Pyroptosis-related Genes Research Articles

A pyroptosis-related signature in colorectal cancer: exploring its prognostic value and immunological characteristics.

The heterogeneity of colorectal cancer (CRC) is the main cause of the disparity of drug sensitivity and the variability of prognosis. Pyroptosis is closely associated with the development and prognosis of various tumors, including CRC. Dividing CRC into distinct subgroups based on pyroptosis is a worthwhile topic for improving the precision treatment and prognosis prediction of CRC. We classified patients into two clusters using the consensus clustering based on the pyroptosis-related genes (PRGs). Next, the prognostic signature was developed with LASSO regression analysis using the screened genes from differentially expressed genes (DEGs) by univariate and multivariate Cox analyses. According to the pyroptosis-related score (PR score) calculated with the signature, patients belonged to two groups with distinct prognosis. Moreover, we assessed the immune profile to explore the relationship between the signature and immunological characteristics. Two single cell sequencing databases were adopted for further exploration of tumor immune microenvironment (TME). In addition, we applied our own cohort and Drugbank to explore the correlation of the signature and clinical therapies. We also studied the expression of key genes by immunohistochemistry. The signature performed well in predicting the prognosis of CRC as the high area under curve (AUC) value demonstrated. Patients with a higher PR score had poorer prognosis and higher expression of immune checkpoints but more abundant infiltration of immune cells. Combining with the indicator of therapeutic analysis, they might benefit more from immune checkpoint blockade (ICB) and neo-adjuvant chemoradiotherapy (nCRT). In conclusion, our study is based on genomics and transcriptomics to investigate the role of PRGs in CRC. We have established a prognostic signature and integrated single-cell data to study the relationship between the signature with the TME in CRC. Its clinical application in reliable prediction of prognosis and personalized treatment was validated by public and own sequencing cohort. It provided a new insight for the personalized treatment of CRC.

Prognostic, immunity, stemness, and anticancer drug sensitivity characterization of pyroptosis related genes in non-small cell lung cancer.

Pyroptosis plays a pivotal role in the tumor immune microenvironment (TME) dynamics, particularly in non-small cell lung cancer (NSCLC). The aim of our study was to explore its effects on tumor progression, TME patterns, and the efficacy of therapeutic interventions in NSCLC. Our investigation encompassed a thorough analysis of pyroptosis-related genes (PRGs), integrating immunohistochemistry (IHC) data, TME characteristics, stemness indices, and anticancer drug sensitivities. We aimed to analyze mRNA expression profiles across various cancers, constructing benchmark datasets to assess the clinical significance of PRGs in NSCLC. This included evaluating their association with clinical responses and efficacy. Notably, both our and HPA IHC data demonstrated significantly elevated GSDMD-N protein levels in lung squamous cell carcinoma (LUSC) tissues. The expression of PRGs differed significantly between tumor and normal tissues across various cancers, as validated by IHC data, and was correlated with prognosis (p < 0.05). Moreover, our investigation revealed significant differences (p < 0.05) in the expression of the PRGs among distinct TME subtypes categorized as C1 (wound healing), C3 (inflammatory), C2 (IFN-gamma dominant), C5 (immunological quiet), C4 (lymphocyte deficient), and C6 (TGF-beta dominant). Additionally, our research on anticancer drug sensitivity uncovered compelling connections between specific anticancer medications and the expression of PRGs, including GSDMD, ELANE, IL18, and CHMP4A (p < 0.05). Our study provided valuable insights into the critical role of PRGs in TME modulation, tumor stemness, and anticancer drug sensitivity across diverse cancers. Our findings illuminate the intricate relationship between pyroptosis and the TME, offering new perspectives for enhancing NSCLC treatment and prognosis.

