Molecular Characteristics, Clinical Implication, and Cancer Immunity Interactions of Pyroptosis-Related Genes in Breast Cancer

Revolutionizing breast cancer treatment: Harnessing the related mechanisms and drugs for regulated cell death (Review)

Breast cancer arises from the malignant transformation of mammary epithelial cells under the influence of various carcinogenic factors, leading to a gradual increase in its prevalence. This disease has become the leading cause of mortality among female malignancies, posing a significant threat to the health of women. The timely identification of breast cancer remains challenging, often resulting in diagnosis at the advanced stages of the disease. Conventional therapeutic approaches, such as surgical excision, chemotherapy and radiotherapy, exhibit limited efficacy in controlling the progression and metastasis of the disease. Regulated cell death (RCD), a process essential for physiological tissue cell renewal, occurs within the body independently of external influences. In the context of cancer, research on RCD primarily focuses on cuproptosis, ferroptosis and pyroptosis. Mounting evidence suggests a marked association between these specific forms of RCD, and the onset and progression of breast cancer. For example, a cuproptosis vector can effectively bind copper ions to induce cuproptosis in breast cancer cells, thereby hindering their proliferation. Additionally, the expression of ferroptosis‑related genes can enhance the sensitivity of breast cancer cells to chemotherapy. Likewise, pyroptosis‑related proteins not only participate in pyroptosis, but also regulate the tumor microenvironment, ultimately leading to the death of breast cancer cells. The present review discusses the unique regulatory mechanisms of cuproptosis, ferroptosis and pyroptosis in breast cancer, and the mechanisms through which they are affected by conventional cancer drugs. Furthermore, it provides a comprehensive overview of the significance of these forms of RCD in modulating the efficacy of chemotherapy and highlights their shared characteristics. This knowledge may provide novel avenues for both clinical interventions and fundamental research in the context of breast cancer.

Gasdermins: a dual role in pyroptosis and tumor immunity

The gasdermin (GSDM) protein family plays a pivotal role in pyroptosis, a process critical to the body's immune response, particularly in combatting bacterial infections, impeding tumor invasion, and contributing to the pathogenesis of various inflammatory diseases. These proteins are adept at activating inflammasome signaling pathways, recruiting immune effector cells, creating an inflammatory immune microenvironment, and initiating pyroptosis. This article serves as an introduction to the GSDM protein-mediated pyroptosis signaling pathways, providing an overview of GSDMs' involvement in tumor immunity. Additionally, we explore the potential applications of GSDMs in both innovative and established antitumor strategies.

Study of Therapeutic Mechanisms of Bupi Yichang Formula against Colon Cancer Based on Network Pharmacology, Machine Learning, and Experimental Verification

Bupi Yichang formula (BPYCF) has shown the anti-cancer potential; however, its effects on colon cancer and the mechanisms remain unknown. This study intended to explore the effects of BPYC on colon cancer and its underlying mechanisms. BPYCF-related and colon cancer-related targets were acquired from public databases, followed by differentially expressed genes (DEG) identification. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using clusterProfiler. A protein-protein interaction (PPI) network was constructed using STRING database. CytoHubba and MCODE to screen the hub targets. A diagnostic model was built using random forest algorithm. Molecular docking was conducted using PyMOL and AutoDock. High-performance liquid chromatograph-mass spectrometry (HPLC-MS) analysis and in vitro validation were performed. Forty-six overlapping targets of BPYCF-related, colon cancer-related targets, and DEGs were obtained. GO and KEGG analyses showed that the targets were mainly enriched in response to lipopolysaccharide, neuronal cell body, protein serine/threonine/tyrosine, as well as C-type lectin receptor, NOD-like receptor, and TNF signaling pathways. Five targets were identified as the pivotal targets, among which, NOS3, CASP8, RIPK3, and TNFRSF10B were stably docked with the core active component, naringenin. Naringenin was also identified from the BPYCF sample through HPLC-MS analysis. In vitro experiments showed that BPYCF inhibited cell viability, reduced NOS3 expression, and elevated CASP8, RIPK3, and TNFRSF10B expression in colon cancer cells. BPYCF might treat colon cancer mainly by regulating NOS3, CASP8, RIPK3, and TN-FRSF10B. This study first revealed the therapeutic effects and mechanisms of BPYCF against colon cancer, paving the path for the development of targeted therapeutic strategies for this cancer in the clinic.

A potential immunotherapy target for breast cancer: parenchymal and immune-stromal expression of the NLRP3 inflammasome pathway

BACKGROUND

The NOD-, LRR- and pyrin domain‑containing 3 (NLRP3) inflammasome is a critical component of the innate immune system. It has been known to play an important role in the carcinogenesis and prognosis of breast cancer patients. While the clinical evidence of the relationship between NLRP3 inflammasome activation and long-term survival is still limited, the possible roles of parenchymal or immune-stromal cells of breast cancer tissues in contributing to such carcinogenesis and progression still need to be clarified. This study is an analysis of patients receiving breast cancer surgery in a previous clinical trial.

METHODS

Immunohistochemistry (IHC) was used to detect the expression levels of NLRP3 inflammasome pathway-related proteins, including NLRP3, caspase-1, apoptosis-associated speck-like protein (ASC), IL-1β, and IL-18, in parenchymal and immune-stromal cells of breast cancer tissues compared to those of adjacent normal tissues, respectively. The relationship between NLRP3 inflammasome expression and clinicopathological characteristics, as well as 5-year survivals were analyzed using the Chi-square test, Kaplan-Meier survival curves, and Cox regression analysis.

