Pyroptosis-Related Gene Markers Can Effectively Diagnose Endometriosis and Predict Prognosis

Pyroptosis-Related Gene Markers Can Effectively Diagnose Endometriosis and Predict Prognosis | 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 Article Pyroptosis-Related Gene Markers Can Effectively Diagnose Endometriosis and Predict Prognosis J.M. S., T. S., Yang Liu, J.S. C., X.L. Y., D.Y. L., J. B., Y. S., and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1935526/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 Endometriosis (EM) is a chronic inflammatory disease, affecting 10% of women and girls of reproductive ages around the globe. Pyroptosis ,a type of pro-inflammatory programmed cell death (PCD), has been associated with EM in recent studies.However,the expression of pyroptosis-related genes (PRGs) in EM and its relationship with diagnosis and prognosis are not clear.In this study,it was discovered that 32 PRGs differed in expression between EM and normal tissues, which were related to diagnosis and prognosis. Firstly, ROC analysis of a single gene was performed based on PRGs ,and then subjected to the corresponding multiomics analysis, prognostic analysis and diagnostic analysis. Secondly,the gene expression profiles of EM group dataset were consistently grouped based on PRGs by the consencesClusterPlus package. Pheatmaps were used to construct a principal component analysis (PCA) diagram of the dataset to determine the potential diagnostic value of these genes and to determine their expression patterns in different subtypes.Thirdly,The Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The results suggested that the risk was related to immune response. In conclusion, PRGs have an important roles in tumour immunity and can be used to predict the prognosis of EM. pyroptosis-related genes diagnosis prognosis classifier immunity endometriosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction EM is a common estrogen-dependent gynecological inflammatory disease that shares similarities with malignant tumors including attachment and infiltration. EM characterized by the presence of endometrial glands and stroma outside the uterine cavity and associated with chronic pain and infertility [1]. EM affects about 10% of women and girls of reproductive age, 25–50% of infertile women and 87% of women with chronic pelvic pain worldwide [2,3]. Due to the lack of prevention and early diagnosis programs,EM signifcantly affects the quality of life in the majority of women and is a major co-factor in infertility.Laparoscopy is the gold standard for EM diagnosis; however, it is a traumatic procedure [4]. Currently, treatment methods include surgery and oral hormonal drugs, but all have significant side effects and cannot completely eradicate the disease [5,6]. Due to the non-specifc symptoms and the risks associated with surgery,it can be over 10 years before women are diagnosed and correctly treated [7]. Considering the limitations of EM therapies ,new therapeutic targets are need to improve the clinical outcome of EM.Therefore,reliable new prognostic models are urgently needed to make targeted therapies more feasible. Pyroptosis is a gasdermins (GSDM)-mediated PCD induced by various stimuli, such as stroke, microbial infections, and cancer, and has been widely studied in many disease models.PCD provides inflammatory and immunogenic signals that act as secondary signals ,inducing the maintenance of homeostasis of the cellular environment and driving adaptive immunity.The term pyroptosis was first identified in infected macrophages in 1992 and was named in 2001 by Cookson et al.In addition to autophagy,apoptosis,and ferroptosis,this newly discovered type of cell death has become a research hotspot in recent years. Pyroptosis characterized by cell swelling, plasma membrane lysis and chromatin fragmentation, accompanied by the release of a large number of intracellular pro-inflammatory cytokines such as interleukin-18 (IL-18) and interleukin-1β(IL-1β)[8–10]. Pyroptosis is dominated and executed by GSDMD and GSDME in the gasdermin family.Caspase-1 and caspase-4/-5/-11 can cleave GSDMD, and caspase-3 can cleave GSDME, leading to the cleavage of N- and C-terminal domains, resulting in the gradual expansion of cells until the plasma membrane ruptures, and releasing a variety of inflammatory factors.During the occurrence of pyroptosis, a variety of risk-related cytokines and signaling molecules are released and activated, accompanied by a strong inflammatory response and activation of the immune system [11].Pyroptosis plays an important role in tumour immune microenvironment. A few studies have suggested that CD8 + T cells and NK cells can induce pyroptosis, and IFN-γ can enhance this process [12,13].The expression of GSDMD is associated with CD8 + cell markers, and the cleavage of GSDMD in cytotoxic T lymphocytes is increased [13]. The mechanism and functions of pyroptosis in EM have been studied, but its relationship with diagnose and prognosis has been ambiguous.The role of PRGs expression in the diagnosis and prognosis of EM remains unclear. To explore the relationship between clinical features of EM and pyroptosis is helpful for its treatment, but the value of pyroptosis in the diagnosis and prognosis of EM has not been reported.Therefore,the study aimed to find out the diagnosis and predictive value of PRGs in differentiating EM tissue from normal tissue. Besides, prognostic riskrelated phenotypes were analyzed. Thus, this study provides a novel understanding of the role of pyroptosis in EM and suggests that PRGs signatures have the potential to diagnose and predict the prognosis. The specific flow chart is shown in Fig. 1 Materials And Methods Data download and data pre-processing The GEOquery package [14] of R software (version 3.6.5, http://r-project.org/) was used to download the EM expression profile dataset from the Gene Expression Omnibus (GEO,https://www.ncbi.nlm.nih.gov/geo/) database with reliable sources GSE51981 [15] and GSE35287 [16] (Table 1).The samples in the dataset were obtained from Homo sapiens and the platform was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array and the GPL6244 [HuGene-1_ 0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version].Using hgu133plus2.db package [17] and hugene10sttranscriptcluster.db packages [18] for clinicopathological characteristics data processing, the genetic data were processed and annotated using the AFFY package for normalization and processing based on the Robust Multi-Array Average (RMA) method. The HUGO Gene Nomenclature Committee (HGNC) [19] is responsible for providing all genes in the human genome with unique, standard, widely disseminated symbols ,including ncRNA genes, protein-coding genes, methylation-associated genes, and other genes. For each human gene, there is a numerical identifier in the HGNC, and mRNA expression profiles are obtained using the HGNC mRNA gene annotation file.A search of the literature on pyroptosis resulted in a total of 32 known PRGs [20] for subsequent analysis (Table 2). Identification of Differentially Expressed Genes We obtained the PRGs expression matrices in GSE51981 and visualized the PRGs expression patterns using pheatmap [21]. The“limma” package [22] was used to identify the differentially expressed genes (DEGs) between EM and normal tissues with the FDR-adjusted p value 2.5 [23]. The correlation of DEGs was analyzed and demonstrated by using the R package “corrplot”package [24]. The protein-protein interaction (PPI) network of DEGs was obtained from the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) database [25]. We submitted the DEGs related to EM from differential expression analysis to the STRING database to obtain their protein interaction networks and then input the networks into Cytoscape [26] software to identify genes that could interact more strongly with other genes and visualize them. The MCODE [27] plug-in in Cytoscape was used to identify subnetworks, and the top scoring subnetworks was obtained based on the scores, which we believe may serve a specific function. a clustering heatmap analysis was performed in GSE51981 using the constructed pheatmap based on hub genes to reveal the expression pattern of the hub gene among different subtypes. Construction and Evaluation of PRGs Based Classifiers for EM Diagnosis and Prognosis PRGs were subjected to prognostic analysis and diagnostic analysis. The receiver operating characteristic (ROC) curves analysis of a single gene was performed using 32 PRGs and then subjected to the corresponding prognostic analysis and diagnostic analysis in GSE51981 and GSE35287. PRGs with the area under ROC curves (AUC) values greater than 0.6 in both datasets were selected as genes with the better diagnostic value for EM. GEPIA2 [28] is a web-based tool used for dynamic analysis of gene expression profile data, capable of analyzing TCGA and GTEx projects with a total of 9736 tumor samples and 8587 normal samples constructed on RNA-seq expression data analysis. We use GEPIA2 for the prognostic analysis of PRGs associated with ovarian serous cystadenocarcinoma (OV) associated with EM in the TCGA database. The cBioPortal for Cancer Genomics [29] is an open access website, open-source resource for interactive exploration of multiple cancer genomics datasets. We entered the PRGs obtained into cBioPortal for mutational analysis with OV. Consensus Clustering Analysis of PRGs We used the ConsensusClusterPlus package [30] in the R language to consistently group the gene expression profiles of the EM group in the GSE51981 dataset based on 32 PRGs.We selected the best cluster and classified it into two subtypes, C1 and C2 groups.DEGs of different subtypes were selected by the “limma” package and DEGs volcano plots were plotted using the ggplot2 package [31] to show the differential expression of DEGs, which satisfied the adj. p value 2.5 thresholds for the cluster heatmap analysis.PCA plots were constructed for the GSE51981 dataset using pheatmaps to determine the potential diagnostic and predictive value of these genes. Cluster heat map analysis was also performed for the GSE51981 dataset using the PRGs-based pheatmap to identify their expression patterns in different subtypes. Gene Sets Enrichment Analysis GO [32] is a database established by the Gene Ontology Consortium to create a semantic vocabulary standard to qualify and describe gene and protein functions for a wide range of species, which is updated as research progresses. KEGG [33] is a comprehensive database that integrates chemical, genomic, and systemic functional information. Metascape [34] is a web tool that provides a variety of functions such as gene enrichment analysis and protein interaction network analysis. The site integrates more than 40 gene function annotation databases and provides diverse visualizations. We used Metascape to perform the GO/KEGG functional enrichment analysis on immune genes differentially expressed among different subtypes, selecting functions with p 1.5. The significant functions and pathways of GO and KEGG were visualized using the R package ggplot2. We downloaded the file "msigdb.v7.4.symbols.gmt" from the GSEA website and used the GSVA package [35] and the MiSigDB GMT file to analyze the GSE51981 expression profile to obtain the functional enrichment score (ES) matrix. The “limma ” package lmFit function was then used to analyze the GO terms and KEGG pathways for differences between the EM and normal groups, with an adj. p value 0.5 defining significant differences. Heatmap representation of differential functions was performed using the pheatmap package in the R package. The main functional differences of KEGG, GO biological process, GO molecular function, and GO cellular components are shown . Differential functions are sorted by FDR from smallest to largest. The STRING database searches for interactions between known proteins and predicted proteins. We submitted the DEGs related to EM from differential expression analysis to the STRING database to obtain their protein interaction networks and then input the networks into Cytoscape software to identify genes that could interact more strongly with other genes and visualize them. The MCODE plug-in in Cytoscape was used to identify subnetworks and to obtain the top scoring subnetworks based on the scores, which we believe may serve a specific function. About 20 hub genes were identified using the cytohubba plugin [36], and a clustering heatmap analysis was performed in GSE51981 using the constructed pheatmap based on hub genes to reveal the expression pattern of the hub gene among different subtypes. Immune Infiltration Analysis CIBERSORT [37] is based on the principle of linear support vector regression to