Risk factors analysis and survival prediction model establishment of patients with lung adenocarcinoma based on different pyroptosis-related gene subtypes

BackgroundLung adenocarcinoma (LUAD) is a common cancer with a poor prognosis. Pyroptosis is an important process in the development and progression of LUAD. We analyzed the risk factors affecting the prognosis of patients and constructed a nomogram to predict the overall survival of patients based on different pyroptosis-related genes (PRGs) subtypes.MethodsThe genomic data of LUAD were downloaded from the TCGA and GEO databases, and all data were filtered and divided into TCGA and GEO cohorts. The process of data analysis and visualization was performed via R software. The data were classified based on different PRGs subtypes using the K-means clustering method. Then, the differentially expressed genes were identified between two different subtypes, and risk factors analysis, survival analysis, functional enrichment analysis, and immune cells infiltration landscape analysis were conducted. The COX regression analysis was used to construct the prediction model.ResultsBased on the PRGs of LUAD, the patients were divided into two subtypes. We found the survival probability of patients in subtype 1 is higher than that in subtype 2. The results of the logistics analysis showed that gene risk score was closely associated with the prognosis of LUAD patients. The results of GO analysis and KEGG analysis revealed important biological processes and signaling pathways involved in the differentially expressed proteins between the two subtypes. Then we constructed a prediction model of patients’ prognosis based on 13 genes, including IL-1A, P2RX1, GSTM2, ESYT3, ZNF682, KCNF1, STK32A, HHIPL2, GDF10, NDC80, GSTA1, BCL2L10, and CCR2. This model was strongly related to the overall survival (OS) and also reflects the immune status in patients with LUAD.ConclusionIn our study, we examined LUAD heterogeneity with reference to pyroptosis and found different prognoses between the two subtypes. And a novel prediction model was constructed to predict the OS of LUAD patients based on different PRGs signatures. The model has shown excellent predictive efficiency through validation.

Pyroptosis-related signatures predict immune characteristics and prognosis in IPF

The purpose of this work was to use integrated bioinformatics analysis to screen for pyroptosis-related genes (PRGs) and possible immunological phenotypes linked to the development and course of IPF. Transcriptome sequencing datasets GSE70866, GSE47460 and GSE150910 were obtained from GEO database. From the GSE70866 database, 34 PRGs with differential expression were found in IPF as compared to healthy controls. In addition, a diagnostic model containing 4 genes PRGs (CAMP, MKI67, TCEA3 and USP24) was constructed based on LASSO logistic regression. The diagnostic model showed good predictive ability to differentiate between IPF and healthy, with ROC-AUC ranging from 0.910 to 0.997 in GSE70866 and GSE150910 datasets. Moreover, based on a combined cohort of the Freiburg and the Siena cohorts from GSE70866 dataset, we identified ten PRGs that might predict prognosis for IPF. We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort. The risk model divided IPF patients from the combined cohort into high-risk and low-risk subgroups. There were significant differences between the two subgroups in terms of IPF survival and GAP stage. There is a close correlation between leukocyte migration, plasma membrane junction, and poor prognosis in a high-risk subgroup. Furthermore, a high-risk score was associated with more plasma cells, activated NK cells, monocytes, and activated mast cells. Additionally, we identified HDAC inhibitors in the cMAP database that might be therapeutic for IPF. To summarize, pyroptosis and its underlying immunological features are to blame for the onset and progression of IPF. PRG-based predictive models and drugs may offer new treatment options for IPF.

