Evaluation of pyroptosis-associated genes in endometrial cancer based on the 101- combination machine learning framework and multi-omics data

Evaluation of pyroptosis-associated genes in endometrial cancer based on the 101- combination machine learning framework and multi-omics data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of pyroptosis-associated genes in endometrial cancer based on the 101- combination machine learning framework and multi-omics data Li Juan Huang, Lin Chen, Min Tang, Shi Tong Zhan, Feng Chen, An Yi Teng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6016108/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Endometrial cancer (EC) represents a common malignancy within gynecological cancers, characterized by a notably high mortality rate. The absence of reliable prognostic biomarkers significantly impairs the effectiveness of predictive, preventive, and personalized medicine (PPPM/3PM) strategies. Pyroptosis, a distinct form of programmed cell death, has been closely linked to anti-cancer immune responses. Nonetheless, the precise role of pyroptosis in the context of EC remains elusive. Methods Pyroptosis-associated genes (PAGs) were screened in Msigdb. We used consensus clustering to classify PAGs from TCGA-UCEC into two clusters, and examined their characteristics. The Seurat package was employed to analyze significant PAGs in EC single-cell data. The mime package was utilized to screen suitable machine learning approaches and build models. A nomogram was constructed to validate the model's performance. Additionally, CIBERSORT was used to evaluate immune infiltration results, and TIDE scores from the TCIA database were applied to assess EC patients' responses to immune checkpoint therapy. Subsequently, we performed PAG-related pathway analysis in EC patients with or without response to PD-1 therapy using the CellChat module in Seurat. Finally, the OncoPredict package was used to predict drug sensitivity in EC patients. Results A consensus PAGs ("CASP3", "CHMP3", "CYCS", "GSDMD", "IRF1", and "NOD1") was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. We observed distinct biological functions and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a negative response to PD-1 in the high-risk group. Potential drugs targeting specific risk subgroups were also identified. Conclusion Our study constructed an PAGs that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in EC. Endometrial cancer Pyroptosis Risk model Immune infiltration Drug sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction With the advancement of economic development, EC has emerged as the second most prevalent gynecological malignancy worldwide, with an incidence rate that continues to rise annually. According to projections by the American Cancer Society, the United States is expected to report 67,880 new cases of EC in 2024, with an estimated 13,250 women succumbing to the disease 1 . Early diagnosis of EC is associated with a five-year survival rate of 95%, the highest among gynecological cancers. However, when diagnosed at an advanced stage, the five-year survival rate drops sharply to 14% 2 . These statistics highlight the critical importance of early diagnosis in improving patient outcomes. Therefore, identifying key proteins and elucidating the mechanisms regulating the progression of EC are crucial for advancing translational research and improving patient prognosis. Adjuvant therapy for cancer can exert antitumor effects through pyroptosis 3 . Pyroptosis is an inflammatory or immunogenic cell death pathway characterized by cellular swelling, osmotic lysis, disruption of membrane integrity, and alterations in electrochemical gradients such as calcium ion (Ca²⁺) flux. This process leads to the release of inflammatory cytokines interleukin-1β (IL-1β) and IL-18, triggering inflammatory responses 4 , 5 . Subsequently, this process facilitates the formation of antigen-specific cytotoxic T lymphocytes (CTLs) capable of recognizing tumor cells, thereby initiating the first step of the "tumor-immunity" cycle and inhibiting tumor progression 6 – 10 . However, pyroptosis is a double-edged sword for cancer treatment 11 . Pyroptosis is also associated with many adverse effects of cancer therapy, such as cytokine release syndrome in CAR T cell therapy 12 or chemotherapy drug damage to normal tissues in chemotherapy 13 . This makes it essential to further investigate the mechanisms and regulation of pyroptosis in cancer therapy. The role and underlying mechanisms of pyroptosis in endometrial cancer have yet to be comprehensively explored. Understanding these processes offers new insights and strategies for improving treatment to endometrium cancer 14 , 15 . In this study, we aimed to explore the prognostic value and regulatory role of pyroptotic genes in EC. By utilizing publicly available EC-related databases, we identified key genes associated with pyroptosis, examined their diagnostic significance, and analyzed their relationship with the immune microenvironment. This research lays a theoretical foundation for further exploration of EC and its potential therapeutic approaches. Methods Data Collection RNA sequencing (RNA-seq) data were obtained from 589 patients in the TCGA-UCEC cohort (accessed via https://portal.gdc.cancer.gov on August 11, 2024). Due to missing survival data for 17 patients, these were excluded from the study. Consequently, our analysis included mRNA expression data for 538 tumor samples and 34 normal tissue samples, along with survival outcomes and times for the corresponding 538 EC patients and 34 normal samples. Somatic mutation data, in Mutation Annotation Format (MAF), were downloaded from TCGA, and copy number variation (CNV) data for TCGA-UCEC patients were retrieved from the UCSC Xena database (accessed via https://xena.ucsc.edu/ on August 11, 2024). Acquisition of Pyroptosis-Associated Genes A list of 52 pyroptosis-associated genes (PAGs) was retrieved from the MSigDB database 16 (accessed via https://www.gsea-msigdb.org/gsea/msigdb ). The genes included in this list are: BAK1, BAX, CASP1, CASP3, CASP4, CASP5, CHMP2A, CHMP2B, CHMP3, CHMP4A, CHMP4B, CHMP4C, CHMP6, CHMP7, CYCS, ELANE, GSDMD, GSDME, GZMB, HMGB1, IL18, IL1A, IL1B, IRF1, IRF2, TP53, TP63, AIM2, CASP6, CASP8, CASP9, GPX4, GSDMA, GSDMB, GSDMC, IL6, NLRC4, NLRP1, NLRP2, NLRP3, NLRP6, NLRP7, NOD1, NOD2, PJVK, PLCG1, PRKACA, PYCARD, SCAF11, TIRAP, TNF, and GZMA. These genes were selected for further analysis in this study. Analysis of PAGs Expression and Mutation In the TCGA-UCEC dataset, the pheatmap package was utilized to generate a heatmap representing the expression of PAGs. Differences in PAGs expression between tumor and normal groups were visualized using boxplots created with the ggplot2 package. For an in-depth examination of the chromosomal distribution of PAGs, the RCircos package was employed to produce circular visualizations. Additionally, mutation data for TCGA-UCEC were downloaded and analyzed using the maftools and oncoplot packages to generate waterfall plots illustrating the mutations present in the selected PAGs. Consensus Clustering Analysis Consensus clustering is a robust resampling-based technique employed to determine subgroup memberships and validate clustering outcomes. In the present study, we leveraged the ConsensusClusterPlus package 17 in R to categorize distinct pyroptosis subtypes based on the expression profiles of pyroptosis-associated genes (PAGs). The survival disparities among these subtypes were evaluated using Kaplan-Meier (K-M) survival curves, which were generated utilizing the survminer package. Screening of PAGs by Single-Cell RNA Sequencing Analysis We acquired single-cell RNA sequencing data from the GSE173682 dataset 18 , which included samples from five EC patients. To identify the gene expression patterns associated with pyroptosis, single-cell RNA sequencing analysis was conducted from the dataset using the "Seurat" package. Clustering was performed using UMAP, and cell types were annotated based on the CellMarker 2.0 database 19 . To mitigate batch effects across the five samples, we applied the Harmony package. Cellular clusters were generated using the FindClusters and FindNeighbors functions, and visualized using the UMAP method. Finally, cells were annotated based on marker genes specific to different cell types. Construction and Evaluation of the Risk Model The TCGA-UCEC dataset was randomly partitioned into a training set and an internal validation set in a 7:3 ratio, ensuring a balanced distribution of clinical characteristics between the two cohorts. We utilized the Mime1 package to apply ten machine learning algorithms 20 , including Lasso, Ridge, stepwise Cox, CoxBoost, random survival forest (RSF), elastic net (Enet), partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling (GBM), and survival support vector machine (survival-SVM), as well as 101 potential combinations of these algorithms. Variable selection and model development were conducted within the TCGA-UCEC training dataset using a ten-fold cross-validation approach. All constructed models were subsequently validated in the TCGA internal validation set. For each model, the concordance index (C-index) was calculated for both the training and internal validation sets to evaluate predictive performance. Models were then ranked based on their average C-index. The most robust and clinically relevant algorithm combinations were selected for further investigation. Based on the median risk score, the TCGA training and internal validation sets were stratified into high-risk and low-risk groups. Kaplan-Meier (KM) survival analysis was performed using the survminer R package to determine if there were significant differences in overall survival (OS) between the high-risk and low-risk groups (log-rank test, p < 0.05). A nomogram and its calibration curve were constructed to validate the risk model. Immune Infiltration and Immunotherapy Analysis We utilized the CIBERSORT 21 package in R to quantify the proportions of 22 immune cell types in tumor samples, including seven T cell subtypes, three B cell subtypes, NK cells, and myeloid cells. The results were saved for subsequent analysis. To provide effective guidance for tumor immunotherapy, we obtained immune phenotype scores (IPS) from The Cancer Immunome Atlas (TCIA) (accessible at https://tcia.at/ ) 22 . These scores were utilized to predict responses to immune checkpoint blockade in the training cohort. Wilcoxon rank-sum tests were employed to evaluate the disparities in responses to cytotoxic T lymphocyte antigen-4 (CTLA-4) inhibitors and anti-PD-1/PD-L1 inhibitors across different risk strata. PAGS and immunotherapy for endometrial cancer GSE251923 is a dataset which indicates responders and non-responders to anti-PD-1 therapy in human EC 23 . Using the Seurat and CellChat packages, we analyzed intercellular communication and its potential connection to pyroptosis. Drug Sensitivity Analysis OncoPredict