deconvolute the transcriptome expression matrix to estimate the composition and abundance of immune cells in a mixture of cells. We downloaded the original codes and the corresponding immune cell files from the official CIBERSORT website and derived the immune cell infiltration matrix in R based on the gene expression profile of the GSE51981 EM and the immune cell files. Heat maps were drawn using the pheatmap package in R language (https://CRAN.R-project.org/package=pheatmap) , showing the distribution of the 22 immune cell infiltrates in each sample. The ggplot2 software package was used to draw 2D PCA clustering plots to visualize sample distribution, and the Corrplot software package was used to plot associated heat maps for data visualization. The ggplot2 package plots box-line plots to visualize the differences between 22 immune cell infiltrates. The igraph package plots [38] were constructed to reveal the correlation network plots of immune cell infiltrates to visualize the interactions of the 22 immune cell infiltrates, using p0.25 as interaction standard. We correlated the resulting PRGs with immune cell infiltration and then visualized the results using the ggplot2 package. Infiltrating stromal cells and immune cells are major components of normal cells in tumor tissues and not only interfere with tumor signaling in molecular studies, but also have an important role in tumor biology. In this article, the ESTIMATE score, immune score, and stromal score were calculated using the R estimate package [39] in the GSE51981 dataset. Violin plots using the ggplot2 package were used to visualize differences in the three immune scores across subtypes. Violin plots were also drawn using the ggplot2 package to reveal the distribution of the two immune checkpoints PD1 and PDL1 between the different subtypes. Statistical analysis All data processing and statistical analyses were completed by R software (version 3.65).The pROC package [40] was used to plot the ROC curves of genes and patients, and the AUC was calculated to assess the diagnostic effects of gene expression in normal tissues versus disease. The correlation between immune cells and PRGs was analyzed using Pearson's correlation coefficient ,and the strength of the correlation was determined using the following absolute values: 0.00–0.19, very weak; 0.20–0.39, weak; 0.40–0.59, moderate; 0.60–0.79, strong; and 0.80–1.0, very strong. This analysis was performed using the corrplot package in R. The correlation between PRGs and immune cells was performed using the Corr.test in the PSYCH package [41] in R. Results Identification of Differentially Expressed PRGs Between Normal and EM Tissues Expression levels of 32 PRGs were compared between 111 normal and 117 EM tissues from TCGAEM data. It was observed that 10 genes,including CASP1, CASP3, CASP4, CASP6, CASP8, GSDME, IL-18, NLRP2, PJVK, and PLCG1 were significantly underexpressed in EM tissues,while 3 genes,invluding GSDMC, GSDMD, and TNF were significantly upexpressed in EM tissues (Figure 2A，Figure 2E）. To further explore the correlation between these PRGs, we used PPI and correlation analysis (Figure 2C). TNF, CASP1, and IL-18 had the strongest interactions with other PRGs of significant importance; The correlation heatmap of the PRGs showed that PRKACA was weakly correlated with CASP3, CASP4, and PJVK, while GSDMD, GPX4, PYCARD, and CASP9 were strongly correlated; GSDMD was weakly associated with CASP3, CASP4, and PJVK, while GPX4, PYCARD, and CASP9 were strongly associated (Figure 2D). Metascape was used to analyze the functional enrichment of PRGs. The results showed that DEGs were related to pyroptosis, NOD-like receptor signaling pathway, positive regulation of interleukin-1 beta production, NLRP1 inflammasome complex, inflammasome complex, NLRP3 inflammasome complex, protein domain specific binding, peptidase activator activity, and neutrophil extracellular trap formation (Figure 2B). Multiomics analysis of Diagnosis and Prognosis value of PRGs PRGs were subjected to multiomics analysis, prognostic analysis, and diagnostic analysis. ROC analysis of a single gene was performed using 32 PRGs and then subjected to the corresponding multiomics analysis, prognostic analysis, and diagnostic analysis in GSE51981 and GSE35287 (Figure 3A-B). Genes with AUC > 0.6 were selected and visualized. We found that the AUC values of GSDME，NLRP2，NOD1，and PLCG1 were all more than 0.6 in both datasets, and GSDME，NLRP2，NOD1 were all more than 0.7 in GSE35287, showing the good diagnostic effects of these 4 PRGs on EM and normal patients. We submitted 32 PRGs in GEPIA2 for OV survival analysis of cancers associated with EM (Figure 3C-E) and found that absent in melanoma 2 (AIM2), PJVK, and PLCG1 demonstrated good prognostic effects (Logrank p<0.1), with AIM2 showing the best prognosis. When 32 PRGs were submitted to the cBioPortal TCGA OV mutation (Figure 3F), most mutations were amplification mutations, with the highest mutation rate of 46% in GSDMD and 43% in GSDMC. The main types of mutations in these two genes were amplifications. The GPX4 mutation rate was 22%, and the main type of mutation was low mRNA expression. Identification of EM Clusters Using Consensus Clustering We divided the EM samples into clusters depend on the gene expression patterns to investigated the therapeutic of PRGs. We used "k" to denote the number of clusters. To clarified the optimal K value for the sample distribution to maximum stability, we employed an empirical CDF method of plotting. The results of the consensus matrices showed that the patients in TCGA-EM could be divided into two distinct and non overlapping clusters at k=2, and the above verification was carried out by PCA(Figure 4) . In the GSE51981 dataset,we identified 682 DEGs in the EM group , including 554 upregulated genes and 128 downregulated genes.The distribution of DEGs is shown in diagram (Figure 5A). We performed PCA analysis of the GSE51981 EM group using DEGs (Figure 5B) and found that the C1 group was clustered into one category, and the C2 group was clustered into another category. Hierarchical cluster analysis of the 682 DEGs in the GSE51981 EM revealed that the C1 samples were clustered into one category and the C2 samples were clustered into one category (Figure 5C). The heatmap showed that the PRGs expression patterns differed significantly between the C1 and C2 samples (Figure 5D). Identification of the Prognostic Related Biological Processes It order to find out biological processes were influenced by the prognostic risk to make them predictive.Functional enrichment analyses was performed.Firstly,The functional enrichment methods of GO and KEGG were used for analysis.The results showed that DEGs were mainly associated with the functions and pathways of mitochondrial envelope, mitochondrial membrane, mitochondrial matrix, positive regulation of cell death, positive regulation of apoptotic processes, positive regulation of programmed cell death, positive regulation of neuron death,pathways in cancer and apoptosis,and so on (Figure 6) (Tables 3 and 4). Detailed enrichment results are shown in Supplement 1. Secondly,to further verify this observation, GSEA was utilized to find enriched pathways in the KEGG database. The results of differential enrichment are shown in the Figure 6.Detailed differential enrichment are shown in Supplement 2.Results showed that the positive regulation of the T-cell receptor signaling pathway and the transforming growth factor beta (TGF-β) signaling pathway were the two significantly enriched pathways(Figure 7). These results proved that the PRGs-based prognostic risk is related to immune responses. Lastly,The PPI protein interaction networks were the submitted to Cytoscape to identify important genes that interacted more strongly with other genes and visualized their interactions (Figure 8A). The MCODE plugin was used to identify the highest scoring subnetworks (Figure 8B), resulting in a total of one module, which we believe may play a specific role in the pathogenesis of EM. Then the Cytohubba plugin was used to obtain the 20 hub genes with the highest scores.(Figure 8C). The 20 hub genes in the GSE51981 EM were analyzed by hierarchical clustering. The C1 samples were clustered in one class and the C2 samples were clustered in another class (Figure 8D).There are significant differences in expression patterns between the two classes. Analysis of immune infiltration assessment with PRGs Based on these findings, we proposed that the effects of PRGs on predicting the prognosis of EM could be related to the immune microenvironment.CIBERSORT was employed to estimate the immune cell component in EM tissues. The proportion of 22 human immune cell subsets, including naive and memory B cells, NK cells, plasma cells, and myeloid subsets, was evaluated. The results of the heatmap representing the infiltration of 22 immune cell types (Figure 9B) showed that activated NK cells were significantly negatively correlated with resting NK cells and M2 macrophages. Resting mast cells showed a significant negative correlation with regulatory T cells (Tregs), plasma cells, and resting NK cells. Memory B cells showed a significant negative correlation with M2 macrophages. There was a significant positive correlation between activated NK cells and resting mast cells, as well as between monocytes and neutrophils. The correlation analysis (Figure 9A) showed that immune cells were clustered into two categories, NK cells resting, plasma cells,macrophages M0,macrophages M1,T cells CD4 memory resting,macrophages M2,B cells naive,T cells gamma delta,dendritic cells resting,and dendritic cells were significantly correlated with expression of GSDME, CASP3, CASP4, GSDMB, PJVK, SCAF11, CASP6, NOD1, IL-18, NLRC4, NOD2, CASP1, and NLRP2 . Other immune cells showed the opposite trend. The results of the box line plot of differences in differences in immune cell infiltration (Figure 9C) showed that compared to C1, the C2 group had higher levels of immune infiltration by memory B cells, activated CD8 T cells, activated NK cells, follicular helper T cells, Tregs, monocytes, and resting mast cells, but lower levels of immune infiltration by plasma cells, resting NK cells, M1 macrophages, M2 macrophages, and resting dendritic cells. The results of the 22 immune cell interactions (Figure 9D) showed that naïve CD4 T cells, M2 macrophages, activated NK cells had the strongest interactions with other immune cells, while gamma delta T cells, monocytes, and activated memory CD4 T cells had the weakest interactions with other immune cells. The heatmap and PCA clustering analysis of immune cell infiltration showed that there was a significant difference in immune cell infiltration between the samples of the C1 group and the samples of the C2 group (Figure 10A-B). Violin plots and Wilcox tests revealed differences in PD1/PD-L1 expression levels (Figure 10C). The ESTIMATE algorithm was used to obtain each immune score for the GSE51981 samples: the ESTIMATE score, immune score, and stromal score. The violin plot of the scores showed that each score was higher in the C2 group than in the C1 group (Figure 10D). Discussion EM is an estrogen-dependent chronic gynecological inflammatory disease and is the main cause of pelvic pain and low fertility in women of reproductive age [1,2,4]. Previous studies have shown that the sensitivity and specificity of serum CA-125, IL-6, ICAM-1,and glycodelin in the diagnosis of EM were not high[42], but the combined detection of multiple markers can improved the accuracy of diagnosis and showed the specificity of menstrual cycle [43].However, the differential expression of proteins in the peritoneal fluid for the diagnosis of EM and the determination of prognosis need to be further explored [44]. Therefore, it is necessary to develop a method to diagnose and predict EM. In recent years, studies have shown that the expression level of pyroptosis is closely related to the occurrence, development, and metastasis of EM, but there is no clear target and mechanism [45, 46]. Therefore, to identify validated diagnostic biomarkers for EM, we searched the GEO database and obtained two datasets (GSE51981 and GSE35287) and searched the literature related to pyroptosis to select 32 known PRGs for comprehensive analysis. A total of 682 DEGs were identified through cross-validation of the EM group. Furthermore, GO enrichment analysis and GSEA indicated that these enrichment modules and pathways were closely associated with mitochondrial dysfunction and cell death observed in EM. Furthermore, the top 20 central genes identified in the PPI network associated with EM had high functional similarity and diagnostic value for EM. In the first part of this study, we used cross-validation