A pyroptosis-related gene expression signature predicts immune microenvironment and prognosis in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive malignancy with a poor prognosis. Pyroptosis triggered by gasdermins family proteins is reported vital for tumor microenvironment and cancer progression. However, pyroptosis-related gene expression and its relationship with immune infiltration and prognosis of HNSCC have not been fully defined. RNA-sequencing data of HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. A pyroptosis-related gene expression signature and infiltrated immune cells were analyzed. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and nomogram analyses were used to construct a clinical-molecular risk model for survival prognosis. HNSCC was classified into three different molecular subtypes based on the expression information of pyroptosis-related genes. Immune cell infiltration was demonstrated to be distinct between the three subtypes. The segregation of patients into the high-risk group and low-risk group, were carried out using the signature of differential expression genes (DEGs) signature among the three molecular subtypes. The precision of this signature was corroborated by Receiver operating characteristic curve (ROC) analysis with the 3-year area under time-dependent ROC curve (AUC) reaching 0.711. The risk model was validated in another dataset from the Gene Expression Omnibus (GEO) database. Subsequently we established a clinical-molecular nomogram which combined the risk score with age and stage. The calibration plots for predicting the overall survival rate of 1-, 3-, and 5-years indicated that the nomogram performs well. The expression signature that encompasses pyroptosis-related genes could be used as molecular classification for HNSCC and pyroptosis might be a promising therapeutic target in HNSCC.

Pyroptosis-Related Subtypes Predict the Response of Clear Cell Renal Cell Carcinoma to Targeted Therapy.

Pyroptosis plays a crucial role in anti-tumor immunity and in formation of the immune microenvironment. However, whether pyroptosis is involved in the progression of clear cell renal cell carcinoma (ccRCC) is still unclear. Personalized treatment of ccRCC requires detailed molecular classification to inform a specific therapy. Molecular subtyping of ccRCC was performed based on consensus clustering of pyroptosis-related genes. The characteristics of these molecular subtypes were explored at the genome, transcriptome and protein levels. Single-cell RNA sequencing and CIBERSORT analysis were used to analyse the immune microenvironment of ccRCC, while Lasso regression was used to develop a prediction model based on hub genes. Expression of the pyroptosis-related gene GSDMB was also investigated at the tissue and cellular levels. Two molecular subtypes were identified based on the clustering of pyroptosis-related genes. Cluster 1 was associated with activation of classical oncogenic pathways, especially the angiogenesis pathway. Cluster 2 was associated with activation of immune-related pathways and high levels of immunosuppressive cells, exhausted CD8+ T cells, and tumor-associated fibroblast infiltration. Clusters 1 and 2 were thus defined as the angiogenic and inflamed subtypes, respectively. The two subtypes were predictive of the response of ccRCC to anti-angiogenic therapy and immunotherapy, with Cluster 1 patients benefiting from anti-angiogenic therapy and Cluster 2 patients showing better response to anti-PD1 inhibitor therapy. Furthermore, a 9-gene expression signature (HJURP, NUF2, KIF15, MELK, TPX2, PLK1, CDCA3, CTLA4, FOXP3) was identified that could predict outcome and response to immune checkpoint blockade therapy in test cohorts. Finally, GSDMB was found to be involved in the development of renal clear cell carcinoma. These results on pyroptosis-related genes in ccRCC provide a theoretical basis for understanding molecular heterogeneity and for the development of individualized treatment strategies.

Novel molecular insights into pyroptosis in triple-negative breast cancer prognosis and immunotherapy.

Patients with triple-negative breast cancer (TNBC) often have a poor prognostic outcome. Current treatment strategies cannot benefit all TNBC patients. Previous findings suggested pyroptosis as a novel target for suppressing cancer development, although the relationship between TNBC and pyroptosis-related genes (PRGs) was still unclear. Gene expression data and clinical follow-up of TNBC patients were collected from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). PRGs were screened using weighted gene co-expression network analysis. Cox regression analysis and the least absolute shrinkage and selection operator (i.e. LASSO) technique were applied to construct a pyroptosis-related prognostic risk score (PPRS) model, which was further combined with the clinicopathological characteristics of TNBC patients to develop a survival decision tree and a nomogram. The model was used to calculate the PPRS, and then the overall survival, immune infiltration, immunotherapy response and drug sensitivity of TNBC patients were analyzed based on the PPRS. The PPRS model was closely related to clinicopathological features and can independently and accurately predict the prognosis of TNBC. According to normalized PPRS, patients in different cohorts were divided into two groups. Compared with the high-PPRS group, the low-PPRS group had significantly higher ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) score, immune score and stromal score, and it also had overexpressed immune checkpoints and significantly reduced Tumor Immune Dysfunction and Exclusion (TIDE) score, as well as higher sensitivity to paclitaxel, veliparib, olaparib and talazoparib. A decision tree and nomogram based on PPRS and clinical characteristics can improve the prognosis stratification and survival prediction for TNBC patients. A PPRS model was developed to predict TNBC patients' immune characteristics and response to immunotherapy, chemotherapy and targeted therapy, as well as their survival outcomes.

