The pyroptosis-related signature predicts prognosis and influences the tumor immune microenvironment in dedifferentiated liposarcoma
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ABSTRACT:
SUBMITTER: Chen W
PROVIDER: S-EPMC10787309 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan-Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.
| S-EPMC9491002 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC) is a highly malignant tumor with a very poor prognosis. Pyroptosis is an inflammatory form of cell death and plays an important role in cancer development. The prognostic value of pyroptosis-related genes (PRGs) in HCC has not been studied extensively.MethodsUnsupervised consensus clustering analysis was performed to identify two subtypes based on the expression profiles of prognostic PRGs in the The Cancer Genome Atlas (TCGA) database, and the differences between the two subtypes were compared. A prognostic model based on four PRGs was established by further least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox regression analysis.ResultsTwo subtypes (clusters 1 and 2) were identified by consensus clustering based on prognostic PRGs in HCC. Survival outcomes, biological function, genomic alterations, immune cell infiltration, and immune checkpoint genes were compared between the subtypes. Cluster 2 had a worse survival outcome than cluster 1. Cluster 2 was enriched for hallmarks of cancer progression, TP53 mutation, tumor-promoting immune cells, and immune checkpoint genes, which may contribute to the poor prognosis. A prognostic risk signature that predicted the overall survival (OS) of patients was constructed and validated. Consequently, a risk score was calculated for each patient. Combined with the clinical characteristics, the risk score was found to be an independent prognostic factor for survival of HCC patients. Further analysis revealed that the risk score was closely associated with the levels of immune cell infiltration and the expression profiles of immune checkpoint genes.ConclusionsCollectively, our study established a prognostic risk signature for HCC and revealed a significant correlation between pyroptosis and the HCC immune microenvironment.
| S-EPMC9164458 | biostudies-literature
Project description: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.
| S-EPMC9081442 | biostudies-literature
Project description:BackgroundGlioma, the most common malignant brain tumor, leads to high recurrence rates and disabilities in patients. Pyroptosis is an inflammasomes-induced programmed cell death in response to infection or chemotherapy. However, the role of pyroptosis in glioma has not yet been elucidated.MethodsRNA-seq data and clinical information of 660 gliomas and 847 samples were downloaded from the TCGA and CGGA, respectively. Then, data of 104 normal brain tissues was retrieved from the GTEx for differential expression analysis. Twelve pairs of peritumoral tissue and glioma samples were used for validation. Gene alteration status of differentially expressed pyroptosis-related regulators in gliomas was detected in cBioPortal algorithm. Consensus clustering was employed to classify gliomas based on differentially expressed pyroptosis-related regulators. Subsequently, a PS-signature was constructed using LASSO-congressional analysis for clinical application. The immune infiltration of glioma microenvironment (TME) was explored using ESTIMATE, CIBERSORT, and the other immune signatures.ResultscBioPortal algorithm revealed alteration of these regulators was correlated to better prognosis of gliomas. Then, our study showed that pyroptosis-related regulators can be used to sort out patients into two clusters with distinct prognostic outcome and immune status. Moreover, a PS-signature for predicting the prognosis of glioma patients was developed based on the identified subtypes. The high PS-score group showed more abundant inflammatory cell infiltration and stronger immune response, but with poorer prognosis of gliomas.ConclusionThe findings of this study provide a therapeutic basis for future research on pyroptosis and unravel the relationship between pyroptosis and glioma prognosis. The risk signature can be utilized as a prognostic biomarker for glioma.
| S-EPMC9972150 | biostudies-literature
Project description:Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named "PS-score." The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.
| S-EPMC8226259 | biostudies-literature
Project description:Background: Pyroptosis is a novel inflammatory form of programmed cell death and a prospective target for cancer therapy. Nevertheless, little is known about the association between pyroptosis-related genes (PRGs) and acute myeloid leukemia (AML) prognosis. Herein, we systematically investigated the specific functions and clinical prognostic value of multiple PRGs in AML. Methods: Univariate and LASSO Cox regression analyses based on TCGA and GTEx databases were used to generate the PRG signature, whose predictive efficacy of survival was evaluated using survival analysis, ROC, univariate and multivariate Cox analyses as well as subgroup analysis. The BeatAML cohort was used for data validation. The association between risk score and immune cell infiltration, HLA, immune checkpoints, cancer stem cell (CSC), tumor mutation burden (TMB), and therapeutic drug sensitivity were also analyzed. Results: Six -PRG signatures, namely, CASP3, ELANE, GSDMA, NOD1, PYCARD, and VDR were generated. The high-risk score represented a poorer prognosis and the PRG risk score was also validated as an independent predictor of prognosis. A nomogram including the cytogenetic risk, age, and risk score was constructed for accurate prediction of 1-, 3-, and 5-year survival probabilities. Meanwhile, this risk score was significantly associated with the tumor immune microenvironment (TIME). A high-risk score is characterized by high immune cell infiltration, HLA, and immune checkpoints, as well as low CSC and TMB. In addition, patients with low-risk scores presented significantly lower IC50 values for ATRA, cytarabine, midostaurin, doxorubicin, and etoposide. Conclusion: Our findings might contribute to further understanding of PRGs in the prognosis and development of AML and provide novel and reliable biomarkers for its precise prevention and treatment.