to obtain expression matrices of 32 PRGs in the GSE51981 dataset. In order to study the BPs of DEGs in normal and EM samples, we performed GO/KEGG functional enrichment analysis of PRGs using Metascape. In the molecular functions (MF) annotations, pyroptosis, NOD-like receptor signaling pathway, positive regulation of IL-1 beta production, NLRP1 inflammasome complex, inflammasome complex, NLRP3 inflammasome complex and protein domain specific binding, peptidase activator activity, and formation of neutrophil extracellular traps were significantly associated with the DEGs. These data are consistent with previous results showing that the focal pathway is involved in the development of EM [47]. In the second part of this study, we used cross-validation to identify 682 DEGs in the datasets.The results showed that mitochondrial envelope, mitochondrial membrane, mitochondrial matrix, positive regulation of cell death, positive regulation of apoptotic process, positive regulation of programmed cell death, positive regulation of neuron death, regulation of neuron death,oxidoreductase activity,o-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase activity, pyrimidine metabolism, pathways in cancer, and apoptosis were significantly associated with the development of EM in MF annotations. Mitochondria play a key role in the development and progression of EM, and the regulation of cellular Ca2 + homeostasis, oxidative stress, and apoptosis. Mitochondrial dysfunction has recently attracted considerable attention because impaired mitochondrial bioenergetics can be linked to inflammation, oxidative stress, and ERβ levels related pathways that underlie the pathophysiology of EM [48]. A previous study concluded that ERβ levels were elevated in endometriotic tissue; ERβ directly regulates mitochondrial DNA (mtDNA) gene expression by interacting with the D loop of mtDNA and polymerase γ. The ERβ in mitochondria resists oxidative damage-induced apoptosis by inducing the ROS scavenger enzyme Mn-superoxide dismutase and antiapoptotic protein Bcl-2 [45]. Together, these observations suggest that mitochondrial dysfunction may be one of the leading causes of EM. In the third part of this study, GSEA was performed to investigate the biological functions of DEGs associated with EM. The results showed that positive regulation of T-cell receptor signaling pathway and TGF-β signaling pathway were two significantly enriched signaling pathways. Interestingly, we noted that the most enriched pathways were associated with immune response, inflammation, and apoptosis in our analysis. TGF-β is one of the main immune and inflammatory factors responsible for the regulation of cell proliferation, angiogenesis,immune responses,and differentiation [46]. Studies in mouse models of EM and females with EM have shown that elevated levels of TGF-β ligands are associated with reduced intraperitoneal immune cell activity and increased survival, attachment, invasion, and proliferation of ectopic endometrial cells during the development of EM [49]. Soluble fibrinogen-like protein 2 secreted by highly active Tregs bias macrophages towards a repair phenotype through the SHP2-ERK1/2-STAT3 signaling pathway, which is associated with the progression of EM [50]. Consistent with our data, analysis of microarray results of EM from other mRNA datasets also suggests that immune and inflammatory responses play a key role in the regulatory network of EM [51]. Our data mining results further confirmed that inflammatory response plays a key role in the etiology of EM. In the PPI network identified in this study, we obtained a module that may play a specific role in the pathogenesis of EM. Meanwhile, 20 central genes, highlighted as the most important, had multiple interactions in the network. Further study of these genes may reveal the pathophysiology of EM. Hierarchical cluster were analyzed of 20 central genes for EM in GSE5181 dataset, and the expression patterns of the two classes differed significantly. In EM, how PRGs interact and whether they are relevant to patients is still unknown. With regard to the diagnostic and prognostic value, we analyzed the AUC of 32 PRGs, which indicated that AUCs of GSDME, NLRP2, NOD1, and PLCG1 were greater than 0.6; thus, these genes have good diagnostic value and may be promising targets for the diagnosis of EM. Among the 32 PRGs, AIM2, PJVK, and PLCG1 exhibited good prognostic values, with AIM2 being the strongest predictor. Most mutations in PRGs in patients with endogeneity were amplifications, and GSDMD had the highest mutation rate. Gasdermin E (GSDME) is one of the member of the gasdermin family [52]. The GSDME gene is highly expressed in many normal human tissues, such as the testes and placenta, and is moderately expressed in the heart and stomach. However, due to epigenetic modifications in the promoter region, GSDME is frequently down-regulated or even silenced in cancer cells [53–55]. Studies have shown that activated caspase-3 can cleave GSDME to generate its N-terminal fragment, which performs secondary necrosis/pyroptosis by the formation of pores in the plasma membrane [56,57]. GSDME expression can control apoptosis and pyroptosis transformation [58]. When overexpressed or moderately expressed, it can lead to cell death through cysteine-3-dependent pyroptosis. When underexpressed, the mode of cell death changed to apoptosis [56,57]. Progression is closely associated with apoptosis and pyroptosis. Therefore, GSDME can be used as a potential diagnostic indicator of EM. A cytoplasmic sensor AIM2, assembles with spot-like proteins associated with apoptosis containing CARD and procaspase-1 in recognition of double-stranded DNA to form the multiprotein complex AIM2 inflammasome [59]. AIM2 activates CASP-1 through ASC-mediated junctional proteins to promote the maturation and release of IL-1β and IL-18 and to promote pyroptosis [60] .AIM2 may play a unique role in different cancer types.Aim2 was found overexpressed in oral cancer, nasopharyngeal carcinoma and non-small cell lung cancer, but it was found to be suppressed in endometrial cancer, gastric cancer and colon cancer [61,62] .Interestingly,in our study, AIM2 seemed to be a cancer-promoting gene,because it was upregulated in EM tissues. AIM2 was identified as a regulator of FOXP3 + Treg cell differentiation and as a potential target for intervention to restore T cell homeostasis [63]. When ROS inhibitors are used, AIM2 expression can be inhibited. Furthermore, overexpression and inhibition of AIM2 expression significantly influences HG-induced migration and the TGF-β/SMAD signaling pathway in vascular smooth muscle cells [64]. In summary, the above pyroptosis factors are of great value in the diagnosis and prognosis of EM. Another important finding of our study showed that 32 PRGs were significantly associated with immune infiltration, and confirmed the important role of pyroptosis in the tumor immune microenvironment [62,65]. Mutations in GSDMD can regulate the tumor immune microenvironment by modulating pyroptosis after inflammatory vesicle activation [66]. Previous studies [67] have also found that PRG GSDMD is associated with immune infiltration. In this study, we used in-depth bioinformatics analysis to identify candidate genes and key signaling pathways that regulate the onset and progression of EM, as well as possible predictive genes. Collectively, these findings provide new insights into the underlying molecular mechanisms and potential drug candidates for the treatment of EM. Therefore, exploring the potential correlation between PRGs in EM and immune factors and EM can help to elucidate the role of pyroptosis and inflammatory factors in disease pathogenesis and generate relevant insights to guide the development of new therapeutic strategies. Our study has some limitations. First, despite the emerging evidence suggesting a series of DEGs in EM, such as PRGs, there are still no reliable candidates to be considered as therapeutic targets for EM. It is necessary to identify more DEGs and explore whether possible targeted genes affect the initiation and progression of EM. Further, although microarray-based bioinformatic analysis is a powerful tool in efficient understanding of molecular mechanisms and identifying potential biomarkers underlying EM, further experimental validation of the identified PRGs are needed at molecular, cellular, and organismal levels. Lastly, to clarify the functions of DEGs and hub genes in EM, loss-of‐function and gain‐of‐function studies with tissue‐type specificity and cell‐type specificity are warranted. Signaling pathways are more diverse than originally thought in EM and include the cancer and apoptosis pathways. Although several pathways have been identified, a series of in vivo and in vitro molecular experiments is needed to further confirm our results and may be useful to provide more detailed and stronger evidence of the possible phenotype and regulation of the pathways of genes predicted to underlie EM. Conclusions In sum,we performed a comprehensive systematic bioinformatic analysis and identified diagnostic genes associated with PRGs in EM patients, including four genes (GSDME, NLRP2, NOD1, and PLCG1) and a prognostic gene signature containing three genes (AIM2, PJVK, and PLCG1). Declarations Acknowledgements Not applicable. Funding Yunnan Provincial Department of Education. Grant Reference Number 2022J0240. Availability of data and materials The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. Authors’ contributions Jiamei Song, Tao Shi,Yang Liu, and Yushi Meng designed research, Jingsi Chen, Xiaoling Yang, and Dongya Li prepared figures 1-10. Jia Bie and Ya Su were prepared tables1-4. All authors read and approved the final version of the manuscript. Yushi Meng and Yang Liu led and oversaw the project. Competing interests The authors declare that they have no competing interests. References Signorile, P. G., Cassano, M., Viceconte, R., Spyrou, M., Marcattilj, V., and Baldi, A. Endometriosis: A retrospective analysis on Diagnostic Data in a cohort of 4,401 patients. In Vivo. 36, 430–438 (2022). Saunders, P. T. K., and Horne, A. W. Endometriosis: etiology,pathobiology, and therapeutic prospects. Cell. 184, 2807–2824 (2021). Parasar, P., Ozcan, P., and Terry, K. L. Endometriosis: epidemiology, diagnosis and clinical management. Curr. Obstet. Gynecol. Rep. 6, 34–41 (2017). Signorile, P. G., Cassano, M., Viceconte, R., Spyrou, M., Marcattilj, V., and Baldi, A. Endometriosis: A retrospective analysis on Diagnostic Data in a cohort of 4,401 patients. In Vivo. 36, 430–438(2022). Ferrero, S., Barra, F., and Leone Roberti Maggiore, U. Current and Emerging Therapeutics for the Management of Endometriosis. Drugs. 78, 995–1012 (2018). Bhurke, A. V., DasMahapatra,et al. Clinical characteristics and surgical management of endometriosis-associated infertility: A multicenter prospective cohort study. Int. J. Gynaecol. Obstet; 10.1002/ijgo.14115 (2022). Rogers, PA., Adamson, GD., Al-Jefout, M., Becker, CM., D'Hooghe, TM., Dunselman, GA., Fazleabas, A., Giudice, LC., Horne, AW., Hull, ML., Hummelshoj, L., Missmer, SA., Montgomery, GW., Stratton, P., Taylor, RN., Rombauts, L., Saunders, PT., Vincent, K., Zondervan, KT. Research Priorities for Endometriosis. REPROD SCI, 24 (2), 202–226 (2016). Morimoto, N., Kono, T., Sakai, M., and Hikima, J. I. Inflammasomes in teleosts: structures and mechanisms that induce pyroptosis during bacterial infection. Int. J. Mol. Sci. 22, 4389 (2021). Zhaolin, Z., Guohua, L., Shiyuan, W., and Zuo, W. Role of pyroptosis in cardiovascular disease. Cell Prolif. 52, e12563(2019). Frank, D., and Vince, J. E. Pyroptosis versus necroptosis: similarities, differences, and crosstalk. Cell Death Differ. 26, 99–114(2019). Ju, A., Tang, J., Chen, S., Fu, Y., and Luo, Y. Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis. Frontiers in oncology, 11, 709077(2021). Zhou, Z., He, H., Wang, K., Shi, X., Wang, Y., Su, Y., Wang, Y., Li, D., Liu, W., Zhang, Y., Shen, L., Han, W., Shen, L., Ding, J., Shao, F. Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells. SCIENCE, 368,6494 (2020). Xi, G., Gao, J., Wan, B., Zhan, P., Xu, W., Lv, T., Song, Y. GSDMD is required for effector CD8 + T cell responses to lung cancer cells. INT IMMUNOPHARMACOL, 74 105713,(2019). Davis, S., and Meltzer, P. S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 23, 1846–1847 (2007). Tamaresis, J. S., Irwin, J. C., Goldfien, G. A., Rabban, J. T., Burney, R. O., Nezhat, C. et al. Molecular classification of endometriosis and disease stage using high-dimensional genomic data. Endocrinology. 155, 4986–4999 (2014). Aghajanova, L., Tatsumi, K., Horcajadas, J. A., Zamah, A. M., Esteban, F. J., Herndon, C. N. et al. Unique transcriptome, pathways, and networks in the human endometrial fibroblast response to progesterone in endometriosis. Biol. Reprod. 