Expression of pyroptosis-associated genes and construction of prognostic model for thyroid cancer.

Thyroid cancer (THCA) is a common endocrine malignancy. This study aimed to explore the expression of pyroptosis-related genes in THCA and establish a prognosis prediction model. Differentially expressed pyroptosis-related genes (DEPRGs) were identified in The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). Subsequently, these genes were subjected to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A least absolute shrinkage and selection operator (LASSO) regression model was employed to establish a DEPRG signature, and its reliability was validated through survival analysis and receiver operating characteristic (ROC) curves. Patients in TCGA-THCA cohort were stratified into two risk groups. The biological functions of the genes between the two risk groups were assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Finally, expression of DEPRGs was validated using datasets from the Gene Expression Omnibus (GEO) and the Human Protein Atlas (HPA) databases. Six DEPRGs were identified in TCGA dataset. Through LASSO Cox regression analysis, we determined IL6, TP63, NOD1, and BAX to be significant. Kaplan-Meier survival analysis demonstrated that patients with THCA expressing high levels of NOD1 and classified as low-risk individuals exhibited prolonged survival. The multifactorial ROC curves yielded area under the curve (AUC) scores exceeding 0.7 for risk score, age, and T-stage, affirming their significance as independent prognostic factors as determined by multivariate analysis. Additionally, we observed elevated expression levels of BAX and NOD1 in THCA using data derived from the HPA database and the GEO dataset. We established a novel DEPRG signature for predicting the prognosis of THCA, potentially offering a promising therapeutic marker for THCA treatment.

GPR124 induces NLRP3 inflammasome-mediated pyroptosis in endothelial cells during ischemic injury

ObjectiveG protein-coupled receptor 124 (GPR124) regulates central nervous system angiogenesis and blood-brain barrier (BBB) integrity, and its deficiency aggravates BBB breakdown and hemorrhagic transformation in ischemic mice. However, excessive GPR124 expression promotes inflammation in atherosclerotic mice. In this study, we aimed to elucidate the role of GPR124 in hypoxia/ischemia-induced cerebrovascular endothelial cell injury. MethodsbEnd.3 cells were exposed to oxygen-glucose deprivation (OGD), and time-dependent changes in GPR124 mRNA and protein expression were evaluated using reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting. The effects of GPR124 overexpression or knockdown on the expression of pyroptosis-related genes were assessed at the mRNA and protein levels. Tadehaginoside (TA) was screened as a potential small molecule targeting GPR124, and its effects on pyroptosis-related signaling pathways were investigated. Finally, the therapeutic efficacy of TA was evaluated using a rat model of transient middle cerebral artery occlusion/reperfusion (tMCAO/R). ResultsDuring OGD, the expression of GPR124 initially increased and then decreased over time, with the highest levels observed 1 h after OGD. The overexpression of GPR124 enhanced the OGD-induced expression of NLRP3, Caspase-1, and Gasdermin D (GSDMD) in bEnd.3 cells, whereas GPR124 knockdown reduced pyroptosis. Additionally, TA exhibited a high targeting ability to GPR124, significantly inhibiting its function and expression and suppressing the expression of pyroptosis-related proteins during OGD. Furthermore, TA treatment significantly reduced the cerebral infarct volume and pyroptotic signaling in tMCAO/R rats. ConclusionsOur findings suggest that GPR124 mediates pyroptotic signaling in endothelial cells during the early stages of hypoxia/ischemia, thereby exacerbating ischemic injury.

