A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration

Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer

Background

Breast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression.

Methods

We obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival.

Results

The NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making.

Conclusion

We identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.

Predictive potentials of glycosylation-related genes in glioma prognosis and their correlation with immune infiltration

Glycosylation is currently considered to be an important hallmark of cancer. However, the characterization of glycosylation-related gene sets has not been comprehensively analyzed in glioma, and the relationship between glycosylation-related genes and glioma prognosis has not been elucidated. Here, we firstly found that the glycosylation-related differentially expressed genes in glioma patients were engaged in biological functions related to glioma progression revealed by enrichment analysis. Then seven glycosylation genes (BGN, C1GALT1C1L, GALNT13, SDC1, SERPINA1, SPTBN5 and TUBA1C) associated with glioma prognosis were screened out by consensus clustering, principal component analysis, Lasso regression, and univariate and multivariate Cox regression analysis using the TCGA-GTEx database. A glycosylation-related prognostic signature was developed and validated using CGGA database data with significantly accurate prediction on glioma prognosis, which showed better capacity to predict the prognosis of glioma patients than clinicopathological factors do. GSEA enrichment analysis based on the risk score further revealed that patients in the high-risk group were involved in immune-related pathways such as cytokine signaling, inflammatory responses, and immune regulation, as well as glycan synthesis and metabolic function. Immuno-correlation analysis revealed that a variety of immune cell infiltrations, such as Macrophage, activated dendritic cell, Regulatory T cell (Treg), and Natural killer cell, were increased in the high-risk group. Moreover, functional experiments were performed to evaluate the roles of risk genes in the cell viability and cell number of glioma U87 and U251 cells, which demonstrated that silencing BGN, SDC1, SERPINA1, TUBA1C, C1GALT1C1L and SPTBN5 could inhibit the growth and viability of glioma cells. These findings strengthened the prognostic potentials of our predictive signature in glioma. In conclusion, this prognostic model composed of 7 glycosylation-related genes distinguishes well the high-risk glioma patients, which might potentially serve as caner biomarkers for disease diagnosis and treatment.

Clinical characteristics and prognostic analysis of patients with HIV and glioma: A case series and literature review

Cerebral glial tumors have become increasingly common in human immunodeficiency virus (HIV)-positive patients. The present study aimed to report a series of such cases, explore their clinical and pathological characteristics and subject all the reported cases to a survival analysis. The characteristics, management and prognosis of 10 HIV-positive patients with brain gliomas enrolled in a single hospital were investigated in detail. Immunohistochemical assessment of CD31, CD68 and CD163 was performed in the 10 HIV-positive patients with glioma and 18 HIV-negative patients with glioma. The relevant literature was also reviewed using relevant search terms. The potential predictive factors were screened by univariate and multivariate logistic regression analyses, and a nomogram was established based on the potential predictive factors. A total of 50 patients, including the 10 primary cases, were included in the survival analysis. The median survival time was 9 months. The gliomas of HIV-negative patients had a lower cell count of CD163+ cells than those of HIV-positive patients. High CD4+ T-cell count and the use of highly active antiretroviral therapy (HAART) tended to increase the median survival duration, although not significantly according to the log-rank analysis. In the univariate analysis, only surgery, radiotherapy (RT) and World Health Organization (WHO) tumor grade had significant associations with overall survival. In the multivariate analysis, only RT and WHO grade were independent predictors. In conclusion, gliomas may occur more frequently in HIV-positive populations than is currently recognized. The survival duration of most HIV-positive patients with glioma is determined by the tumor rather than HIV status. Adjuvant radiotherapy and the WHO grade of the glioma are predicted to be independent prognostic factors. Surgical resection followed by RT plus regular HAART is recommended for patients with glioma who are HIV-positive.