| S-EPMC9399441 | biostudies-literature
Project description:Pancreatic cancer (PC) has a poor prognosis, which is attributable to its high aggressiveness and lack of effective therapies. Although immunotherapy has been used for the treatment of various tumor, its efficacy in pancreatic cancer is not satisfactory. As a caspase-1-dependent programmed cell death, pyroptosis s involved in the pathological process of many tumors. Nevertheless, the vital role of the pyroptosis-related gene (PRG) in PC remains unknown. In this study, univariate COX regression was performed for 33 pyroptosis-related genes. Based on these prognosis-related PRGs, all PC patients in the Cancer Genome Atlas (TCGA) database were divided into four subtypes. Then, pyroptosis score (PP-score) was established to quantify pyroptosis level for individual PC patients using principal component analysis (PCA) algorithms. Assessment of pyroptosis level within individual PC patients may predict tumor classification and patient prognosis. Finally, a signature was constructed in TCGA and verified in ICGC. In addition, immunocheckpoint analysis revealed the possibility that the low-risk group would benefit more from immunocheckpoint therapy. Taken together, pyroptosis-related genes play a significant role in tumor immunotherapy and can be utilized to predict the prognosis of PC patients.
| S-EPMC9192978 | biostudies-literature
Project description:Chronic lymphocytic leukemia (CLL) is the most common leukemia in the Western world with great heterogeneity. Pyroptosis has recently been recognized as an inflammatory form of programmed cell death (PCD) and shares a close relationship with apoptosis. Although the role of apoptosis in CLL was comprehensively studied and successfully applied in clinical treatment, the relationship between pyroptosis genes and CLL remained largely unknown. In this study, eight differentially expressed pyroptosis-related genes (PRGs) were identified between CLL and normal B cells. In order to screen out the prognostic value of differentially expressed PRGs, univariate and multivariate Cox regression analyses were conducted and a risk model with three PRG signatures (GSDME, NLRP3, and PLCG1) was constructed. All CLL samples were stratified into high- and low-risk subgroups according to risk scores. The risk model showed high efficacy in predicting both overall survival (OS) and time to first treatment (TTFT). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) showed the dysregulation of immune and inflammatory response in the high-risk group. Single-sample GSEA (ssGSEA) of immune cell infiltration and the activity of immune-related pathways also displayed decreased antitumor immunity in the high-risk group. In conclusion, PRGs are of prognostic value in CLL and may play important roles in tumor immunity, and the underlying relationship between PRGs and CLL needs to be explored further.
| S-EPMC9513039 | biostudies-literature
Project description:Ovarian cancer (OV) is the most fatal gynecological malignant tumor worldwide, with high recurrence rates and great heterogeneity. Pyroptosis is a newly-acknowledged inflammatory form of cell death with an essential role in cancer progression, though studies focusing on prognostic patterns of pyroptosis in OV are still lacking. Our research filtered 106 potential pyroptosis-related genes (PRGs) among the 6406 differentially expressed genes (DEGs) between the 376 TCGA-OV samples and 180 normal controls. Through the LASSO-Cox analysis, the 6-gene prognostic signature, namely CITED2, EXOC6B, MIA2, NRAS, SETBP1, and TRPV46, was finally distinguished. Then, the K-M survival analysis and time-dependent ROC curves demonstrated the promising prognostic value of the 6-gene signature (p-value < 0.0001). Furthermore, based on the signature and corresponding clinical features, we constructed and validated a nomogram model for 1-year, 2-year, and 3-year OV survival, with reliable prognostic values in TCGA-OV (p-value < 0.001) and ICGC-OV cohort (p-value = 0.040). Pathway analysis enriched several critical pathways in cancer, refer to the pyroptosis-related signature, while the m6A analysis indicated greater m6A level in high-risk group. We assessed tumor immune microenvironment through the CIBERSORT algorithm, which demonstrated the upregulation of M1 Macrophages and activated DCs and high expression of key immune checkpoint molecules (CTLA4, PDCD1LG2, and HAVCR2) in high-risk group. Interestingly, the high-risk group exhibited poor sensitivity towards immunotherapy and better sensitivity towards chemotherapies, including Vinblastine, Docetaxel, and Sorafenib. Briefly, the pyroptosis-related signature was a promising tool to predict prognosis and evaluate immune responses, in order to assist decision-making for OV patients in the realm of precision medicine.
| S-EPMC10512632 | biostudies-literature
Project description:Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women's health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironment in BC are limited. Therefore, our study aimed to investigate the potential prognostic value of pyroptosis-related genes (PRGs) and their relationship to immune microenvironment in BC. First, we identified 38 differentially expressed PRGs between BC and normal tissues. Further on, the least absolute shrinkage and selection operator (LASSO) Cox regression and computational biology techniques were applied to construct a four-gene signature based on PRGs and patients in The Cancer Genome Atlas (TCGA) cohort were classified into high- and low-risk groups. Patients in the high-risk group showed significantly lower survival possibilities compared with the low-risk group, which was also verified in an external cohort. Furthermore, the risk model was characterized as an independent factor for predicting the overall survival (OS) of BC patients. What is more important, functional enrichment analyses demonstrated the robust correlation between risk score and immune infiltration, thereby we summarized genetic mutation variation of PRGs, evaluated the relationship between PRGs, different risk group and immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint blockers (ICB), which indicated that the low-risk group was enriched in higher TMB, more abundant immune cells, and subsequently had a brighter prognosis. Except for that, the lower expression of PRGs such as GZMB, IL18, IRF1, and GZMA represented better survival, which verified the association between pyroptosis and immune landscape. In conclusion, we performed a comprehensive bioinformatics analysis and established a four-PRG signature consisting of GZMB, IL18, IRF1, and GZMA, which could robustly predict the prognosis of BC patients.
| S-EPMC9021921 | biostudies-literature