84, 801–815 (2011). Carlson, M. hgu133plus2.Db: Affymetrix Human Genome U133 Plus 2.0 Array Annotation Data (Chip hgu133plus2). R Package Version 3.2.3 (2016). Langkilde A, Olsen LC, Sætrom P, Drabløs F, Besenbacher S, Raaby L, Johansen C, Iversen L. Pathway analysis of skin from psoriasis patients after adalimumab treatment reveals new early events in the anti-inflammatory mechanism of Anti-TNF-α. PLOS ONE. 11, e0167437(2016). Tweedie, S., Braschi, B., Gray, K., Jones, T., Seal, R. L., Yates, B., & Bruford, E. A. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 49, D939-D946(2021). Xia, X., Wang, X., Cheng, Z., Qin, W., Lei, L., Jiang, J., & Hu, J. The role of pyroptosis in cancer: pro-cancer or pro-‘host’? Cell Death Dis. 10, 650 (2019). Yao, S.and Liu, T. Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database. Oncol. Lett. 15, 8319–8324 (2018). Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47(2015). Zhou, J., Zhu, Y., Ma, S., Li, Y., Liu, K., Xu, S., Li, X., Li, L., Hu, J., & Liu, Y. Bioinformatics analysis identifies DYNC1I1 as prognosis marker in male patients with liver hepatocellular carcinoma. PLOS ONE. 16, e0258797(2021). Parvathi, A., Catena, M., Jasna, V., Phadke, N., and Gogate, N. Influence of hydrological factors on bacterial community structure in a tropical monsoonal estuary in India. Environ. Sci. Pollut. Res. Int. 28, 50579–50592(2021). von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., and Snel, B. String: a database of predicted functional associations between proteins. Nucleic Acids Res. 31, 258–261 (2003). Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504(2003). Deng, J. L., Xu, Y. H., and Wang, G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front. Genet. 10, 695 (2019). Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556-W560 (2019). Gao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., Cerami, E., Sander, C., & Schultz, N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, p11(2013). Wilkerson, M. D. and Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 26, 1572–1573(2010). Wu, X., Sui, Z., Zhang, H., Wang, Y., and Yu, Z. Integrated analysis of lncRNA-mediated ceRNA network in lung adenocarcinoma. Front. Oncol. 10, 554759(2020). The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330-D338 (2019). Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., and Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017). Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. Metascape provides a biologist-oriented resource for the analysis of systEM-level datasets. Nat. Commun. 10, 1523(2019). Hänzelmann, S., Castelo, R., and Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 14, 7 (2013). Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., and Lin, C. Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8 (Suppl 4), S11(2014). Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., and Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243–259(2018). Baciu, C., Pasini, E., Angeli, M., Schwenger, K., Afrin, J., Humar, A., Fischer, S., Patel, K., Allard, J., & Bhat, M. Systematic integrative analysis of gene expression identifies HNF4A as the central gene in pathogenesis of non-alcoholic steatohepatitis. PLOS ONE. 12, e0189223 (2017). Juge, P. A., Lee, J. S., Lau, J., Kawano-Dourado, L., Rojas Serrano, J., Sebastiani, M., Koduri, G., Matteson, E., Bonfiglioli, K., Sawamura, M., Kairalla, R., Cavagna, L., Bozzalla Cassione, E., Manfredi, A., Mejia, M., Rodríguez-Henriquez, P., González-Pérez, M. I., Falfán-Valencia, R., Buendia-Roldán, I., Pérez-Rubio, G., … Dieudé, P. Methotrexate and rheumatoid arthritis associated interstitial lung disease. The European respiratory journal, 57(2), 2000337 (2021). Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Müller, M. pROC: an Open-Source Package for R and S + to Analyze and Compare ROC Curves. BMC Bioinformatics. 12, 77(2011). Robles-Jimenez, L. E., Aranda-Aguirre, E., Castelan-Ortega, O. A., Shettino-Bermudez, B. S., Ortiz-Salinas, R., Miranda, M., Li, X., Angeles-Hernandez, J. C., Vargas-Bello-Pérez, E., & Gonzalez-Ronquillo, M. Worldwide traceability of antibiotic residues from livestock in wastewater and soil: A systematic review. Animals (Basel). 12, 60(2021). Mosbah, A., Nabiel, Y., and Khashaba, E. Interleukin-6, intracellular adhesion molecule-1, and glycodelin A levels in serum and peritoneal fluid as biomarkers for endometriosis. Int. J. Gynaecol. Obstet. 134, 247–251 (2016). Irungu, S, Mavrelos, D, Worthington, J, Blyuss, O, Saridogan, E, Timms, JF. Discovery of non-invasive biomarkers for the diagnosis of endometriosis. CLIN PROTEOM, 16 CLIN PROTEOM(2019). Janša, V., Klančič, T., Pušić, M., Klein, M., Vrtačnik Bokal, E., Ban Frangež, H., and Rižner, T. L. Proteomic analysis of peritoneal fluid identified COMP and TGFBI as new candidate biomarkers for endometriosis. Scientific reports, 11(1), 20870 (2021). Liao, T. L., Lee, Y. C., Tzeng, C. R., Wang, Y. P., Chang, H. Y., Lin, Y. F., & Kao, S. H. Mitochondrial translocation of estrogen receptor β affords resistance to oxidative insult-induced apoptosis and contributes to the pathogenesis of endometriosis. Free Radic. Biol. Med. 134, 359–373 (2019). Sikora, J., Smycz-Kubańska, M., Mielczarek-Palacz, A., Bednarek, I., and Kondera-Anasz, Z. The involvement of multifunctional TGF-β and related cytokines in pathogenesis of endometriosis. Immunol. Lett. 201, 31–37 (2018). Hang, Y., Tan, L., Chen, Q., Liu, Q., and Jin, Y. E3 ubiquitin ligase TRIM24 deficiency promotes NLRP3/caspase-1/IL-1β-mediated pyroptosis in endometriosis. Cell Biol. Int. 45, 1561–1570(2021). Anderson, G. Endometriosis pathoetiology and pathophysiology: roles of vitamin A, estrogen, immunity, adipocytes, gut microbiome and melatonergic pathway on mitochondria regulation. Biomol. Concepts. 10, 133–149(2019). Young, V. J., Ahmad, S. F., Duncan, W. C., and Horne, A. W. The role of TGF-β in the pathophysiology of peritoneal endometriosis. Hum. Reprod. Update. 23, 548–559(2017). Hou, X. X., Wang, X. Q., Zhou, W. J., and Li, D. J. Regulatory T cells induce polarization of pro-repair macrophages by secreting sFGL2 into the endometriotic milieu. Commun. Biol. 4, 499 (2021). Björk, E., Vinnars, M. T., Nagaev, I., Nagaeva, O., Lundin, E., Ottander, U., & Mincheva-Nilsson, L. Enhanced local and systemic inflammatory cytokine mRNA expression in women with endometriosis evokes compensatory adaptive regulatory mRNA response that mediates immune suppression and impairs cytotoxicity. Am. J. Reprod. Immunol. 4, 84, e13298 (2020). Shi, J., Gao, W., and Shao, F. Pyroptosis: gasdermin-mediated programmed necrotic cell death. Trends Biochem. Sci. 42, 245–254 (2017). Akino, K., Toyota, M., Suzuki, H., Imai, T., Maruyama, R., Kusano, M., Nishikawa, N., Watanabe, Y., Sasaki, Y., Abe, T., Yamamoto, E., Tarasawa, I., Sonoda, T., Mori, M., Imai, K., Shinomura, Y., & Tokino, T. Identification of DFNA5 as a target of epigenetic inactivation in gastric cancer. Cancer Sci. 98, 88–95(2007). Kim, M. S., Chang, X., Yamashita, K., Nagpal, J. K., Baek, J. H., Wu, G., Trink, B., Ratovitski, E. A., Mori, M., & Sidransky, D. Aberrant promoter methylation and tumor suppressive activity of the DFNA5 gene in colorectal carcinoma. Oncogene. 27, 3624–3634 (2008). Van Laer, L., Huizing, E. H., Verstreken, M., van Zuijlen, D., Wauters, J. G., Bossuyt, P. J. et al. Nonsyndromic hearing impairment is associated with a mutation in DFNA5. Nat. Genet. 20, 194–197(1998). Wang, Y., Gao, W., Shi, X., Ding, J., Liu, W., He, H., Wang, K., & Shao, F. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin. Nature. 547, 99–103 (2017). Rogers, C., Fernandes-Alnemri, T., Mayes, L., Alnemri, D., Cingolani, G., and Alnemri, E. S. Cleavage of DFNA5 by caspase-3 during apoptosis mediates progression to secondary necrotic/pyroptotic cell death. Nat. Commun. 8, 14128(2017). Mai, F. Y., He, P., Ye, J. Z., Xu, L. H., Ouyang, D. Y., Li, C. G., Zeng, Q. Z., Zeng, C. Y., Zhang, C. C., He, X. H., & Hu, B. Caspase-3-mediated GSDME activation contributes to cisplatin- and doxorubicin-induced secondary necrosis in mouse macrophages. Cell Prolif. 52, e12663(2019). Wang, B., Bhattacharya, M., Roy, S., Tian, Y., and Yin, Q. Immunobiology and structural biology of AIM2 inflammasome. Mol. Aspects Med. 76, 100869 (2020). Kumari, P, Russo, AJ, Shivcharan, S, Rathinam, VA. AIM2 in health and disease: Inflammasome and beyond. IMMUNOL REV, 297 IMMUNOL REV (2020). Choubey, D. Absent in melanoma 2 proteins in the development of cancer. CMLS-CELL MOL LIFE S, 73 CMLS-CELL MOL LIFE S (2016). Ye, Y., Dai, Q., and Qi, H. A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer. Cell death discovery, 7(1), 71 (2021). Lozano-Ruiz, B., Tzoumpa, A., Martínez-Cardona, C., Moreno, D., Aransay, A. M., Cortazar, A. R., Picó, J., Peiró, G., Lozano, J., Zapater, P., Francés, R., & González-Navajas, J. M. Absent in melanoma 2 (AIM2) regulates the stability of regulatory T cells. Int. J. Mol. Sci. 23, 2230(2022). Pan, J., Lu, L., Wang, X., Liu, D., Tian, J., Liu, H., Zhang, M., Xu, F., & An, F. AIM2 regulates vascular smooth muscle cell migration in atherosclerosis. Biochem. Biophys. Res. Commun. 497, 401–409(2018). Chao, B., Jiang, F., Bai, H., Meng, P., Wang, L., and Wang, F. Predicting the prognosis of glioma by pyroptosis-related signature. Journal of cellular and molecular medicine, 26(1), 133–143 (2022). Liu, Z., Wang, C., Rathkey, J. K., Yang, J., Dubyak, G. R., Abbott, D. W., & Xiao, T. S. Structures of the gasdermin D C-terminal domains reveal mechanisms of autoinhibition. Structure. 26, 778–784.e3 (2018). Aglietti, R. A., Estevez, A., Gupta, A., Ramirez, M. G., Liu, P. S., Kayagaki, N., Ciferri, C., Dixit, V. M., & Dueber, E. C. GsdmD p30 elicited by caspase-11 during pyroptosis forms pores in membranes. Proc. Natl Acad. Sci. U. S. A. 113, 7858–7863 (2016). Tables Table 1. Related information of dataset Platform (Affymetrix Human Genome U133 Plus 2.0 Array和Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]) Dataset Patient Control PMID GSE51981 77 71 25243856 GSE35287 40 40 20864642 Table2: The 32 original pyroptosis-related genes that were used in this study. Genes Name AIM2 Absent in melanoma 2 CASP1 cysteine-aspartic acid protease-1 CASP3 cysteine-aspartic acid protease-3 CASP4 cysteine-aspartic acid protease-4 CASP5 cysteine-aspartic acid protease-5 CASP6 cysteine-aspartic acid protease-6 CASP8 cysteine-aspartic acid protease-8 CASP9 cysteine-aspartic acid protease-9 ELANE elastase, neutrophil expressed GPX4 glutathione peroxidase 4 GSDMB gasdermin B GSDMC gasdermin C GSDMD gasdermin D GSDME gasdermin E IL1B interleukin 1 beta IL6 interleukin 6 IL18 interleukin 18 NLRC4 NLR family CARD domain containing 4 NLRP1 NLR family pyrin domain containing 1 NLRP2 NLR family pyrin domain containing 2 NLRP3 NLR family pyrin domain containing 3 NLRP6 NLR family pyrin domain containing 6 NLRP7 NLR family pyrin domain containing 7 NOD1 nucleotide binding oligomerization domain containing 1 NOD2 nucleotide binding oligomerization domain containing 2 PJVK pejvakin/deafness, autosomal recessive 59 PLCG1 phospholipase C gamma 1 PRKACA protein kinase cAMP-activated catalytic subunit alpha PYCARD PYD and CARD domain containing SCAF11 SR-related CTD associated factor 11 TIRAP TIR domain containing adaptor protein TNF tumor necrosis factor Table3: GO功能富集分析 Category GO Description Count LogP GO Biological Processes GO:0010942 positive regulation of cell death 36 -6.795237394 GO Biological Processes GO:0043065 positive regulation of apoptotic process 33 -6.725948242 GO Biological Processes GO:0043068 positive regulation of programmed cell death 33 -6.468562417 GO Biological Processes GO:1901216 positive regulation of neuron death 8 -3.102600975 GO Biological Processes GO:1901214 regulation of neuron death 17 -3.074072761 GO Biological Processes GO:0043525 positive regulation of neuron apoptotic process 6 -2.96232387 GO Biological Processes GO:0043523 regulation of neuron apoptotic process 11 -2.113727913 GO Biological Processes GO:0044283 small molecule biosynthetic process 27 -5.803088323 GO Biological Processes GO:0046394 carboxylic acid biosynthetic process 17 -4.007782365 GO Biological Processes GO:0016053 organic acid biosynthetic process 17 -3.967401467 GO Molecular Functions GO:0016491 oxidoreductase activity 40 -6.431827336 GO Molecular Functions GO:0033829 O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase activity 3 -4.941011776 GO Molecular Functions GO:0004540 ribonuclease activity 8 -2.289962761 GO Molecular Functions GO:0000175 3'-5'-exoribonuclease activity 4 -2.111383633 GO Molecular Functions GO:0019901 protein kinase binding 31 -3.799284977 GO Molecular