Glioma stem cells remodel immunotolerant microenvironment in GBM and are associated with therapeutic advancements

Glioma is the most common primary tumor of the central nervous system (CNS). Glioblastoma (GBM) is incurable with current treatment strategies. Additionally, the treatment of recurrent GBM (rGBM) is often referred to as terminal treatment, necessitating hospice-level care and management. The presence of the blood-brain barrier (BBB) gives GBM a more challenging or "cold" tumor microenvironment (TME) than that of other cancers and gloma stem cells (GSCs) play an important role in the TME remodeling, occurrence, development and recurrence of giloma. In this review, our primary focus will be on discussing the following topics: niche-associated GSCs and macrophages, new theories regarding GSC and TME involving pyroptosis and ferroptosis in GBM, metabolic adaptations of GSCs, the influence of the cold environment in GBM on immunotherapy, potential strategies to transform the cold GBM TME into a hot one, and the advancement of GBM immunotherapy and GBM models.

MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients

N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases 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.

Dysregulation of inflammasome activation in glioma

Gliomas are the most common brain tumors characterized by complicated heterogeneity. The genetic, molecular, and histological pathology of gliomas is characterized by high neuro-inflammation. The inflammatory microenvironment in the central nervous system (CNS) has been closely linked with inflammasomes that control the inflammatory response and coordinate innate host defenses. Dysregulation of the inflammasome causes an abnormal inflammatory response, leading to carcinogenesis in glioma. Because of the clinical importance of the various physiological properties of the inflammasome in glioma, the inflammasome has been suggested as a promising treatment target for glioma management. Here, we summarize the current knowledge on the contribution of the inflammasomes in glioma and therapeutic insights. Video Abstract.

Multienzyme-Mimicking LaCoO3 Nanotrigger for Programming Cancer-Cell Pyroptosis

Pyroptosis, a distinct paradigm of programmed cell death, is an efficient strategy against cancer by overcoming resistance to apoptosis. In this study, LaCoO3 (LCO) lanthanide-based nanocrystals with multienzyme characteristics are rationally designed and engineered to trigger the generation of cytotoxic reactive oxygen species (ROS) and the release of lanthanum ions, ultimately inducing lung cancer cell pyroptosis. The peroxidase- and oxidase-mimicking activities of LCO nanocrystals endow LCO with ROS production capacity in tumor tissues with an acidic pH and high hydrogen peroxide content. Concurrently, the LCO nanoenzyme exhibits catalase- and glutathione peroxidase-like activities, reversing the hypoxic microenvironment, destroying the activated antioxidant system of tumor cells, and amplifying the sensitivity of tumor cells to ROS. The use of ultrasound further accelerates the enzymatic kinetic rate. Most importantly, the La3+ ions released by LCO robustly destroy the lysosomal membrane, finally inducing canonical pyroptotic cell death, together with ROS. LCO-nanocrystal-triggered programmed cell pyroptosis amplifies the therapeutic effects both in vitro and in vivo, effectively restraining lung cancer growth and metastasis. This study paves a new avenue for the efficient treatment of lung cancer and metastasis through US-enhanced lanthanum-based nanoenzyme platforms and pyroptotic cell death.

Changes in the Expression Profile of Pyroptosis-Related Genes in Senescent Retinal Pigment Epithelial Cells after Lutein Treatment

Retinal pigment epithelium (RPE) is a specialized structure essential for proper vision, which is constantly exposed to oxidative damage. With aging, this damage accumulates within the RPE cells, causing various diseases, including age-related macular degeneration (AMD). Numerous antioxidant substances are used to prevent this process in humans, including lutein. This study aims to determine the differences in the expression patterns of pyroptosis genes in senescent human retinal pigment epithelial cell line ARPE-19 exposed to lutein. Changes in the expression of pyroptosis-related genes were assessed by oligonucleotide microarrays, and the results were validated by real-time RT-qPCR. The microarray analysis showed seven transcripts were differentially expressed both in the H₂O₂-treated cells versus the controls and in the lutein/H₂O₂-treated cells compared to the H₂O₂-treated cells (FC > 2.0). Depending on the used lutein, H₂O₂, or co-treatment of ARPE-19 cells, statistically significant differences in the expression of TXNIP, CXCL8, BAX, and CASP1 genes were confirmed by the RT-qPCR (p < 0.05). A STRING database analysis showed that the proteins encoded by the analyzed genes form a strong interaction network (p < 0.001). These data indicate that lutein modulates the expression level of pyroptosis-related genes, which may be useful for the development of new methods preventing pyroptosis pathway activation in the future.

