The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer

The Pyroptosis-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immunotherapeutic Efficiency in Hepatocellular Carcinoma

Pyroptosis was recently demonstrated to be an inflammatory form of gasdermin-regulated programmed cell death characterized by cellular lysis and the release of several proinflammatory factors and participates in tumorigenesis. However, the effects of pyroptosis-related long noncoding RNAs (lncRNAs) on hepatocellular carcinoma (HCC) have not yet been completely elucidated. Based on the regression coefficients of ZFPM2-AS1, KDM4A-AS1, LUCAT1, NRAV, CRYZL2P-SEC16B, AL031985.3, SNHG4, AL049840.5, AC008549.1, MKLN1-AS, AC099850.3, and LINC01224, HCC patients were classified into a low- or high-risk group. The high-risk score according to pyroptosis-related lncRNA signature was significantly associated with poor overall survival even after adjusting for age and clinical stage. Receiver operating characteristic curves and principal component analysis further supported the accuracy of the model. Our study revealed that a higher pyroptosis-related lncRNA risk score was significantly associated with tumor staging, pathological grade, and tumor-node-metastasis stages. The nomogram incorporating the pyroptosis-related lncRNA risk score and clinicopathological factors demonstrated good accuracy. Furthermore, we observed distinct tumor microenvironment cell infiltration characteristics between high- and low-risk tumors. Notably, based on the risk model, we found that the risk score is closely related to the expression of immune checkpoint genes, immune subtypes of tumors, and the sensitivity of HCC to chemotherapy drugs and immunotherapy. In conclusion, our novel risk score of pyroptosis-related lncRNA can serve as a promising prognostic biomarker for HCC patients and provide help for HCC patients to guide precision drug treatment and immunotherapy.

Comprehensive Analysis of a Ferroptosis Pattern and Associated Prognostic Signature in Acute Myeloid Leukemia

Ferroptosis is a widespread form of programmed cell death. The environment of cancer cells makes them vulnerable to ferroptosis, including AML cells, yet the specific association between ferroptosis and AML outcome is little known. In this study, we utilized ferroptosis-related genes to distinguish two subtypes in TCGA cohort, which were subsequently validated in independent AML cohorts. The subtypes were linked with tumor-related immunological abnormalities, mutation landscape and pathway dysregulation, and clinical outcome. Further, we developed a 13-gene prognostic model for AML from DEG analysis in the two subtypes. A risk score was calculated for each patient, and then the overall group was stratified into high- and low-risk groups; the higher risk score correlated with short survival. The model was validated in both independent AML cohorts and pan-cancer cohorts, which demonstrated robustness and extended the usage of the model. A nomogram was constructed that integrated risk score, FLT3-ITD, TP53, and RUNX1 mutations, and age. This model had the additional value of discriminating the sensitivity of several chemotherapeutic drugs and ferroptosis inducers in the two risk groups, which increased the translational value of this model as a potential tool in clinical management. Through integrated analysis of ferroptosis pattern and its related model, our work shed new light on the relationship between ferroptosis and AML, which may facilitate clinical application and therapeutics.

Integrative Analysis of Pyroptosis-Related Prognostic Signature and Immunological Infiltration in Lung Squamous Cell Carcinoma

Background

Lung cancer is one of leading causes of human health threatening with approximately 2.09 million initially diagnosed cases and 1.76 million deaths worldwide annually. Pyroptosis is a programmed cell death mediated by Gasdermin family proteins. Pyroptosis could suppress the tumor oncogenesis and progression; nevertheless, pyroptosis could promote tumor growth by forming a suitable microenvironment.

Methods

LASSO Cox regression analysis was performed to construct prognostic pyroptosis-related gene (PRG) signature. A ceRNA was constructed to explore the potential lncRNA-miRNA-mRNA regulatory axis in LUSC.

Results

The expression of 26 PRGs were increased or decreased in LUSC. We also summarized simple nucleotide variation and copy number variation landscape of PRGs in LUSC. Prognosis analysis suggested a poor overall survival rate in LUSC patients with high expression of IL6, IL1B, ELANE, and CASP6. A pyroptosis-related prognostic signature was developed based on four prognostic PRGs. High-risk score LUSC patients had a poor overall survival rate versus low-risk score patients with an AUC of 0.565, 0.641, and 0.619 in 1-year, 3-year, and 5-year ROC curves, respectively. Moreover, the risk score was correlated with immune infiltration in LUSC. Further analysis revealed that pyroptosis-related prognostic signature was correlated with immune cell infiltration, tumor mutation burden, microsatellite instability, and drug sensitivity. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR-328-3p/IL1B regulatory axis for LUSC.

Conclusion

A bioinformatics method was performed to develop a pyroptosis-related prognostic signature containing four genes (IL6, IL1B, ELANE, and CASP4) in LUSC. We also constructed a ceRNA network and identified a lncRNA KCNQ1OT1/miR-328-3p/IL1B regulatory axis for LUSC. Further in vivo and in vitro studies should be conducted to verify these results.

A Novel Pyroptosis-Associated Gene Signature to Predict Prognosis in Patients with Colorectal Cancer

Background

Pyroptosis is a form of cell death characterized by cell swelling and plasma membrane bubbling in association with inflammatory and immune responses. To date, the association between pyroptosis and colorectal cancer remains unclear. We aimed to establish a novel pyroptosis-associated model for the prognosis of colorectal cancer.

Methods

Pyroptosis-related genes were extracted using Gene Set Enrichment Analysis. A least absolute shrinkage and selection operator regression model was constructed to identify a pyroptosis-related gene signature using the Cancer Genome Atlas and Gene Expression Omnibus databases. Then, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology and GSEA were performed to better understand the potential mechanisms and the functional pathways associated with pyroptosis involved in colorectal cancer. The relationship between the pyroptosis-related signature and immune infiltration was investigated using Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts and MCPcounter.

Results

A 12 pyroptosis-related gene signature was identified. Then, patients were classified into high- and low-risk groups. Kaplan–Meier and receiver operating characteristic analyses confirmed that the high-risk groups showed worse overall survival, progression-free survival, or relapse-free survival probability. Functional enrichment analysis showed that pyroptosis was associated with extracellular matrix-related pathways. Furthermore, the pyroptosis risk score was associated with immune infiltration. The low-risk group exhibited a higher percentage of plasma cells, CD4 T cells, activated dendritic cells, and activated mast cells. M2 macrophages and M0 macrophages were positively related to the risk score.

Conclusion

Our research yielded a novel pyroptosis-related prognostic signature for colorectal cancer that was related to immune cell infiltration, and it provided an immunological perspective for developing personalized therapies.

Pyroptosis-related genes regulate proliferation and invasion of pancreatic cancer and serve as the prognostic signature for modeling patient survival

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and poor prognosis. Pyroptosis can influence the prognosis of patients by regulating the proliferation, invasion, and metastasis of cancer cells. However, the role of pyroptosis-related genes (PRGs) in PDAC remains unclear.

METHODS

In this study, based on the Cancer Genome Atlas (TCGA) cohort of PDAC samples, univariate Cox analysis and LASSO regression analysis were used to screen the prognostic PRGs and establish the gene signature. To further evaluate the functional significance of CASP4 and NLRP1 in PDAC, we also conducted an in vitro study to explore the mechanism of CASP4 and NLRP1 regulating the occurrence and development of PDAC. Finally, we investigated the relationship between CASP4 and NLRP1 expression levels and drug sensitivity in pancreatic cancer cells.

RESULTS

A risk prediction model based on CASP4 and NLRP1 was established, which can distinguish high-risk patients from low-risk patients (P < 0.001). Both internal validation and external GEO data sets validation demonstrate good predictive capability of the model (AUC = 0.732, AUC = 0.802, AUC = 0.632, P < 0.05). In vitro, CCK8 and Transwell assay suggested that CASP4 may accelerate the progression of PDAC by promoting proliferation and migration of pancreatic cancer cells, while NLRP1 has been found to have tumor suppressive effect. It should be noted that knockdown of CASP4 reduced the level of coke death, the expression levels of acetyl-CoA carboxylase, FASN, SREBP-1 and SREBP-2 were decreased, and the number of lipid droplets was also significantly reduced. Moreover, the enrichment of signaling pathways showed that NLRP1 was significantly correlated with MAPK and RAS/ERK signaling pathways, and knocking down NLRP1 could indeed up-regulate p-ERK expression. Finally, high expression of CASP4 and low expression of NLRP1 increased the sensitivity of pancreatic cancer cells to ERK inhibitors.