RESULTS

In the parenchymal cells, ASC and IL-18 protein levels were significantly up-regulated in breast cancer tissues compared with adjacent normal tissues (P<0.05). In the immune-stromal cells, all the five NLRP3 inflammasome pathway-related proteins were significantly elevated in breast cancer tissues compared with adjacent normal tissues (P < 0.05). Carcinoma cell embolus was found to significantly correlate with high NLRP3 expression in parenchymal cells of the tumor (x2=4.592, P=0.032), while the expression of caspase-1 was negatively correlated with tumor progression. Histological grades were found to have a positive correlation with IL-18 expression in immune-stromal cells of the tumor (x2=14.808, P=0.001). Kaplan-Meier survival analysis revealed that high IL-18 expression in the immune-stromal cells and the positive carcinoma cell embolus were both associated with poor survival (P < 0.05). The multivariable Cox proportional hazards regression model implied that the high IL-18 expression and positive carcinoma cell embolus were both independent risk factors for unfavorable prognosis.

CONCLUSIONS

The activation of NLRP3 inflammasome pathways in immune-stromal and tumor parenchymal cells in the innate immune system was not isotropic and the main functions are somewhat different in breast cancer patients. Caspase-1 in parenchymal cells of the tumor was negatively correlated with tumor progression, and upregulation of IL-18 in immune-stromal cells of breast cancer tissues is a promising prognostic biomarker and a potential immunotherapy target.

TRIAL REGISTRATION

This clinical trial has been registered at the Chictr.org.cn registry system on 21/08/2018 (ChiCTR1800017910).

Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer

Background

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.

Methods

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.

Results

PMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

Conclusion

PMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.

Integrated Analysis of Immune Infiltration and Hub Pyroptosis-Related Genes for Multiple Sclerosis

Purpose

Studies on overall immune infiltration and pyroptosis in patients with multiple sclerosis (MS) are limited. This study explored immune cell infiltration and pyroptosis in MS using bioinformatics and experimental validation.

Methods

The GSE131282 and GSE135511 microarray datasets including brain autopsy tissues from controls and MS patients were downloaded for bioinformatic analysis. The gene expression-based deconvolution method, CIBERSORT, was used to determine immune infiltration. Differentially expressed genes (DEGs) and functional enrichments were analyzed. We then extracted pyroptosis-related genes (PRGs) from the DEGs by using machine learning strategies. Their diagnostic ability for MS was evaluated in both the training set (GSE131282 dataset) and validation set (GSE135511 dataset). In addition, messenger RNA (mRNA) expression of PRGs was validated using quantitative real-time polymerase chain reaction (qRT-PCR) in cortical tissue from an experimental autoimmune encephalomyelitis (EAE) model of MS. Moreover, the functional enrichment pathways of each hub PRG were estimated. Finally, co-expressed competitive endogenous RNA (ceRNA) networks of PRGs in MS were constructed.

Results

Among the infiltrating cells, naive CD4+ T cells (P=0.006), resting NK cells (P=0.002), activated mast cells (P=0.022), and neutrophils (P=0.002) were significantly higher in patients with MS than in controls. The DEGs of MS were screened. Analysis of enrichment pathways showed that the pathways of transcriptional regulatory mechanisms and ion channels associating with pyroptosis. Four PRGs genes CASP4, PLCG1, CASP9 and NLRC4 were identified. They were validated in both the GSE135511 dataset and the EAE model by using qRT-PCR. CASP4 and NLRC4 were ultimately identified as stable hub PRGs for MS. Single-gene Gene Set Enrichment Analysis showed that they mainly participated in biosynthesis, metabolism, and organism resistance. ceRNA networks containing CASP4 and NLRC4 were constructed.

Conclusion

MS was associated with immune infiltration. CASP4 and NLRC4 were key biomarkers of pyroptosis in MS.

Role of gasdermin family proteins in cancers (Review)

The gasdermin (GSDM) family comprises six proteins, including GSDMA‑GSDME and Pejvakin. Most of these proteins have a crucial role in inducing pyroptosis; in particular, GSDMD and GSDME are the most extensively studied proteins as the executioners of the pyroptosis process. Pyroptosis is a highly pro‑inflammatory form of programmed cell death and is closely associated with the incidence, development and prognosis of multiple cancer types. The present review focused on the current knowledge of the molecular mechanism of GSDM‑mediated pyroptosis, its intricate role in cancer and the potential therapeutic value of its anti‑tumor effects.

Pyrimidine metabolism regulator-mediated molecular subtypes display tumor microenvironmental hallmarks and assist precision treatment in bladder cancer

Background

Bladder cancer (BLCA) is a common urinary system malignancy with a significant morbidity and death rate worldwide. Non-muscle invasive BLCA accounts for over 75% of all BLCA cases. The imbalance of tumor metabolic pathways is associated with tumor formation and proliferation. Pyrimidine metabolism (PyM) is a complex enzyme network that incorporates nucleoside salvage, de novo nucleotide synthesis, and catalytic pyrimidine degradation. Metabolic reprogramming is linked to clinical prognosis in several types of cancer. However, the role of pyrimidine metabolism Genes (PyMGs) in the BLCA-fighting process remains poorly understood.

Methods

Predictive PyMGs were quantified in BLCA samples from the TCGA and GEO datasets. TCGA and GEO provided information on stemness indices (mRNAsi), gene mutations, CNV, TMB, and corresponding clinical features. The prediction model was built using Lasso regression. Co-expression analysis was conducted to investigate the relationship between gene expression and PyM.

