Identification of a Pyroptosis-Related Gene Signature for Prediction of Overall Survival in Lung Adenocarcinoma

Predicting Diagnostic Biomarkers Associated with Pyroptosis in Neuropathic Pain Based on Machine Learning and Experimental Validation

Purpose

Previous studies have shown that pyroptosis plays a vital role in the progress of neuropathic pain (NP), but the molecular mechanisms have not been fully elucidated. The aim of this study was to identify crucial pyroptosis-related genes (PRGs) in NP.

Methods

We identified pyroptosis-related differentially expressed genes (PRDEGs) in NP by machine learning analysis of the GSE24982 and GSE60670 datasets. Furthermore, these PRDEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Friends analysis, respectively. Meanwhile, receiver operator characteristic (ROC) analysis was performed to assess the diagnostic value of PRDEGs in NP. Finally, we performed immune infiltration analysis of key PRDEGs using CIBERSORTR R package.

Results

We found that 5 PRDEGs by least absolute shrinkage and selection operator (LASSO) regression and random forest and verified by RT-qPCR. GO, KEGG and GSEA revealed that these PRDEGs were mainly enriched in regulation of neuron death, IL-4 signaling, IL-23 pathway, and NF-κB pathway. ROC analysis revealed that most of the PRDEGs performed well in diagnosing NP. We also revealed transcription factors, miRNA regulatory networks and drug interaction networks of PRDEGs. For immune infiltration analysis, PRDEGs were mainly correlated with dendritic cells, monocytes and follicular T helper cells, suggested that it might be involved in the regulation of neuroimmune-related signaling.

Conclusion

A total of five PRDEGs were can be employed as NP biomarkers, particularly Tlr4, Il1b and Casp8, and provide additional evidence for a vital role of pyroptosis in NP.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

Xihuang pill facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis

Glioma is the most common malignancy in the central nervous system. This study aims to disclose the impacts of Xihuang pill (XHP), a traditional Chinese formula, on glioma cell pyroptosis and relevant molecular mechanism. U251 and SHG-44 cells were treated with XHP alone or together with oe-POU4F1 and sh-STAT3. CCK8 assay detected the viability, flow cytometry evaluated pyroptosis, and microscopy observed cell morphology. LDH release was determined by the LDH kit and the levels of IL-1β and IL-18 were detected by ELISA. Immunofluorescence showed NLRP3 expression in glioma cells and western blotting measured the levels of POU4F1, STAT3, NLRP3, ASC, cleaved caspase-1, and IL-1β. The binding of POU4F1 to STAT3 was verified. Primary glioma model was established to observe tumor change by in vivo imaging, determine the levels of Ki67 and NLRP3 by immunochemistry, and detect relevant protein levels by western blotting. XHP treatment alone downregulated POU4F1 and STAT3 levels, aroused pyroptotic appearance in glioma cells such as ballooning swelling, reduced cell viability and number of pyroptotic cells, increased LDH release and IL-1β and IL-18 levels, formed NLRP3 sports in cells, and elevated the levels of pyroptosis-related proteins. However, POU4F1 overexpression or STAT3 silencing suppressed XHP-promoted pyroptosis. Mechanistically, POU4F1 acted as a transcription factor of STAT3 and regulated its transcription. In primary glioma models, XHP enhanced glioma cell pyroptosis and blocked glioma growth. XHP facilitates glioma cell pyroptosis via the POU4F1/STAT3 axis.

Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns

BACKGROUND

Lung adenocarcinoma (LUAD) seriously threatens people's health worldwide. Programmed cell death (PCD) plays a critical role in regulating LUAD growth and metastasis as well as in therapeutic response. However, currently, there is a lack of integrative analysis of PCD-related signatures of LUAD for accurate prediction of prognosis and therapeutic response.