CONCLUSIONS

In especial, CASP4 can promote tumor progression by promoting the synthesis and accumulation of fatty acids, while NLRP1 acts on RAS/ERK signaling pathway. Both of genes play an important role in the diagnosis and treatment of PDAC, which may also affect the inhibitors of MAPK/ERK efficiency.

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.

Extended Application of Genomic Selection to Screen Multi-Omics Data for the Development of Novel Pyroptosis-Immune Signatures and Predicting Immunotherapy of Glioma

Glioma is one of the most human malignant diseases and the leading cause of cancer-related deaths worldwide. Nevertheless, the present stratification systems do not accurately predict the prognosis and treatment benefit of glioma patients. Currently, no comprehensive analyses of multi-omics data have been performed to better understand the complex link between pyroptosis and immune. In this study, we constructed four pyroptosis immune subgroups by pyroptosis regulators and obtained nine pyroptosis immune signatures by analyzing the differentially expressed genes between the four pyroptosis immune subgroups. Nine novel pyroptosis immune signatures were provided for assessing the complex heterogeneity of glioma by the analyses of multi-omics data. The pyroptosis immune prognostic model (PIPM) was constructed by pyroptosis immune signatures, and the PIPM risk score was established for glioma cohorts with a total of 1716 samples. Then, analyses of the tumor microenvironment revealed an unanticipated correlation of the PIPM risk score with stemness, immune checkpoint expression, infiltrating the immune system, and therapy response in glioma. The low PIPM risk score patients had a better response to immunotherapy and showed sensitivity to radio-chemotherapy. The results of the pan-cancer analyses revealed the significant correlation between the PIPM risk score and clinical outcome, immune infiltration, and stemness. Taken together, we conclude that pyroptosis immune signatures may be a helpful tool for overall survival prediction and treatment guidance for glioma and other tumors patients.

A Multi-Omics Study on the Effect of Helicobacter Pylori-Related Genes in the Tumor Immunity on Stomach Adenocarcinoma

Background

Helicobacter pylori (HP), a gram-negative spiral-shaped microaerophilic bacterium, colonizes the stomach of approximately 50% of the world’s population, which is considered a risk factor for gastritis, peptic ulcers, gastric cancer, and other malignancies. HP is also considered carcinogenic since it involves the mutation and damage of multiple HP-related genes. Stomach adenocarcinoma (STAD) is a common stom5ach cancer with a poor prognosis and high risk of metastasis in the advanced stage. Therefore, an early diagnosis and targeted therapies are needed to ensure a better prognosis. In this study, a scoring system was constructed based on three HP infection–related candidate genes to enable a more accurate prediction of tumor progression and metastasis and response to immunotherapies.

Methods

HP infection–induced mutation patterns of STAD samples from six cohorts were comprehensively assessed based on 73 HP-related genes, which were then correlated with the immune cell–infiltrating characteristics of the tumor microenvironment (TME). The risk signature was constructed to quantify the influence of HP infection on individual tumors. Subsequently, an accurate nomogram was generated to improve the clinical applicability of the risk signature. We conducted immunohistochemical experiments and used the Affiliated Hospital of Youjiang Medical University for Nationalities (AHYMUN) cohort data set with survival information to further verify the clinical value of this risk signature.

Results

Two distinct HP-related mutation patterns with different immune cell–infiltrating characteristics (ICIC) and survival possibility were identified. We demonstrated that the evaluation of HP infection–induced mutation patterns of tumor could assist the prediction of stages, phenotypes, stromal activity, genetic diversity, and patient prognosis. A low risk score involved an increased mutation burden and activation of immune responses, with a higher 5-year survival rate and enhanced response to anti-PD-1/L1 immunotherapy, while a high risk score involved stromal activation and poorer survival. The efficiency of the risk signature was further evidenced by the nomogram.

Conclusions

STAD patients with a low risk score demonstrated significant therapeutic advantages and clinical benefits. HP infection–induced mutations play a nonnegligible role in STAD development. Quantifying the HP-related mutation patterns of individual tumors will contribute to phenotype classification, guide more effective targeted and personalized therapies, and enable more accurate predictions of metastasis and prognosis.

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 of the Pyroptosis-Related Prognosis Gene Signature and Immune Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignancies worldwide with a dismal prognosis. Lack of efficient biomarkers, early detection, and prognosis is still a challenge for HCC. Pyroptosis is a new discovery inflammatory form of programmed cell death. There is growing evidence revealed that pyroptosis plays a role in physiological and pathological conditions of human cancers. However, the prognostic evaluation of these pyroptosis-related genes (PRGs) in HCC remains blank. Consensus clustering of PRGs was used to classify 374 patients with HCC from the TCGA-LIHC cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 2-gene prognostic gene model (PLCG1 and GSDMC) was built and indicated the survival rate in HCC with medium-to-high accuracy. Then, the median risk score from the TCGA cohort was utilized; the prognostic gene model was also accurate in Gene Expression Omnibus (GEO) cohort. The functional enrichment analysis indicated that the oncogenic properties are associated with prominent hallmarks of cancer. The ssGSEA analyses and TIMER database indicated that immune infiltration tumor microenvironment in the HCC. In conclusion, our findings provide a foundation for further research targeting PRGs and their immune microenvironment.

The Expression of Pyroptosis-Related Gene May Influence the Occurrence, Development, and Prognosis of Uterine Corpus Endometrial Carcinoma

BACKGROUND

Increasing evidence has demonstrated that pyroptosis exerts key roles in the occurrence, development, and prognosis of uterine corpus endometrial carcinoma (UCEC). However, the mechanism of pyroptosis and its predictive value for prognosis remain largely unknown.

METHODS

UCEC data were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes in UCEC vs. normal cases were selected to perform a weighted correlation network analysis (WGCNA). Forty-two UCEC-associated pyroptosis-related genes were identified via applying differential expression analysis. Protein-protein interaction (PPI) and gene correlation analyses were applied to explore the relationship between 21 UCEC key genes and 42 UCEC-associated pyroptosis-related genes. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also calculated. The immune environment of UCEC was evaluated. Furthermore, pyroptosis-related genes were filtered out by the co-expression. Univariate and a least absolute shrinkage and selection operator (LASSO) Cox analyses were implemented to yield a pyroptosis-related gene model. We also performed consensus classification to regroup UCEC samples into two clusters. A clinically relevant heatmap and survival analysis curve were implemented to explore the clinicopathological features and relationship between two clusters. Furthermore, a Kaplan-Meier survival analysis was implemented to analyze the risk model.

RESULTS

Twenty-one UCEC key genes and 42 UCEC-associated pyroptosis-related genes were identified. The PPI and gene correlation analysis showed a clear relationship. The expression of 42 UCEC-associated pyroptosis-related genes of different grades was also depicted. A risk model based on pyroptosis-related genes was then developed to forecast overall survival among UCEC patients. Finally, Cox regression analysis verified this model as an independent risk factor for UCEC patients.

CONCLUSIONS

The expression of pyroptosis-related gene may influence UCEC occurrence, development, and prognosis.

Genome-wide Exploration of a Pyroptosis-Related Long Non-Coding RNA Signature Associated With the Prognosis and Immune Response in Patients With Bladder Cancer

Background: Bladder cancer (BLCA) is a malignant tumor with a complex molecular mechanism and high recurrence rate in the urinary system. Studies have shown that pyroptosis regulates tumor cell proliferation and metastasis and affects the prognosis of cancer patients. However, the role of pyroptosis-related (PR) genes or long non-coding RNAs (lncRNAs) in BLCA development is not fully understood.

Methods: We comprehensively analyzed the molecular biological characteristics of PR genes in BLCA, including copy number variation, mutations, expression and prognostic value based on TCGA database. We then identified PR lncRNAs with prognostic value based on the expression of PR genes and performed a consistent clustering analysis of 407 BLCA patients according to the expression of prognosis-related PR lncRNAs and identified two clusters. The least absolute shrinkage and selection operator (LASSO) regression was used to establish a PR lncRNA signature and calculate the risk score associated with the prognosis of patients with BLCA. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) were used to evaluate the possible functions of PR lncRNA signature. We also evaluated the relationship between the risk score and tumor immune microenvironment (TIME).

Results: A total of 33 PR genes were obtained in our study and 194 prognosis-related PR lncRNAs were identified. We also constructed a signature consisting of eight-PR-lncRNAs and divided patients into high- and low-risk groups. The overall survival rate of patients with a high risk was significantly lower than patients with a low risk. The risk score was significantly correlated with the degree of infiltration of multiple immune cell subtypes and positively correlated with multiple immune checkpoint genes expression in BLCA. Enrichment analyses showed that these lncRNAs are involved in human immune regulatory functions and immune-related pathways.