Results

PyMGs were overexpressed in the high-risk sample in the absence of other clinical symptoms, demonstrating their predictive potential for BLCA outcome. Immunological and tumor-related pathways were identified in the high-risk group by GSWA. Immune function and m6a gene expression varied significantly between the risk groups. In BLCA patients, DSG1, C6orf15, SOST, SPRR2A, SERPINB7, MYBPH, and KRT1 may participate in the oncology process. Immunological function and m6a gene expression differed significantly between the two groups. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and drug sensitivity all showed significant gene connections.

Conclusions

BLCA-associated PyMGs are available to provide guidance in the prognostic and immunological setting and give evidence for the formulation of PyM-related molecularly targeted treatments. PyMGs and their interactions with immune cells in BLCA may serve as therapeutic targets.

Stat5 inhibits NLRP3-mediated pyroptosis to enhance chemoresistance of breast cancer cells via promoting miR-182 transcription

The treatment of breast cancer (BC) calls for targeted methods to overcome chemoresistance (CR). This study is expected to figure out the mechanism of signal transducer and activator of transcription 5 (STAT5) in NOD-like receptor family pyrin domain containing 3 (NLRP3)-mediated pyroptosis and CR in BC cells. BC cell lines resistant to paclitaxel (PTX) and cis-diamminedichloro-platinum (DDP) were prepared. Expressions of Stat5, miR-182, and NLRP3 were detected. The 50% inhibition concentration (IC50 ), proliferation, colony formation, apoptosis rate, and levels of pyroptosis-related factors were appraised and determined. The binding relationships of Stat5 and miR-182, and miR-182 and NLRP3 were testified. Stat5 and miR-182 were highly expressed in drug-resistant BC cells. Silencing Stat5 reduced proliferation and colony formation of drug-resistant BC cells, coincided with elevated levels of pyroptosis-related factors. Stat5 bound to the promoter region of miR-182 to promote miR-182 expression. miR-182 inhibition reversed the role of silencing Stat5 in BC cells. miR-182 inhibited NLRP3. Overall, Stat5 bound to the promoter region of miR-182 to promote miR-182 expression and inhibit NLRP3 transcription, thereby suppressing pyroptosis and enhancing CR of BC cells.

Glutamine metabolism genes prognostic signature for stomach adenocarcinoma and immune infiltration: potential biomarkers for predicting overall survival

Background

Stomach adenocarcinoma (STAD), caused by mutations in stomach cells, is characterized by poor overall survival. Chemotherapy is commonly administered for stomach cancer patients following surgical resection. An imbalance in tumor metabolic pathways is connected to tumor genesis and growth. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. Metabolic reprogramming is associated with clinical prognosis in various cancers. However, the role of glutamine metabolism genes (GlnMgs) in the fight against STAD remains poorly understood.

Methods

GlnMgs were determined in STAD samples from the TCGA and GEO datasets. The TCGA and GEO databases provide information on stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and clinical characteristics. Lasso regression was performed to build the prediction model. The relationship between gene expression and Gln metabolism was investigated using co-expression analysis.

Results

GlnMgs, found to be overexpressed in the high-risk group even in the absence of any symptomatology, demonstrated strong predictive potential for STAD outcomes. GSEA highlighted immunological and tumor-related pathways in the high-risk group. Immune function and m6a gene expression differed significantly between the low- and high-risk groups. AFP, CST6, CGB5, and ELANE may be linked to the oncology process in STAD patients. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and medication sensitivity all revealed a strong link to the gene.

Conclusion

GlnMgs are connected to the genesis and development of STAD. These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

OBJECTIVES

Breast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m¹A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m¹A RNA methylation-related genes in BRCA is indistinct.

METHODS

The RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m¹A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m¹A-related genes were selected through overlapping m¹A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m¹A score subgroups. The m¹A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).

RESULTS

Eighty-five differentially expressed m¹A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.

CONCLUSIONS

An m¹A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

A novel risk score model based on pyroptosis-related genes for predicting survival and immunogenic landscape in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer worldwide, with high incidence and mortality. Pyroptosis, a form of inflammatory-regulated cell death, is closely associated with oncogenesis.

METHODS

Expression profiles of HCC were downloaded from the TCGA database and validated using the ICGC and GEO databases. Consensus clustering analysis was used to determine distinct clusters. The pyroptosis-related genes (PRGs) included in the pyroptosis-related signature were selected by univariate Cox regression and LASSO regression analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to estimate the prognostic potential of the model. The characteristics of infiltration of immune cells between different groups of HCC were explored.

RESULTS

Two independent clusters were identified according to PRG expression. Cluster 2 showed upregulated expression, poor prognosis, increased immune cell infiltration and worse immunotherapy response than cluster 1. A prognostic risk signature consisting of five genes (GSDME, NOD1, PLCG1, NLRP6 and NLRC4) was identified. In the high-risk score group, HCC patients showed decreased survival rates. In particular, multiple clinicopathological characteristics and immune cell infiltration were significantly associated with the risk score. Notably, the 5 PRGs in the risk score have been implicated in carcinogenesis, immunological pathways and drug sensitivity.

CONCLUSIONS

A prognostic signature comprising five PRGs can be used as a potential prognostic factor for HCC. The PRG-related signature provides an in-depth understanding of the association between pyroptosis and chemotherapy or immunotherapy for HCC patients.

Targeting pyroptosis in breast cancer: biological functions and therapeutic potentials on It

Pyroptosis is a lytic and inflammatory type of programmed cell death that is mediated by Gasdermin proteins (GSDMs). Attractively, recent evidence indicates that pyroptosis involves in the development of tumors and can serve as a new strategy for cancer treatment. Here, we present a basic knowledge of pyroptosis, and an overview of the expression patterns and roles of GSDMs in breast cancer. In addition, we further summarize the available evidence of pyroptosis in breast cancer progression and give insight into the clinical potential of applying pyroptosis in anticancer strategies for breast cancer. This review will deepen our understanding of the relationship between pyroptosis and breast cancer, and provide a novel potential therapeutic avenue for breast cancer.