Quercetin is a Potential Therapy for Rheumatoid Arthritis via Targeting Caspase-8 Through Ferroptosis and Pyroptosis.

Rheumatoid arthritis (RA) is one of the most common chronic inflammatory autoimmune diseases. However, the underlying molecular mechanisms of its pathogenesis are unknown. This study aimed to identify the common biomarkers of ferroptosis and pyroptosis in RA and screen potential drugs. The RA-related differentially expressed genes (DEGs) in GSE55235 were screened by R software and intersected with ferroptosis and pyroptosis gene libraries to obtain differentially expressed ferroptosis-related genes (DEFRGs) and differentially expressed pyroptosis-related genes (DEPRGs). We performed Gene Ontology (GO), Kyoto Encyclopedia of the Genome (KEGG), ClueGO, and Protein-Protein Interaction (PPI) analysis for DEFRGs and DEPRGs and validated them by machine learning. The microRNA/transcription factor (TF)-hub genes regulatory network was further constructed. The key gene was validated using the GSE77298 validation set, cellular validation was performed in in vitro experiments, and immune infiltration analysis was performed using CIBERSORT. Network pharmacology was used to find key gene-targeting drugs, followed by molecular docking and molecular dynamics simulations to analyze the binding stability between small-molecule drugs and large-molecule proteins. Three hub genes (CASP8, PTGS2, and JUN) were screened via bioinformatics, and the key gene (CASP8) was validated and obtained through the validation set, and the diagnostic efficacy was verified to be excellent through the receiver operating characteristic (ROC) curves. The ferroptosis and pyroptosis phenotypes were constructed by fibroblast-like synoviocytes (FLS), and caspase-8 was detected and validated as a common biomarker for ferroptosis and pyroptosis in RA, and quercetin can reduce caspase-8 levels. Quercetin was found to be a potential target drug for caspase-8 by network pharmacology, and the stability of their binding was further verified using molecular docking and molecular dynamics simulations. Caspase-8 is an important biomarker for ferroptosis and pyroptosis in RA, and quercetin is a potential therapy for RA via targeting caspase-8 through ferroptosis and pyroptosis.

Identification of IRF1 as a Novel Pyroptosis-Related Prognostic Biomarker of Atopic Dermatitis.

Purpose: The aim of this study was to characterize key biomarkers associated with pyroptosis in atopic dermatitis (AD). Materials and methods: To identify the differentially expressed pyroptosis-related genes (DEPRGs), the gene expression profiles GSE16161 and GSE32924 from the Gene Expression Omnibus (GEO) database were utilized. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to determine the potential biological functions and involved pathways. Furthermore, protein-protein interaction network analyses were performed to identify hub genes. The types and proportions of infiltrating immune cells were detected by immune filtration analysis using CIBERSORT. A 12-axis competing endogenous RNA (ceRNA) network was constructed utilizing the miRNet database. Immunohistochemistry (IHC) further validated the differential expression of a key gene IRF1 in the skin tissues collected from AD patients. The collection of skin tissue from human subjects in this study were reviewed and approved by the IRB of Yueyang Integrated Chinese and Western Medicine Hospital (KYSKSB2020-125). Results: The study identified a total of 76 DEPRGs, which were enriched in genes associated with the inflammatory response and immune regulation. There was a higher percentage of activated dendritic cells and a lower percentage of resting mast cells in AD samples. PVT1 expression was associated with upregulation of hub genes including CXCL8, IRF1, MKI67, and TP53 in the ceRNA network and was correlated with activated dendritic cells in AD. As a transcription factor, IRF1 could regulate the production of downstream inflammatory factors. The IHC study revealed that IRF1 was overexpressed in the skin tissues of AD patients, which were consistent with the results of the bioinformatic study. Conclusions: IRF1 and its related genes were identified as key pyroptosis-related biomarkers in AD, which is a crucial pathway in the pathogenesis of AD.