Construction and comprehensive analysis of a novel prognostic signature associated with pyroptosis molecular subtypes in patients with pancreatic adenocarcinoma

Background

Treatment of cancer with pyroptosis is an emerging strategy. Molecular subtypes based on pyroptosis-related genes(PRGs) seem to be considered more conducive to individualized therapy. It is meaningful to construct a pyroptosis molecular subtypes-related prognostic signature (PMSRPS) to predict the overall survival (OS) of patients with pancreatic adenocarcinoma(PAAD) and guide treatment.

Methods

Based on the transcriptome data of 23 PRGs, consensus clustering was applied to divide the TCGA and GSE102238 combined cohort into three PRGclusters. Prognosis-related differentially expressed genes(DEGs) among PRGclusters were subjected to LASSO Cox regression analysis to determine a PMSRPS. External cohort and in vitro experiments were conducted to verify this PMSRPS. The CIBERSORT algorithm, the ESTIMATE algorithm and the Immunophenoscore (IPS) were used to analyze the infiltrating abundance of immune cells, the tumor microenvironment (TME), and the response to immunotherapy, respectively. Wilcoxon analysis was used to compare tumor mutational burden (TMB) and RNA stemness scores (RNAss) between groups. RT-qPCR and in vitro functional experiments were used for evaluating the expression and function of SFTA2.

Results

Based on three PRGclusters, 828 DEGs were obtained and a PMSRPS was subsequently constructed. In internal and external validation, patients in the high-risk group had significantly lower OS than those in the low-risk group and PMSRPS was confirmed to be an independent prognostic risk factor for patients with PAAD with good predictive performance. Immune cell infiltration abundance and TME scores indicate patients in the high-risk group have typical immunosuppressive microenvironment characteristics. Analysis of IPS suggests patients in the high-risk group responded better to novel immune checkpoint inhibitors (ICIs) than PD1/CTLA4. The high-risk group had higher TMB and RNAss. In addition, 10 potential small-molecule compounds were screened out. Finally, we found that the mRNA expression of SFTA2 gene with the highest risk coefficient in PMSRPS was significantly higher in PAAD than in paracancerous tissues, and knockdown of it significantly delayed the progression of PAAD.

Conclusions

PMSRPS can well predict the prognosis, TME and immunotherapy response of patients with PAAD, identify potential drugs, and provide treatment guidance based on individual needs.

A pyroptosis-related signature predicts prognosis and indicates immune microenvironment infiltration in glioma

BACKGROUND

Glioma, the most common malignant brain tumor, leads to high recurrence rates and disabilities in patients. Pyroptosis is an inflammasomes-induced programmed cell death in response to infection or chemotherapy. However, the role of pyroptosis in glioma has not yet been elucidated.

METHODS

RNA-seq data and clinical information of 660 gliomas and 847 samples were downloaded from the TCGA and CGGA, respectively. Then, data of 104 normal brain tissues was retrieved from the GTEx for differential expression analysis. Twelve pairs of peritumoral tissue and glioma samples were used for validation. Gene alteration status of differentially expressed pyroptosis-related regulators in gliomas was detected in cBioPortal algorithm. Consensus clustering was employed to classify gliomas based on differentially expressed pyroptosis-related regulators. Subsequently, a PS-signature was constructed using LASSO-congressional analysis for clinical application. The immune infiltration of glioma microenvironment (TME) was explored using ESTIMATE, CIBERSORT, and the other immune signatures.

RESULTS

cBioPortal algorithm revealed alteration of these regulators was correlated to better prognosis of gliomas. Then, our study showed that pyroptosis-related regulators can be used to sort out patients into two clusters with distinct prognostic outcome and immune status. Moreover, a PS-signature for predicting the prognosis of glioma patients was developed based on the identified subtypes. The high PS-score group showed more abundant inflammatory cell infiltration and stronger immune response, but with poorer prognosis of gliomas.

CONCLUSION

The findings of this study provide a therapeutic basis for future research on pyroptosis and unravel the relationship between pyroptosis and glioma prognosis. The risk signature can be utilized as a prognostic biomarker for glioma.