is a computational tool tailored for predicting the responsiveness of tumor patients to a range of chemotherapy and targeted therapeutic agents 24 . Leveraging patient gene expression profiles and established drug sensitivity data, it generates predictive models. In the present study, patients were stratified into distinct expression clusters (PagCluster) based on the median expression levels of prognostic genes. Subsequently, the drug sensitivity of these clusters to commonly used chemotherapy drugs was evaluated. Results Expression and Mutation Analysis of PAGs In the TCGA-UCEC dataset, we employed the pheatmap package to generate a heatmap illustrating the expression patterns of PAGs (Fig. 1 A). For visualizing the chromosomal distribution of PAGs, we utilized the RCircos package, revealing their near-ubiquitous presence across all autosomes, with notable exceptions being chromosomes 9, 10, 15, 18, 21, and 22 (Fig. 1 B). To analyze the mutational landscape of PAGs among the 338 patients with available data, we leveraged the maftools and oncoplot packages to produce waterfall plots. These plots highlighted TP53, CASP8, NLRP3 as the most frequently mutated genes, each exceeding a mutation rate of 10%. Missense mutations were the predominant type, followed by nonsense mutations (Fig. 1 C). Furthermore, using the ggplot2 package, we generated boxplots to compare PAGs expression between tumor and normal samples. Notably, expression levels of CASP4, CHMP4B, ELANE, IL-1A, and TNF were significantly higher in the tumor group (P < 0.05) (Fig. 1 D). Consensus Clustering Analysis in EC classification Consensus clustering is a widely adopted technique in the field of cancer classification. Utilizing the TCGA-UCEC dataset, we conducted a consensus clustering analysis on tumor samples based on the expression of PAGs using the ConsensusClusterPlus package. The results of this analysis were visualized through the cumulative distribution function (CDF) plot, Delta Area Plot, and matrix heatmap (Figs. 2 A-C). Our analysis demonstrated robust consistency at k = 2, resulting in the stratification of patients into two distinct subtypes, designated as pagCluster A and pagCluster B. A subsequent differential gene expression analysis between these two clusters revealed that pagCluster B exhibited significantly elevated expression levels of PAGs (Fig. 2 D). Group A represents the low pyroptosis expression group, while Group B is defined as the high pyroptosis expression group (all p < 0.05 but ELANE). To assess potential survival differences between the two identified subtypes, Kaplan-Meier (K-M) survival curves were generated using the survminer package, both groups have a high survival rate in 3 years. However, over time, the survival rates and cluster A has a slightly higher survival rate compared to cluster B throughout the entire duration of the study (though p = 0.295). (Fig. 2 E). Notably, the subtype characterized by lower PAGs expression (pagCluster A) demonstrated a higher survival rate, particularly evident beyond the 9-year mark. These findings suggest that reduced expression of pyroptosis-related genes may be associated with improved survival outcomes in endometrial cancer, implying a detrimental pyroptotic effect on patient prognosis. The pyroptosis scores of PAGs in single cells To identify the gene expression patterns associated with pyroptosis, single-cell RNA sequencing analysis was conducted on data from the PRJNA699347 dataset.The analysis was conducted using the Seurat package. Initially, quality control (QC) was performed to retain cells with mitochondrial gene content below 20% and genes expressed in at least three cells within an expression range of 500 to 6000.Subsequently, we identified 1500 highly variable genes for further analysis. Using UMAP for dimensionality reduction and cell annotation with the ACT database, we identified a total of 18 clusters, including nine types of cells: "Stem cells," "Stromal cells," "Unknown," "Epithelial cells," "Macrophages," "Smooth muscle cells," "NK cells," "Fibroblasts," and "T cells" (Fig. 3 A). To quantify the activity of PAGs in each cell and assess pyroptosis scores, we utilized the "AddModuleScore" function in Seurat. The results indicated that PAG activity was significantly higher in macrophages (0.09) and minimally expressed in stromal cells (-0.03) (Fig. 3 B-C). Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs) between cells with high and low PAG scores (p < 0.05). This analysis yielded a set of Pyroptosis-Associated Differentially Expressed Genes (PADEGs), which are listed in Table 1 . Visualization of PADEGs using UMAP revealed that the genes CASP1, TNF, GSDME, IL1A, NLRP3, IL6, NOD2, IL18 and IL1B are strongly associated with macrophages that exhibit "active" PAGs (Fig. 3 D). It could imply that these macrophages play an important role in the immune microenvironment, particularly in response to tumorigenic or inflammatory signals. The high expression of these genes (CASP1, TNF, GSDME, etc) may indicate their involvement in inflammatory or immune responses. Table 1 PADEGs in single cell Gene p_val avg_log2FC pct.1 pct.2 p_val_adj CHMP4A 0 1.37534716 0.405 0.205 0 BAX 6.143E-288 1.08899767 0.443 0.268 1.37E-283 IL1B 8.815E-283 6.54089959 0.135 0.03 1.966E-278 GSDMD 1.206E-241 1.34656643 0.258 0.12 2.689E-237 CASP8 9.941E-223 1.60427271 0.219 0.095 2.217E-218 CASP4 1.425E-202 1.14851609 0.323 0.185 3.177E-198 CASP1 3.62E-199 2.39127742 0.12 0.034 8.072E-195 IL18 8.077E-184 2.40895224 0.102 0.026 1.801E-179 PYCARD 1.154E-136 1.40846291 0.172 0.083 2.573E-132 TNF 7.81E-122 3.00916778 0.079 0.024 1.742E-117 IL1A 2.381E-117 4.43685994 0.05 0.008 5.308E-113 CASP6 8.803E-107 1.25646108 0.127 0.059 1.963E-102 GZMB 1.798E-106 3.37715548 0.052 0.011 4.01E-102 CHMP4C 1.4081E-83 1.21074597 0.106 0.05 3.1397E-79 CHMP6 2.2612E-74 1.10457859 0.118 0.062 5.0419E-70 PRKACA 3.1219E-73 1.12379997 0.112 0.057 6.9611E-69 TP53 1.6038E-67 1.0309549 0.119 0.065 3.576E-63 GSDME 2.3472E-67 1.57602404 0.059 0.022 5.2337E-63 IL6 3.6123E-62 1.98527293 0.124 0.072 8.0547E-58 GZMA 4.5224E-61 3.50867058 0.022 0.003 1.0084E-56 CASP9 1.3908E-40 1.20829617 0.048 0.021 3.1011E-36 NLRP2 6.1952E-39 1.267805 0.04 0.017 1.3814E-34 NLRP3 2.6467E-34 1.91118671 0.031 0.012 5.9017E-30 NOD2 1.5627E-24 2.07572334 0.018 0.006 3.4845E-20 NOD1 1.3482E-19 1.27444705 0.025 0.012 3.0062E-15 GSDMB 8.6712E-17 1.19040758 0.027 0.014 1.9335E-12 TIRAP 1.796E-16 1.57603085 0.02 0.009 4.0046E-12 Construction and Evaluation of a Risk Prediction Model Using the TCGA-UCEC dataset, we randomly assigned 80% of the cohort to the training set and the remaining 20% to the validation set for the purpose of identifying relevant “active” pyroptosis-associated genes. To integrate the advantages of multiple algorithms, improve prediction accuracy, robustness, and generalization ability, and reduce overfitting and underfitting, multiple predictive models were developed within the training set utilizing the Mime1 package in R. Following a rigorous evaluation in both the training and validation sets, the model that combined LASSO regression with Random Survival Forest (RSF) was selected as the optimal model. This model exhibited the highest average C-index of 0.77, with individual C-indices of 0.96 in the training set and 0.58 in the validation set (Figs. 4 A). The model is also relatively accurate in predicting one-year survival rates, with an AUC of 1 for the training set and an AUC of 0.68 for the test set, and HR > 1, indicating that, overall, high expression of PAGs is associated with poor prognosis (Figs. 4 B). We combined LASSO regression for feature selection with RSF modeling to predict patient survival outcomes. LASSO was used to identify key prognostic factors. By analyzing the relationship graph between partial likelihood deviance and the regularization parameter λ, we determined that λ = 4 is the optimal regularization parameter for the LASSO regression model, achieving the best balance between model complexity and predictive accuracy (Fig. 4 C). Based on the estimated regression coefficients for this parameter, the non-zero factors selected correspond to the genes that are significantly associated with the PAGs: "CASP3", "CHMP3", "CYCS", "GSDMD", "IRF1", "NOD1", which were then incorporated into the RSF model to estimate individual survival risk scores. Time-dependent AUC at 1-year, 3-year, and 5-year intervals was computed to assess the model's predictive accuracy, showing that the RSF model effectively discriminates between high- and low-risk groups (Fig. 4 D). Besides, to assess survival differences between high- and low-risk groups, Kaplan-Meier (K-M) survival curves were generated using the survminer package. The results clearly demonstrated that the low-risk group had significantly better long-term survival(p < 0.05) (Fig. 4 E). In order to understand the relationship between significantly associated PAGs ("CASP3," "CHMP3," "CYCS," "GSDMD," "IRF1," and "NOD1") and prognosis, we used the rms package to construct a nomogram for further evaluation of the model. CASP3, CHMP3, and NOD1 were all associated with patient survival, with CASP3 showing a negative correlation (Fig. 4 F). Additionally, the calibration curve indicated that the model had a good fit (Fig. 4 G). Immune Checkpoint Analysis and Immune Infiltration Analysis To facilitate the guiding role of pyroptosis gene scoring in EC immunotherapy, we acquired immune phenotype scores (IPS) from The Cancer Immunome Atlas (TCIA) to predict responses to immune checkpoint blockade in the training cohort. Higher IPS scores are predictive of a more favorable response to immune checkpoint inhibitor (ICI) therapy, encompassing PD-1 inhibitor and CTLA4 inhibitor therapies19. Utilizing the Wilcoxon test, we evaluated the differences in response to CTLA-4 and anti-PD-1/PD-L 1 blockade between high- and low-risk groups. Our findings revealed that the IPS was significantly higher in the low-risk group for CTLA4+/PD1- treatment, suggesting that patients in the low-risk group exhibited a more favorable response to anti-CTLA4 therapy. So the high expression of PAGs may be associated with a diminished response to PD-1 therapy (Fig. 5 A). The CIBERSORT package was employed to assess the immune infiltration status, and the correlation between PAGs and immune cells was visualized. PAGs show a negative correlation trend with the infiltration of macrophages M2 and monocytes (Figs. 5 B). The pheatmap package was used to evaluate the immune infiltration status and to visualize the correlation between immune cells and overall survival (OS), OS time (days), and risk score. Regulatory T cells (Tregs) exhibited a significant positive correlation with OS status, whereas macrophage M0 and eosinophils demonstrated a significant negative correlation with the risk score (Fig. 5 C). PAGS in immunotherapy for endometrial cancer To further investigate the role of PAGS in immunotherapy for endometrial cancer, we analyzed data from GSE251923, a single-cell