Identification and Immune Characteristics Study of Pyroptosis‑Related Genes in Endometriosis.

Endometriosis (EMT) is a prevalent gynecological disorder characterized by pain and infertility associated with the menstrual cycle. Pyroptosis, an emerging cell death mechanism, has been implicated in the pathogenesis of diverse diseases, highlighting its pivotal role in disease progression. Therefore, our study aimed to investigate the impact of pyroptosis in EMT using a comprehensive bioinformatics approach. We initially obtained two datasets from the Gene Expression Omnibus database and performed differential expression analysis to identify pyroptosis-related genes (PRGs) that were differentially expressed between EMT and non-EMT samples. Subsequently, several machine learning algorithms, namely least absolute shrinkage selection operator regression, support vector machine-recursive feature elimination, and random forest algorithms were used to identify a hub gene to construct an effective diagnostic model for EMT. Receiver operating characteristic curve analysis, nomogram, calibration curve, and decision curve analysis were applied to validate the performance of the model. Based on the selected hub gene, differential expression analysis between high- and low-expression groups was conducted to explore the functions and signaling pathways related to it. Additionally, the correlation between the hub gene and immune cells was investigated to gain insights into the immune microenvironment of EMT. Finally, a pyroptosis-related competing endogenous RNA network was constructed to elucidate the regulatory interactions of the hub gene. Our study revealed the potential contribution of a specific PRG to the pathogenesis of EMT, providing a novel perspective for clinical diagnosis and treatment of EMT.

Pyroptosis-related gene signature: A predictor for overall survival, immunotherapy response, and chemosensitivity in patients with pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is a lethal malignancy with high levels of heterogeneity. Pyroptosis is thought to influence the development of various tumors. Nevertheless, the role of pyroptosis-related genes (PRGs) in prognostic risk stratification and therapeutic guidance for PAAD remains ambiguously. MethodsTranscriptome profile and clinical information of PAAD patients were retrieved from The Cancer Genome Atlas (TCGA) as well as Gene Expression Omnibus (GEO) databases, followed by differential analysis. Patients were divided into distinct pyroptosis phenotype subtypes based on the characteristic of differently expressed PRGs (DEPRGs). Then a PRG signature was established through univariate analysis and LASSO algorithm in the training set to assess the prognostic risk, and its reliability was verified in the validation set using receiver operating characteristic(ROC) curve. The correlation of risk score with tumor microenvironment(TME), TMB and chemotherapeutic drug sensitivity were also analyzed. In addition, a nomogram was constructed to promote better clinical application. ResultsA total of 28 DEPRGs were determined in the integrated TCGA-GEO datasets. Patients were divided into three pyroptosis phenotype subtypes, Kaplan-Meier curve suggested patients in cluster B had a worse prognosis than those in cluster A and C. Then a price signature comprised of 8 PRGs was generated. TME analysis suggested that the low-risk subgroup displayed potential stronger antitumor immune effect and might respond better to immune checkpoint inhibitors (ICIs) therapy. Furthermore, PRG signature exhibited favorable discriminatory ability for TMB status and the sensitivity of multiple conventional chemotherapeutic agents including paclitaxel. Ultimately, we constructed a promising nomogram according to the risk score and N stage with good predictive accuracy compared with the actual overall survival (OS) probabilities. ConclusionWe established an 8-gene signature that could be regarded as an independent prognostic risk factor for PAAD patients. The 8-gene signature could provide rationale for immunotherapy and chemotherapy, which might help clinicians make precise individualized treatment regimens.