Conclusion: Our study comprehensively studied the molecular biological characteristics of PR genes BLCA, and the eight-PR-lncRNA signature we identified might play a crucial role in tumor immunity and may be able to predict the prognosis of BLCA patients, providing a theoretical basis for an in-depth study of the relationship between the prognosis and TIME.

Pyroptosis-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Glioma

Objective: Gliomas are the most common primary tumors in the central nervous system with a bad prognosis. Pyroptosis, an inflammatory form of regulated cell death, plays a vital role in the progression and occurrence of tumors. However, the value of pyroptosis related genes (PRGs) in glioma remains poorly understood. This study aims to construct a PRGs signature risk model and explore the correlation with clinical characteristics, prognosis, tumor microenviroment (TME), and immune checkpoints.

Methods: RNA sequencing profiles and the relevant clinical data were obtained from the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA), the Repository of Molecular Brain Neoplasia Data (REMBRANDT), and the Genotype-Tissue Expression Project (GTEx-Brain). Then, the differentially expressed pyroptosis related genes (PRGs) were identified, and the least absolute shrinkage and selection operator (LASSO) and mutiCox regression model was generated using the TCGA-train dataset. Then the expression of mRNA and protein levels of PRGs signature was detected through qPCR and human protein atlas (HPA). Further, the predictive ability of the PRGs-signature, prognostic analysis, and stratification analysis were utilized and validated using TCGA-test, CGGA, and REMBRANDT datasets. Subsequently, we constructed the nomogram by combining the PRGs signature and other key clinical features. Moreover, we used gene set enrichment analysis (GSEA), GO, KEGG, the tumor immune dysfunction and exclusion (TIDE) single-sample GSEA (ssGSEA), and Immunophenoscore (IPS) to determine the relationship between PRGs and TME, immune infiltration, and predict the response of immune therapy in glioma.

Results: A four-gene PRGs signature (CASP4, CASP9, GSDMC, IL1A) was identified and stratified patients into low- or high-risk group. Survival analysis, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group than in the low-risk group. Correlation analysis showed that the risk score was correlated with poor disease features. Furthermore, GSEA and immune infiltrating and IPS analysis showed that the PRGs signature could potentially predict the TME, immune infiltration, and immune response in glioma.

Conclusion: The newly identified four-gene PRGs signature is effective in diagnosis and could robustly predict the prognosis of glioma, and its impact on the TME and immune cell infiltrations may provide further guidance for immunotherapy.

Antitumor Effect of Simvastatin in Combination With DNA Methyltransferase Inhibitor on Gastric Cancer via GSDME-Mediated Pyroptosis

Gasdermin E (GSDME) is one of the executors of pyroptosis, a type of programmed lytic cell death, which can be triggered by caspase-3 activation upon stimulation. Silenced GSDME expression due to promoter hypermethylation is associated with gastric cancer (GC), which is confirmed in the present study by bioinformatics analysis and methylation-specific PCR (MSP) test of GC cell lines and clinical samples. GC cell lines and mouse xenograft models were used to investigate the pyroptosis-inducing effect of the common cholesterol-depleting, drug simvastatin (SIM), allied with upregulating GSDME expression by doxycycline (DOX)- inducible Tet-on system or DNA methyltransferase inhibitor 5-Aza-2′-deoxycytidine (5-Aza-CdR). Cell viability assessment and xenograft tumour growth demonstrated that the tumour inhibition effects of SIM can be enhanced by elevated GSDME expression. Morphological examinations and assays measuring lactate dehydrogenase (LDH) release and caspase-3/GSDME protein cleavage underlined the stimulation of pyroptosis as an important mechanism. Using short hairpin RNA (shRNA) knockdown of caspase-3 or GSDME, and caspase-3-specific inhibitors, we provided evidence of the requirement of caspase-3/GSDME in the pyroptosis process triggered by SIM. We conclude that reactivating GSDME expression and thereby inducing cancer cell-specific pyroptosis could be a potential therapeutic strategy against GC.

Pyroptosis impacts the prognosis and treatment response in gastric cancer via immune system modulation

Pyroptosis plays a vital role in the development of cancers; however, its role in regulating immune cell infiltration in tumor microenvironment (TME) and pyroptosis-related molecular subtypes remain unclear. Herein, we comprehensively analyzed the molecular subtypes mediated by the pyroptosis-related genes (PRGs) in gastric cancer (GC). Three pyroptosis patterns were determined with distinct TME cell-infiltrating characteristics and prognosis. Principal component analysis was performed to establish the pyroptosis score. The high pyroptosis score group was featured by increased activated CD4+ T cell infiltration, better prognosis, elevated tumor mutation burden, higher immune and stromal scores, and enhanced response to immunotherapy. However, the low pyroptosis score group was characterized by poorer survival, decreased immune infiltration, and glycerolipid and histidine metabolism pathways. Additionally, high pyroptosis score was confirmed as an independent favorable prognostic factor for overall survival. Three cohorts designed to analyze the response to immunotherapy verified that patients with higher pyroptosis score showed treatment benefit. In summary, our study demonstrated that pyroptosis regulates the complex TME. Assessing the pyroptosis patterns will advance our understanding on TME features and tumor immunology and provide the rationale for designing personalized immunotherapy strategies.

A Novel Pyroptotic and Inflammatory Gene Signature Predicts the Prognosis of Cutaneous Melanoma and the Effect of Anticancer Therapies

Purpose

The purpose of this study was to construct a gene signature comprising genes related to both inflammation and pyroptosis (GRIPs) to predict the prognosis of patients with cutaneous melanoma patients and the efficacy of immunotherapy, chemotherapy, and targeted therapy in these patients.

Methods

Gene expression profiles were collected from The Cancer Genome Atlas. Weighted gene co-expression network analysis was performed to identify GRIPs. Univariable Cox regression and Lasso regression further selected key prognostic genes. Multivariable Cox regression was used to construct a risk score, which stratified patients into high- and low-risk groups. Areas under the ROC curves (AUCs) were calculated, and Kaplan-Meier analyses were performed for the two groups, following validation in an external cohort from Gene Expression Omnibus (GEO). A nomogram including the GRIP signature and clinicopathological characteristics was developed for clinical use. Gene set enrichment analysis illustrated differentially enriched pathways. Differences in the tumor microenvironment (TME) between the two groups were assessed. The efficacies of immune checkpoint inhibitors (ICIs), chemotherapeutic agents, and targeted agents were predicted for both groups. Immunohistochemical analyses of the GRIPs between the normal and CM tissues were performed using the Human Protein Atlas data. The qRT-PCR experiments validated the expression of genes in CM cell lines, Hacat, and PIG1 cell lines.

Results

A total of 185 GRIPs were identified. A novel gene signature comprising eight GRIPs (TLR1, CCL8, EMP3, IFNGR2, CCL25, IL15, RTP4, and NLRP6) was constructed. The signature had AUCs of 0.714 and 0.659 for predicting 3-year overall survival (OS) in the TCGA entire and GEO validation cohorts, respectively. Kaplan-Meier analyses revealed that the high-risk group had a poorer prognosis. Multivariable Cox regression showed that the GRIP signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The nomogram showed good accuracy and reliability in predicting 3-year OS (AUC = 0.810). GSEA and TME analyses showed that the high-risk group had lower levels of pyroptosis, inflammation, and immune response, such as lower levels of CD8+ T-cell infiltration, CD4+ memory-activated T-cell infiltration, and ICI. In addition, low-risk patients whose disease expressed PD-1 or CTLA-4 were likely to respond better to ICIs, and several chemotherapeutic and targeted agents. Immunohistochemical analysis confirmed the distinct expression of five out of the eight GRIPs between normal and CM tissues.

Conclusion

Our novel 8-GRIP signature can accurately predict the prognosis of patients with CM and the efficacies of multiple anticancer therapies. These GRIPs might be potential prognostic biomarkers and therapeutic targets for CM.