Functions GO:0019900 kinase binding 31 -2.963031033 GO Molecular Functions GO:0016616 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 9 -2.593933958 GO Molecular Functions GO:0016614 oxidoreductase activity, acting on CH-OH group of donors 9 -2.335341726 GO Molecular Functions GO:0016229 steroid dehydrogenase activity 4 -2.156406636 GO Molecular Functions GO:0033764 steroid dehydrogenase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor 4 -2.354071397 GO Cellular Components GO:0005740 mitochondrial envelope 46 -8.330134083 GO Cellular Components GO:0031966 mitochondrial membrane 40 -6.462012134 GO Cellular Components GO:0005759 mitochondrial matrix 29 -5.83494067 GO Cellular Components GO:0019866 organelle inner membrane 31 -5.419918031 GO Cellular Components GO:0005743 mitochondrial inner membrane 26 -4.205371038 GO Cellular Components GO:0000315 organellar large ribosomal subunit 7 -3.68980253 GO Cellular Components GO:0005762 mitochondrial large ribosomal subunit 7 -3.68980253 GO Cellular Components GO:0098798 mitochondrial protein-containing complex 17 -3.605514366 GO Cellular Components GO:0000313 organellar ribosome 8 -3.31070908 GO Cellular Components GO:0005761 mitochondrial ribosome 8 -3.31070908 Table4:KEGG功能富集分析 Category GO Description Count LogP KEGG Pathway hsa00240 Pyrimidine metabolism 5 -2.004746308 KEGG Pathway hsa05168 Herpes simplex virus 1 infection 24 -3.366329528 KEGG Pathway hsa00740 Riboflavin metabolism 3 -3.229659261 KEGG Pathway hsa05165 Human papillomavirus infection 18 -3.227120893 KEGG Pathway hsa04390 Hippo signaling pathway 10 -2.508029284 KEGG Pathway hsa05225 Hepatocellular carcinoma 10 -2.300246423 KEGG Pathway hsa05200 Pathways in cancer 21 -2.000779506 KEGG Pathway hsa01524 Platinum drug resistance 7 -2.886007438 KEGG Pathway hsa04210 Apoptosis 9 -2.417907689 KEGG Pathway hsa05142 Chagas disease 7 -2.073577812 KEGG Pathway hsa04064 NF-kappa B signaling pathway 7 -2.029329462 KEGG Pathway hsa05164 Influenza A 10 -2.247064717 KEGG Pathway hsa00120 Primary bile acid biosynthesis 3 -2.21110722 KEGG Pathway hsa04360 Axon guidance 10 -2.063595963 KEGG Pathway hsa00532 Glycosaminoglycan biosynthesis - chondroitin sulfate / dermatan sulfate 3 -2.008477972 Additional Declarations No competing interests reported. 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M.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBCDBDZmxoYDHyok5OSJ18Le3PhwxhkLY8MGYrUw8BxvNuZtq0hkOEBAqW577+HXvG11eXwSiW0SvPMkEhgbmB8+uoFHi9mZc2nWvG2Hi9lAWiS3SeSxM7AZG+fg03IjxwzongNA9UBkuE2imLGBh00ar5b7b0Ba6iBaEudIJDYcIKTlBo/xY9425sQ2noPNBgcbiNFyJseMcc65w4lt7I2NDxuOSRgbNhPyy/Ezxh/elNUlzm9mf3D4T02dnDx788PH+LQAAZsUDwqfGb9ysJKPPwgrGgWjYBSMgpEMAJDMT7heU+s0AAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Y.S.","middleName":"","lastName":"M.","suffix":""}],"badges":[],"createdAt":"2022-08-06 10:14:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1935526/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1935526/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25158560,"identity":"2c39f148-b21b-4c00-baaa-6aa5468dd28c","added_by":"auto","created_at":"2022-08-12 20:08:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1388862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eflow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/a9537197386976dc5723ce42.png"},{"id":25158446,"identity":"a3ecc72b-8366-4fc4-860e-7354e70506b8","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2040831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRG expression pattern.\u003c/strong\u003e \u0026nbsp;A. Heatmap of PRG expression. B. GO/KEGG top enrichment functions bar plot. C. PPI protein interactions network of SCORCH death-related genes, larger nodes, and thicker lines indicate more important genes. D. Heatmap of the correlation of PRGs. Blue indicates a positive correlation, and red indicates a negative correlation; the darker the color, the stronger the correlation. E. Box line plot of PRGs. Red represents the EM group, and blue represents the normal group. * p \u0026lt; 0.05; * * p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/776dd9076d7f875d018488bd.png"},{"id":25158557,"identity":"a419e2d2-1460-4e64-b8a9-fb4c60964e84","added_by":"auto","created_at":"2022-08-12 20:08:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4813251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiomics analysis. \u003c/strong\u003e\u0026nbsp;A. ROC analysis of a single gene associated with PRGs GSE51981. B. ROC analysis of a single gene associated with PRGs GSE35287. C.AIM2 survival curve KM plot. D. PJVK survival curve KM plot. E. PLCG1 survival curve KM plot. F. Mutation oncoprint plot of 32 PRGs in TCGA OV.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/0db453c115ab718dcb366ea2.png"},{"id":25158447,"identity":"119666c4-78c9-4b7b-a68d-e8c98c0938a7","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":807044,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsistent clustering analysis.\u0026nbsp;\u003c/strong\u003eA. Consistency cluster graph when K=2. B. Consistent clustering cumulative distribution function (CDF). C. Relative change in area under the CDF curve from 2 to 10 for k. D. Tracking plot.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/682a20e370a0ba36f84e6d94.png"},{"id":25158683,"identity":"984da30d-6396-4c41-8e9d-26ef03575be1","added_by":"auto","created_at":"2022-08-12 20:13:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2725642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis.\u0026nbsp;\u003c/strong\u003eA. Volcano map of differentially expressed genes (DEGs); red represents up-regulated DEGs, blue represents down-regulated DEGs, and gray represents no DEGs. B. PCA map of the GSE51981 EM dataset. C. Heatmap showing the clustering of the GSE51981 EM groups; red represents group C2, and blue represents group C1. D. Heatmap of genes associated with PRGs; red represents group C2, and blue represents group C1.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/adbde6bff6523ea7ce4fd50f.png"},{"id":25158452,"identity":"db1c3add-a097-41c9-b3ee-7e9d812a256a","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2667981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO/KEGG functional enrichment analysis.\u003c/strong\u003e \u0026nbsp;A. Network plot of the top 20 enriched functions, with cluster IDs represented by different colors, each node being an enriched term. B. Network plot of the top 20 enriched functions, with P-values as colors. Each node is an enriched term. C. Bar graph of GO-enriched functions, with the length of functional columns shown by P value. D. KEGG enrichment results dot plot. E. Apoptosis pathway diagram. F. Pathways in cancer.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/4f8ab79fd0b3f724fe1cd7f9.png"},{"id":25158449,"identity":"9545a92c-0847-43d1-9055-2f9fad2e14c7","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2459830,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSVA functional enrichment analysis.\u0026nbsp;\u003c/strong\u003eA. GO Biological Process. B. GO Molecular Function. C. GO Cellular Component; D. KEGG Pathway top differential enrichment heatmap. All differential results are ordered by FDR.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/3c8599575c1d8b5a1d81162c.png"},{"id":25158559,"identity":"afe3c1b9-a2af-49f7-87e7-7a0a80c7c58d","added_by":"auto","created_at":"2022-08-12 20:08:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2071737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI protein network analysis. \u003c/strong\u003e\u0026nbsp;A. PPI protein interaction network obtained from the STRING database. B. Consider the sub-networks with the highest score identified by the MCODE plug-in as relatively important functional modules. C. Top 20 scoring hub genes obtained with the Cytohubba plug-in. D. Hierarchical clustering heat map based on the 20 hub genes.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/acd9149114b956bbbd5683d8.png"},{"id":25158456,"identity":"16b2ac6b-d5da-44c8-a520-fce86126b4d0","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3131583,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of immune cell infiltration and correlation analysis with PRGs.\u0026nbsp;\u003c/strong\u003eA. Correlation analysis of 22 kinds of immune cell infiltrations with PRGs; red represents positive correlation, and blue represents negative correlation. B. Heat map of the correlation of 22 types of immune cell infiltrations, blue represents a positive correlation, red represents a negative correlation; the darker the color, the stronger the correlation. C. Box line plot of the ratio of 2 types of immune cell infiltrations; red represents the C2 group, and blue represents the C1 group. * p\u0026lt;0.05,** p\u0026lt;0.01. D. Interaction of 22 kinds of immune cell infiltrations, the size of the circle represents the strength of interactions with other immune cells; the larger the circle, the stronger the interactions with other immune cells.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/fdbfd57cc7f760b2ce016382.png"},{"id":25158455,"identity":"7448ac9e-03b8-4501-aa19-9f21360e6dbd","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":983176,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation and visualization of immune cell infiltration.\u0026nbsp;\u003c/strong\u003eA. Heatmap of immune cell infiltration between groups C1 and C2. B. PCA clustering map of immune cell infiltration between groups C1 and C2. C. Violin map of the expression of PD1/PD-L1. D. Violin plot of the immune score.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/cb30ee7ceb802c496876bc12.png"},{"id":35159536,"identity":"db301eff-32b0-4767-877c-f7ab3f830049","added_by":"auto","created_at":"2023-04-02 11:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7787255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/b70c17fa-5560-4a32-8485-336b0bfa015d.pdf"},{"id":25158454,"identity":"7fa7d7ff-f211-4e5c-addf-8a8c4756baf4","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":102282,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/cb2c06459d24caa309987037.xlsx"},{"id":25158457,"identity":"e1f4a3a2-16c5-4f8c-b256-203d54b3264f","added_by":"auto","created_at":"2022-08-12 20:03:05","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":2799247,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1935526/v1/62d124f3f97152eaf207a562.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pyroptosis-Related Gene Markers Can Effectively Diagnose Endometriosis and Predict Prognosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEM is a common estrogen-dependent gynecological inflammatory disease that shares similarities with malignant tumors including attachment and infiltration. EM characterized by the presence of endometrial glands and stroma outside the uterine cavity and associated with chronic pain and infertility [1]. EM affects about 10% of women and girls of reproductive age, 25\u0026ndash;50% of infertile women and 87% of women with chronic pelvic pain worldwide [2,3]. Due to the lack of prevention and early diagnosis programs,EM signifcantly affects the quality of life in the majority of women and is a major co-factor in infertility.Laparoscopy is the gold standard for EM diagnosis; however, it is a traumatic procedure [4]. Currently, treatment methods include surgery and oral hormonal drugs, but all have significant side effects and cannot completely eradicate the disease [5,6]. Due to the non-specifc symptoms and the risks associated with surgery,it can be over 10 years before women are diagnosed and correctly treated [7]. Considering the limitations of EM therapies ,new therapeutic targets are need to improve the clinical outcome of EM.Therefore,reliable new prognostic models are urgently needed to make targeted therapies more feasible.\u003c/p\u003e \u003cp\u003ePyroptosis is a gasdermins (GSDM)-mediated PCD induced by various stimuli, such as stroke, microbial infections, and cancer, and has been widely studied in many disease models.PCD provides inflammatory and immunogenic signals that act as secondary signals ,inducing the maintenance of homeostasis of the cellular environment and driving adaptive immunity.The term pyroptosis was first identified in infected macrophages in 1992 and was named in 2001 by Cookson et al.In addition to autophagy,apoptosis,and ferroptosis,this newly discovered type of cell death has become a research hotspot in recent years.