dataset comparing the response to PD-1 treatment in endometrial cancer, which was designated as the partly-response group (PR) and progressive disease group (PD). After dimensionality reduction using UMAP, the PR group exhibited increased T cell infiltration compared to the PD group (Fig. 6 A). Additionally, by using the CellChat package to plot the number of interactions among the common cell types—B cells, Mast cells, Macrophages, and Epithelial cells—after achieving a partly response with PD-1, it was observed that Macrophages' communication with almost all other cells has been reduced, indicating that PD-1 may also affect the function of Macrophages (Fig. 6 B). The signaling pathways of various cells in the PR group and PD group were shown in Fig. 6 C. In macrophages, signaling pathways such as CD56 and BAFF are generally reduced. The analysis mentioned we mentioned above found that PAGs have a significant effect on the interaction in T cells and Macrophages. We performed pyroptosis scoring on cells from both groups using the "AddModuleScore" function, In the PR group, epithelial cells exhibited significantly lower pyroptosis scores, this may suggest that the relationship between epithelial cells and pyroptosis is substantial (Fig. 6 D). Drug Sensitivity Analysis To further investigate the relationship between pyroptosis-risk scores and chemotherapy response, we utilized the R package OncoPredict to compute the IC50 values for commonly used chemotherapy and targeted therapy drugs across all patients (Fig. 7 ). A lower IC50 value (µM) is indicative of higher drug efficacy. Notably, 174 drugs exhibited statistically significant differen 8 ces in IC50 values between the high- and low-risk groups (p < 0.01) (Supplementary Table 1). Subsequently, Wilcoxon tests were conducted to assess the disparities in IC50 values (p < 0.001) between the high- and low-risk groups for drugs with IC50 values less than 1. The IC50 values were transformed using a negative logarithmic scale, and the top 10 drugs were represented using a box-and-whisker plot. The results revealed that Epirubicin, Podophyllotoxin bromide, Sabutoclax, AZD8055, CDK9_5576 and Gemcitabine demonstrated favorable response efficacy. Additionally, Gemcitabine and Topotecan exhibited potential for enhanced therapeutic efficacy in the "high-risk" group (Supplementary Fig. 1). Discussion Pyroptosis, a proinflammatory form of regulated cell death, has emerged as a key factor in tumor immune evasion and treatment resistance. Pyroptosis plays a complex and context-dependent role in cancer 8 , 25 . It can promote tumor growth and metastasis through inflammation and immune evasion 7 , and it also activates antitumor immunity by recruiting immune cells and enhancing immunotherapy response 25 – 27 . This dual role of pyroptosis highlights its potential as a therapeutic target and prognostic indicator in cancer. In light of these complexities, our study aimed to bridge the gap between pyroptosis and clinical outcomes by focusing on pyroptosis-associated genes (PAGs). By leveraging advanced computational strategies, we sought to unravel the prognostic potential of these genes and their interplay with immune modulation. The shift from apoptosis to pyroptosis mediated by PD-L1 can promote tumor necrosis 28 , potentially facilitating tumor growth and hindering antitumor immunity 29 . Gao et al. found that higher GSDMD expression might contribute to tumor evasion of innate immune responses and is associated with poor prognosis in non-small cell lung cancer (NSCLC) 30 . However, another study demonstrated that GSDME acts as a tumor suppressor by activating pyroptosis and enhancing antitumor immunity 31 . Pyroptosis-induced inflammation within the tumor microenvironment (TME) can stimulate the immune system by activating immune cells and pathways, thereby improving the efficacy of cancer immunotherapy 32 . How pyroptosis functions in tumor tissues and impacts the survival of EC patients remains unclear. To identify a stable and robust prognosis signature for EC, we developed a novel computational framework incorporating ten machine learning algorithms and their 101 combinations. A signature composed of 6 PAGs—CASP3, CHMP3, CYCS, GSDMD, IRF1, and NOD1—was selected for analysis, which exhibits higher predictive accuracy and better clinical translational implications than previous studies that attempt to develop programmed cell death-related prognosis classifiers for EC 33 – 36 . Caspase-3 /GSDME pathway, acts as a “switch” determining the mode of cell death. When GSDME is highly expressed, active Caspase-3 cleaves it, leading to the formation of pores in the cell membrane and ultimately causing pyroptosis 37 ; conversely, when GSDME expression is low, Caspase-3 activation results in apoptosis 38 . Recent studies have demonstrated that 39 CHMP3 promotes the progression of hepatocellular carcinoma by inhibiting caspase-1-dependent pyroptosis. Additionally, CHMP3 is also one of the pyroptosis-related prognostic feature genes in breast cancer 40 , multiple myeloma 41 , and pediatric acute myeloid leukemia 42 , which indicates its great value in predicting the prognosis of various tumors. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin especially in GSDMD 13 . In head and neck cancer, researchers discovered that CTLA-4 blockade in CD8(+) T cells of head and neck squamous cell carcinoma induces pyroptosis through the STAT1/IRF1 axis 43 . Thus the PAGs allows for the stratification of patient risk, which has significant implication to improve patient prognosis. We used the PAGs to perform risk stratification for EC patients, and analyzed their response to immunotherapy and their sensitivity to new sensitive drugs for EC, including Polo-like kinase 1 inhibitors (PLK1), Topotecan etc. These findings provide rational guidance for administering immunotherapy and chemotherapy in clinical practice, which represents an important step towards more effective personalized medicine approaches. Furthermore, unlike previous studies that focused solely on the prognostic value of the signature, we performed a comprehensive multi-omics analysis, including genome, single-cell transcriptome, and bulk transcriptome, to gain a deeper understanding of the PAGs. These findings provide biological evidence and explanation for the PAGs in guiding personalized medicine approaches. Drug resistance is a common occurrence due to the highly dynamic and heterogeneous nature of the tumor microenvironment. Therefore, it is crucial to identify populations that are sensitive to specific small molecule drugs in order to improve therapeutic efficacy and prognosis in EC patients. In this study, we analyzed the sensitivity of PAGs risk subgroups to 138 drugs. We observed significant differences in the IC50 values of these small molecule drugs between the PAGs risk subgroups. Specifically, Gemcitabine, Topotecan exhibited lower IC50 values in the high-risk group, while epirubicin and sabutoclax had lower IC50 values in the low-risk group. This may provide alternative treatment options for patients with endometrial cancer who are resistant to first-line therapy. Our study highlights the role of PAGs in guiding targeted prevention and personalized medicine for EC, which may aid to improve patient outcomes and reduce unnecessary treatment costs. Overall, the PAGs can serve as a promising tool to provide critical information for clinicians in personalized medicine selection. However, in some studies, GSDMD-triggered pyroptosis inhibits the growth of subcutaneous EC xenografts in nude mice, revealing for the first time that GSDMD-mediated pyroptosis is a crucial pathway for suppressing tumor growth in endometrial cancer 44 . So more samples should be evaluated, in vitro and in vivo studies are required to elucidate the biological functions of PAGs in EC. Additional large-scale, multi-center prospective study is necessary to further confirm our findings. Lastly, although we predicted the sensitivity of PAGs risk subgroups to various small molecule drugs, in vitro drug assays and clinical trials are required to validate our predictions. Conclusion In this study, we employed the 101-type machine learning framework to evaluate pyroptosis-related features in endometrial cancer and explore their potential as clinical biomarkers. By integrating multi-omics data, including gene expression profiles, mutation data, etc, we developed a machine learning-based predictive model to identify key genes associated with pyroptosis in EC. Using this model, we performed prognostic analyses across various clinical subtypes and immune phenotypes of EC, revealing that specific pyroptosis-related genes are strongly correlated with patient survival and responses to immunotherapy. Additionally, we validated the expression patterns of these genes in tumor tissues and performed internal validation using the TCGA database. Our results suggest that pyroptosis-related genes may serve as valuable prognostic biomarkers in EC. This study provides new molecular targets for the personalized treatment and immunotherapy of endometrial cancer, holding significant promise for clinical application. Declarations Clinical trial number Not applicable. Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this article. Consent for publication: Not applicable. Funding This work was supported by Medical and health projects of scientific and technological breakthrough in Songjiang District (2023SJKWGG080), National Natural Science Foundation of China (82472727), Natural Science Foundation of Shanghai Municipality (23ZR1451600). Author Contribution HLJ, CL and YY analyzed and interpreted the data and drafted the initial manuscript. SWL, ZLN, and YY revised the manuscript. ZST, CF, TAY and TM collected information. HLJ, CL, SWL, ZLN and YY worked equally as major contributors in writing the manuscript. All authors read and approved the final manuscript. References Siegel, R.L., Giaquinto, A.N. & Jemal, A. Cancer statistics, 2024. CA: a cancer journal for clinicians 74, 12–49 (2024). Omozuwa, E. & Atoe, K.J.I.J.o.F.M.I. 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Pyroptosis relates to tumor microenvironment remodeling and prognosis: A pan-cancer perspective. Frontiers in immunology 13, 1062225 (2022). Hou, J., et al. PD-L1-mediated gasdermin C expression switches apoptosis to pyroptosis in cancer cells and facilitates tumour necrosis. Nature cell biology 22, 1264–1275 (2020). Vakkila, J. & Lotze, M.T.J.N.R.I. Inflammation and necrosis promote tumour growth. 4, 641–648 (2004). Gao, J., et al. Downregulation of GSDMD attenuates tumor proliferation via the intrinsic mitochondrial apoptotic pathway and inhibition of EGFR/Akt signaling and predicts a good prognosis in non–small cell lung cancer. 40, 1971–1984 (2018). Zhang, Z., et al. Gasdermin E suppresses tumour growth by activating anti-tumour immunity. 579, 415–420 (2020). Minton, K.J.N.R.I. Pyroptosis heats tumour immunity. 