: Overall, a risk signature based on five PRGs was generated, providing a novel perspective on the determinants of prognosis and survival in LUAD, as well as a basis for the development of individualized regimes.

Identification and Validation of a Gene Signature for Lower-Grade Gliomas Based on Pyroptosis-Related Genes to Predict Survival and Response to Immune Checkpoint Inhibitors

Pyroptosis plays a critical role in the immune response to immune checkpoint inhibitors (ICIs) by mediating the tumor immune microenvironment. However, the impact of pyroptosis-related biomarkers on the prognosis and efficacy of ICIs in patients with lower-grade gliomas (LGGs) is unclear. An unsupervised clustering analysis identified pyroptosis-related subtypes (PRSs) based on the expression profile of 47 pyroptosis-related genes in The Cancer Genome Atlas-LGG cohort. A PRS gene signature was established using univariate Cox regression, random survival forest, least absolute shrinkage and selection operator, and stepwise multivariable Cox regression analyses. The predictive power of this signature was validated in the Chinese Glioma Genome Atlas database. We also investigated the differences between high- and low-risk groups in terms of the tumor immune microenvironment, tumor mutation, and response to target therapy and ICIs. The PRS gene signature comprised eight PRS genes, which independently predicted the prognosis of LGG patients. High-risk patients had a worse overall survival than did the low-risk patients. The high-risk group also displayed a higher proportion of M1 macrophages and CD8⁺ T cells and higher immune scores, tumor mutational burden, immunophenoscore, IMmuno-PREdictive Score, MHC I association immune score, and T cell-inflamed gene expression profile scores, but lower suppressor cells scores, and were more suitable candidates for ICI treatment. Higher risk scores were more frequent in patients who responded to ICIs using data from the ImmuCellAI website. The presently established PRS gene signature can be validated in melanoma patients treated with real ICI treatment. This signature is valuable in predicting prognosis and ICI treatment of LGG patients, pending further prospective verification.