\u003c/p\u003e \u003cp\u003ePyroptosis characterized by cell swelling, plasma membrane lysis and chromatin fragmentation, accompanied by the release of a large number of intracellular pro-inflammatory cytokines such as interleukin-18 (IL-18) and interleukin-1β(IL-1β)[8\u0026ndash;10]. Pyroptosis is dominated and executed by GSDMD and GSDME in the gasdermin family.Caspase-1 and caspase-4/-5/-11 can cleave GSDMD, and caspase-3 can cleave GSDME, leading to the cleavage of N- and C-terminal domains, resulting in the gradual expansion of cells until the plasma membrane ruptures, and releasing a variety of inflammatory factors.During the occurrence of pyroptosis, a variety of risk-related cytokines and signaling molecules are released and activated, accompanied by a strong inflammatory response and activation of the immune system [11].Pyroptosis plays an important role in tumour immune microenvironment. A few studies have suggested that CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells can induce pyroptosis, and IFN-γ can enhance this process [12,13].The expression of GSDMD is associated with CD8\u0026thinsp;+\u0026thinsp;cell markers, and the cleavage of GSDMD in cytotoxic T lymphocytes is increased [13].\u003c/p\u003e \u003cp\u003eThe mechanism and functions of pyroptosis in EM have been studied, but its relationship with diagnose and prognosis has been ambiguous.The role of PRGs expression in the diagnosis and prognosis of EM remains unclear. To explore the relationship between clinical features of EM and pyroptosis is helpful for its treatment, but the value of pyroptosis in the diagnosis and prognosis of EM has not been reported.Therefore,the study aimed to find out the diagnosis and predictive value of PRGs in differentiating EM tissue from normal tissue. Besides, prognostic riskrelated phenotypes were analyzed. Thus, this study provides a novel understanding of the role of pyroptosis in EM and suggests that PRGs signatures have the potential to diagnose and predict the prognosis. The specific flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData download and data pre-processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GEOquery package [14] of R software (version 3.6.5, http://r-project.org/) was used to download the\u0026nbsp;EM\u0026nbsp;expression profile dataset from the Gene Expression Omnibus (GEO,https://www.ncbi.nlm.nih.gov/geo/)\u0026nbsp;database with reliable sources\u0026nbsp;GSE51981 [15] and GSE35287 [16] (Table 1).The samples in the dataset were obtained from Homo sapiens and the platform was based on the GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array and the GPL6244 [HuGene-1_ 0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version].Using hgu133plus2.db package [17] and hugene10sttranscriptcluster.db packages [18] for clinicopathological characteristics data processing, the genetic data were processed and annotated using the AFFY package for normalization and processing based on the Robust Multi-Array Average (RMA) method. The HUGO Gene Nomenclature Committee (HGNC) [19] is responsible for providing all genes in the human genome with unique, standard, widely disseminated symbols ,including ncRNA genes, protein-coding genes, methylation-associated\u0026nbsp;genes,\u0026nbsp;and other genes.\u0026nbsp;For each human gene, there is a numerical identifier in the HGNC, and\u0026nbsp;mRNA expression profiles are obtained using the HGNC mRNA gene annotation file.A search of the literature on pyroptosis resulted in a total of 32 known PRGs [20]\u0026nbsp;for subsequent analysis\u0026nbsp;(Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of Differentially Expressed Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained\u0026nbsp;the PRGs\u0026nbsp;expression\u0026nbsp;matrices\u0026nbsp;in GSE51981 and visualized\u0026nbsp;the PRGs\u0026nbsp;expression\u0026nbsp;patterns\u0026nbsp;using pheatmap [21].\u0026nbsp;The\u0026ldquo;limma\u0026rdquo;\u0026nbsp;package [22] was used to identify the differentially expressed genes (DEGs) between EM and normal tissues with the FDR-adjusted p value \u0026lt; 0.05 and | log2FC|\u0026gt; 2.5 [23]. The correlation of DEGs was analyzed and demonstrated by using the R package\u0026nbsp;\u0026ldquo;corrplot\u0026rdquo;package [24].\u0026nbsp;The\u0026nbsp;protein-protein interaction (PPI) network of DEGs was obtained from the Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/) \u0026nbsp;database [25].\u0026nbsp;We submitted the DEGs related to EM from differential expression analysis to the STRING database to obtain their protein interaction networks and then input the networks into Cytoscape [26] software to identify genes that could interact more strongly with other genes and visualize them. The MCODE [27] plug-in in Cytoscape was used to identify subnetworks, and the top scoring subnetworks was obtained based on the scores, which we believe may serve a specific function. \u0026nbsp;a clustering heatmap analysis was performed in GSE51981 using the constructed pheatmap based on hub genes to reveal the expression pattern of the hub gene among different subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and Evaluation of PRGs Based Classifiers for EM Diagnosis and Prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePRGs were subjected to prognostic analysis and diagnostic analysis.\u0026nbsp;The receiver operating characteristic (ROC) curves\u0026nbsp;analysis of a single gene was performed using 32 PRGs and then subjected to the corresponding prognostic analysis and diagnostic analysis in GSE51981 and GSE35287.\u0026nbsp;PRGs\u0026nbsp;with\u0026nbsp;the area under ROC curves (AUC) values greater than 0.6 in both datasets were selected as\u0026nbsp;genes with\u0026nbsp;the better diagnostic\u0026nbsp;value for EM.\u0026nbsp;GEPIA2 [28] is a web-based tool used for dynamic analysis of gene expression profile data, capable of analyzing TCGA and GTEx projects with a total of 9736 tumor samples and 8587 normal samples constructed on RNA-seq expression data analysis. We use GEPIA2 for the prognostic analysis of PRGs associated with ovarian serous cystadenocarcinoma (OV) associated with EM in the TCGA database. The cBioPortal for Cancer Genomics [29] is an open access website, open-source resource for interactive exploration of multiple cancer genomics datasets. We entered the PRGs obtained into cBioPortal for mutational analysis with OV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsensus Clustering Analysis of PRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used\u0026nbsp;the ConsensusClusterPlus package [30]\u0026nbsp;in the\u0026nbsp;R\u0026nbsp;language\u0026nbsp;to consistently\u0026nbsp;group\u0026nbsp;the gene expression profiles of the\u0026nbsp;EM\u0026nbsp;group\u0026nbsp;in the\u0026nbsp;GSE51981 dataset based on 32\u0026nbsp;PRGs.We selected the best cluster and classified it into two subtypes, C1 and C2 groups.DEGs of different subtypes were selected by the \u0026ldquo;limma\u0026rdquo; package and DEGs volcano plots were plotted using the ggplot2 package [31] to show the differential expression of DEGs, which satisfied the adj. p value \u0026lt; 0.05 and | log2FC|\u0026gt; 2.5 thresholds for the cluster heatmap analysis.PCA plots were constructed for the GSE51981 dataset using pheatmaps to determine the potential diagnostic and predictive value of these genes. Cluster heat map analysis was also performed for the GSE51981 dataset using the PRGs-based pheatmap to identify their expression patterns in different subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Sets Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO [32] is a database established by the Gene Ontology Consortium to create a semantic vocabulary standard to qualify and describe gene and protein functions for a wide range of species, which is updated as research progresses. KEGG [33] is a comprehensive database that integrates chemical, genomic, and systemic functional information. Metascape [34] is a web tool that provides a variety of functions such as gene enrichment analysis and protein interaction network analysis. The site integrates more than 40 gene function annotation databases and provides diverse visualizations. We used Metascape to perform the GO/KEGG functional enrichment analysis on immune genes differentially expressed among different subtypes, selecting functions with p\u0026lt;0.01, minimum count of 3, and enrichment factor \u0026gt; 1.5. The significant functions and pathways of GO and KEGG were visualized using the R package ggplot2.\u003c/p\u003e\n\u003cp\u003eWe downloaded the file \u0026quot;msigdb.v7.4.symbols.gmt\u0026quot; from the GSEA website and used the GSVA package\u0026nbsp;[35]\u0026nbsp;and\u0026nbsp;the MiSigDB\u0026nbsp;GMT\u0026nbsp;file\u0026nbsp;to analyze the GSE51981 expression profile to obtain the functional enrichment score (ES) matrix.\u0026nbsp;The \u0026ldquo;limma \u0026rdquo; package\u0026nbsp;lmFit\u0026nbsp;function\u0026nbsp;was then used to\u0026nbsp;analyze\u0026nbsp;the GO terms\u0026nbsp;and KEGG pathways\u0026nbsp;for differences between the\u0026nbsp;EM\u0026nbsp;and normal groups,\u0026nbsp;with an\u0026nbsp;adj. p value \u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt; 0.5\u0026nbsp;defining significant differences.\u0026nbsp;Heatmap representation of differential functions was performed using the pheatmap package in\u0026nbsp;the\u0026nbsp;R package. The\u0026nbsp;main\u0026nbsp;functional differences of KEGG, GO\u0026nbsp;biological process,\u0026nbsp;GO molecular\u0026nbsp;function,\u0026nbsp;and GO\u0026nbsp;cellular components\u0026nbsp;are shown\u0026nbsp;. Differential functions are sorted by FDR from smallest to largest.\u003c/p\u003e\n\u003cp\u003eThe STRING database searches for interactions between known proteins and predicted proteins. We submitted the DEGs related to EM from differential expression analysis to the STRING database to obtain their protein interaction networks and then input the networks into Cytoscape software to identify genes that could interact more strongly with other genes and visualize them. The MCODE plug-in in Cytoscape was used to identify subnetworks and to obtain the top scoring subnetworks based on the scores, which we believe may serve a specific function. About 20 hub genes were identified using the cytohubba plugin [36], and a clustering heatmap analysis was performed in GSE51981 using the constructed pheatmap based on hub genes to reveal the expression pattern of the hub gene among different subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Infiltration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT [37] is based on the principle of linear support vector regression to deconvolute the transcriptome expression matrix to estimate the composition and abundance of immune cells in a mixture of cells. We downloaded the original codes and the corresponding immune cell files from the official CIBERSORT website and derived the immune cell infiltration matrix in R based on the gene expression profile of the GSE51981 EM and the immune cell files. Heat maps were drawn using the pheatmap package in R language (https://CRAN.R-project.org/package=pheatmap) , showing the distribution of the 22 immune cell infiltrates in each sample. The ggplot2 software package was used to draw 2D PCA clustering plots to visualize sample distribution, and the Corrplot software package was used to plot associated heat maps for data visualization. The ggplot2 package plots box-line plots to visualize the differences between 22 immune cell infiltrates. The igraph package plots [38] were constructed to reveal the correlation network plots of immune cell infiltrates to visualize the interactions of the 22 immune cell infiltrates, using p\u0026lt;0.05 and｜correlation coefficient｜\u0026gt;0.25 as interaction standard. We correlated the resulting PRGs with immune cell infiltration and then visualized the results using the ggplot2 package. Infiltrating stromal cells and immune cells are major components of normal cells in tumor tissues and not only interfere with tumor signaling in molecular studies, but also have an important role in tumor biology. In this article, the ESTIMATE score, immune score, and stromal score were calculated using the R estimate package [39] in the GSE51981 dataset. Violin plots using the ggplot2 package were used to visualize differences in the three immune scores across subtypes. Violin plots were also drawn using the ggplot2 package to reveal the distribution of the two immune checkpoints PD1 and PDL1 between the different subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data processing and statistical analyses were completed by R software (version 3.65).The pROC package [40] was used to plot the ROC curves of genes and patients, and the AUC was calculated to assess the diagnostic effects of gene expression in normal tissues versus disease. The correlation between immune cells and PRGs was analyzed using Pearson\u0026apos;s correlation coefficient ,and the strength of the correlation was determined using the following absolute values: 0.00\u0026ndash;0.19, very weak; 0.20\u0026ndash;0.39, weak; 0.40\u0026ndash;0.59, moderate; 0.60\u0026ndash;0.79, strong; and 0.80\u0026ndash;1.0, very strong. This analysis was performed using the corrplot package in R. The correlation between PRGs and immune cells was performed using the Corr.test in the PSYCH package [41] in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of Differentially Expressed PRGs Between Normal and EM Tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression levels of 32 PRGs were compared between 111 normal and 117 EM tissues from TCGAEM data. It was observed that 10 genes,including CASP1, CASP3, CASP4, CASP6, CASP8, GSDME, IL-18, NLRP2, PJVK, and PLCG1 were significantly underexpressed in EM tissues,while 3 genes,invluding GSDMC, GSDMD,\u0026nbsp;and\u0026nbsp;TNF were significantly upexpressed in EM tissues (Figure\u0026nbsp;2A，Figure\u0026nbsp;2E）.