METHODS

The bulk transcriptome and clinical information of LUAD were obtained from TCGA and GEO databases. A total of 1382 genes involved in regulating 13 various PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, alkaliptosis and disulfidptosis) were included in the study. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to identify PCD-associated differential expression genes (DEGs). An unsupervised consensus clustering algorithm was used to explore the potential subtypes of LUAD based on the expression profiles of PCD-associated DEGs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF) analysis and stepwise multivariate Cox analysis were performed to construct a prognostic gene signature. The "oncoPredict" algorithm was utilized for drug-sensitive analysis. GSVA and GSEA were utilized to perform function enrichment analysis. MCPcounter, quanTIseq, Xcell and ssGSEA algorithms were used for tumor immune microenvironment analysis. A nomogram incorporating PCDI and clinicopathological characteristics was established to predict the prognosis of LUAD patients.

RESULTS

Forty PCD-associated DEGs related to LUAD were obtained by WGCNA analysis and differential expression analysis, followed by unsupervised clustering to identify two LUAD molecular subtypes. A programmed cell death index (PCDI) with a five-gene signature was established by machine learning algorithms. LUAD patients were then divided into a high PCDI group and a low PCDI group using the median PCDI as a cutoff. Survival and therapeutic analysis revealed that the high PCDI group had a poor prognosis and was more sensitive to targeted drugs but less sensitive to immunotherapy compared to the low PCDI group. Further enrichment analysis showed that B cell-related pathways were significantly downregulated in the high PCDI group. Accordingly, the decreased tumor immune cell infiltration and the lower tumor tertiary lymphoid structure (TLS) scores were also found in the high PCDI group. Finally, a nomogram with reliable predictive performance PCDI was constructed by incorporating PCDI and clinicopathological characteristics, and a user-friendly online website was established for clinical reference ( https://nomogramiv.shinyapps.io/NomogramPCDI/ ).

CONCLUSION

We performed the first comprehensive analysis of the clinical relevance of genes regulating 13 PCD patterns in LUAD and identified two LUAD molecular subtypes with distinct PCD-related gene signature which indicated differential prognosis and treatment sensitivity. Our study provided a new index to predict the efficacy of therapeutic interventions and the prognosis of LUAD patients for guiding personalized treatments.

Development and clinical validation of a necroptosis-related gene signature for prediction of prognosis and tumor immunity in lung adenocarcinoma

Necroptosis is a new programmed formation of necrotizing cell death, which plays important role in tumor biological regulation, including tumorigenesis and immunity. In this study, we aimed to establish and validate a prediction model based on necroptosis-related genes (NRGs) for lung adenocarcinoma (LUAD) prognosis and tumor immunity. The training set consisted of samples from The Cancer Genome Atlas (TCGA) dataset (n = 334), and the validation sets consisted of samples from the Gene Expression Omnibus (GEO) (n = 439) and clinical (n = 20) datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that 28 necroptosis-related differentially expressed genes (DEGs) were enriched in cell death and immune regulation. RT-qPCR and western blot results showed the low expression of necroptosis markers in LUAD cells. A prognostic gene signature based on 6 NRGs (PYGB, IL1A, IFNAR2, BIRC3, H2AFY2, and H2AFX) was constructed and the risk score was calculated. Multivariate Cox regression analysis showed that the risk score was an independent risk factor [hazard ratio (HR) = 1.220, 95% confidence interval (CI): 1.154-1.290, P<0.001]. In the TCGA cohort, a high-risk score was associated with poor prognosis, weak immune infiltration, and low expression at immune checkpoints, which was validated in the GEO and clinical cohorts. Our findings showed that the patients in the low-risk group had a better progression-free survival (PFS) [not reached vs. 8.5 months, HR = 0.18, 95% CI: 0.04-0.72, P<0.001] than those in the high-risk score group. Immunotherapy tolerance was found to be correlated with the high-risk score, and the risk score combined with PD-L1 (AUC = 0.808, 95% CI: 0.613-1.000) could better predict the immunotherapy response of LUAD. A nomogram was shown to have a strong ability to predict the individual survival rate of patients with LUAD in the TCGA and GSE68465 cohorts. We constructed and validated a potential prognostic signature consisting of 6 NRGs to predict the prognosis and tumor immunity of LUAD, which may be helpful to guide the individualized immunotherapy of LUAD.