Pyroptosis-Related Signature Predicts Prognosis and Immunotherapy Efficacy in Muscle-Invasive Bladder Cancer

Pyroptosis has profound impacts on tumor cell proliferation, invasion, and metastasis and is of great clinical significance for different cancers. However, the role of pyroptosis in the progression and prognosis of muscle invasive bladder cancer (MIBC) remains poorly characterized. Here, we collected multicenter MIBC data and performed integrated analysis to dissect the role of pyroptosis in MIBC and provide an optimized treatment for this disease. Based on transcriptomic data, we developed a novel prognostic model named the pyroptosis-related gene score (PRGScore), which summarizes immunological features, genomic alterations, and clinical characteristics associated with the pyroptosis phenotype. Samples with high PRGScore showed enhancement in CD8⁺ T cell effector function, antigen processing machinery and immune checkpoint and better response to immunotherapy by programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors, which indicates that PRGScore is a valuable signature in the identification of populations sensitive to immune checkpoint inhibitors. Collectively, our study provides insights into further research targeting pyroptosis and its tumor immune microenvironment (TME) and offers an opportunity to optimize the treatment of MIBC.

Characterization of Pyroptosis-Related Subtypes via RNA-Seq and ScRNA-Seq to Predict Chemo-Immunotherapy Response in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. There is an urgent need to screen for reliable indices, especially immunotherapy-associated biomarkers that can predict patient outcomes. Pyroptosis, as an inflammation-induced type of programmed cell death, is shown to create a tumor-suppressive environment and improve the chemotherapeutic response in multiple tumors. However, the specific therapeutic effect of pyroptosis in TNBC remains unclear. In this study, we present a consensus clustering by pyroptosis-related signatures of 119 patients with TNBC into two subtypes (clusterA and clusterB) with distinct immunological and prognostic characteristics. First, clusterB, associated with better outcomes, was characterized by a significantly higher pyroptosis-related signature expression, tumor microenvironment prognostic score, and upregulation of immunotherapy checkpoints. A total of 262 differentially expressed genes between the subtypes were further identified and the Ps-score was built using LASSO and COX regression analyses. The external GEO data set demonstrated that cohorts with low Ps-scores consistently had higher expression of pyroptosis-related signatures, immunocyte infiltration levels, and better prognosis. In addition, external immunotherapy and chemotherapy cohorts validated that patients with lower Ps-scores exhibited significant therapeutic response and clinical benefit. Combined with other clinical characteristics, we successfully constructed a nomogram to effectively predict the survival rate of patients with TNBC. Finally, using the scRNA-seq data sets, we validated the landscape of cellular subtypes of TNBC and successfully constructed an miRNA-Ps-score gene interaction network. These findings indicated that the systematic assessment of tumor pyroptosis and identification of Ps-scores has potential clinical implications and facilitates tailoring optimal immunotherapeutic strategies for TNBC.

Prognosis and Characterization of Immune Microenvironment in Head and Neck Squamous Cell Carcinoma through a Pyroptosis-Related Signature

Pyroptosis, as a novel identified programmed cell death, is closely correlated with tumor immunity and shows potential roles in cancer treatment. Discerning a pyroptosis-related gene signature and its correlations with tumor immune microenvironment is critical in head and neck squamous cell carcinoma (HNSCC). Transcriptome data and corresponding clinical data were downloaded from TCGA and GEO databases. Tumor mutation burden (TMB) data were obtained from TCGA database. Firstly, univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct a six pyroptosis-related gene signature. Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA) results verified that the risk model has good performance in predicting the survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that the pyroptosis-related gene signature was immune related. Finally, the immune landscape and immunotherapy sensitivity prediction capabilities of the risk model were further explored. There were close correlations between the overall survival (OS) and various immune cells and immune functions. Single-sample gene set enrichment analysis (ssGSEA) showed that high risk group had decreased expression of various immune cells and lower activities of immune functions. Meanwhile, tumor mutation burden (TMB) data combining risk score could well predict the OS of HNSCC patients. However, tumor immune dysfunction and exclusion (TIDE) analysis revealed that there was no significant difference in the sensitivity to immunotherapies between high and low risk groups. Finally, a nomogram based on risk score and clinicopathological parameters was constructed. And, the risk model demonstrated better sensitivity and specificity than TIDE scores and T-cell-inflamed signature (TIS). In conclusion, although the risk model could not well predict the immune escape and response to immunotherapies, the signature established by pyroptosis-related genes, with better sensitivity and specificity than TIDE scores and TIS signature, could be used for predicting prognosis and immune status of HNSCC patients.

Pyroptosis-Related LncRNA Signature Predicts Prognosis and Is Associated With Immune Infiltration in Hepatocellular Carcinoma

Pyroptosis is an inflammatory form of programmed cell death that is involved in various cancers, including hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) were recently verified as crucial mediators in the regulation of pyroptosis. However, the role of pyroptosis-related lncRNAs in HCC and their associations with prognosis have not been reported. In this study, we constructed a prognostic signature based on pyroptosis-related differentially expressed lncRNAs in HCC. A co-expression network of pyroptosis-related mRNAs-lncRNAs was constructed based on HCC data from The Cancer Genome Atlas. Cox regression analyses were performed to construct a pyroptosis-related lncRNA signature (PRlncSig) in a training cohort, which was subsequently validated in a testing cohort and a combination of the two cohorts. Kaplan-Meier analyses revealed that patients in the high-risk group had poorer survival times. Receiver operating characteristic curve and principal component analyses further verified the accuracy of the PRlncSig model. Besides, the external cohort validation confirmed the robustness of PRlncSig. Furthermore, a nomogram based on the PRlncSig score and clinical characteristics was established and shown to have robust prediction ability. In addition, gene set enrichment analysis revealed that the RNA degradation, the cell cycle, the WNT signaling pathway, and numerous immune processes were significantly enriched in the high-risk group compared to the low-risk group. Moreover, the immune cell subpopulations, the expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. Finally, the expression levels of the five lncRNAs in the signature were validated by quantitative real-time PCR. In summary, our PRlncSig model shows significant predictive value with respect to prognosis of HCC patients and could provide clinical guidance for individualized immunotherapy.

Generation and Analysis of Pyroptosis-Based and Immune-Based Signatures for Kidney Renal Clear Cell Carcinoma Patients, and Cell Experiment

Background: Pyroptosis is a programmed cell death caused by inflammasomes, which is closely related to immune responses and tumor progression. The present study aimed to construct dual prognostic indices based on pyroptosis-associated and immune-associated genes and to investigate the impact of the biological signatures of these genes on Kidney Renal Clear Cell Carcinoma (KIRC).

Materials and Methods: All the KIRC samples from the Cancer Genome Atlas (TCGA) were randomly and equally divided into the training and testing datasets. Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to screen crucial pyroptosis-associated genes (PAGs), and a pyroptosis-associated genes prognostic index (PAGsPI) was constructed. Immune-associated genes (IAGs) related to PAGs were identified, and then screened through Cox and LASSO regression analyses, and an immune-associated genes prognostic index (IAGsPI) was developed. These two prognostic indices were verified by using the testing and the Gene Expression Omnibus (GEO) datasets and an independent cohort. The patients’ response to immunotherapy was analyzed. A nomogram was constructed and calibrated. qRT-PCR was used to detect the expression of PAGs and IAGs in the tumor tissues and normal tissues. Functional experiment was carried out.

Results: 86 PAGs and 1,774 differentially expressed genes (DEGs) were obtained. After intersecting PAGs with DEGs, 22 differentially expressed PAGs (DEPAGs) were included in Cox and LASSO regression analyses, identifying 5 crucial PAGs. The PAGsPI was generated. Patients in the high-PAGsPI group had a poor prognosis. 82 differentially expressed IAGs (DEIAGs) were highly correlated with DEPAGs. 7 key IAGs were screened out, and an IAGsPI was generated. Patients in the high-IAGsPI group had a poor prognosis. PAGsPI and IAGsPI were verified to be robust and reliable. The results revealed patients in low-PAGsPI group and high-IAGsPI group may be more sensitive to immunotherapy. The calibrated nomogram was proved to be reliable. An independent cohort study also proved that PAGsPI and IAGsPI performed well in prognosis prediction. We found that the expression of AIM2 may affect proliferation of KIRC cells.

Conclusion: PAGsPI and IAGsPI could be regarded as potential biomarkers for predicting the prognosis of patients with KIRC.