20, 274–275 (2020). Bhardwaj, V., et al. Machine Learning for Endometrial Cancer Prediction and Prognostication. Frontiers in oncology 12, 852746 (2022). Liu, X., et al. Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms. Molecular therapy. Nucleic acids 35, 102155 (2024). Praiss, A.M., et al. Using machine learning to create prognostic systems for endometrial cancer. Gynecologic oncology 159, 744–750 (2020). Shazly, S.A., et al. Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics 161, 760–768 (2023). Hu, L., et al. Chemotherapy-induced pyroptosis is mediated by BAK/BAX-caspase-3-GSDME pathway and inhibited by 2-bromopalmitate. Cell death & disease 11, 281 (2020). Jiang, M., Qi, L., Li, L. & Li, Y. The caspase-3/GSDME signal pathway as a switch between apoptosis and pyroptosis in cancer. Cell death discovery 6, 112 (2020). Zheng, Y., et al. CHMP3 promotes the progression of hepatocellular carcinoma by inhibiting caspase–1–dependent pyroptosis. International journal of oncology 64(2024). Zhou, Y., Zheng, J., Bai, M., Gao, Y. & Lin, N. Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer. Frontiers in oncology 12, 948169 (2022). Li, C., Liang, H., Bian, S., Hou, X. & Ma, Y. Construction of a Prognosis Model of the Pyroptosis-Related Gene in Multiple Myeloma and Screening of Core Genes. ACS omega 7, 34608–34620 (2022). He, X., Jiang, Y., Yu, X., He, F. & Gao, H. A Gene Signature Comprising Seven Pyroptosis-Related Genes Predicts Prognosis in Pediatric Patients with Acute Myeloid Leukemia. Acta haematologica 145, 627–641 (2022). Wang, S., et al. CTLA-4 blockade induces tumor pyroptosis via CD8(+) T cells in head and neck squamous cell carcinoma. Molecular therapy: the journal of the American Society of Gene Therapy 31, 2154–2168 (2023). Yang, Y., et al. Hydrogen inhibits endometrial cancer growth via a ROS/NLRP3/caspase-1/GSDMD-mediated pyroptotic pathway. BMC Cancer 20, 28 (2020). Additional Declarations No competing interests reported. Supplementary Files S1.bmp Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6016108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415887874,"identity":"c52b99d9-3217-4a06-965e-d0849d78a1d6","order_by":0,"name":"Li Juan Huang","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"Juan","lastName":"Huang","suffix":""},{"id":415887875,"identity":"bfa2455a-cdfc-4b00-a841-3bede227a5f5","order_by":1,"name":"Lin Chen","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Chen","suffix":""},{"id":415887876,"identity":"debdfe29-fba4-4e27-b96c-003d9ab3e1ac","order_by":2,"name":"Min Tang","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Tang","suffix":""},{"id":415887877,"identity":"c1869118-29f6-45c4-a044-e88763dd4d3a","order_by":3,"name":"Shi Tong Zhan","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shi","middleName":"Tong","lastName":"Zhan","suffix":""},{"id":415887878,"identity":"27c27a6a-1a95-40eb-94a6-e8c2491abe70","order_by":4,"name":"Feng Chen","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Chen","suffix":""},{"id":415887879,"identity":"96fb5f56-14c1-42d6-983d-f1700ddf695a","order_by":5,"name":"An Yi Teng","email":"","orcid":"","institution":"Shanghai Songjiang District Maternal and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"An","middleName":"Yi","lastName":"Teng","suffix":""},{"id":415887880,"identity":"9fd23245-6465-46f9-a760-173d69d1d6b7","order_by":6,"name":"Wei Lin Sang","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"Lin","lastName":"Sang","suffix":""},{"id":415887881,"identity":"90c910ef-781a-4f1d-9716-4ffe8e9664dd","order_by":7,"name":"Li Na Zhou","email":"","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"Na","lastName":"Zhou","suffix":""},{"id":415887882,"identity":"191ee45d-976e-4f25-8034-f879a44b4653","order_by":8,"name":"Ye Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIie3LsQqCUBTG8SPBbcnmI1H2CEZjL3NdctG5xUQoblO70NArODYawnVxjhsIObkFrg1B9QQdt6D7n77h+wHodD/cBBBYNzLvTtyYTJwiZvg4rb3jYXepIawIpMyYtS+LIK3KlQOyIRDFGZpCBin6SzTinEBuNbOeQnp2QiYK2MgUIQflSRqxSnezGItsliqfIZcEMizy8/UuIttOvAbbkECmmbFFgM9z4AD/DgDsGHotQPSe/ZoCdDqd7g97AY6gPmrbRyHPAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-02-12 14:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6016108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6016108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76495153,"identity":"e32f04af-69e4-420f-b610-a36768a5878d","added_by":"auto","created_at":"2025-02-17 17:58:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":370633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of PAGs in Endometrial Cancer.\u003c/strong\u003e\u003cbr\u003e\n(A) Heatmap of PAGs expression. The blue bar above represents tumors, while the red bar represents normal tissues. PAGs exhibit medium to high expression in both normal endometrium and endometrial cancer.\u003c/p\u003e\n\u003cp\u003e(B) Chromosomal distribution of PAGs.\u003cbr\u003e\n(C) Mutation landscape of PAGs in endometrial cancer. TMB distribution plot illustrating the genetic alterations in 518 samples, with 330 (63.71%) showing mutated genes. The x-axis represents different gene names, while the y-axis indicates the number of alterations for each gene. The bar chart, with various colors, depicts different types of mutations, including missense mutations, frameshift insertions/deletions, nonsense mutations, etc. The percentage bar chart on the right displays the proportion of samples with each type of mutation.\u003c/p\u003e\n\u003cp\u003eColor code: Blue: Missense Mutations; Orange: Frameshift Insertions/Deletions; Pink: Frameshift Substitutions; Red: Non-frameshift Insertions; Green: Nonsense Mutations; Gray: Non-stop Codon Mutationsormal tissues.\u003cbr\u003e\n(D) Differential expression of PAGs between tumor and normal tissues. Red represents tumor tissue, and blue represents non-tumor tissue; CASP4, CHMP4B, IL-1A, and TNF are significantly overexpressed in tumors, while ELANE is more highly expressed in normal tissues.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/a940759bff15297f7d00daf5.jpg"},{"id":76495278,"identity":"d33c9b06-f2d5-467d-917f-3bb7941171c0","added_by":"auto","created_at":"2025-02-17 18:06:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus Clustering Analysis of TCGA-UCEC Tumor Samples\u003c/strong\u003e\u003cbr\u003e\n(A) Cumulative Distribution Function (CDF) plot of consensus clustering. It describes the changing trends of the CDF of the consensus index under different \u003cstrong\u003ek\u003c/strong\u003e values. The colors from red to pink represent K values ranging from 2 to 9. As \u003cstrong\u003ek\u003c/strong\u003e increases, the consensus index shows noticeable changes.\u003cbr\u003e\n(B) Delta Area plot.The X-axis represents the \u003cstrong\u003ek\u003c/strong\u003e values, while the Y-axis shows the relative Delta area. As \u003cstrong\u003ek\u003c/strong\u003e increases, the Delta area gradually decreases.\u003cbr\u003e\n(C) A consensus matrix heatmap is shown for \u003cstrong\u003ek\u003c/strong\u003e = 2.\u003cbr\u003e\n(D) Differential gene expression between different pagClusters. The X-axis represents the names of different genes, and the Y-axis represents the expression level of the gene. The blue box represents the expression level of cluster A, and the red box represents the expression level of cluster B.\u003cbr\u003e\n(E) Kaplan-Meier survival analysis of different pagClusters. Cluster A shows a better survival rate in the long term.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/8844200676d303b0175306c2.jpg"},{"id":76495157,"identity":"0d2ab43e-81e5-47b8-8de7-cadcea461f9f","added_by":"auto","created_at":"2025-02-17 17:58:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":235862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePAGs Scoring and Distribution in Endometrial Cancer Single Cells\u003c/strong\u003e\u003cbr\u003e\n(A) Clustering and annotation of single cells in endometrial cancer.\u003cbr\u003e\n(B-C) PAGs activity scores across single cells. PAGs were scored by “AddModuleScore” function (B) and visualized using violin plots (C). Macrophages exhibit a high PAG score.\u003cbr\u003e\n(D) Expression distribution of PADEGs2. CASP1, TNF, GSDME, IL1B, IL18, IL1A, NLRP3, NOD2 and IL6 showed aggregation in macrophages.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/fe1a6dc74fcdbe6855fd382d.jpg"},{"id":76495168,"identity":"b62a468f-c09c-4d78-8925-f35a16ffdf26","added_by":"auto","created_at":"2025-02-17 17:58:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the Risk Model\u003c/strong\u003e\u003cbr\u003e\n(A) C-index values of 101 machine learning models. LASSO+RSF showed the highest C-index (0.77 in average, framed in red). \u003cbr\u003e\n(B) AUC at 1-year values of 101 machine learning models. LASSO+RSF showed the highest AUC (0.84 in average, framed in red).\u003c/p\u003e\n\u003cp\u003e(C) Genes selected based on LASSO regression. The x-axis represents the Log(λ) value, and the y-axis represents the partial likelihood deviation. The red dotted line represents the coefficient change path corresponding to each feature in the LASSO regression; the gray dashed line represents the performance of the model under different compression levels. When the Log(λ) value increases, the coefficients of most features tend to be stable or close to zero, which indicates that these features contribute less to the final model. At the same time, the cross-validation mean square error reaches the lowest value at the inflection point, indicating that the model here has good generalization ability.\u003c/p\u003e\n\u003cp\u003e(D) 1-year, 3-year and 5-year ROC of the best model (LASSO+RSF). (represented by red, green, and blue respectively)\u003c/p\u003e\n\u003cp\u003e(E) Kaplan-Meier curve of the COX model. Divided by the median, red represents the high-risk group and green represents the low-risk group. The low-risk group enjoys a higher survival rate. (p=0.039)\u003c/p\u003e\n\u003cp\u003e(F) A constructed nomogram for prognostic prediction of a patient with EC. The patient was 58 years old and received pharmaceutical therapy. Density plot of total points shows their distribution. The importance of each variable was ranked according to the standard deviation along nomogram scales. To use the nomogram, the specific points (black dots) of individual patients are located on each variable axis. Red lines and dots are drawn upward to determine the points received by each variable; the sum (270) of these points is located on the Total Points axis, and a line is drawn downward to the survival axes to determine the probability of 1-year (95.2%), 5‐ year (82.5%) and 10‐year (52.3%) overall survival.\u003c/p\u003e\n\u003cp\u003e(G) Calibration curves of the nomograms. Calibration curves of 1--year overall survival for UCEC patients in training cohort (the top) and internal validation cohort (the bottom). The dotted line represents the ideal reference line, where the predicted probability would match the observed survival rate. The red dots are calculated and represent the nomogram performance. The blue line represents the confidence interval of the predicted result. The closer the red solid dots are to the dashed line, and the shorter the blue line, the higher the accuracy of the model’s prediction of overall survival time.