Pyroptosis-based risk score predicts prognosis and drug sensitivity in lung adenocarcinoma

Pyroptosis is a recently identified form of programmed cell death; however, its role in lung adenocarcinoma (LUAD) remains unclear. Therefore, we set out to explore the prognostic potential of pyroptosis-related genes in LUAD. The pyroptosis-related risk score (PRRS) was developed by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression. We found that PRRS was an independent prognostic factor for LUAD. LUAD patients in the high-PRRS group showed a significantly shorter overall survival (OS) and enriched in cell proliferation-related pathways. Then pathway enrichment analyses, mutation profile, tumor microenvironment, and drug sensitivity analysis were further studied in PRRS stratified LUAD patients. Tumor purity (TP) analyses revealed that L-PRRS LUAD patients had a lower TP, and patients in L-TP + L-PRRS subgroup had the most prolonged OS. Mutation analyses suggested that the L-PRRS LUAD patients had a lower tumor mutation burden (TMB), and patients in H-TMB + L-PRRS subgroup had the most prolonged OS. Drug sensitivity analyses showed that PRRS was significantly negatively correlated with the sensitivity of cisplatin, besarotene, etc., while it was significantly positively correlated with the sensitivity of kin001-135. Eventually, a nomogram was constructed based on PRRS and clinical characters of LUAD. Overall, the pyroptosis-related signature is helpful for prognostic prediction and in guiding treatment for LUAD patients.

A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer

Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that pyroptosis is inextricably involved in the development of cancer and may activate tumor-specific immunity and/or enhance the effectiveness of existing therapies. We constructed a novel prognostic prediction model for BC, based on pyroptosis-related clusters, according to RNA-seq and clinical data downloaded from TCGA. The proportions of tumor-infiltrating immune cells differed significantly in the two pyroptosis clusters, which were determined according to 38 pyroptosis-related genes, and the immune-related pathways were activated according to GO and KEGG enrichment analysis. A 56-gene signature, constructed using univariate and multivariate Cox regression, was significantly associated with progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) of patients with BC. Cox analysis revealed that the signature was significantly associated with the PFI and DSS of patients with BC. The signature could efficiently distinguish high- and low-risk patients and exhibited high sensitivity and specificity when predicting the prognosis of patients using KM and ROC analysis. Combined with clinical risk, patients in both the gene and clinical low-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of the clinical event than those who did not. This study presents a novel 56-gene prognostic signature significantly associated with PFI, DSS, and OS in patients with BC, which, combined with the TNM stage, might be a potential therapeutic strategy for individualized clinical decision-making.

Identification and validation of a pyroptosis-related prognostic model for colorectal cancer

In this study, we explored the pyroptosis-related biomarkers and signatures of colorectal cancer (CRC). Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA)-COADREAD and were analyzed for differentially expressed genes (DEGs). DEGs in CRC‒pyroptosis-related genes (CRC‒PRGs) were obtained by intersecting DEGs associated with CRC and PRGs. The CRC‒PRGs were verified; functional enrichment analysis was performed with Gene Ontology (GO) followed by cluster analysis. Cox analyses and LASSO regression were used in TCGA dataset to construct a prognostic model for patients with CRC. A prognostic risk assessment model was constructed and efficacy was evaluated. Decision curve analysis was utilized to assess the role of the Lasso-Cox regression prognostic model for clinical utility at 1, 3, and 5 years. Twelve CRC‒PRGs were identified as prognostic pyroptosis-related DEGs. CXCL8, IL13RA2, MELK, and POP1 were selected as prognostic genes to construct features with a good prognostic performance in GEO and TCGA. Functional enrichment indicated that the 4-gene signature might be involved in CRC tumorigenesis and development through various pathways by playing a prognostic role in CRC. Furthermore, the results of the immune landscape analysis showed that the expression of CXCL8 and IL13RA2 in TCGA-COADREAD dataset was positively correlated with significant differential enrichment of most immune cells. A novel prognostic model consisting of four key genes, CXCL8, IL13RA2, MELK, and POP1, can accurately predict the survival of patients with CRC. This finding may provide a new perspective for the treatment of pyroptosis-related CRC.

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival

Background

Stomach adenocarcinoma (STAD) arises from the mutations of stomach cells and has poor overall survival. Chemotherapy is commonly indicated for patients with stomach cancer following surgical resection. The most prevalent alteration that affects cancer growth is N6-methyladenosine methylation (m6A), although the possible function of m6A in STAD prognosis is not recognized.

Method

The research measured predictive FRGs in BLCA samples from the TCGA and GEO datasets. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from TCGA and GEO. STAD from TCGA and GEO at 24 m6A was investigated. Lasso regression was used to construct the prediction model to assess the m6A prognostic signals in STAD. In addition, the correlation between m6a and immune infiltration in STAD patients was discussed using GSVA and ssGSEA analysis. Based on these genes, GO and KEGG analyses were performed to identify key biological functions and key pathways.