Identification of a Pyroptosis-Related Prognostic Signature Combined With Experiments in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor prognosis. There is a necessary search for improvement in diagnosis and treatment methods to improve the prognosis. Some useful prognostic markers of HCC are still lacking. Pyroptosis is a type of programmed cell death caused by the inflammasome. It is still unknown whether pyroptosis-related genes (PRGs) are involved in the prognosis in HCC. The gene expression and clinical data of LIHC (liver hepatocellular carcinoma) patients were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database (ICGC). In this study, we identified 40 PRGs that were differentially expressed between LIHC and normal liver tissues. Based on the TCGA-LIHC cohort, a 9-gene prediction model was established with the Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression. The risk score was calculated according to the model in the TCGA-LIHC cohort and the ICGC-LIHC cohort. Utilizing the median risk score from the TCGA cohort, LIHC patients from the ICGC-LIHC cohort were divided into two risk subgroups. The Kaplan–Meier (KM) survival curves demonstrated that patients with lower risk scores had significantly favorable overall survival (OS). Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of LIHC patients in both the TCGA-LIHC cohort and the ICGC-LIHC cohort. Functional enrichment and immune function analysis were carried out. Furthermore, a nomogram based on risk score, age, gender, and tumor stage was used to predict mortality of patients with LIHC. Moreover, KM survival analysis was performed for 9 genes in the risk model, among which CHMP4A, SCAF11, and GSDMC had significantly different results and the ceRNA network was constructed. Based on the core role of SCAF11, we performed loss-of-function experiments to explore the function of SCAF11 in vitro. Suppression of SCAF11 expression inhibited the proliferation, attenuated the migration and invasion, and induced apoptosis of liver cancer cell lines. In conclusion, the pyroptosis-related model and nomogram can be utilized for the clinical prognostic prediction in LIHC. This study has demonstrated for the first time that SCAF11 promotes the progression of liver cancer.

Pyroptosis-related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.

A new pyroptosis model can predict the immunotherapy response and immune microenvironment characteristics and prognosis of patients with cutaneous melanoma based on TCGA and GEO databases

Background

Recent studies have shown that pyroptosis is related to cancer development. Our previous study also found that gasdermins (GSDMs) was associated with the tumor immune microenvironment. Therefore, we wanted to observe the relationship between pyroptosis and the immune microenvironment and prognosis of skin cutaneous melanoma (SKCM).

Methods

Pyroptosis-related genes were used for pan-cancer prognostic analysis using the GEPIA2 online analysis website. Prognosis-related genes were clustered using R software and related R packages, and the best clustering results were screened for prognosis analysis. The prognosis-related genes were also used to establish a prognosis-related model. Assess the predictive power of a model by comparing area under the curve (AUC). The t-test was used to analyze the differences of immune-related indicators between the two clusters and between high and low risk groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on the differential genes.

Results

By clustering the prognosis-related genes, SKCM could be divided into 2 clusters with significant differences in prognosis P<0.05. A prognostic model can be established using prognosis-related genes. The AUC value of 1 year, 2 years and 3 years was 0.696, 0.702 and 0.664, respectively. The risk score was significantly associated with prognosis in both univariate and multivariate Cox analyses P<0.001. The low-risk group or C2 cluster with better prognosis had higher expression of pyroptosis-related genes, and tended to have a lower exclusion score, greater chemokine expression, more immune cells and higher immune score. However, the C2 cluster or low-risk group was also associated with a higher dysfunction score. At the same time, the C2 or low-risk group was more suitable for immunotherapy because of the higher immunophenoscore (IPS) score P<0.001. Correlation analysis also demonstrated that the risk score was positively correlated with the gene expression of most immunoinhibitors, MHC molecules, immunostimulators, and chemokines and their receptors.

Conclusions

Pyroptosis is associated with melanoma immune microenvironment, immunotherapy response, and prognoses. The constructed risk scores could effectively predict the characteristics of the immune microenvironment, the sensitivity to immunotherapy, and the prognosis of melanoma patients.

Integrated Analysis of Multi-Omics Alteration, Immune Profile, and Pharmacological Landscape of Pyroptosis-Derived lncRNA Pairs in Gastric Cancer

Background: Recent evidence demonstrates that pyroptosis-derived long non-coding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNAs (PDLs) in gastric cancer (GC) remain elusive.

Methods: We comprehensively analyzed the multi-omics data of 839 GC patients from three independent cohorts. The previous gene set enrichment analysis embedding algorithm was utilized to identify PDLs. A gene pair pipeline was developed to facilitate clinical translation via qualitative relative expression orders. The LASSO algorithm was used to construct and validate a pyroptosis-derived lncRNA pair prognostics signature (PLPPS). The associations between PLPPS and multi-omics alteration, immune profile, and pharmacological landscape were further investigated.

Results: A total of 350 PDLs and 61,075 PDL pairs in the training set were generated. Cox regression revealed 15 PDL pairs associated with overall survival, which were utilized to construct the PLPPS model via the LASSO algorithm. The high-risk group demonstrated adverse prognosis relative to the low-risk group. Remarkably, genomic analysis suggested that the lower tumor mutation burden and gene mutation frequency (e.g., TTN, MUC16, and LRP1B) were found in the high-risk group patients. The copy number variants were not significantly different between the two groups. Additionally, the high-risk group possessed lower immune cell infiltration abundance and might be resistant to a few chemotherapeutic drugs (including cisplatin, paclitaxel, and gemcitabine).

Conclusion: PDLs were closely implicated in the biological process and prognosis of GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.

Identification and Validation of a Pyroptosis-Related Prognostic Model for Gastric Cancer

Pyroptosis is an inflammatory form of programmed cell death triggered by caspase-1/4/5/11 that plays an important role in the occurrence and development of gastric cancer (GC). We investigated the prognostic value of pyroptosis-related genes in GC. The “LIMMA” R package and univariate Cox analysis were used to find pyroptosis-related genes with differential expression and prognostic value in the TCGA cohort and the identified genes were analyzed for GO enrichment and KEGG pathways. The selected genes were then included in a multivariate Cox proportional hazard regression analysis, and a ten genes prognostic model (BIRC2, CD274, IRGM, ANXA2, GBP5, TXNIP, POP1, GBP1, DHX9, and TLR2) was established. To evaluate the predictive value of the risk score on prognosis, patients were divided into high-risk and low-risk groups according to the median risk score, and survival analysis was carried out. Compared with the low-risk group, the OS of GC patients in the high-risk group was significantly worse. Additionally, these results were verified in the GSE84437 and GSE66229 datasets. Finally, through the combination of prognostic gene characteristics and clinicopathological features, a nomogram was established to predict individual survival probability. The results show that the genetic risk characteristics related to clinical features can be used as independent prognostic indicators for patients with GC. In summary, the pyroptosis-related risk signals proposed in this study can potentially predict the prognosis of patients with GC. In addition, we also found significant infiltration of dendritic cells, macrophages, and neutrophils in tissues of high-risk patients.

A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

BACKGROUND

Pyroptosis can not only inhibit the occurrence and development of tumors but also develop a microenvironment conducive to cancer growth. However, pyroptosis research in prostate cancer (PCa) has rarely been reported.

METHODS

The expression profile and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were divided into different clusters using consensus clustering analysis, and differential genes were obtained. We developed and validated a prognostic biomarker for biochemical recurrence (BCR) of PCa using univariate Cox analysis, Lasso-Cox analysis, Kaplan-Meier (K-M) survival analysis, and time-dependent receiver operating characteristics (ROC) curves.

RESULTS

The expression levels of most pyroptosis-related genes (PRGs) are different not only between normal and tumor tissues but also between different clusters. Cluster 2 patients have a better prognosis than cluster 1 patients, and there are significant differences in immune cell content and biological pathway between them. Based on the classification of different clusters, we constructed an eight genes signature that can independently predict the progression-free survival (PFS) rate of a patient, and this signature was validated using a GEO data set (GSE70769). Finally, we established a nomogram model with good accuracy.

CONCLUSIONS

In this study, PRGs were used as the starting point and based on the expression profile and clinical data, a prognostic signature with a high predictive value for biochemical recurrence (BCR) following radical prostatectomy (RP) was finally constructed, and the relationship between pyroptosis, immune microenvironment, and PCa was explored, providing important clues for future research on pyroptosis and immunity.

A Novel Risk Model Identified Based on Pyroptosis-Related lncRNA Predicts Overall Survival and Associates With the Immune Landscape of GC Patients

Gastric cancer (GC) is one of the most common malignant gastrointestinal tumors worldwide. Pyroptosis was widely reported to exert a crucial function in tumor development. In addition, pyroptosis was also proved to be associated with the immune landscape. However, whether pyroptosis-related lncRNAs are associated with the prognosis and the immune landscape of GC remains unclear. In the present study, we first constructed a novel risk model by using pyroptosis-related lncRNAs. We identified 11 pyroptosis-related lncRNAs for the establishment of the risk model. The risk model could be used to predict the survival outcome and immune landscape of GC patients. The results of survival analysis and AUC value of a time-related ROC curve proved that our risk model has an elevated efficiency and accuracy in predicting the survival outcome of patients. We also found that the risk model was also associated with the immune landscape, drug sensitivity, and tumor mutation burden of GC patients. In conclusion, our risk model plays a crucial role in the tumor immune microenvironment and could be used to predict survival outcomes of GC patients.