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore the\u0026nbsp;correlation between\u0026nbsp;these PRGs,\u0026nbsp;we used\u0026nbsp;PPI and correlation analysis (Figure\u0026nbsp;2C). TNF,\u0026nbsp;CASP1,\u0026nbsp;and IL-18 had the strongest interactions with other PRGs\u0026nbsp;of\u0026nbsp;significant\u0026nbsp;importance;\u0026nbsp;The\u0026nbsp;correlation\u0026nbsp;heatmap\u0026nbsp;of\u0026nbsp;the\u0026nbsp;PRGs showed that PRKACA was weakly correlated with CASP3, CASP4, and PJVK, while GSDMD,\u0026nbsp;GPX4, PYCARD,\u0026nbsp;and CASP9 were strongly correlated; GSDMD was weakly associated with CASP3,\u0026nbsp;CASP4,\u0026nbsp;and PJVK, while GPX4,\u0026nbsp;PYCARD,\u0026nbsp;and CASP9 were strongly associated (Figure\u0026nbsp;2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetascape was used to analyze the functional enrichment of PRGs. The results showed that DEGs were related to pyroptosis, NOD-like receptor signaling pathway, positive regulation of interleukin-1 beta production, NLRP1 inflammasome complex, inflammasome complex, NLRP3 inflammasome complex, protein domain specific\u0026nbsp;binding, peptidase activator activity, and neutrophil extracellular trap formation (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiomics analysis of Diagnosis and Prognosis value of PRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePRGs were subjected to\u0026nbsp;multiomics\u0026nbsp;analysis, prognostic analysis, and diagnostic analysis. ROC analysis\u0026nbsp;of a single gene\u0026nbsp;was performed using 32 PRGs\u0026nbsp;and\u0026nbsp;then subjected to the corresponding\u0026nbsp;multiomics\u0026nbsp;analysis, prognostic analysis, and diagnostic analysis in GSE51981 and GSE35287 (Figure 3A-B). Genes with AUC \u0026gt; 0.6 were selected and visualized. We found that\u0026nbsp;the AUC values of GSDME，NLRP2，NOD1，and PLCG1 were all more than 0.6\u0026nbsp;in both datasets, and\u0026nbsp;GSDME，NLRP2，NOD1\u0026nbsp;were all more than 0.7 in GSE35287, showing the good diagnostic effects of these 4 PRGs on EM and normal patients.\u003c/p\u003e\n\u003cp\u003eWe submitted 32 PRGs in GEPIA2 for OV survival analysis of cancers associated with EM (Figure 3C-E) and found that\u0026nbsp;absent in melanoma 2 (AIM2), PJVK, and PLCG1 demonstrated good prognostic effects (Logrank p\u0026lt;0.1), with AIM2 showing\u0026nbsp;the best prognosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen\u0026nbsp;32 PRGs\u0026nbsp;were submitted to the\u0026nbsp;cBioPortal\u0026nbsp;TCGA OV\u0026nbsp;mutation\u0026nbsp;(Figure 3F),\u0026nbsp;most\u0026nbsp;mutations were amplification\u0026nbsp;mutations, with the highest mutation rate of 46% in GSDMD and 43% in GSDMC.\u0026nbsp;The main types of mutations in these two genes\u0026nbsp;were amplifications.\u0026nbsp;The\u0026nbsp;GPX4\u0026nbsp;mutation rate was 22%,\u0026nbsp;and the main type of mutation was low mRNA\u0026nbsp;expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of EM Clusters Using Consensus Clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe divided the EM samples into clusters depend on the gene expression patterns to investigated the therapeutic of PRGs. We used \u0026quot;k\u0026quot; to denote the number of clusters. To clarified the optimal K value for the sample distribution to maximum stability, we employed an empirical CDF method of plotting. The results of the consensus matrices showed that the patients in TCGA-EM could be divided into two distinct and non overlapping clusters at k=2, and the above verification was carried out by PCA(Figure 4)\u0026nbsp;. In the GSE51981 dataset,we identified 682 DEGs in the EM\u0026nbsp;group\u0026nbsp;, including 554 upregulated genes and 128 downregulated genes.The distribution of\u0026nbsp;DEGs\u0026nbsp;is shown in diagram (Figure 5A).\u0026nbsp;We performed PCA analysis of the GSE51981\u0026nbsp;EM\u0026nbsp;group using\u0026nbsp;DEGs\u0026nbsp;(Figure 5B) and found that the C1 group was clustered into one category, and the C2 group was clustered into\u0026nbsp;another\u0026nbsp;category. Hierarchical\u0026nbsp;cluster analysis of the 682 DEGs in the GSE51981\u0026nbsp;EM\u0026nbsp;revealed\u0026nbsp;that the C1 samples were clustered into one category and the C2 samples were clustered into one category (Figure 5C). The heatmap showed that\u0026nbsp;the PRGs\u0026nbsp;expression\u0026nbsp;patterns\u0026nbsp;differed significantly between the C1 and C2 samples (Figure 5D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of the Prognostic Related Biological Processes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt order to find out biological processes were influenced by the prognostic risk to make them predictive.Functional enrichment analyses was performed.Firstly,The\u0026nbsp;functional enrichment methods of GO and KEGG were used\u0026nbsp;for\u0026nbsp;analysis.The results showed that DEGs were mainly associated with the functions and pathways of mitochondrial envelope, mitochondrial membrane, mitochondrial matrix, positive regulation of cell death, positive regulation of apoptotic processes, positive regulation of programmed cell\u0026nbsp;death, positive regulation of neuron death,pathways in cancer and apoptosis,and so on (Figure 6) (Tables 3 and 4). Detailed enrichment results are shown in Supplement 1.\u0026nbsp;Secondly,to further verify this observation, GSEA was utilized to find enriched pathways in the KEGG database. The\u0026nbsp;results of\u0026nbsp;differential enrichment are shown in the Figure 6.Detailed differential enrichment are shown in Supplement 2.Results showed that the positive regulation of the T-cell receptor signaling pathway and the transforming growth factor beta (TGF-\u0026beta;) signaling pathway were the two significantly enriched pathways(Figure 7). These results proved that the PRGs-based prognostic risk is related to immune responses. Lastly,The PPI protein interaction networks were the submitted to Cytoscape to identify important genes that interacted more strongly with other genes and visualized their interactions (Figure\u0026nbsp;8A). The MCODE plugin was used to identify the\u0026nbsp;highest\u0026nbsp;scoring\u0026nbsp;subnetworks\u0026nbsp;(Figure 8B), resulting in a total of one module, which we believe may play a specific role in the pathogenesis of EM. Then the Cytohubba plugin was used to obtain the 20 hub genes with the highest scores.(Figure 8C). The 20 hub genes in\u0026nbsp;the\u0026nbsp;GSE51981 EM were analyzed by hierarchical clustering. The\u0026nbsp;C1 samples were clustered in one class and\u0026nbsp;the\u0026nbsp;C2 samples were clustered\u0026nbsp;in another\u0026nbsp;class (Figure 8D).There are significant differences in expression patterns between the two classes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of immune infiltration assessment with PRGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on these findings, we proposed that the effects of PRGs on predicting the prognosis of\u0026nbsp;EM could be related to the immune microenvironment.CIBERSORT was employed to estimate the immune cell component in\u0026nbsp;EM tissues. The proportion of 22 human immune cell subsets, including naive and memory B cells, NK cells,\u0026nbsp;plasma cells, and myeloid subsets, was evaluated. The results of\u0026nbsp;the\u0026nbsp;heatmap representing the infiltration of 22 immune cell\u0026nbsp;types\u0026nbsp;(Figure 9B) showed that activated\u0026nbsp;NK cells were\u0026nbsp;significantly negatively correlated with\u0026nbsp;resting\u0026nbsp;NK cells\u0026nbsp;and M2\u0026nbsp;macrophages.\u0026nbsp;Resting mast\u0026nbsp;cells showed a significant negative correlation with regulatory\u0026nbsp;T cells\u0026nbsp;(Tregs),\u0026nbsp;plasma cells,\u0026nbsp;and resting\u0026nbsp;NK cells. Memory\u0026nbsp;B cells showed a significant negative correlation with\u0026nbsp;M2 macrophages. There was a\u0026nbsp;significant positive correlation between\u0026nbsp;activated\u0026nbsp;NK cells and\u0026nbsp;resting mast cells, as well as between \u0026nbsp;monocytes\u0026nbsp;and\u0026nbsp;neutrophils.\u0026nbsp;The correlation analysis (Figure 9A)\u0026nbsp;showed that immune cells were clustered into two categories, NK cells resting, plasma cells,macrophages M0,macrophages M1,T cells CD4 memory resting,macrophages M2,B cells naive,T cells gamma delta,dendritic cells resting,and dendritic cells were significantly correlated with expression of GSDME, CASP3, CASP4, GSDMB, PJVK, SCAF11, CASP6, NOD1, IL-18, NLRC4, NOD2, CASP1, and NLRP2 .\u0026nbsp;Other immune cells showed the opposite trend. The results of the box line plot\u0026nbsp;of differences in differences in\u0026nbsp;immune cell\u0026nbsp;infiltration\u0026nbsp;(Figure 9C) showed that compared\u0026nbsp;to\u0026nbsp;C1, the C2 group\u0026nbsp;had\u0026nbsp;higher levels of\u0026nbsp;immune infiltration by\u0026nbsp;memory\u0026nbsp;B cells, activated CD8\u0026nbsp;T cells, activated\u0026nbsp;NK cells,\u0026nbsp;follicular helper T cells,\u0026nbsp;Tregs, monocytes, and\u0026nbsp;resting\u0026nbsp;mast cells, but\u0026nbsp;lower levels of immune infiltration by plasma cells,\u0026nbsp;resting\u0026nbsp;NK cells, M1\u0026nbsp;macrophages,\u0026nbsp;M2\u0026nbsp;macrophages,\u0026nbsp;and\u0026nbsp;resting\u0026nbsp;dendritic cells. The results of the 22 immune cell interactions (Figure\u0026nbsp;9D) showed that\u0026nbsp;na\u0026iuml;ve CD4\u0026nbsp;T cells,\u0026nbsp;M2\u0026nbsp;macrophages,\u0026nbsp;activated\u0026nbsp;NK cells\u0026nbsp;had the strongest interactions with other immune cells, while gamma delta T cells, monocytes,\u0026nbsp;and activated\u0026nbsp;memory CD4 T cells\u0026nbsp;had the weakest interactions with other immune cells.\u0026nbsp;The heatmap and PCA clustering analysis of immune cell infiltration showed that there was a significant difference in immune cell infiltration between the\u0026nbsp;samples of the\u0026nbsp;C1 group and\u0026nbsp;the\u0026nbsp;samples of the\u0026nbsp;C2 group (Figure\u0026nbsp;10A-B). Violin plots and Wilcox\u0026nbsp;tests\u0026nbsp;revealed differences in PD1/PD-L1 expression levels (Figure 10C).\u0026nbsp;The ESTIMATE algorithm was used to obtain each immune score for the GSE51981 samples:\u0026nbsp;the ESTIMATE score, immune score,\u0026nbsp;and stromal score. The violin plot of the scores showed that each score was higher in the C2 group than in the C1 group (Figure 10D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEM is an estrogen-dependent chronic gynecological inflammatory disease and is the main cause of pelvic pain and low fertility in women of reproductive age [1,2,4]. Previous studies have shown that the sensitivity and specificity of serum CA-125, IL-6, ICAM-1,and glycodelin in the diagnosis of EM were not high[42], but the combined detection of multiple markers can improved the accuracy of diagnosis and showed the specificity of menstrual cycle [43].However, the differential expression of proteins in the peritoneal fluid for the diagnosis of EM and the determination of prognosis need to be further explored [44]. Therefore, it is necessary to develop a method to diagnose and predict EM. In recent years, studies have shown that the expression level of pyroptosis is closely related to the occurrence, development, and metastasis of EM, but there is no clear target and mechanism [45, 46]. Therefore, to identify validated diagnostic biomarkers for EM, we searched the GEO database and obtained two datasets (GSE51981 and GSE35287) and searched the literature related to pyroptosis to select 32 known PRGs for comprehensive analysis. A total of 682 DEGs were identified through cross-validation of the EM group. Furthermore, GO enrichment analysis and GSEA indicated that these enrichment modules and pathways were closely associated with mitochondrial dysfunction and cell death observed in EM. Furthermore, the top 20 central genes identified in the PPI network associated with EM had high functional similarity and diagnostic value for EM.