\u003c/p\u003e\n\u003cp\u003e*:p\u0026lt;0.05;**: p\u0026lt;0.01;***:p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/615141efa34446214e5058cf.jpg"},{"id":76495709,"identity":"342cb48a-3e0a-4720-9699-61220e5703dd","added_by":"auto","created_at":"2025-02-17 18:14:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":235304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Checkpoint Analysis and immune Infiltration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) IPS scores in different risk groups. Red represents high-risk group, and blue represents low-risk group. The high-risk group showed a negative predictive result for PD-1 treatment.\u003c/p\u003e\n\u003cp\u003e(B) The correlation between PAGs and immune cells. Macrophages M2 and monocytes show a negative correlation significantly with PAGs (framed in black). \u003cbr\u003e\n(C) Immune cell infiltration and survival, risk score. \u003cbr\u003e\n*:p\u0026lt;0.05;**: p\u0026lt;0.01;***:p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/3523d43abccdb7ae83bcab0e.jpg"},{"id":76495279,"identity":"fdb9f6af-334c-47c0-9f15-452aee2caf1d","added_by":"auto","created_at":"2025-02-17 18:06:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":249342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell analysis of the relationship between anti-PD-1 therapy, immunity, and pyroptosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The umap plot of celltypes in the PD and PR group.UMAP visualization results show the distribution of different cell types in two dimensions. The PR group includes B cells, epithelial cells, macrophages, mast cells, progenitor cells, smooth muscle cells, and unknown cells. The PD group contains B cells, epithelial cells, macrophages, mast cells, progenitor cells, T cells, trophoblast cells, and unknown cells. The legend lists the corresponding colors for each cell type.\u003c/p\u003e\n\u003cp\u003e(B) Interactions of same cells in the two groups. Blue represents Mast cells, green represents Macrophages, pink represents B cells, and purple represents Epithelial cells. The numbers represent the number of interactions.\u003c/p\u003e\n\u003cp\u003e(C) Overall signal pantterns of the two groups.\u003c/p\u003e\n\u003cp\u003e(D) Pyroptosis scores in each celltype. The score of each cell type is presented in the form of a scatter plot, with the x-axis representing the cell type and the y-axis representing the score value.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/c3fbf820c435a27e877bfbc5.jpg"},{"id":76495169,"identity":"62bf00aa-5e3b-474e-b6e6-57d8b9174181","added_by":"auto","created_at":"2025-02-17 17:58:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":263752,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis in EC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the RS group, the color scheme differentiates the low-risk cohort with green and the high-risk cohort with red. The transition in IC50 values from low to high is depicted through a gradient ranging from blue to red. The visualization clearly indicates that the IC50 for the low-risk group is inferior to that of the high-risk group, suggesting that a lower expression of PAGs may confer increased sensitivity to chemotherapeutic agents.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/1fceb1135956ba064c2ba2f8.jpg"},{"id":76496606,"identity":"4692e9f0-0308-4da1-8e1e-18b8c4135b24","added_by":"auto","created_at":"2025-02-17 18:30:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2636875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/43ed8f0d-76d9-4405-a0be-7b2ac813ae54.pdf"},{"id":76495154,"identity":"2b22d03e-6af5-4dfc-a584-74a6a4a6838e","added_by":"auto","created_at":"2025-02-17 17:58:24","extension":"bmp","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":356446,"visible":true,"origin":"","legend":"","description":"","filename":"S1.bmp","url":"https://assets-eu.researchsquare.com/files/rs-6016108/v1/47a237a410db63bc6431a03a.bmp"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of pyroptosis-associated genes in endometrial cancer based on the 101- combination machine learning framework and multi-omics data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the advancement of economic development, EC has emerged as the second most prevalent gynecological malignancy worldwide, with an incidence rate that continues to rise annually. According to projections by the American Cancer Society, the United States is expected to report 67,880 new cases of EC in 2024, with an estimated 13,250 women succumbing to the disease\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early diagnosis of EC is associated with a five-year survival rate of 95%, the highest among gynecological cancers. However, when diagnosed at an advanced stage, the five-year survival rate drops sharply to 14%\u003csup\u003e2\u003c/sup\u003e. These statistics highlight the critical importance of early diagnosis in improving patient outcomes. Therefore, identifying key proteins and elucidating the mechanisms regulating the progression of EC are crucial for advancing translational research and improving patient prognosis.\u003c/p\u003e \u003cp\u003eAdjuvant therapy for cancer can exert antitumor effects through pyroptosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Pyroptosis is an inflammatory or immunogenic cell death pathway characterized by cellular swelling, osmotic lysis, disruption of membrane integrity, and alterations in electrochemical gradients such as calcium ion (Ca\u0026sup2;⁺) flux. This process leads to the release of inflammatory cytokines interleukin-1β (IL-1β) and IL-18, triggering inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Subsequently, this process facilitates the formation of antigen-specific cytotoxic T lymphocytes (CTLs) capable of recognizing tumor cells, thereby initiating the first step of the \"tumor-immunity\" cycle and inhibiting tumor progression \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, pyroptosis is a double-edged sword for cancer treatment \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Pyroptosis is also associated with many adverse effects of cancer therapy, such as cytokine release syndrome in CAR T cell therapy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e or chemotherapy drug damage to normal tissues in chemotherapy\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This makes it essential to further investigate the mechanisms and regulation of pyroptosis in cancer therapy. The role and underlying mechanisms of pyroptosis in endometrial cancer have yet to be comprehensively explored. Understanding these processes offers new insights and strategies for improving treatment to endometrium cancer\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this study, we aimed to explore the prognostic value and regulatory role of pyroptotic genes in EC. By utilizing publicly available EC-related databases, we identified key genes associated with pyroptosis, examined their diagnostic significance, and analyzed their relationship with the immune microenvironment. This research lays a theoretical foundation for further exploration of EC and its potential therapeutic approaches.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003eRNA sequencing (RNA-seq) data were obtained from 589 patients in the TCGA-UCEC cohort (accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e on August 11, 2024). Due to missing survival data for 17 patients, these were excluded from the study. Consequently, our analysis included mRNA expression data for 538 tumor samples and 34 normal tissue samples, along with survival outcomes and times for the corresponding 538 EC patients and 34 normal samples. Somatic mutation data, in Mutation Annotation Format (MAF), were downloaded from TCGA, and copy number variation (CNV) data for TCGA-UCEC patients were retrieved from the UCSC Xena database (accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e on August 11, 2024).\u003c/p\u003e \u003cp\u003eAcquisition of Pyroptosis-Associated Genes\u003c/p\u003e \u003cp\u003eA list of 52 pyroptosis-associated genes (PAGs) was retrieved from the MSigDB database \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (accessed via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The genes included in this list are: BAK1, BAX, CASP1, CASP3, CASP4, CASP5, CHMP2A, CHMP2B, CHMP3, CHMP4A, CHMP4B, CHMP4C, CHMP6, CHMP7, CYCS, ELANE, GSDMD, GSDME, GZMB, HMGB1, IL18, IL1A, IL1B, IRF1, IRF2, TP53, TP63, AIM2, CASP6, CASP8, CASP9, GPX4, GSDMA, GSDMB, GSDMC, IL6, NLRC4, NLRP1, NLRP2, NLRP3, NLRP6, NLRP7, NOD1, NOD2, PJVK, PLCG1, PRKACA, PYCARD, SCAF11, TIRAP, TNF, and GZMA. These genes were selected for further analysis in this study.\u003c/p\u003e \u003cp\u003eAnalysis of PAGs Expression and Mutation\u003c/p\u003e \u003cp\u003eIn the TCGA-UCEC dataset, the pheatmap package was utilized to generate a heatmap representing the expression of PAGs. Differences in PAGs expression between tumor and normal groups were visualized using boxplots created with the ggplot2 package. For an in-depth examination of the chromosomal distribution of PAGs, the RCircos package was employed to produce circular visualizations. Additionally, mutation data for TCGA-UCEC were downloaded and analyzed using the maftools and oncoplot packages to generate waterfall plots illustrating the mutations present in the selected PAGs.\u003c/p\u003e \u003cp\u003eConsensus Clustering Analysis\u003c/p\u003e \u003cp\u003eConsensus clustering is a robust resampling-based technique employed to determine subgroup memberships and validate clustering outcomes. In the present study, we leveraged the ConsensusClusterPlus package\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e in R to categorize distinct pyroptosis subtypes based on the expression profiles of pyroptosis-associated genes (PAGs). The survival disparities among these subtypes were evaluated using Kaplan-Meier (K-M) survival curves, which were generated utilizing the survminer package.\u003c/p\u003e \u003cp\u003eScreening of PAGs by Single-Cell RNA Sequencing Analysis\u003c/p\u003e \u003cp\u003eWe acquired single-cell RNA sequencing data from the GSE173682 dataset\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which included samples from five EC patients. To identify the gene expression patterns associated with pyroptosis, single-cell RNA sequencing analysis was conducted from the dataset using the \"Seurat\" package. Clustering was performed using UMAP, and cell types were annotated based on the CellMarker 2.0 database\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To mitigate batch effects across the five samples, we applied the Harmony package. Cellular clusters were generated using the FindClusters and FindNeighbors functions, and visualized using the UMAP method. Finally, cells were annotated based on marker genes specific to different cell types.