Development of a prognostic pyroptosis-related gene signature for head and neck squamous cell carcinoma patient

Objective

Head and neck squamous cell carcinoma (HNSCC) is a major threat to public health. Pyroptosis is a form of inflammatory programmed cell death that is still incompletely understood. The role of pyroptotic cell death in HNSCC remains to be fully defined. As such, the present study was developed to explore the potential prognostic utility of a pyroptosis-related gene (PRG) signature in HNSCC.

Methods

PRG expression patterns and the associated mutational landscape in HNSCC were analyzed, after which a 6-gene prognostic model was constructed through least absolute shrinkage and selection operator (LASSO) and Cox regression analyses using the TCGA dataset, followed by validation with two GEO datasets (GSE41643 and GSE65858). The relative expression of the genes in the prognostic model was assessed via RT-qPCR in tumor and paired adjacent normal tissue samples from a 32-patient cohort. Potential predictors of patient outcomes associated with this 6-gene model were identified through topological degree analyses of a protein–protein interaction network. Moreover, the prognostic value of NLRP3 as a predictor of HNSCC patient prognosis was established through immunohistochemical (IHC) analyses of samples from 176 HNSCC patients. Lastly, in vitro studies were performed to further demonstrate the relevance of NLRP3 in the context of HNSCC development.

Results

Differentially expressed PRGs were able to readily differentiate between HNSCC tumors and normal tissues. Risk scores derived from the 6-gene PRG model were independent predictors of HNSCC patient prognosis, and genes that were differentially expressed between low- and high-risk groups were associated with tumor immunity. RT-qPCR assays also showed the potential protective role of NLRP3 in HNSCC patients. IHC analyses further supported the value of NLRP3 as a predictor of HNSCC patient outcomes. Invasion and migration assays demonstrated the potential role of NLRP3 in the inhibition of HNSCC development.

Conclusions

Overall, these results highlight a novel prognostic gene signature that offers value in the context of HNSCC patient evaluation, although additional research will be essential to elucidate the mechanisms linking these PRGs to HNSCC outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02476-3.

The pyroptosis-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) remains one of the most common malignant tumors with poor survival. Pyroptosis is a kind of programmed cell death that can regulate the proliferation, invasion, and metastasis of tumor cells. However, the expression levels of pyroptosis-related genes (PRGs) in HCC and their relationship with prognosis are still unclear.

Methods

Our study identified 35 PRGs through bioinformatics analysis that were differentially expressed between tumor samples and nontumor samples. According to these differentially expressed genes, HCC patients could be divided into two groups, cluster 1 and cluster 2. The least absolute shrinkage and selection operator (LASSO) Cox regression method was performed to construct a 10-gene signature that classified HCC patients in the cancer genome atlas (TCGA) database into low-risk and high-risk groups.

Results

The results showed that the survival rate of HCC patients in the low-risk group was significantly higher than that in the high-risk group (p < 0.001). The validation cohort, the Gene Expression Omnibus (GEO) cohort, was divided into two risk groups based on the median risk score calculated by the TCGA cohort. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group (p = 0.007). Univariate and multivariate Cox regression analyses revealed that the risk score was an independent factor in predicting OS in HCC patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that immune-related high-risk groups were rich in genes and had reduced immune status.

Conclusions

PRGs play a significant role in tumor immunity and have the potential capability to predict the prognosis of HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00445-0.

A Novel Pyroptosis-Related Prognostic Signature for Cervical Squamous Cell Carcinoma

Purpose

Pyroptosis has vital roles in tumorigenesis and cancer development; however, its relationship with cervical squamous cell cancer (CSCC) remains unexplored. In this study, we aimed to develop a CSCC prediction signature related to pyroptosis.

Patients and Methods

Consensus clustering analysis was conducted to detect pyroptosis-related subclusters for CSCC. Next, differentially expressed genes (DEGs) between subclusters were identified. Univariate, least absolute shrinkage and selection operator, and stepwise multivariate Cox regression analyses were applied to establish a prognostic model and a nomogram drawn. Additionally, functional enrichment analysis, tumor mutation burden, and immune characteristics associated with this signature were investigated.

Results

We constructed a seven-gene signature that functions as an independent predictor of prognosis in CSCC using data from The Cancer Genome Atlas. Patients with CSCC were divided into two groups based on median risk score, and patients in the low-risk group had significantly longer survival time than those in the high-risk group. Our findings were validated using Gene Expression Omnibus cohort data. We also established a nomogram, to expand the clinical applicability of our findings. The seven gene signature was associated with various molecular pathways, tumor mutation status, and immune microenvironment.

Conclusion

The pyroptosis-related risk signature consisting of seven genes developed here represents a potential robust biomarker for predicting prognosis and immunotherapy response in patients with CSCC.

A novel pyroptosis-related signature predicts prognosis and response to treatment in breast carcinoma

Background: Pyroptosis is a new form of programmed cell death (PCD), also known as cellular inflammatory necrosis. Its discovery has resulted in a novel approach to the progression and medication resistance of breast cancer (BC). However, there is still a significant gap in the investigation of pyroptosis-related genes in BC.

Methods: The mRNA expression profiles and clinical data of BC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Then, using the TCGA cohort, we created a predictive multigene signature including pyroptosis-related genes and verified it using the two GEO cohorts. A pyroptosis-related gene signature was created by combining several bioinformatics and statistical methodologies to predict patient prognosis and responses to immunotherapy and chemotherapy. Furthermore, a nomogram based on the gene signature and clinicopathological markers was created to better classify the risk and quantify the risk assessment of individual patients.

Results: A pyroptosis-related gene signature consisting of 15 genes was established. The pyroptosis-related gene signature classified the patients into two groups: high-risk and low-risk. When combined with clinical variables, the risk score was discovered to be an independent predictor of overall survival (OS) in BC patients. Some immunological pathways and genes were linked to pyroptosis, according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) evaluations. Patients in the high-risk group had a worse prognosis and were not very sensitive to immunotherapy. However, several chemotherapeutic agents were predicted to have greater potential for patients in the high-risk group. Finally, a nomogram was developed that included a classifier based on the 15 pyroptosis-related genes, tumor stage, age, and histologic grade. This nomogram demonstrated good classification capacity and might help with clinical decision-making in BC.

The Pyroptosis-Related Gene Signature Predicts the Prognosis of Hepatocellular Carcinoma

Objective: Hepatocellular carcinoma (HCC) is a genetically and phenotypically heterogeneous tumor, and the prediction of its prognosis remains a challenge. In the past decade, studies elucidating the mechanisms that induce tumor cell pyroptosis has rapidly increased. The elucidation of their mechanisms is essential for the clinical development optimal application of anti-hepatocellular carcinoma therapeutics.

Methods: Based on the different expression profiles of pyroptosis-related genes in HCC, we constructed a LASSO Cox regression pyroptosis-related genes signature that could more accurately predict the prognosis of HCC patients.

Results: We identified seven pyroptosis-related genes signature (BAK1, CHMP4B, GSDMC, NLRP6, NOD2, PLCG1, SCAF11) in predicting the prognosis of HCC patients. Kaplan Meier survival analysis showed that the pyroptosis-related high-risk gene signature was associated with poor prognosis HCC patients. Moreover, the pyroptosis-related genes signature performed well in the survival analysis and ICGC validation group. The hybrid nomogram and calibration curve further demonstrated their feasibility and accuracy for predicting the prognosis of HCC patients. Meanwhile, the evaluation revealed that our novel signature predicted the prognosis of HCC patients more accurately than traditional clinicopathological features. GSEA analysis further revealed the novel signature associated mechanisms of immunity response in high-risk groups. Moreover, analysis of immune cell subsets with relevant functions revealed significant differences in aDCs, APC co-stimulation, CCR, check-point, iDCs, Macrophages, MHC class-I, Treg, and type II INF response between high- and low-risk groups. Finally, the expression of Immune checkpoints was enhanced in high-risk group, and m6A-related modifications were expressed differently between low- and high-risk groups.

Conclusion: The novel pyroptosis-related genes signature can predict the prognosis of patients with HCC and insight into new cell death targeted therapies.

Signature Construction and Molecular Subtype Identification Based on Pyroptosis-Related Genes for Better Prediction of Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort (n = 231) and the First Affiliated Hospital of Wenzhou Medical University cohort (n = 180). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.