\u003c/p\u003e \u003cp\u003eIn the first part of this study, we used cross-validation to obtain expression matrices of 32 PRGs in the GSE51981 dataset. In order to study the BPs of DEGs in normal and EM samples, we performed GO/KEGG functional enrichment analysis of PRGs using Metascape. In the molecular functions (MF) annotations, pyroptosis, NOD-like receptor signaling pathway, positive regulation of IL-1 beta production, NLRP1 inflammasome complex, inflammasome complex, NLRP3 inflammasome complex and protein domain specific binding, peptidase activator activity, and formation of neutrophil extracellular traps were significantly associated with the DEGs. These data are consistent with previous results showing that the focal pathway is involved in the development of EM [47].\u003c/p\u003e \u003cp\u003eIn the second part of this study, we used cross-validation to identify 682 DEGs in the datasets.The results showed that mitochondrial envelope, mitochondrial membrane, mitochondrial matrix, positive regulation of cell death, positive regulation of apoptotic process, positive regulation of programmed cell death, positive regulation of neuron death, regulation of neuron death,oxidoreductase activity,o-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase activity, pyrimidine metabolism, pathways in cancer, and apoptosis were significantly associated with the development of EM in MF annotations. Mitochondria play a key role in the development and progression of EM, and the regulation of cellular Ca2\u0026thinsp;+\u0026thinsp;homeostasis, oxidative stress, and apoptosis. Mitochondrial dysfunction has recently attracted considerable attention because impaired mitochondrial bioenergetics can be linked to inflammation, oxidative stress, and ERβ levels related pathways that underlie the pathophysiology of EM [48]. A previous study concluded that ERβ levels were elevated in endometriotic tissue; ERβ directly regulates mitochondrial DNA (mtDNA) gene expression by interacting with the D loop of mtDNA and polymerase γ. The ERβ in mitochondria resists oxidative damage-induced apoptosis by inducing the ROS scavenger enzyme Mn-superoxide dismutase and antiapoptotic protein Bcl-2 [45]. Together, these observations suggest that mitochondrial dysfunction may be one of the leading causes of EM.\u003c/p\u003e \u003cp\u003eIn the third part of this study, GSEA was performed to investigate the biological functions of DEGs associated with EM. The results showed that positive regulation of T-cell receptor signaling pathway and TGF-β signaling pathway were two significantly enriched signaling pathways. Interestingly, we noted that the most enriched pathways were associated with immune response, inflammation, and apoptosis in our analysis. TGF-β is one of the main immune and inflammatory factors responsible for the regulation of cell proliferation, angiogenesis,immune responses,and differentiation [46]. Studies in mouse models of EM and females with EM have shown that elevated levels of TGF-β ligands are associated with reduced intraperitoneal immune cell activity and increased survival, attachment, invasion, and proliferation of ectopic endometrial cells during the development of EM [49]. Soluble fibrinogen-like protein 2 secreted by highly active Tregs bias macrophages towards a repair phenotype through the SHP2-ERK1/2-STAT3 signaling pathway, which is associated with the progression of EM [50]. Consistent with our data, analysis of microarray results of EM from other mRNA datasets also suggests that immune and inflammatory responses play a key role in the regulatory network of EM [51]. Our data mining results further confirmed that inflammatory response plays a key role in the etiology of EM.\u003c/p\u003e \u003cp\u003eIn the PPI network identified in this study, we obtained a module that may play a specific role in the pathogenesis of EM. Meanwhile, 20 central genes, highlighted as the most important, had multiple interactions in the network. Further study of these genes may reveal the pathophysiology of EM. Hierarchical cluster were analyzed of 20 central genes for EM in GSE5181 dataset, and the expression patterns of the two classes differed significantly.\u003c/p\u003e \u003cp\u003eIn EM, how PRGs interact and whether they are relevant to patients is still unknown. With regard to the diagnostic and prognostic value, we analyzed the AUC of 32 PRGs, which indicated that AUCs of GSDME, NLRP2, NOD1, and PLCG1 were greater than 0.6; thus, these genes have good diagnostic value and may be promising targets for the diagnosis of EM. Among the 32 PRGs, AIM2, PJVK, and PLCG1 exhibited good prognostic values, with AIM2 being the strongest predictor. Most mutations in PRGs in patients with endogeneity were amplifications, and GSDMD had the highest mutation rate. Gasdermin E (GSDME) is one of the member of the gasdermin family [52]. The GSDME gene is highly expressed in many normal human tissues, such as the testes and placenta, and is moderately expressed in the heart and stomach. However, due to epigenetic modifications in the promoter region, GSDME is frequently down-regulated or even silenced in cancer cells [53\u0026ndash;55]. Studies have shown that activated caspase-3 can cleave GSDME to generate its N-terminal fragment, which performs secondary necrosis/pyroptosis by the formation of pores in the plasma membrane [56,57]. GSDME expression can control apoptosis and pyroptosis transformation [58]. When overexpressed or moderately expressed, it can lead to cell death through cysteine-3-dependent pyroptosis. When underexpressed, the mode of cell death changed to apoptosis [56,57]. Progression is closely associated with apoptosis and pyroptosis. Therefore, GSDME can be used as a potential diagnostic indicator of EM. A cytoplasmic sensor AIM2, assembles with spot-like proteins associated with apoptosis containing CARD and procaspase-1 in recognition of double-stranded DNA to form the multiprotein complex AIM2 inflammasome [59]. AIM2 activates CASP-1 through ASC-mediated junctional proteins to promote the maturation and release of IL-1β and IL-18 and to promote pyroptosis [60] .AIM2 may play a unique role in different cancer types.Aim2 was found overexpressed in oral cancer, nasopharyngeal carcinoma and non-small cell lung cancer, but it was found to be suppressed in endometrial cancer, gastric cancer and colon cancer [61,62] .Interestingly,in our study, AIM2 seemed to be a cancer-promoting gene,because it was upregulated in EM tissues. AIM2 was identified as a regulator of FOXP3\u0026thinsp;+\u0026thinsp;Treg cell differentiation and as a potential target for intervention to restore T cell homeostasis [63]. When ROS inhibitors are used, AIM2 expression can be inhibited. Furthermore, overexpression and inhibition of AIM2 expression significantly influences HG-induced migration and the TGF-β/SMAD signaling pathway in vascular smooth muscle cells [64]. In summary, the above pyroptosis factors are of great value in the diagnosis and prognosis of EM.\u003c/p\u003e \u003cp\u003eAnother important finding of our study showed that 32 PRGs were significantly associated with immune infiltration, and confirmed the important role of pyroptosis in the tumor immune microenvironment [62,65]. Mutations in GSDMD can regulate the tumor immune microenvironment by modulating pyroptosis after inflammatory vesicle activation [66]. Previous studies [67] have also found that PRG GSDMD is associated with immune infiltration.\u003c/p\u003e \u003cp\u003eIn this study, we used in-depth bioinformatics analysis to identify candidate genes and key signaling pathways that regulate the onset and progression of EM, as well as possible predictive genes. Collectively, these findings provide new insights into the underlying molecular mechanisms and potential drug candidates for the treatment of EM. Therefore, exploring the potential correlation between PRGs in EM and immune factors and EM can help to elucidate the role of pyroptosis and inflammatory factors in disease pathogenesis and generate relevant insights to guide the development of new therapeutic strategies.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, despite the emerging evidence suggesting a series of DEGs in EM, such as PRGs, there are still no reliable candidates to be considered as therapeutic targets for EM. It is necessary to identify more DEGs and explore whether possible targeted genes affect the initiation and progression of EM. Further, although microarray-based bioinformatic analysis is a powerful tool in efficient understanding of molecular mechanisms and identifying potential biomarkers underlying EM, further experimental validation of the identified PRGs are needed at molecular, cellular, and organismal levels. Lastly, to clarify the functions of DEGs and hub genes in EM, loss-of‐function and gain‐of‐function studies with tissue‐type specificity and cell‐type specificity are warranted. Signaling pathways are more diverse than originally thought in EM and include the cancer and apoptosis pathways. Although several pathways have been identified, a series of \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e molecular experiments is needed to further confirm our results and may be useful to provide more detailed and stronger evidence of the possible phenotype and regulation of the pathways of genes predicted to underlie EM.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn sum,we performed a comprehensive systematic bioinformatic analysis and identified diagnostic genes associated with PRGs in EM patients, including four genes (GSDME, NLRP2, NOD1, and PLCG1) and a prognostic gene signature containing three genes (AIM2, PJVK, and PLCG1).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunnan Provincial Department of Education. Grant Reference Number 2022J0240.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiamei Song, Tao Shi,Yang Liu, and Yushi Meng designed research, Jingsi Chen, Xiaoling Yang, and Dongya Li prepared figures 1-10. Jia Bie and Ya Su were prepared tables1-4. All authors read and approved the final version of the manuscript. Yushi Meng and Yang Liu led and oversaw the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSignorile, P. G., Cassano, M., Viceconte, R., Spyrou, M., Marcattilj, V., and Baldi, A. Endometriosis: A retrospective analysis on Diagnostic Data in a cohort of 4,401 patients. In Vivo. 36, 430\u0026ndash;438 (2022). \u003c/li\u003e\n\u003cli\u003eSaunders, P. T. K., and Horne, A. W. Endometriosis: etiology,pathobiology, and therapeutic prospects. Cell. 184, 2807\u0026ndash;2824 (2021).\u003c/li\u003e\n\u003cli\u003eParasar, P., Ozcan, P., and Terry, K. L. Endometriosis: epidemiology, diagnosis and clinical management. Curr. Obstet. Gynecol. Rep. 6, 34\u0026ndash;41 (2017).\u003c/li\u003e\n\u003cli\u003eSignorile, P. G., Cassano, M., Viceconte, R., Spyrou, M., Marcattilj, V., and Baldi, A. Endometriosis: A retrospective analysis on Diagnostic Data in a cohort of 4,401 patients. In Vivo. 36, 430\u0026ndash;438(2022).\u003c/li\u003e\n\u003cli\u003eFerrero, S., Barra, F., and Leone Roberti Maggiore, U. Current and Emerging Therapeutics for the Management of Endometriosis. Drugs. 78, 995\u0026ndash;1012 (2018).\u003c/li\u003e\n\u003cli\u003eBhurke, A. V., DasMahapatra,et al. Clinical characteristics and surgical management of endometriosis-associated infertility: A multicenter prospective cohort study. Int. J. Gynaecol. Obstet; 10.1002/ijgo.14115 (2022).\u003c/li\u003e\n\u003cli\u003eRogers, PA., Adamson, GD., Al-Jefout, M., Becker, CM., D'Hooghe, TM., Dunselman, GA., Fazleabas, A., Giudice, LC., Horne, AW., Hull, ML., Hummelshoj, L., Missmer, SA., Montgomery, GW., Stratton, P., Taylor, RN., Rombauts, L., Saunders, PT., Vincent, K., Zondervan, KT. Research Priorities for Endometriosis. REPROD SCI, 24 (2), 202\u0026ndash;226 (2016).\u003c/li\u003e\n\u003cli\u003eMorimoto, N., Kono, T., Sakai, M., and Hikima, J. I. Inflammasomes in teleosts: structures and mechanisms that induce pyroptosis during bacterial infection. Int. J. Mol. Sci. 22, 4389 (2021).\u003c/li\u003e\n\u003cli\u003eZhaolin, Z., Guohua, L., Shiyuan, W., and Zuo, W. Role of pyroptosis in cardiovascular disease. Cell Prolif. 52, e12563(2019).\u003c/li\u003e\n\u003cli\u003eFrank, D., and Vince, J. E. Pyroptosis versus necroptosis: similarities, differences, and crosstalk. Cell Death Differ. 26, 99\u0026ndash;114(2019).\u003c/li\u003e\n\u003cli\u003eJu, A., Tang, J., Chen, S., Fu, Y., and Luo, Y. Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis. Frontiers in oncology, 11, 709077(2021).\u003c/li\u003e\n\u003cli\u003eZhou, Z., He, H., Wang, K., Shi, X., Wang, Y., Su, Y., Wang, Y., Li, D., Liu, W., