A Pyroptosis-Related Gene Prognostic Index Correlated with Survival and Immune Microenvironment in Glioma

Programmed cell death disrupts inflammatory tumor microenvironment (TME) and promotes glioblastoma evolution

Glioblastoma (GBM) is the most common malignant brain tumor and has a dismal prognosis even under the current first-line treatment, with a 5-year survival rate less than 7%. Therefore, it is important to understand the mechanism of treatment resistance and develop new anti-tumor strategies. Induction of programmed cell death (PCD) has become a promising anti-tumor strategy, but its effectiveness in treating GBM remains controversial. On the one hand, PCD triggers tumor cell death and then release mediators to draw in immune cells, creating a pro-inflammatory tumor microenvironment (TME). One the other hand, mounting evidence suggests that PCD and inflammatory TME will force tumor cells to evolve under survival stress, leading to tumor recurrence. The purpose of this review is to summarize the role of PCD and inflammatory TME in the tumor evolution of GBM and promising methods to overcome tumor evolution.

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.

Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma

BACKGROUND

Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients.

METHODS

RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs.

RESULTS

A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined.

CONCLUSIONS

A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.

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 model based on necroptosis-related genes for predicting immune status and prognosis in glioma

Background

Glioma is a highly aggressive brain cancer with a poor prognosis. Necroptosis is a form of programmed cell death occurring during tumor development and in immune microenvironments. The prognostic value of necroptosis in glioma is unclear. This study aimed to develop a prognostic glioma model based on necroptosis.

Methods

A necroptosis-related risk model was constructed by Cox regression analysis based on The Cancer Genome Atlas (TCGA) training set, validated in two Chinese Glioma Genome Atlas (CGGA) validation sets. We explored the differences in immune infiltration and immune checkpoint genes between low and high risk groups and constructed a nomogram. Moreover, we compiled a third validation cohort including 43 glioma patients. The expression of necroptosis-related genes was verified in matched tissues using immunochemical staining in the third cohort, and we analyzed their relationship to clinicopathological features.

Results

Three necroptosis-related differentially expressed genes (EZH2, LEF1, and CASP1) were selected to construct the prognostic model. Glioma patients with a high risk score in the TCGA and CGGA cohorts had significantly shorter overall survival. The necroptosis-related risk model and nomogram exhibited good predictive performance in the TCGA training set and the CGGA validation sets. Furthermore, patients in the high risk group had higher immune infiltration status and higher expression of immune checkpoint genes, which was positively correlated with poorer outcomes. In the third validation cohort, the expression levels of the three proteins encoded by EZH2, LEF1, and CASP1 in glioma tissues were significantly higher than those from paracancerous tissues. They were also closely associated with disease severity and prognosis.

Conclusions

Our necroptosis-related risk model can be used to predict the prognosis of glioma patients and improve prognostic accuracy, which may provide potential therapeutic targets and a theoretical basis for treatment.

A prognostic pyroptosis-related LncRNA classifier associated with the immune landscape and therapy efficacy in glioma

Background: Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of “personalised medicine” remains a significant challenge.

Method: The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the “timeROC” package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the “oncoPredict” package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively.

Results: A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy.

Conclusion: We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.

Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

Background

Pyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related long non-coding RNAs (lncRNAs) (PRLs) in PCa remains elusive. This study aimed to explore the relationship between PRL and PCa prognosis.

Methods

Gene expression and clinical signatures were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A PRL risk prediction model was established by survival random forest analysis and least absolute shrinkage and selection operator regression. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by the single-sample gene set enrichment analysis, gene set variation analysis, and copy number variation analysis. PRL expression was verified in PCa cells. Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, transwell, and Western blotting assay were used to detect the proliferation, migration, invasion, and pyroptosis of PCa cells, respectively.

Results

Prognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and the area under the curve of the 1-, 3-, and 5-year receiver operating characteristic curves in the testing cohort was 1, 0.93, and 0.92, respectively. Moreover, the external cohort validation confirmed the robustness of the PRL risk prediction model. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints was also reduced in the high-score group. Gene mutation proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Six PRLs were upregulated in PCa cells. Silencing of AC005253.1 inhibited cell proliferation, migration, and invasion in DU145 and PC-3 cells. Moreover, silencing of AC005253.1 promoted pyroptosis and inflammasome AIM2 expression.

Conclusions

Overall, we constructed a prognostic model of PCa with six PRLs and identified their expression in PCa cells. The experimental verification showed that AC005253.1 could affect the proliferation, migration, and invasion abilities of PCa cells. Meanwhile, AC005253.1 may play an important role in PCa by affecting pyroptosis through the AIM2 inflammasome. This result requires further research for verification.