Identification of the role of pyroptosis-related genes in chronic rhinosinusitis based on WGCNA

BackgroundChronic rhinosinusitis (CRS) is a complex chronic inflammatory disease of the nose, paranasal sinus, and upper respiratory tract. Its treatment methods mainly include antibiotic treatment and surgical treatment. However, the molecular mechanism of its inflammation is still unclear. Pyroptosis is a programmed cell death. As an important natural immune response, pyroptosis plays an essential role in fighting infection. MethodsIn this paper, a weighted co-expression network (WGCNA) was used to screen gene modules significantly related to CRS. Then it intersects with the genes related to scorching death (PRGs). We evaluated the immune landscape of CRS by the expression of intersecting genes. In addition, in the enrichment analysis of intersection genes and PPI network analysis, we verified the pathways closely related to CRS and hub genes. Finally, the interaction network between the hub gene, miRNA, and TF was constructed. In this paper, qRT-qPCR technology was also used to detect the hub gene related to CRS. ResultsHub genes (CASP3, IL18, NAIP, NLRC4, and TP53) found in this paper are directly or indirectly related to CRS, and these genes were proved to be of diagnostic significance to CRS by ROC curve and qRT-qPCR vertification. In the infiltration abundance of CRS and its control group, the infiltration abundance of Plasma cells, T cells follicular helper, Macrophages M2, Dendritic cells activated, and Neutrophils cells in the two groups were significantly different. We also constructed the interaction network between the hub genes and miRNAs and the interaction network between hub genes and TFs. Most of these miRNAs and TFs were also related to CRS. ConclusionsWith the help of the WGCNA and PPI analysis, our results provide a better understanding of the role of biomarkers CASP3, IL18, NAIP, NLRC4, and TP53 in the development of CRS and provide a research basis for the mining of biomarkers related to the diagnosis and treatment of CRS.

Integrative bioinformatics analysis of biomarkers and pathways for exploring the mechanisms and molecular targets associated with pyroptosis in type 2 diabetes mellitus.

Research has shown that pyroptosis contributes greatly to the progression of diabetes and its complications. However, the exact relationship between this particular cell death process and the pathology of type 2 diabetes mellitus (T2DM) remains unclear. In this study, we used bioinformatic tools to identify the pyroptosis-related genes (PRGs) associated with T2DM and to analyze their roles in the disease pathology. Two microarray datasets, GSE7014 and GSE25724, were obtained from the GEO database and assessed for differentially expressed genes (DEGs). The T2DM-associated DEGs that overlapped with differentially expressed PRGs were noted as T2DM-PRGs. Subsequently, 25 T2DM-PRGs were validated and subjected to functional enrichment analysis through Gene Ontology annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis (GSEA). The diagnostic and predictive value of the T2DM-PRGs was evaluated using receiver operating characteristic curves (ROC). Additionally, a single-sample GSEA algorithm was applied to study immune infiltration in T2DM and assess immune infiltration levels. We identified 25 T2DM-PRGs that were significantly enriched in the nuclear factor-kappa B signaling and prostate cancer pathways. The top five differentially expressed prognostic T2DM-PRGs targeted by miRNAs were PTEN, BRD4, HSP90AB1, VIM, and PKN2. The top five differentially expressed T2DM-PRGs associated with transcription factors were HSP90AB1, VIM, PLCG1, SCAF11, and PTEN. The genes PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, BCL2, SERPINB1, ACE2, DRD2, DDX58, and BTK showed excellent diagnostic performance. The immune infiltration analysis revealed notable differences in immune cells between T2DM and normal tissues in both datasets. These findings suggest that T2DM-PRGs play a crucial role in the development and progression of T2DM and could be used as potential diagnostic biomarkers and therapeutic targets. Investigating the mechanisms and biomarkers associated with pyroptosis may offer valuable insights into the pathophysiology of T2DM and lead to novel therapeutic approaches to treat the disease.

System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma

BackgroundSkin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive.MethodsA total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package “pRRophetic” was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using “immuneeconv” R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells.ResultsBased on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan–Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells.ConclusionIn this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.

The role of pyroptosis-related genes in the diagnosis and subclassification of sepsis.

Pyroptosis is a new form of programmed cell death recognized as crucial in developing sepsis. However, there is limited research on the mechanism of pyroptosis-related genes in sepsis-related from the Gene Expression Omnibus (GEO) database and standardized. The expression levels of pyroptosis-related genes were extracted, and differential expression analysis was conducted. A prediction model was constructed using random forest (RF), support vector machine (SVM), weighted gene co-expression new analysis (WGCNA), and nomogram techniques to assess the risk of sepsis. The relationship between pyroptosis-related subgroups and the immune microenvironment and inflammatory factors was studied using consistent clustering algorithms, principal component analysis (PCA), single-sample genomic enrichment analysis (ssGSEA), and immune infiltration. A risk prediction model based on 3 PRGs has been constructed and can effectively predict the risk of sepsis. Patients with sepsis can be divided into two completely different subtypes of pyroptosis-related clusters. Cluster B is highly correlated with the lower proportion of Th17 celld and has lower levels of expression of inflammatory factors. This study utilizes mechanical learning methods to further investigate the pathogenesis of sepsis, explore potential biomarkers, provide effective molecular targets for its diagnosis and treatment of sepsis.

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

ObjectiveTo study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS. MethodsWe retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes. ResultsWe obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores. ConclusionThe study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing.

Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.

Transcriptome analysis reveals the mechanism of pyroptosis-related genes in septic cardiomyopathy.

Septic cardiomyopathy (SC) is characterized by myocardial dysfunction caused by sepsis and constitutes one of the serious complications of sepsis. Pyroptosis is a unique proinflammatory programmed cell death process. However, the role of pyroptosis in the development of SC remains unclear, and further study is required. The purpose of this study is to identify pyroptosis-related genes (PRGs) in SC and explore the mechanism of pyroptosis involved in the regulation of SC formation and progression. Differential expression analysis and enrichment analysis were performed on the SC-related dataset GSE79962 to identify differentially expressed genes (DEGs). PRGs were screened by intersecting genes associated with pyroptosis in previous studies with the DEGs obtained from GSE79962. The expression pattern of them was studied based on their raw expression data. Additionally, corresponding online databases were used to predict miRNAs, transcription factors (TFs) and therapeutic agents of PRGs. Lipopolysaccharide (LPS)-induced cell damage models in H9C2 and AC16 cell lines were constructed, cell activity was detected by CCK-8 and cell pyroptosis were detected by Hoechst33342/PI staining. Furthermore, these PRGs were verified in the external datasets (GSE53007 and GSE142615) and LPS-induced cell damage model. Finally, the effect of siRNA-mediated PRGs knockdown on the pyroptosis phenotype was examined. A total of 1,206 DEGs were screened, consisting of 663 high-expressed genes and 543 low-expressed genes. Among them, ten PRGs (SOD2, GJA1, TIMP3, TAP1, TIMP1, NOD1, TP53, CPTP, CASP1 and SAT1) were identified, and they were mainly enriched in "Pyroptosis", "Ferroptosis", "Longevity regulating pathway", and "NOD-like receptor signaling pathway". A total of 147 miRNAs, 31 TFs and 13 therapeutic drugs were predicted targeting the PRGs. The expression trends of SOD2 were confirmed in both the external datasets and LPS-induced cell damage models. Knockdown of SOD2 induced increased pyroptosis in the AC16 LPS-induced cell damage model. In this study, we demonstrated that SOD2 is highly expressed in both the SC and LPS-induced cell damage models. Knockdown of SOD2 led to a significant increase in pyroptosis in the AC16 LPS-induced cell damage model. These findings suggest that SOD2 may serve as a potential target for the diagnosis and treatment of SC.