A Novel Defined Pyroptosis-Related Gene Signature for the Prognosis of Acute Myeloid Leukemia

Pyroptosis is an inflammatory form of programmed necrotic cell death, but its potential prognostic value in acute myeloid leukemia (AML) remains unclear. On the basis of available AML data from TCGA and TARGET databases, a 10-gene signature model was constructed to effectively predict AML prognosis by performing LASSO Cox regression analysis, which showed that patients with a low-risk score had a significantly better prognosis than that of the high-risk group, and receiver operator characteristic (ROC) analysis achieved superior performance in the prognostic model. The model was further well-verified in an external GEO cohort. Multivariable Cox regression analysis showed that, in addition to age, the risk score was an independent poor survival factor for AML patients, and a nomogram model was constructed with high accuracy. Moreover, the high-risk group generally had higher cytolytic activity and increased levels of infiltrating immune cells, including tumor-infiltrating lymphocytes (TILs) and regulatory T cells (Tregs), which could be related to the expression of immune checkpoint genes. Additionally, low-risk AML patients may have a better response from traditional chemotherapeutic drugs. In conclusion, a pyroptosis-related gene signature can independently predict the prognosis of AML patients with sufficient predictive power, and pyroptosis plays an important role in the immune microenvironment of AML, which may be used to develop a new effective therapeutic method for AML in the future.

Identification of cuproptosis-related lncRNAs for prognosis and immunotherapy in glioma

Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis-related genes have prognostic predictive value has not been clarified. In this study, uni-/multi-variate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis-related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high- and low-risk groups. Either for grade 2-3 or for grade 4, glioma patients with high-risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high-risk group was functionally associated with immune responses and cancer-related pathways. The high-risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high-risk group were significantly higher than those in the low-risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T-cell co-stimulation pathways. In conclusion, we established a risk model based on cuproptosis-related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno-microenvironment status of glioma.

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).

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.

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.

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.

Comprehensive analysis of pyroptosis regulation patterns and their influence on tumor immune microenvironment and patient prognosis in glioma

BACKGROUND

Glioma is the most common intracranial malignancy with a poor prognosis. Although remarkable advances have been made in the study of diagnostic and prognostic biomarkers, the efficacy of current treatment strategies is still unsatisfactory. Therefore, developing novel and reliable targets is desperately needed for glioma patients. Pyroptosis reshapes tumor immune microenvironment (TME) and promotes the destruction of the tumor by the immune system. Moreover, pyroptosis levels correlate with prognosis and immunotherapy response in many cancer patients. This study performed a comprehensive analysis of pyroptosis in the glioma, unveiling its potential value in glioma prognosis prediction and therapy efficacy.

METHODS

Firstly, the pyroptosis regulation patterns were comprehensively evaluated on 33 pyroptosis-related genes in 1716 glioma samples. The correlations were analyzed between pyroptosis regulation patterns and TME immune cell infiltration properties. Next, pyroptosis regulation patterns were measured by the PSscore model based on principal component analysis algorithms. The correlations were analyzed between PSscore and tumor mutational burden (TMB), immune checkpoint blockade (ICB) therapeutic advantages. Last, the findings were validated in an independently collected external clinical cohort.

RESULTS

We determined two distinct pyroptosis regulation patterns. The cluster-A was high immune cell infiltration with a poor prognosis (p < 0.001), whereas the cluster-B was low immune cell infiltration with a better prognosis (p < 0.001). We developed the PSscore as a measure for pyroptosis regulation patterns. The high PSscore with an inflamed TME phenotype, a high TMB (p < 0.0001), increased innate immune response, and a poor prognosis (p < 0.001). It was in stark contrast to the low PSscore (p < 0.001). Analysis of PSscore with checkpoint therapy indicated high PSscore were correlated with enhanced response to anti-PD-1 immunotherapy (p = 0.0046). For validation, we utilized in vitro experiments on an external clinical cohort. The results demonstrated that GSDMD expression level in the high PSscore group was significantly upregulated compared to the low PSscore group (p < 0.001); the CD3+ T cells and the CD3+PD-1+ cells significantly increased in the high PSscore group compared to the low PSscore group (p < 0.01).

CONCLUSIONS

The PSscore of pyroptosis regulation pattern is a reliable biomarker, and it is valuable to predict prognosis, TME, and ICB therapeutic efficiency in glioma patients.