New insights into the role of dipeptidyl peptidase 8 and dipeptidyl peptidase 9 and their inhibitors

Dipeptidyl peptidase 8 (DPP8) and 9 (DPP9) are widely expressed in mammals including humans, mainly locate in the cytoplasm. The DPP8 and DPP9 (DPP8/9) belong to serine proteolytic enzymes, they can recognize and cleave N-terminal dipeptides of specific substrates if proline is at the penultimate position. Because the localization of DPP8/9 is different from that of DPP4 and the substrates for DPP8/9 are not yet completely clear, their physiological and pathological roles are still being further explored. In this article, we will review the recent research advances focusing on the expression, regulation, and functions of DPP8/9 in physiology and pathology status. Emerging research results have shown that DPP8/9 is involved in various biological processes such as cell behavior, energy metabolism, and immune regulation, which plays an essential role in maintaining normal development and physiological functions of the body. DPP8/9 is also involved in pathological processes such as tumorigenesis, inflammation, and organ fibrosis. In recent years, related research on immune cell pyroptosis has made DPP8/9 a new potential target for the treatment of hematological diseases. In addition, DPP8/9 inhibitors also have great potential in the treatment of tumors and chronic kidney disease.

Construction of prognostic signature of patients with oral squamous cell carcinoma based on pyroptosis-related long non-coding RNAs

Background and objective

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.

Methods

The RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.

Results

Eight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.

Conclusion

The prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.

Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas

Background

Cuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.

Methods

In this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).

Results

Two distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.

Conclusion

Collectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

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.

PD-L2 Serves as a Potential Prognostic Biomarker That Correlates With Immune Infiltration and May Predict Therapeutic Sensitivity in Lower-Grade Gliomas

Although patients with lower-grade gliomas (LGGs; grades II and III) have a relatively favorable prognosis, patients frequently relapse and tend to progress to higher-grade gliomas, leading to treatment resistance, poor survival, and ultimately treatment failure. However, until now, thorough research has not yet been reported on the relationship between PD-L2 and immune infiltration and therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy of LGGs. In this study, we found that the expression of PD-L2 is upregulated in glioma, with high PD-L2 expression predicting a worse prognosis. Univariate and multivariate Cox regression analysis both indicated that PD-L2 represented an independent prognostic factor with high accuracy in survival prediction for LGGs. A nomogram comprising of age, grade, IDH mutation, and PD-L2 was established for predicting OS. Additionally, PD-L2 was found to be remarkably correlated with immune infiltration and some anti-tumor immune functions. The degree of PD-L2 expression was also found to be strongly related to the prediction of therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy. Furthermore, immunohistochemistry demonstrated that PD-L2 and the macrophage biomarker CD68 were both increased in glioma, with PD-L2 expression having a strong positive connection with CD68 expression. Taken together, PD-L2 is a prognostic biomarker for LGGs patients that may provide novel insights into glioma individualized therapeutic strategies and guide effective immunotherapy and chemotherapy.

A Pyroptosis-Related Gene Signature Associated with Prognosis and Tumor Immune Microenvironment in Gliomas

Background

Pyroptosis is a novel form of cell death that plays a significant role in cancer, while the prognostic values of pyroptosis-related genes in gliomas have not been revealed.

Methods

We analyzed the RNA-seq and clinical data of gliomas from the University of California Santa Cruz (UCSC) Xena database to determine differentially expressed pyroptosis-related genes. Based on these genes, a pyroptosis genes signature was constructed after univariate Cox analysis and Lasso Cox analyses. The sensitivity and specificity of pyroptosis genes signature were verified by the Chinese Glioma Genome Atlas (CGGA) dataset. Finally, we explored the association of risk signatures with tumor microenvironment and immune cell infiltration.

Results

Of 15 differentially expressed pyroptosis-related genes, three genes of BCL2 associated X (BAX), caspase 3 (CASP3), and caspase 4 (CASP4) were used to construct the risk signature. The effectiveness of risk signature for predicting survival at 1, 3, 5 years was performed by the receiver operating characteristic curve (ROC), and the area under curves (AUC) was 0.739, 0.817, and 0.800, respectively. Functional enrichment results showed signal transduction, cell adhesion, immune response, and inflammatory response were enriched. The immune analysis revealed that pyroptosis had a remarkable effect on the immune microenvironment.

Conclusion

In this study, we constructed a pyroptosis-related gene signature, which can serve as a potential biomarker for predicting the survival of glioma patients. Additionally, we suggested that pyroptosis may promote gliomas development by inducing chronic inflammation microenvironment.

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.