Result

A significant relationship was discovered between numerous m6A clusters and the tumor immune microenvironment, as well as three m6A alteration patterns with different clinical outcomes. Furthermore, GSVA and ssGSEA showed that m6A clusters were significantly associated with immune infiltration in the STAD. The low-m6Ascore group had a lower immunotherapeutic response than the high-m6Ascore group. ICIs therapy was more effective in the group with a higher m6Ascore. Three writers (VIRMA, ZC3H13, and METTL3) showed significantly lower expression, whereas five authors (METTL14, METTL16, WTAP, RBM15, and RBM15B) showed considerably higher expression. Three readers (YTHDC2, YTHDF2, and LRPPRC) had higher levels of expression, whereas eleven readers (YTHDC1, YTHDF1, YTHDF3, HNRNPC, FMR1, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX) had lower levels. As can be observed, the various types of m6 encoders have varied ramifications for STAD control.

Conclusion

STAD occurrence and progression are linked to m6A-genes. Corresponding prognostic models help forecast the prognosis of STAD patients. m6A-genes and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in STAD, which requires further trials. In addition, the m6a-related gene signature offers a viable alternative to predict bladder cancer, and these m6A-genes show a prospective research area for STAD targeted treatment in the future.

A Novel and Validated 8-Pyroptosis-Related Genes Based Risk Prediction Model for Diffuse Large B Cell Lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL), the most common type of Non-Hodgkin's Lymphoma (NHL), has a lethal nature. Thus, the establishment of a novel model to predict the prognosis of DLBCL and guide its therapy is an urgency. Meanwhile, pyroptosis is engaged in the progression of DLBCL with further investigations required to reveal the underlying mechanism.

METHODS

LASSO regression was conducted to establish a risk model based on those PRGs. External datasets, RT-qPCR and IHC images from The Human Protein Alta (HPA) database were utilized to validate the model. ssGSEA was utilized to estimate the score of immune components in DLBCL.

RESULTS

A model based on 8 PRGs was established to generate a risk score. Validation of the model confirmed its robust performance. The risk score was associated with advanced clinical stages and shorter overall survivals. Two novel second-line chemotherapies were found to be potential treatments for high-risk patients. The risk score was also found to be correlated with immune components in DLBCL.

CONCLUSION

This novel model can be utilized in clinical practices to predict the prognosis of DLBCL and guide the treatment of patients at high risk, providing an overview of immune regulatory program via pyroptosis in DLBCL.

Biallelic NLRP7 variants in patients with recurrent hydatidiform mole: A review and expert consensus

Hydatidiform mole (HM) is an abnormal human pregnancy characterized by excessive growth of placental trophoblasts and abnormal early embryonic development. Following a first such abnormal pregnancy, the risk for women of successive molar pregnancies significantly increases. To date variants in seven maternal-effect genes have been shown to cause recurrent HMs (RHM). NLRP7 is the major causative gene for RHM and codes for NOD-like receptor (NLR) family pyrin domain containing 7, which belongs to a family of proteins involved in inflammatory disorders. Since its identification, all NLRP7 variants have been recorded in Infevers, an online registry dedicated to autoinflammatory diseases (https://infevers.umai-montpellier.fr/web/). Here, we reviewed published and unpublished recessive NLRP7 variants associated with RHM, scored their pathogenicity according to the American College of Medical Genetics classification, and recapitulated all functional studies at the level of both the patients and the conceptions. We also provided data on further variant analyses of 32 patients and genotypes of 36 additional molar pregnancies. This comprehensive review integrates published and unpublished data on NLRP7 and aims at guiding geneticists and clinicians in variant interpretation, genetic counseling, and management of patients with this rare condition.

Development of a prognostic prediction model based on a combined multi-omics analysis of head and neck squamous cell carcinoma cell pyroptosis-related genes

This study aimed to understand the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) and to develop and validate a prognostic model for HNSCC based on pyroptosis-associated genes (PAGs) in nasopharyngeal carcinoma. The Cancer Genome Atlas database was used to identify differentially expressed PAGs. These genes were analyzed using the Kyoto Encyclopedia of Genes and Genomes functional annotation analyses and Gene Ontology analyses. The NLR family pyrin domain containing 1 (NLRP1) gene, charged multivesicular body protein 7 (CHMP7) gene, and cytochrome C (CYCS) gene were used to create a prognostic model for HNSCC. The results of the Kaplan-Meier (K-M) and Cox regression analyses indicated that the developed model served as an independent risk factor for HNSCC. According to the K-M analysis, the overall survival of high-risk patients was lower than that of low-risk patients. The hazard ratios corresponding to the risk scores determined using the multivariate and univariate Cox regression analyses were 1.646 (95% confidence interval (CI): 1.189–2.278) and 1.724 (95% CI: 1.294–2.298), respectively, and the area under the receiver operator characteristic curve was 0.621. The potential mechanisms associated with the functions of the identified genes were then identified, and the tumor microenvironment and levels of immune cell infiltration achieved were analyzed. The immune infiltration analysis revealed differences in the distribution of Th cells, tumor-infiltrating lymphocytes, regulatory T cells, follicular helper T cells, adipose-derived cells, interdigitating dendritic cells, CD8⁺ T cells, and B cells. However, validating bioinformatics analyses through biological experiments is still recommended. This study developed a prognostic model for HNSCC that included NLRP1, CHMP7, and CYCS.

Constructing and Validating a Pyroptosis-Related Genes Prognostic Signature for Stomach Adenocarcinoma and Immune Infiltration: Potential Biomarkers for Predicting the Overall Survival

Background

Stomach adenocarcinoma (STAD) is a kind of cancer that begins in the stomach cells and has a poor overall survival rate. Following resection surgery, chemotherapy has been suggested as a curative method for stomach cancer. However, it is ineffective. Pyroptosis, a kind of inflammatory programmed cell death, has been shown to play a significant role in the development and progression of STAD. However, whether pyroptosis-related genes (PRGs) can be utilized to predict the diagnosis and prognosis of gastric cancer remains unknown.