Construction and Evaluation of a Preoperative Prediction Model for Lymph Node Metastasis of cIA Lung Adenocarcinoma Using Random Forest

Background

Lymph node metastasis (LNM) is the main route of metastasis in lung adenocarcinoma (LA), and preoperative prediction of LNM in early LA is key for accurate medical treatment. We aimed to establish a preoperative prediction model of LNM of early LA through clinical data mining to reduce unnecessary lymph node dissection, reduce surgical injury, and shorten the operation time.

Methods

We retrospectively collected imaging data and clinical features of 1121 patients with early LA who underwent video-assisted thoracic surgery at the First Hospital of China Medical University from 2004 to 2021. Logistic regression analysis was used to select variables and establish the preoperative diagnosis model using random forest classifier (RFC). The prediction results from the test set were used to evaluate the prediction performance of the model.

Results

Combining the results of logistic analysis and practical clinical application experience, nine clinical features were included. In the random forest classifier model, when the number of nodes was three and the n-tree value is 500, we obtained the best prediction model (accuracy = 0.9769), with a positive prediction rate of 90% and a negative prediction rate of 98.69%.

Conclusion

We established a preoperative prediction model for LNM of early LA using a machine learning random forest method combined with clinical and imaging features. More excellent predictors may be obtained by refining imaging features.

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.

Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality and is usually associated with therapeutic resistance and poor prognosis because of individual biological heterogeneity. There is an unmet need to screen for reliable parameters, especially immunotherapy-related biomarkers to predict the patient’s outcomes. Necroptosis is a special caspase-independent form of necrotic cell death associated with the pathogenesis, progression, and prognosis of multiple tumors but the potential connection between necroptosis-related genes (NRGs) and LUAD still remains unclear. In this study, we expounded mutational and transcriptional alterations of 67 NRGs in 522 LUAD samples and proposed a consensus-clustering subtype of these patients into two cohorts with distinct immunological and clinical prognosis characteristics. Cluster B patients were associated with a better prognosis and characterized by relatively lower expression of NRGs, higher immune scores in the tumor microenvironment (TME), more mild clinical stages, and downregulated expression of immunotherapy checkpoints. Subsequently, the NRG score was further established to predict the overall survival (OS) of LUAD patients using univariate Cox, LASSO, and multivariate Cox regression analyses. The immunological characteristics and potential predictive capability of NRG scores were further validated by 583 LUAD patients in external datasets. In addition to better survival and immune-activated conditions, low-NRG-score cohorts exhibited a significant positive correlation with the mRNA stem index (mRNAsi) and tumor mutation burden (TMB) levels. Combined with classical clinical characteristics and NRG scores, we successfully defined a novel necroptosis-related nomogram to accurately predict the 1/3/5-year survival rate of individual LUAD patients, and the potential predictive capability was further estimated and validated in multiple test datasets with high AUC values. Integrated transcriptomic analysis helps us seek vital NRGs and supplements a novel clinical application of NRG scores in predicting the overall survival and therapeutic benefits for LUAD patients.

Prognosis and Tumour Immune Microenvironment of Patients With Hepatocellular Carcinoma by a Novel Pyroptosis-Related lncRNA Signature

Worldwide, hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. However, the survival rate of patients with HCC continues to be poor. The recent literature has revealed that long non-coding RNAs (lncRNAs) and the occurrence of pyroptosis can perform a substantial task in predicting the prognosis of the respective condition along with the response to immunotherapy among HCC patients. Thus, screening and identifying lncRNAs corelated with pyroptosis in HCC patients are critical. In the current study, pyroptosis-related lncRNAs (PR-lncRNAs) have been obtained by co-expression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regression assessments have been performed to develop a PR-lncRNA prognostic model. The risk model was analysed using Kaplan–Meier analysis, principal component analysis (PCA), functional enrichment annotation, and a nomogram. The risk model composed of five PR-lncRNAs was identified as an independent prognostic factor. The tumour immune microenvironment (TIME) was assessed using model groupings. Finally, we validated the five PR-lncRNAs in vitro using a quantitative real-time polymerase chain reaction (qRT-PCR).

Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma

Programmed cell death (PCD) is a process that regulates the homeostasis of cells in the body, and it plays an important role in tumor immunity. However, the expression profile and clinical characteristics of PCD-related genes remain unclear. In this study, we comprehensively analysed the PCD genes with the tumor microenvironment (TME), drug sensitivity, immunothearapy response, and evaluated their prognostic value through systematic bioinformatics methods.We identified 125 PCD-related regulatory factors, which were expressed differently in lung adenocarcinoma (LUAD) and normal lung tissues. 32 PCD related prognostic genes associated with LUAD were identified by univariate Cox analysis. 23 PCD-related gene signature was constructed, and all LUAD patients in the Cancer Genome Atlas (TCGA) dataset were stratified as low-risk or high-risk groups according to the risk score. This signature had a powerful prognostic value, which was validated in three independent data sets and clinical subtypes. Additionally, it has unique properties in TME. Further analysis showed that different risk groups have different immune cell infiltration, immune inflammation profile, immune pathways, and immune subtypes. In addition, the low-risk group had a better immunotherapy response with higher levels of multiple immune checkpoints and lower Tumor immune dysfunction and exclusion (TIDE) score, while the high-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In short, this is the first model to predict the prognosis and immunological status of LUAD patients based on PCD-related genes. It may be used as a predictor of immunotherapy response to achieve customized treatment of LUAD.

Pyroptosis-Related Gene Signature and Expression Patterns in the Deterioration of Atherosclerosis

Background

Pyroptosis has been shown to be involved in the overall process of atherosclerosis. This study was aimed at investigating pyroptosis-related gene expression patterns in atherosclerosis and their diagnostic significance.

Methods and Results

In GSE100927, fifty-four pyroptosis-related genes were identified. Between atherosclerotic plaques and normal samples, the expression patterns of pyroptosis-related genes were significantly different. In order to construct a pyroptosis-related risk score signature (PRSS), the least absolute shrinkage and selection operator (LASSO) was combined with multivariate logistic regression to screen twelve genes. The diagnostic efficiency of the PRSS performed well in GSE43292, as shown by the results of receiver-operating characteristics (ROCs). Consensus clustering identified two expression patterns of pyroptosis-related genes in different statuses of atherosclerotic plaque in GSE163154. The biological behavior of the different clusters was examined by the gene set variation analysis (GSVA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses revealed that the differentially expressed genes (DEGs) in the two clusters were enriched in the immune response. The Cytoscape software was used to construct protein-protein interaction (PPI) networks for hub gene screening. Following that, the Drug Gene Interaction Database (DGIdb) was utilized to find 47 possible medicines and chemical compounds that interact with hub genes in atherosclerotic plaques.

Conclusion

The results of this study showed that pyroptosis-related genes contribute to the progression of atherosclerosis and may serve as biomarkers in clinical diagnosis as well as novel therapeutic targets for the treatment of AS.

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.

Integratively Genomic Analysis Reveals the Prognostic and Immunological Characteristics of Pyroptosis and Ferroptosis in Pancreatic Cancer for Precision Immunotherapy

The non-apoptotic cell death processes including pyroptosis and ferroptosis have been implicated in the progression and therapeutic responses of pancreatic adenocarcinoma (PAAD). However, the extent to which pyroptosis and ferroptosis influence tumor biology remains ambiguous, especially in PAAD, which is characterized with “cold” immunity. Considering the heterogeneity among different patients, it was more practical to quantify distinct cell death profiles in an individual tumor sample. Herein, we developed a pyroptosis-ferroptosis (P-F) score for PAAD patients in The Cancer Genome Atlas (TCGA) database. A high P-F score was associated with active immune phenotype, decreased genomic alterations, and significantly longer survival. Good accuracy of the P-F score in predicting overall survival (OS) was further confirmed in the TCGA-PAAD, ICGC-PACA-CA, and E-MTAB-6134 cohorts. Besides, one immunotherapy cohort (IMvigor210 dataset) has verified that patients with high P-F scores exhibited significant advantages in therapeutic responses and clinical benefits. The sensitivity to chemotherapeutics was analyzed through the Genomics of Drug Sensitivity in Cancer (GDSC), and patients with low P-F score might be more sensitive to paclitaxel and 5-fluorouracil. Collectively, the P-F score based on the systematic evaluation of cell death profiles could serve as an effective biomarker in predicting the outcomes and responses of PAAD patients to treatments with chemotherapeutic agents or immunotherapies.

Pan-cancer analyses of pyroptosis with functional implications for prognosis and immunotherapy in cancer

Background

Programmed cell death is an active and orderly form of cell death regulated by intracellular genes that plays an important role in the normal occurrence and development of the immune system, and pyroptosis has been found to be involved in tumorigenesis and development. However, compressive analysis and biological regulation of pyroptosis genes are lacking in cancers.

Methods

Using data from The Cancer Genome Atlas, we established a score level model to quantify the pyroptosis level in cancer. Multiomics bioinformatic analyses were performed to assess pyroptosis-related molecular features and the effect of pyroptosis on immunotherapy in cancer.

Results

In the present study, we performed a comprehensive analysis of pyroptosis and its regulator genes in cancers. Most pyroptosis genes were aberrantly expressed in different types of cancer, attributed to the CAN frequency and differences in DNA methylation levels. We established a pyroptosis level model and found that pyroptosis had dual roles across cancers, while the pyroptosis levels were different among multiple cancers and were significantly associated with clinical prognosis. The dual role of pyroptosis was also shown to affect immunotherapeutic efficacy in several cancers. Multiple pyroptosis genes showed close associations with drug sensitivity across cancers and may be considered therapeutic targets in cancer.

Conclusions

Our comprehensive analyses provide new insight into the functions of pyroptosis in the initiation, development, progression and treatment of cancers, suggesting corresponding prognostic and therapeutic utility.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03313-x.

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.

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer

Objective. Bladder cancer (BC) is the most common malignancy in the urinary system and is prone to recurrence and metastasis. Pyroptosis is a kind of cell necrosis that is triggered by the gasdermin protein family. lncRNAs are noncoding RNAs that are more than 200 nucleotides long. Both pyroptosis and lncRNAs are associated with tumor development and progression. This study is aimed at exploring and establishing a prognostic signature of BC based on pyroptosis-related lncRNAs. Methods. In this study, The Cancer Genome Atlas (TCGA) database provided us with the RNA sequencing transcriptome data of bladder cancer patients, and we identified differentially expressed pyroptosis-related lncRNAs in bladder cancer. Then, the prognostic significance of these lncRNAs was assessed using univariate Cox regression analysis and LASSO regression analysis. Subsequently, 4 pyroptosis-related lncRNAs, namely, AL121652.1, AL161729.4, AC007128.1, and AC124312.3, were identified by multivariate Cox regression analysis, thus constructing the prognostic risk model. Then, we compared the levels of immune infiltration, differences in cell function, immune checkpoints, and m6A-related gene expression levels between the high- and low-risk groups. Result. Patients were divided into low-risk or high-risk groups based on the median risk score. Kaplan–Meier survival analysis indicated that the overall survival of bladder cancer patients in the low-risk group was substantially superior to that in the high-risk group ( $p<0.001$ ). The receiver operating characteristic (ROC) curve further confirmed the credibility of our model. Moreover, gene set enrichment analysis (GSEA) indicated that these were different signal pathways significantly enriched between the two groups. Immune infiltration, immune checkpoint, and N6-methyladenosine-related gene analysis also reflected that there were notable differences between the two groups. Conclusion. Therefore, this prognostic risk model is based on the level of pyroptotic lncRNAs, which is conducive to individualized assessment of the risk of patients and provides a reference for clinical treatment. This will also help provide insights into the prognosis and treatment of bladder cancer.

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

Purpose

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

Patients and Methods

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

Results

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

Conclusion

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