A Pyroptosis-Based Prognostic Model for Immune Microenvironment Estimation of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), an aggressive malignant tumor, has a high incidence and unfavorable prognosis. Recently, the synergistic effect of pyroptosis in antitumor therapy and regulation of tumor immune microenvironment has made it possible to become a novel therapeutic method, but its potential mechanism still needs further exploration.

Methods

Differentially expressed genes with prognostic value in Liver Hepatocellular Carcinoma Project of The Cancer Genome Atlas (TCGA-LIHC) cohort were screened and incorporated into the risk signature by Cox proportional hazards regression model and least absolute shrinkage and selection operator. Kaplan-Meier (KM) curves and receiver operating characteristic (ROC) curves were applied to conduct survival comparisons and estimate prediction ability. The dataset of Liver Cancer-RIKEN, Japan Project from International Cancer Genome Consortium (ICGC-LIRI-JP) cohort was used to verify the reliability of the signature. Correlation analysis between clinicopathological characteristics, immune infiltration, drug sensitivities, and risk scores was conducted. Functional annotation analyses were performed for the genes differentially expressed between high-risk and low-risk groups.

Results

A risk signature consisting of 6 pyroptosis-related genes in HCC was developed and validated. KM curves and ROC curves revealed its considerable predictive accuracy. Higher risk scores meant more advanced grade, higher alpha-fetoprotein level, and stronger invasive ability. Overexpressed genes in high-risk population were more enriched in the immune-associated pathways, and these patients might be more sensitive to immune checkpoint inhibitors instead of Sorafenib. Intriguingly, 6 identified genes were promising to be prognostic biomarkers and therapeutic targets of HCC.

Conclusions

The signature may have crucial clinical significance in predicting survival prognosis, immune infiltration, and drug efficacy based on pyroptosis-related genes.

Comprehensive Pan-Cancer Analyses of Pyroptosis-Related Genes to Predict Survival and Immunotherapeutic Outcome

Simple Summary

Pyroptosis is a type of programmed cell death accompanied by inflammation. Although the dysregulation of pyroptosis has been reported to be involved in carcinogenesis, its function in cancer progression and therapy remains largely unknown and controversial because of the inconsistency across different cancer types. This study provides the most complete gene set of pyroptosis-related genes (PRGs), depicts their expression changes across 31 cancer types for the first time, and constructs a novel prognostic risk model to predict cancer patient survival. In addition, the effects of pyroptosis on immune cell infiltration and immunotherapy were dissected at the pan-cancer level. Small-molecule compounds, which may be beneficial to immunotherapy, were screened on the basis of differentially expressed PRGs. These results lay the foundation for the study of pyroptosis in cancer.

Abstract

Pyroptosis is a newly characterized type of programmed cell death. However, its function in cancer progression and its response to treatments remain controversial. Here, we extensively and systematically compiled genes associated with pyroptosis, integrated multiomics data and clinical data across 31 cancer types from The Cancer Genome Atlas, and delineated the global alterations in PRGs at the transcriptional level. The underlying transcriptional regulations by copy number variation, miRNAs, and enhancers were elucidated by integrating data from the Genotype-Tissue Expression and International Cancer Genome Consortium. A prognostic risk model, based on the expression of PRGs across 31 cancer types, was constructed. To investigate the role of pyroptosis in immunotherapy, we found five PRGs associated with effectiveness by exploring the RNA-Seq data of patients with immunotherapy, and further identified two small-molecule compounds that are potentially beneficial for immunotherapy. For the first time, from a pyroptosis standpoint, this study establishes a novel strategy to predict cancer patient survival and immunotherapeutic outcomes.

Molecular investigation of candidate genes for pyroptosis-induced inflammation in diabetic retinopathy

BACKGROUND

Diabetic retinopathy is a diabetic microvascular complication. Pyroptosis, as a way of inflammatory death, plays an important role in the occurrence and development of diabetic retinopathy, but its underlying mechanism has not been fully elucidated. The purpose of this study is to identify the potential pyroptosis-related genes in diabetic retinopathy by bioinformatics analysis and validation in a diabetic retinopathy model and predict the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) interacting with them. Subsequently, the competing endogenous RNA (ceRNA) regulatory network is structured to explore their potential molecular mechanism.

METHODS

We obtained mRNA expression profile dataset GSE60436 from the Gene Expression Omnibus (GEO) database and collected 51 pyroptosis-related genes from the PubMmed database. The differentially expressed pyroptosis-related genes were obtained by bioinformatics analysis with R software, and then eight key genes of interest were identified by correlation analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis. Then, the expression levels of these key pyroptosis-related genes were validated with quantitative real-time polymerase chain reaction (qRT-PCR) in human retinal endothelial cells with high glucose incubation, which was used as an in vitro model of diabetic retinopathy. Western blot was performed to measure the protein levels of gasdermin D (GSDMD), dasdermin E (GSDME) and cleaved caspase-3 in the cells. Moreover, the aforementioned genes were further confirmed with the validation set. Finally, the ceRNA regulatory network was structured, and the miRNAs and lncRNAs which interacted with CASP3, TLR4, and GBP2 were predicted.

RESULTS

A total of 13 differentially expressed pyroptosis-related genes were screened from six proliferative diabetic retinopathy patients and three RNA samples from human retinas, including one downregulated gene and 12 upregulated genes. A correlation analysis showed that there was a correlation among these genes. Then, KEGG pathway and GO enrichment analyses were performed to explore the functional roles of these genes. The results showed that the mRNA of these genes was mainly related to inflammasome complex, interleukin-1 beta production, and NOD-like receptor signaling pathway. In addition, eight hub genes-CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD, and GBP1-were identified by PPI network analysis using Cytoscape software. High glucose increased the protein level of GSDMD and GSDME, as critical effectors of pyroptosis, in retinal vascular endothelial cells. Verified by qRT-PCR, the expression of all these eight hub genes in the in vitro model of diabetic retinopathy was consistent with the results of the bioinformatics analysis of mRNA chip. Among them, CASP4, GBP1, CASP3, TLR4, and GBP2 were further validated in the GSE179568 dataset. Finally, 20 miRNAs were predicted to target three key genes-CASP3, GBP2, and TLR4, and 22 lncRNAs were predicted to potentially bind to these 20 miRNAs. Then, we constructed a key ceRNA network that is expected to mediate cellular pyroptosis in diabetic retinopathy.

CONCLUSION

Through the data analysis of the GEO database by R software and verification by qRT-PCR and validation set, we successfully identified potential pyroptosis-related genes involved in the occurrence of diabetic retinopathy. The key ceRNA regulatory network associated with these genes was structured. These findings might improve the understanding of molecular mechanisms underlying pyroptosis in diabetic retinopathy.

Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

A Novel Risk Signature with Seven Pyroptosis-Related Genes for Prognosis Prediction in Glioma

BACKGROUND

Increasing evidence indicates that pyroptosis is closely linked to the occurrence and progression of cancer. However, the expression and prognostic role of most pyroptosis-related genes in glioma have not been fully elucidated.

METHODS

Herein, we explored the expression profiles and prognostic value of 33 pyroptosis-related genes in glioma. LASSO regression analysis was then used to construct a risk signature to predict glioma outcomes in The Cancer Genome Atlas (TCGA) cohort. Furthermore, we constructed a nomogram based on independent prognostic factors and performed external validation. Finally, functional enrichment analysis was performed to explore the potential biological role of the pyroptosis-related signature in glioma.

RESULTS

The expression of most pyroptosis-related genes (31/33) was significantly different between normal brain and glioma tissue. By univariate Cox regression analysis, 24 genes were found to be significantly correlated with glioma overall survival (OS). Subsequently, we constructed a 7-gene risk signature in the TCGA training cohort, which demonstrated good performance in predicting glioma survival through multidatabase validation. Moreover, a nomogram was established based on independent prognostic factors (age, WHO grade, IDH status and signature) and confirmed to be more effective and accurate through internal evaluation and external validation. Finally, functional enrichment analyses suggested that the signature might be related to invasion ability and immune function.

CONCLUSIONS

The risk signature based on seven pyroptosis-related genes can effectively predict the clinical outcomes of glioma patients. Our study provides novel insights for further understanding the association between pyroptosis-related genes and glioma prognosis.

Identification of an Aging-Related Gene Signature in Predicting Prognosis and Indicating Tumor Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most commonly diagnosed malignancy accompanied by high invasion and metastasis features. Importantly, emerging studies have supported that aging is a key clue that participates in the immune state and development of BC. Nevertheless, there are no studies concerning the aging-related genes (AGs) in constructing the prognosis signature of BC. Here, to address this issue, we initially performed a systematic investigation of the associations between AGs and BC prognosis and accordingly constructed a prognosis risk model with 10 AGs including PLAU, JUND, IL2RG, PCMT1, PTK2, HSPA8, NFKBIA, GCLC, PIK3CA, and DGAT1 by using the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis. Meanwhile, our analysis further confirmed that the nomogram possessed a robust performance signature for predicting prognosis compared to clinical characteristics of BC patients, including age, clinical stage, and TNM staging. Moreover, the risk score was confirmed as an independent prognostic index of BC patients and was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Furthermore, in the external clinical sample validation, AGs were expressed differentially in patients from different risk groups, and tumor-associated macrophage markers were elevated in high-risk BC tissues with more co-localization of AGs. In addition, the proliferation, transwell, and wound healing assays also confirmed the promoting effect of DGAT1 in BC cell proliferation and migration. Therefore, this well-established risk model could be used for predicting prognosis and immunotherapy in BC, thus providing a powerful instrument for combating BC.

Pyroptosis-Related Gene Signature Is a Novel Prognostic Biomarker for Sarcoma Patients

Sarcoma is a rare and an extremely aggressive form of cancer that originates from mesenchymal cells. Pyroptosis exerts a dual effect on tumours by inhibiting tumour cell proliferation while creating a microenvironment suitable for tumour cell development and proliferation. However, the significance of pyroptosis-related gene (PRG) expression in sarcoma has not yet been evaluated. Here, we conduct a retrospective analysis to examine PRG expression in 256 sarcoma samples from The Cancer Genome Atlas database. We identified the PRGs that had a significant correlation with overall patient survival in sarcoma by performing a univariate Cox regression analysis. Subsequently, we conducted a LASSO regression analysis and created a risk model for a six-PRG signature. As indicated from the Kaplan–Meier analysis, this signature revealed a significant difference between high- and low-risk sarcoma patients. A receiver operating characteristic curve analysis confirmed that this signature could predict overall patient survival in sarcoma patients with high sensitivity and specificity. Gene ontology annotation and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses revealed that five independent PRGs were closely associated with increased immune activity. Moreover, we also deciphered that increased number of immune cells infiltrated the tumour microenvironment in sarcoma. In brief, the PRG signature can effectively act as novel prognostic biomarker for sarcoma patients and is associated with the tumour immune microenvironment.

A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.

Development of a Prognostic Model of Glioma Based on Pyroptosis-Related Genes

BACKGROUND

Glioma is the most malignant tumor of the central nervous system, with a poor prognosis. Pyroptosis is known to regulate the malignant phenotype of tumor cells, thus affecting the prognosis of patients. However, the role of pyroptosis-related genes (PRGs) in glioma remains unclear.

METHODS

We used the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Rembrandt database of patients with glioma to construct a PRG-based prognostic model and analyzed the relationship between the prognostic model and tumor immune microenvironment. The Wilcox test was used to compare the expression of PRGs in glioma and normal tissues based on TCGA. Univariate Cox and LASSO regression were used to construct the prognostic model. The CGGA and Rembrandt database were used as validation sets to validate the model.

RESULTS

Five genes were included in the model (BAX, CASP1, CASP3, CASP6, and NOD1). The survival of patients in the high-risk group was lower than that in the low-risk group. The receiver operating characteristic curve showed that the model had good prognostic evaluation ability and accuracy in all 3 cohorts of patients with glioma. The correlation analysis between the prognostic model and immune infiltration showed that the degree of immune cell infiltration, immune response process, and the expression level of immune checkpoints in the high-risk group were higher than those in the low-risk group.

CONCLUSIONS

We have constructed a reliable PRG-related prognostic model, which can provide reference for the prognostic evaluation of patients with glioma.

Identification of pyroptosis-related signature for cervical cancer predicting prognosis

Cervical cancer (CC) is one of the most common malignancies encountered in gynecology practice. However, there is a paucity of information about specific biomarkers that assist in the diagnosis and prognosis of CC. Pyroptosis is a form of programmed cell death whose different elements are related to the occurrence, invasion, and metastasis of tumors. However, the role of pyroptosis phenomena in the progression of CC has not yet been elucidated. This study focuses on the development of a pyroptosis-associated prognostic signature for CC using integrated bioinformatics to delineate the relationships among the signature, tumor microenvironment, and immune response of the patients. In this respect, we identified a prognostic signature that depends on eight pyroptosis-related genes (PRGs) that designate with better prognostic survival in the low-risk group (P<0.05) and where AUC values were greater than 0.7. A multi-factor Cox regression analysis indicated that such a signature could be used as an independent prognostic factor, and both the DCA and the Nomogram suggested that the proposed prognostic signature had good predictive capabilities. Interestingly, this prognostic signature can be applied to multiple tumors and thus, is versatile from a clinical point of view. In addition, there were significant differences in the tumor microenvironment and immune infiltration status between the high- and low-risk groups (P<0. 05). The core gene granzyme B (GZMB) was screened and the CC-associated regulatory axis, GZMB/ miR-378a/TRIM52-AS1, was constructed, which may promote CC progression, and further experimentation is needed to validate these results.

A Pyroptosis-Related Gene Panel in Prognosis Prediction and Immune Microenvironment of Human Endometrial Cancer

As the second common diagnosed cancer among gynecological tumors, endometrial cancer (EC) has heterogeneous pathogenesis and clinical manifestations. Therefore, prognosis prediction that considers gene expression value and clinical characteristics, is helpful to patients with EC. We downloaded RNA expression and clinical data from the TCGA database. We achieved 4 DEPRGs and constructed the PRG panel by univariate, lasso and multivariate Cox analysis. Based on the median value of the risk score, patients were divided into two groups. The Kaplan–Meier curve suggested that the patients with lower risk scores had better clinical outcomes of EC. AUC of ROC curves suggested the panel can be used as an independent predictor. Future analysis indicated the positive correlations between risk score and clinical characteristics. What’s more, we performed GO and KEGG functional analysis and immune environment exploration to get an understanding of the potential molecular mechanism and immunotherapeutic target. To future validate the panel, we found that the relapse-free and overall survival probability of 4 prognostic DEPRGs between high-expression group and low-expression group were different through the Kaplan–Meier plotter in UCEC. In addition, GEPIA database and RT-PCR experiment indicated GPX4 and GSDMD were highly expressed in UCEC compared to normal endometrial tissue, and TIRAP and ELANE were downregulated. This study identified a PRG panel to predict the prognosis immune microenvironment in human EC. Then, Kaplan–Meier analysis and AUC below the ROC curves was used to validate the panel. In addition, Chi-square was used to show the clinical significance. GO, KEGG and GSEA were used to show the functional differences. Different immune-related databases were used to analyze the immune characteristics. The Kaplan–Meier plotter website was used to assess the effect of genes on survival. GEPIA and RT-PCR were used to analyze the expression level. In summary, we identified 4 prognosis-associated pyroptosis-related genes (ELANE, GPX4, GSDMD, and TIRAP). The panel can also predict prognosis prediction and immune microenvironment in human endometrial cancer.

A novel identified pyroptosis-related prognostic signature of colorectal cancer

Colorectal cancer (CRC), one of the most common malignancies worldwide, leads to abundant cancer-related mortalities annually. Pyroptosis, a new kind of programmed cell death, plays a critical role in immune response and tumor progression. Our study aimed to identify a prognostic signature for CRC based on pyroptosis-related genes (PRGs). The difference in PRGs between CRC tissues and normal tissues deposited in the TCGA database was calculated by "limma" R package. The tumor microenvironment (TME) of CRC cases was accessed by the ESTIMATE algorithm. The prognostic PRGs were identified using Cox regression analysis. A least absolute shrinkage and selector operation (LASSO) algorithm was used to calculate the risk scores and construct a clinical predictive model of CRC. Gene Set Enrichment Analysis (GSEA) was performed for understanding the function annotation of the signature in the tumor microenvironment. We found that most PRGs were significantly dysregulated in CRC. Through the LASSO method, three key PRGs were selected to calculate the risk scores and construct the prognostic model for CRC. The risk score was an independent indicator of patient's prognosis. In addition, we classified the CRC patients into two clusters based on risk scores and discovered that CRC patients in cluster 2 underwent worse overall survival and owned higher expression levels of immune checkpoint genes in tumor tissues. In conclusion, our study identified a PRG-related prognostic signature for CRC, according to which we classified the CRC patients into two clusters with distinct prognosis and immunotherapy potential.

Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

Background

Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear.

Objective

This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival.

Methods

A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort.

Results

A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues.

Conclusion

In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02231-0.