Method

The research measured at predictive PRGs in STAD samples from TCGA and GEO. Lasso regression was used to build the prediction model. Coexpression analysis revealed that gene expression was linked to pyroptosis. PRGs were found to be overexpressed in high-risk individuals, implying that they could be used in a model to predict STAD prognosis.

Result

Immunological and tumor-related pathways were discovered using GSEA. In STAD patients, the genes GPX3, PDGFRL, RGS2, and SERPINE1 may be connected to the cancer process. The levels of expression also differed between the two risk groups.

Conclusion

The purpose of this study is to identify and verify STAD-associated PRGs that can effectively guide prognosis and the immunological milieu in STAD patients as well as offer evidence for the development of pyroptosis-related molecularly targeted therapeutics. Therefore, PRGs and the link between immunological and PRGs in STAD may be therapeutic targets.

Pyroptosis related genes signature predicts prognosis and immune infiltration of tumor microenvironment in hepatocellular carcinoma

Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan–Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer

The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.

Identification of a novel necroptosis-related classifier to predict prognosis and guide immunotherapy in breast invasive carcinoma

Background

Necroptosis plays a crucial function in the progression of breast invasive carcinoma (BRCA). It may be triggered in cancer therapy to enhance anti-tumor immunity. However, the functions of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remain largely unclear.

Methods

Necroptosis-related genes (NRGs) were collated from high-quality literature reviews. A robust risk model was constructed to systematically evaluate the clinical value, functional status, effects exerted by the risk model on the TME, and the genomic variations based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) meta-cohorts.

Results

A risk model was constructed which comprised of six NRGs, including TNF receptor-associated factor 5 (TRAF5), Toll-like receptor 3 (TLR3), a riboflavin kinase (RFK), Fas ligand (FASLG), Fas-associated protein with death domain (FADD), and baculoviral IAP repeat-containing 3 (BIRC3). The stability and accuracy of the risk model were demonstrated for both the training and validation cohorts and its utility as an independent prognostic model for BRCA was verified. Patients in the low-risk group exhibited “hot” tumors having active immune and cell killing functions, while those in the high-risk group showed “cold” tumors having active tumor proliferation and immunosuppression. Moreover, patients in the high-risk group had a greater number of CNV events in their genome, while the somatic mutations were fewer. Furthermore, patients in the low-risk group showed high sensitivity toward immunotherapy and chemotherapy.

Conclusion

A reliable risk model based on NRGs to assess patient prognoses and guide clinical decision-making was constructed and validated. Our findings may contribute to the understanding of necroptosis and aid clinical management, along with precision treatment in BRCA.

The role of pyroptosis and its crosstalk with immune therapy in breast cancer

Pyroptosis is a brand-new category of programmed cell death (PCD) that is brought on by multitudinous inflammasomes, which can recognize several stimuli to pilot the cleavage of and activate inflammatory cytokines like IL-18 and IL-1β is believed to have dual effects on the development of multiple cancers including breast cancer. However, pyroptosis has different effects on cancers depending on the type of tissues and their distinct heredity. Recently, the association between pyroptosis and breast cancer has received more and more attention, and it is thought that inducing pyroptosis could be used as a cancer treatment option. In addition, a great deal of evidence accumulating over the past decades has evinced the crosstalk between pyroptosis and tumor immunological therapy. Thus, a comprehensive summary combining the function of pyroptosis in breast cancer and antitumor immunity is imperative. We portray the prevalent knowledge of the multidimensional roles of pyroptosis in cancer and summarize the pyroptosis in breast cancer principally. Moreover, we elucidate the influence of inflammasomes and pyroptosis-produced cytokines on the tumor microenvironment (TME) of breast cancer. Taken together, we aim to provide a clue to harness pyroptosis rationally and apply it to augment immunotherapy efficiency for breast cancer.

Emerging roles of ferroptosis in glioma

Glioma is the most common primary malignant tumor in the central nervous system, and directly affects the quality of life and cognitive function of patients. Ferroptosis, is a new form of regulated cell death characterized by iron-dependent lipid peroxidation. Ferroptosis is mainly due to redox imbalance and involves multiple intracellular biology processes, such as iron metabolism, lipid metabolism, and antioxidants synthesis. Induction of ferroptosis could be a new target for glioma treatment, and ferroptosis-related processes are associated with chemoresistance and radioresistance in glioma. In the present review, we provide the characteristics, key regulators and pathways of ferroptosis and the crosstalk between ferroptosis and other programmed cell death in glioma, we also proposed the application and prospect of ferroptosis in the treatment of glioma.

The pyroptosis-related gene signature predicts prognosis and reveals characterization of the tumor immune microenvironment in acute myeloid leukemia

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.

A novel pyroptosis-related prognostic lncRNAs signature, tumor immune microenvironment and the associated regulation axes in bladder cancer

Bladder cancer (BC) is the most common malignancy of the urinary system. Pyroptosis is a host programmed cell death. However, the effects of pyroptosis-related lncRNAs (PRLs) on BC have not yet been completely elucidated. In this study, a prognostic PRLs model and two ceRNA networks were established using sufficient bioinformatics analysis and preliminary RT-qPCR validation in vitro. 6 PRLs were identified to construct a prognostic model. Then, the prognostic model risk score was verified to be an effective independent factor (Training cohort: Univariate analysis: HR = 1.786, 95% Cl = 1.416-2.252, p &lt; 0.001; multivariate analysis: HR = 1.664, 95% Cl = 1.308-2.116, p &lt; 0.001; testing cohort: Univariate analysis: HR = 1.268, 95% Cl = 1.144-1.405, p &lt; 0.001; multivariate analysis: HR = 1.141, 95% Cl = 1.018-1.280, p = 0.024). Moreover, ROC and nomogram were performed to assess the accuracy of this signature (1-year-AUC = 0.764, 3-years-AUC = 0.769, 5-years-AUC = 0.738). Consequently, we evaluated the survival curves of these 6 lncRNAs using Kaplan–Meier survival analysis, demonstrating that MAFG-DT was risk lncRNA, while OCIAD1-AS1, SLC25A25-AS1, SNHG18, PSMB8-AS1 and TRM31-AS1 were protective lncRNAs. We found a strong correlation between PRLs and tumor immune microenvironment by Pearson’s correlation analysis. As for sensitivity of anti-tumor drugs, the high-risk group was more sensitive to Sorafenib, Bicalutamide and Cisplatin, while the low-risk group was more sensitive to AKT.inhibitor.VIII, Salubrinal and Lenalidomide, etc. Meanwhile, we identified lncRNA OCIAD1-AS1/miR-141-3p/GPM6B and lncRNA OCIAD1-AS1/miR-200a-3p/AKAP11 regulatory axes, which may play a potential role in the progression of BC.

A Predictive Model Based on Pyroptosis-Related Gene Features Can Effectively Predict Clear Cell Renal Cell Carcinoma Prognosis and May Be an Underlying Target for Immunotherapy

Methods

The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed.

Results

The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group.

Conclusion

Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.

A novel Pyroptosis-related long non-coding RNA signature for predicting the prognosis and immune landscape of head and neck squamous cell carcinoma

BACKGROUND

Pyroptosis plays an essential function in carcinogenesis and the antitumor immune response. Herein, we constructed a pyroptosis-related long noncoding RNA (prLncRNA) signature to predict therapeutic effects and outcomes for head and neck squamous cell carcinoma (HNSCC) patients.

METHODS

Patients obtained from the TCGA-HNSC project were divided randomly into the training as well as the validation sets at a ratio of 7:3. A novel prognostic prLncRNA signature was constructed from the results of the training set using the least absolute shrinkage and selection operation. The medium value was used as the basis for categorizing all HNSCC patients into a low- or high-risk cohort. Cox regression and Kaplan-Meier (KM) survival analyses were executed to estimate the prognostic value. We also evaluated the functional enrichment, tumor microenvironment, immune cell infiltration, and the sensitivity to chemotherapy and immunotherapy between the high- and low-risk cohorts.

RESULTS

Nineteen prognostic prlncRNAs were identified to establish the prognostic signature. Multivariate Cox regression and KM survival analyses confirmed that this prlncRNA signature might serve as an independent prognostic indicator of patient survival, which was subsequently confirmed using a validating dataset. Multiple ROC curves indicated the prlncRNA signature presented a more predictive power than clinicopathological factors (age, sex, tumor grade, and tumor stage). GO, KEGG, and GSEA enrichment analysis disclosed several immune-related pathways which appeared to be enhanced in the low-risk cohort. ESTIMATE, CIBERSORT, and ssGSEA algorithms indicated considerable differences in the tumor microenvironment and immune cell infiltration in the low- and high-risk cohorts. Furthermore, the low-risk cohort was predicted to achieve a better response to immunotherapeutic drugs, while in contrast, the high-risk cohort would be more sensitive to chemotherapy drugs.

CONCLUSIONS

Our findings robustly demonstrate that our constructed prlncRNA signature could serve as an efficient indicator of prognosis, immunotherapy response, and chemosensitivity for HNSCC patients.

A Prognostic Signature Consisting of Pyroptosis-Related Genes and SCAF11 for Predicting Immune Response in Breast Cancer

Pyroptosis, characterized as an inflammasome-mediated cell death pathway, may be participated in tumorigenesis and progression. However, the underlying molecular function and mechanism of pyroptosis in BRCA remain unclear. In our study, we aimed to develop a prognostic signature in BRCA based on pyroptosis-associated genes. Data was downloaded from TCGA database, and then we screened 760 female BRCA samples and 104 normal breast tissues as the training set. Seven pyroptosis-related genes (CASP9, GPX4, IL18, NLRC4, SCAF11, TIRAP, and TNF) were identified as the pyroptosis-related prognostic model for BRCA using LASSO Cox regression. We subsequently tested the prognostic value of pyroptosis-associated gene signature in a validation set, GSE 20685. Time-dependent receiver operating characteristic analysis demonstrated the credible predictive capacity of this pyroptosis-associated gene signature. The area under the curves were 0.806 at 3 years, 0.787 at 5 years, 0.775 at 8 years, and 0.793 at 10 years in the training set, and 0.824 at 5 years, 0.808 at 8 years, and 0.790 at 10 years in the validation set. Furthermore, there are currently few data on SCAF11 regulating pyroptosis. To clarify this issue, we performed integrative bioinformatics and experimental analysis. Knocking down SCAF11 possessed an anti-cancer effect in terms of inhibiting cell viability and suppressing colony-formation in in-vitro functional assays. Meanwhile, the biological functions of SCAF11 in BRCA were further validated with several algorithms, such as Xiantao tool, LinkedOmics, GEPIA2, and TISIDB. These findings indicated that the expression of SCAF11 was significantly correlated with diverse tumor-infiltrating lymphocytes (TILs), including T central memory cell (Tcm), and type 2 T helper cell (Th2), etc. Functional enrichment analysis suggested that co-expression genes of SCAF11 primarily participated in inflammation and immune-related signaling pathways, such as oxidative phosphorylation, antimicrobial humoral response, and immunoglobulin complex. Moreover, SCAF11 expression was positively correlated with several immune checkpoints, including PD-L1, B7H3, and PDCD1LG2. Taken together, this study uncovered that pyroptosis-associated gene signature might be applied as an effective independent predictor in patients with BRCA. The pyroptosis-related gene SCAF11 might play potential roles in the regulation of immune microenvironment in BRCA.

miR-1290 modulates the radioresistance of triple-negative breast cancer by targeting NLRP3-mediated pyroptosis

PURPOSE

To explore the roles and underlying mechanisms of miR-1290 in determining the sensitivity of triple-negative breast cancer (TNBC) to radiation therapy.

METHODS

ELISA was performed to detect the levels of IL-18 and IL-1β in radiosensitive cells and serum samples. The level of miR-1290 in radiosensitive cells and tissues was assessed by qRT-PCR assay. A luciferase reporter assay was performed to confirm NLRP3 as the target of miR-1290. Functionally, the roles of miR-1290 in TNBC radioresistance were analyzed by transfection of either miR-1290 mimic or miR-1290 inhibitor. Moreover, the involvement of the miR-1290/NLRP3 axis in TNBC radioresistance was analyzed by experiments with a miR-1290 mimic and NLRP3 overexpression. MTT and colony formation assays were used to detect radiation-induced cell viability and proliferation. qRT-PCR and western blot assays were used to detect pyroptosis markers (NLRP3, ACS and caspase-1).

RESULTS

The results showed that radioresistance in TNBC cells was associated with a reduction in pyroptosis. miR-1290 expression was increased in radioresistant cells, and it had higher expression levels in the radioresistant tumor tissues of TNBC patients compared to the radiosensitive samples. The miR-1290 mimic suppressed radiation-induced pyroptosis and reduced the radiosensitivity of TNBC cells. Moreover, we found that NLRP3 was a potential target of miR-1290. Overexpression of NLRP3 partly reversed the effects of miR-1290 on pyroptosis and radioresistance. The mouse models showed that miR-1290 suppressed tumor size, tumor weight and pyroptosis.

CONCLUSIONS

miR-1290/NLRP3-mediated pyroptosis may play an important role in determining radioresistance in TNBC and serve as a novel therapeutic option.

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.

Identification of a Pyroptosis-Related Gene Signature and Effect of Silencing the CHMP4C and CASP4 in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor with an extremely poor prognosis. Pyroptosis has been demonstrated to play an important role in tumor prognosis. However, the expression of pyroptosis-related genes in PAAD and their correlations with prognosis remains unclear.

Methods

In this study, the 36 pyroptosis-related genes that were differentially expressed between normal pancreatic tissues and PAAD tissues were identified via the “limma” R package. Based on these differentially expressed genes (DEGs), a five-gene signature was established by applying the least absolute shrinkage and selection operator Cox regression in the TCGA cohort and was validated in the GEO cohort. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of DEGs based on the risk model indicated that immune-associated biological processes and pathways were enriched. In vivo, we detected the expressions of CASP4 and CHMP4C by immunohistochemistry in tumor tissues and adjacent normal tissues. In vitro, we silenced CASP4 and CHMP4C to explore their effects on pancreatic cancer cells.

Results

PAAD patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group. The expressions of CASP4 and CHMP4C in tumor tissue were higher than those in the adjacent normal tissues in vivo. The knockdown of CASP4 significantly inhibited the invasion and migration but not the proliferation of PANC-1 cells. The knockdown of CHMP4C obviously inhibited the proliferation, migration, and invasion of PANC-1 cells.

Conclusion

Pyroptosis-related genes play important roles in predicting the prognosis of PAAD, and CASP4 and CHMP4C affect the metastasis of PAAD.

Polydatin down-regulates the phosphorylation level of STAT3 and induces pyroptosis in triple-negative breast cancer mice with a high-fat diet

Background

To explore the impact of polydatin on mice with triple-negative breast cancer (TNBC) receiving a high-fat diet, as well as the underlying processes.

Methods

A total of 40 female Balb/c mice were randomly separated into 4 groups (4T1 + polydatin + fat diet group, 4T1 + high-fat diet group, 4T1 + polydatin group, and 4T1 group). To establish the obese TNBC mouse model, TNBC was xenografted 1×10⁵ 4T1 cells/50 µL per mouse at the right fourth mammary fat pad under anesthesia and the mice were fed a high fat diet. When the experiment was completed, total plasma cholesterol (TC) and cancer antigen (CA)15-3 were measured. The enzyme-linked immunosorbent assay (ELISA) method was used detect CA15-3. Oil red O staining was used to observe the morphological changes. Western blot analysis and reverse transcription polymerase chain reaction (RT-PCR) were used to detect the corresponding protein expression and the messenger RNA (mRNA) level.

Results

Polydatin decreased the degree of fatty liver, as determined by oil red O staining. The TC level in the 4T1 + fat diet group was significantly higher, and it was decreased in the 4T1 + polydatin group. The results of ELISA showed that compared with the 4T1 group, CA15-3 was significantly increased in the 4T1 + fat diet group, and polydatin was shown to significantly reduce the expression of CA15-3. Polydatin inhibited p-JAK2 and p-STAT3 mRNA and protein levels. Polydatin increased pyroptosis-related gene mRNA and protein level.

Conclusions

We believe that polydatin can effectively reduce blood lipid levels in TNBC mice with a high-fat diet, and play an anticancer role in TNBC. The underlying mechanism may be related to the JAK2/STAT3 signaling pathway and pyroptosis in TNBC. Our results contribute to validating the traditional use of polydatin in the treatment of TNBC with hyperlipidemia.

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.