\u003c/p\u003e \u003cp\u003eConstruction and Evaluation of the Risk Model\u003c/p\u003e \u003cp\u003eThe TCGA-UCEC dataset was randomly partitioned into a training set and an internal validation set in a 7:3 ratio, ensuring a balanced distribution of clinical characteristics between the two cohorts. We utilized the Mime1 package to apply ten machine learning algorithms\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, including Lasso, Ridge, stepwise Cox, CoxBoost, random survival forest (RSF), elastic net (Enet), partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling (GBM), and survival support vector machine (survival-SVM), as well as 101 potential combinations of these algorithms. Variable selection and model development were conducted within the TCGA-UCEC training dataset using a ten-fold cross-validation approach. All constructed models were subsequently validated in the TCGA internal validation set.\u003c/p\u003e \u003cp\u003eFor each model, the concordance index (C-index) was calculated for both the training and internal validation sets to evaluate predictive performance. Models were then ranked based on their average C-index. The most robust and clinically relevant algorithm combinations were selected for further investigation. Based on the median risk score, the TCGA training and internal validation sets were stratified into high-risk and low-risk groups. Kaplan-Meier (KM) survival analysis was performed using the survminer R package to determine if there were significant differences in overall survival (OS) between the high-risk and low-risk groups (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A nomogram and its calibration curve were constructed to validate the risk model.\u003c/p\u003e \u003cp\u003eImmune Infiltration and Immunotherapy Analysis\u003c/p\u003e \u003cp\u003eWe utilized the CIBERSORT\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e package in R to quantify the proportions of 22 immune cell types in tumor samples, including seven T cell subtypes, three B cell subtypes, NK cells, and myeloid cells. The results were saved for subsequent analysis.\u003c/p\u003e \u003cp\u003eTo provide effective guidance for tumor immunotherapy, we obtained immune phenotype scores (IPS) from The Cancer Immunome Atlas (TCIA) (accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/\u003c/span\u003e\u003cspan address=\"https://tcia.at/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e. These scores were utilized to predict responses to immune checkpoint blockade in the training cohort. Wilcoxon rank-sum tests were employed to evaluate the disparities in responses to cytotoxic T lymphocyte antigen-4 (CTLA-4) inhibitors and anti-PD-1/PD-L1 inhibitors across different risk strata.\u003c/p\u003e \u003cp\u003ePAGS and immunotherapy for endometrial cancer\u003c/p\u003e \u003cp\u003eGSE251923 is a dataset which indicates responders and non-responders to anti-PD-1 therapy in human EC\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Using the Seurat and CellChat packages, we analyzed intercellular communication and its potential connection to pyroptosis.\u003c/p\u003e \u003cp\u003eDrug Sensitivity Analysis\u003c/p\u003e \u003cp\u003eOncoPredict is a computational tool tailored for predicting the responsiveness of tumor patients to a range of chemotherapy and targeted therapeutic agents\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Leveraging patient gene expression profiles and established drug sensitivity data, it generates predictive models. In the present study, patients were stratified into distinct expression clusters (PagCluster) based on the median expression levels of prognostic genes. Subsequently, the drug sensitivity of these clusters to commonly used chemotherapy drugs was evaluated.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003eExpression and Mutation Analysis of PAGs\u003c/p\u003e \u003cp\u003eIn the TCGA-UCEC dataset, we employed the pheatmap package to generate a heatmap illustrating the expression patterns of PAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For visualizing the chromosomal distribution of PAGs, we utilized the RCircos package, revealing their near-ubiquitous presence across all autosomes, with notable exceptions being chromosomes 9, 10, 15, 18, 21, and 22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). To analyze the mutational landscape of PAGs among the 338 patients with available data, we leveraged the maftools and oncoplot packages to produce waterfall plots. These plots highlighted TP53, CASP8, NLRP3 as the most frequently mutated genes, each exceeding a mutation rate of 10%. Missense mutations were the predominant type, followed by nonsense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, using the ggplot2 package, we generated boxplots to compare PAGs expression between tumor and normal samples. Notably, expression levels of CASP4, CHMP4B, ELANE, IL-1A, and TNF were significantly higher in the tumor group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsensus Clustering Analysis in EC classification\u003c/p\u003e \u003cp\u003eConsensus clustering is a widely adopted technique in the field of cancer classification. Utilizing the TCGA-UCEC dataset, we conducted a consensus clustering analysis on tumor samples based on the expression of PAGs using the ConsensusClusterPlus package. The results of this analysis were visualized through the cumulative distribution function (CDF) plot, Delta Area Plot, and matrix heatmap (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Our analysis demonstrated robust consistency at k\u0026thinsp;=\u0026thinsp;2, resulting in the stratification of patients into two distinct subtypes, designated as pagCluster A and pagCluster B. A subsequent differential gene expression analysis between these two clusters revealed that pagCluster B exhibited significantly elevated expression levels of PAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Group A represents the low pyroptosis expression group, while Group B is defined as the high pyroptosis expression group (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 but ELANE). To assess potential survival differences between the two identified subtypes, Kaplan-Meier (K-M) survival curves were generated using the survminer package, both groups have a high survival rate in 3 years. However, over time, the survival rates and cluster A has a slightly higher survival rate compared to cluster B throughout the entire duration of the study (though p\u0026thinsp;=\u0026thinsp;0.295). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notably, the subtype characterized by lower PAGs expression (pagCluster A) demonstrated a higher survival rate, particularly evident beyond the 9-year mark. These findings suggest that reduced expression of pyroptosis-related genes may be associated with improved survival outcomes in endometrial cancer, implying a detrimental pyroptotic effect on patient prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe pyroptosis scores of PAGs in single cells\u003c/p\u003e \u003cp\u003eTo identify the gene expression patterns associated with pyroptosis, single-cell RNA sequencing analysis was conducted on data from the PRJNA699347 dataset.The analysis was conducted using the Seurat package. Initially, quality control (QC) was performed to retain cells with mitochondrial gene content below 20% and genes expressed in at least three cells within an expression range of 500 to 6000.Subsequently, we identified 1500 highly variable genes for further analysis. Using UMAP for dimensionality reduction and cell annotation with the ACT database, we identified a total of 18 clusters, including nine types of cells: \"Stem cells,\" \"Stromal cells,\" \"Unknown,\" \"Epithelial cells,\" \"Macrophages,\" \"Smooth muscle cells,\" \"NK cells,\" \"Fibroblasts,\" and \"T cells\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). To quantify the activity of PAGs in each cell and assess pyroptosis scores, we utilized the \"AddModuleScore\" function in Seurat. The results indicated that PAG activity was significantly higher in macrophages (0.09) and minimally expressed in stromal cells (-0.03) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs) between cells with high and low PAG scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This analysis yielded a set of Pyroptosis-Associated Differentially Expressed Genes (PADEGs), which are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVisualization of PADEGs using UMAP revealed that the genes CASP1, TNF, GSDME, IL1A, NLRP3, IL6, NOD2, IL18 and IL1B are strongly associated with macrophages that exhibit \"active\" PAGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). It could imply that these macrophages play an important role in the immune microenvironment, particularly in response to tumorigenic or inflammatory signals. The high expression of these genes (CASP1, TNF, GSDME, etc) may indicate their involvement in inflammatory or immune responses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePADEGs in single cell\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep_val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eavg_log2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epct.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epct.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_val_adj\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHMP4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37534716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.143E-288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08899767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37E-283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.815E-283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54089959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.966E-278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSDMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.206E-241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34656643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.689E-237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCASP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.941E-223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60427271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.217E-218\u003c/p\u003e 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align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.072E-195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.077E-184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.40895224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.801E-179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePYCARD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.154E-136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40846291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.573E-132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.81E-122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00916778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e 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align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2612E-74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10457859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.0419E-70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRKACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1219E-73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12379997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9611E-69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6038E-67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0309549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.576E-63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSDME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20829617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1011E-36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1952E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.267805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3814E-34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6467E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91118671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.9017E-30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5627E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.07572334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4845E-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3482E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.27444705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0062E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSDMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.6712E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19040758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9335E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.796E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.57603085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0046E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConstruction and Evaluation of a Risk Prediction Model\u003c/p\u003e \u003cp\u003eUsing the TCGA-UCEC dataset, we randomly assigned 80% of the cohort to the training set and the remaining 20% to the validation set for the purpose of identifying relevant \u0026ldquo;active\u0026rdquo; pyroptosis-associated genes. To integrate the advantages of multiple algorithms, improve prediction accuracy, robustness, and generalization ability, and reduce overfitting and underfitting, multiple predictive models were developed within the training set utilizing the Mime1 package in R. Following a rigorous evaluation in both the training and validation sets, the model that combined LASSO regression with Random Survival Forest (RSF) was selected as the optimal model. This model exhibited the highest average C-index of 0.77, with individual C-indices of 0.96 in the training set and 0.58 in the validation set (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The model is also relatively accurate in predicting one-year survival rates, with an AUC of 1 for the training set and an AUC of 0.68 for the test set, and HR\u0026thinsp;\u0026gt;\u0026thinsp;1, indicating that, overall, high expression of PAGs is associated with poor prognosis (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe combined LASSO regression for feature selection with RSF modeling to predict patient survival outcomes. LASSO was used to identify key prognostic factors. By analyzing the relationship graph between partial likelihood deviance and the regularization parameter λ, we determined that λ\u0026thinsp;=\u0026thinsp;4 is the optimal regularization parameter for the LASSO regression model, achieving the best balance between model complexity and predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Based on the estimated regression coefficients for this parameter, the non-zero factors selected correspond to the genes that are significantly associated with the PAGs: \"CASP3\", \"CHMP3\", \"CYCS\", \"GSDMD\", \"IRF1\", \"NOD1\", which were then incorporated into the RSF model to estimate individual survival risk scores. Time-dependent AUC at 1-year, 3-year, and 5-year intervals was computed to assess the model's predictive accuracy, showing that the RSF model effectively discriminates between high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Besides, to assess survival differences between high- and low-risk groups, Kaplan-Meier (K-M) survival curves were generated using the survminer package. The results clearly demonstrated that the low-risk group had significantly better long-term survival(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eIn order to understand the relationship between significantly associated PAGs (\"CASP3,\" \"CHMP3,\" \"CYCS,\" \"GSDMD,\" \"IRF1,\" and \"NOD1\") and prognosis, we used the rms package to construct a nomogram for further evaluation of the model. CASP3, CHMP3, and NOD1 were all associated with patient survival, with CASP3 showing a negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Additionally, the calibration curve indicated that the model had a good fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eImmune Checkpoint Analysis and Immune Infiltration Analysis\u003c/p\u003e \u003cp\u003eTo facilitate the guiding role of pyroptosis gene scoring in EC immunotherapy, we acquired immune phenotype scores (IPS) from The Cancer Immunome Atlas (TCIA) to predict responses to immune checkpoint blockade in the training cohort. Higher IPS scores are predictive of a more favorable response to immune checkpoint inhibitor (ICI) therapy, encompassing PD-1 inhibitor and CTLA4 inhibitor therapies19. Utilizing the Wilcoxon test, we evaluated the differences in response to CTLA-4 and anti-PD-1/PD-L 1 blockade between high- and low-risk groups. Our findings revealed that the IPS was significantly higher in the low-risk group for CTLA4+/PD1- treatment, suggesting that patients in the low-risk group exhibited a more favorable response to anti-CTLA4 therapy. So the high expression of PAGs may be associated with a diminished response to PD-1 therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CIBERSORT package was employed to assess the immune infiltration status, and the correlation between PAGs and immune cells was visualized. PAGs show a negative correlation trend with the infiltration of macrophages M2 and monocytes (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The pheatmap package was used to evaluate the immune infiltration status and to visualize the correlation between immune cells and overall survival (OS), OS time (days), and risk score. Regulatory T cells (Tregs) exhibited a significant positive correlation with OS status, whereas macrophage M0 and eosinophils demonstrated a significant negative correlation with the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003ePAGS in immunotherapy for endometrial cancer\u003c/p\u003e \u003cp\u003eTo further investigate the role of PAGS in immunotherapy for endometrial cancer, we analyzed data from GSE251923, a single-cell dataset comparing the response to PD-1 treatment in endometrial cancer, which was designated as the partly-response group (PR) and progressive disease group (PD). After dimensionality reduction using UMAP, the PR group exhibited increased T cell infiltration compared to the PD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Additionally, by using the CellChat package to plot the number of interactions among the common cell types\u0026mdash;B cells, Mast cells, Macrophages, and Epithelial cells\u0026mdash;after achieving a partly response with PD-1, it was observed that Macrophages' communication with almost all other cells has been reduced, indicating that PD-1 may also affect the function of Macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The signaling pathways of various cells in the PR group and PD group were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC. In macrophages, signaling pathways such as CD56 and BAFF are generally reduced. The analysis mentioned we mentioned above found that PAGs have a significant effect on the interaction in T cells and Macrophages. We performed pyroptosis scoring on cells from both groups using the \"AddModuleScore\" function, In the PR group, epithelial cells exhibited significantly lower pyroptosis scores, this may suggest that the relationship between epithelial cells and pyroptosis is substantial (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDrug Sensitivity Analysis\u003c/p\u003e \u003cp\u003eTo further investigate the relationship between pyroptosis-risk scores and chemotherapy response, we utilized the R package OncoPredict to compute the IC50 values for commonly used chemotherapy and targeted therapy drugs across all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A lower IC50 value (\u0026micro;M) is indicative of higher drug efficacy. Notably, 174 drugs exhibited statistically significant differen\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003eces in IC50 values between the high- and low-risk groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Supplementary Table\u0026nbsp;1). Subsequently, Wilcoxon tests were conducted to assess the disparities in IC50 values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the high- and low-risk groups for drugs with IC50 values less than 1. The IC50 values were transformed using a negative logarithmic scale, and the top 10 drugs were represented using a box-and-whisker plot. The results revealed that Epirubicin, Podophyllotoxin bromide, Sabutoclax, AZD8055, CDK9_5576 and Gemcitabine demonstrated favorable response efficacy. Additionally, Gemcitabine and Topotecan exhibited potential for enhanced therapeutic efficacy in the \"high-risk\" group (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePyroptosis, a proinflammatory form of regulated cell death, has emerged as a key factor in tumor immune evasion and treatment resistance. Pyroptosis plays a complex and context-dependent role in cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It can promote tumor growth and metastasis through inflammation and immune evasion\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and it also activates antitumor immunity by recruiting immune cells and enhancing immunotherapy response\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This dual role of pyroptosis highlights its potential as a therapeutic target and prognostic indicator in cancer.\u003c/p\u003e \u003cp\u003eIn light of these complexities, our study aimed to bridge the gap between pyroptosis and clinical outcomes by focusing on pyroptosis-associated genes (PAGs). By leveraging advanced computational strategies, we sought to unravel the prognostic potential of these genes and their interplay with immune modulation. The shift from apoptosis to pyroptosis mediated by PD-L1 can promote tumor necrosis\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, potentially facilitating tumor growth and hindering antitumor immunity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Gao et al. found that higher GSDMD expression might contribute to tumor evasion of innate immune responses and is associated with poor prognosis in non-small cell lung cancer (NSCLC)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, another study demonstrated that GSDME acts as a tumor suppressor by activating pyroptosis and enhancing antitumor immunity\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Pyroptosis-induced inflammation within the tumor microenvironment (TME) can stimulate the immune system by activating immune cells and pathways, thereby improving the efficacy of cancer immunotherapy\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. How pyroptosis functions in tumor tissues and impacts the survival of EC patients remains unclear.\u003c/p\u003e \u003cp\u003eTo identify a stable and robust prognosis signature for EC, we developed a novel computational framework incorporating ten machine learning algorithms and their 101 combinations. A signature composed of 6 PAGs\u0026mdash;CASP3, CHMP3, CYCS, GSDMD, IRF1, and NOD1\u0026mdash;was selected for analysis, which exhibits higher predictive accuracy and better clinical translational implications than previous studies that attempt to develop programmed cell death-related prognosis classifiers for EC\u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Caspase-3 /GSDME pathway, acts as a \u0026ldquo;switch\u0026rdquo; determining the mode of cell death. When GSDME is highly expressed, active Caspase-3 cleaves it, leading to the formation of pores in the cell membrane and ultimately causing pyroptosis\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e; conversely, when GSDME expression is low, Caspase-3 activation results in apoptosis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Recent studies have demonstrated that\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e CHMP3 promotes the progression of hepatocellular carcinoma by inhibiting caspase-1-dependent pyroptosis. Additionally, CHMP3 is also one of the pyroptosis-related prognostic feature genes in breast cancer\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, multiple myeloma\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, and pediatric acute myeloid leukemia\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, which indicates its great value in predicting the prognosis of various tumors. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin especially in GSDMD\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In head and neck cancer, researchers discovered that CTLA-4 blockade in CD8(+) T cells of head and neck squamous cell carcinoma induces pyroptosis through the STAT1/IRF1 axis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Thus the PAGs allows for the stratification of patient risk, which has significant implication to improve patient prognosis.\u003c/p\u003e \u003cp\u003eWe used the PAGs to perform risk stratification for EC patients, and analyzed their response to immunotherapy and their sensitivity to new sensitive drugs for EC, including Polo-like kinase 1 inhibitors (PLK1), Topotecan etc. These findings provide rational guidance for administering immunotherapy and chemotherapy in clinical practice, which represents an important step towards more effective personalized medicine approaches. Furthermore, unlike previous studies that focused solely on the prognostic value of the signature, we performed a comprehensive multi-omics analysis, including genome, single-cell transcriptome, and bulk transcriptome, to gain a deeper understanding of the PAGs. These findings provide biological evidence and explanation for the PAGs in guiding personalized medicine approaches.\u003c/p\u003e \u003cp\u003eDrug resistance is a common occurrence due to the highly dynamic and heterogeneous nature of the tumor microenvironment. Therefore, it is crucial to identify populations that are sensitive to specific small molecule drugs in order to improve therapeutic efficacy and prognosis in EC patients. In this study, we analyzed the sensitivity of PAGs risk subgroups to 138 drugs. We observed significant differences in the IC50 values of these small molecule drugs between the PAGs risk subgroups. Specifically, Gemcitabine, Topotecan exhibited lower IC50 values in the high-risk group, while epirubicin and sabutoclax had lower IC50 values in the low-risk group. This may provide alternative treatment options for patients with endometrial cancer who are resistant to first-line therapy.\u003c/p\u003e \u003cp\u003eOur study highlights the role of PAGs in guiding targeted prevention and personalized medicine for EC, which may aid to improve patient outcomes and reduce unnecessary treatment costs. Overall, the PAGs can serve as a promising tool to provide critical information for clinicians in personalized medicine selection. However, in some studies, GSDMD-triggered pyroptosis inhibits the growth of subcutaneous EC xenografts in nude mice, revealing for the first time that GSDMD-mediated pyroptosis is a crucial pathway for suppressing tumor growth in endometrial cancer \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSo more samples should be evaluated, in vitro and in vivo studies are required to elucidate the biological functions of PAGs in EC. Additional large-scale, multi-center prospective study is necessary to further confirm our findings. Lastly, although we predicted the sensitivity of PAGs risk subgroups to various small molecule drugs, in vitro drug assays and clinical trials are required to validate our predictions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we employed the 101-type machine learning framework to evaluate pyroptosis-related features in endometrial cancer and explore their potential as clinical biomarkers. By integrating multi-omics data, including gene expression profiles, mutation data, etc, we developed a machine learning-based predictive model to identify key genes associated with pyroptosis in EC. Using this model, we performed prognostic analyses across various clinical subtypes and immune phenotypes of EC, revealing that specific pyroptosis-related genes are strongly correlated with patient survival and responses to immunotherapy. Additionally, we validated the expression patterns of these genes in tumor tissues and performed internal validation using the TCGA database. Our results suggest that pyroptosis-related genes may serve as valuable prognostic biomarkers in EC. This study provides new molecular targets for the personalized treatment and immunotherapy of endometrial cancer, holding significant promise for clinical application.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eClinical trial number\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of Interest\u003c/strong\u003e \u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Medical and health projects of scientific and technological breakthrough in Songjiang District (2023SJKWGG080), National Natural Science Foundation of China (82472727), Natural Science Foundation of Shanghai Municipality (23ZR1451600).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHLJ, CL and YY analyzed and interpreted the data and drafted the initial manuscript. SWL, ZLN, and YY revised the manuscript. ZST, CF, TAY and TM collected information. HLJ, CL, SWL, ZLN and YY worked equally as major contributors in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R.L., Giaquinto, A.N. \u0026amp; Jemal, A. Cancer statistics, 2024. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e 74, 12\u0026ndash;49 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmozuwa, E. \u0026amp; Atoe, K.J.I.J.o.F.M.I. Evaluating the Efficacy of Biomarkers in the Diagnosis and Treatment of Gyneacological Malignancies: A Review. 10(2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, L.-R., Chen, L. \u0026amp; Sun, Z.-J. Igniting hope: Harnessing NLRP3 inflammasome-GSDMD-mediated pyroptosis for cancer immunotherapy. 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BMC Cancer 20, 28 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial cancer, Pyroptosis, Risk model, Immune infiltration, Drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-6016108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6016108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndometrial cancer (EC) represents a common malignancy within gynecological cancers, characterized by a notably high mortality rate. The absence of reliable prognostic biomarkers significantly impairs the effectiveness of predictive, preventive, and personalized medicine (PPPM/3PM) strategies. Pyroptosis, a distinct form of programmed cell death, has been closely linked to anti-cancer immune responses. Nonetheless, the precise role of pyroptosis in the context of EC remains elusive.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePyroptosis-associated genes (PAGs) were screened in Msigdb. We used consensus clustering to classify PAGs from TCGA-UCEC into two clusters, and examined their characteristics. The Seurat package was employed to analyze significant PAGs in EC single-cell data. The mime package was utilized to screen suitable machine learning approaches and build models. A nomogram was constructed to validate the model's performance. Additionally, CIBERSORT was used to evaluate immune infiltration results, and TIDE scores from the TCIA database were applied to assess EC patients' responses to immune checkpoint therapy. Subsequently, we performed PAG-related pathway analysis in EC patients with or without response to PD-1 therapy using the CellChat module in Seurat. Finally, the OncoPredict package was used to predict drug sensitivity in EC patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA consensus PAGs (\"CASP3\", \"CHMP3\", \"CYCS\", \"GSDMD\", \"IRF1\", and \"NOD1\") was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. We observed distinct biological functions and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a negative response to PD-1 in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study constructed an PAGs that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in EC.\u003c/p\u003e","manuscriptTitle":"Evaluation of pyroptosis-associated genes in endometrial cancer based on the 101- combination machine learning framework and multi-omics data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 17:58:20","doi":"10.21203/rs.3.rs-6016108/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9c54fb5-c165-420a-8bf3-b5852cbe2a92","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T17:58:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-17 17:58:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6016108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6016108","identity":"rs-6016108","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}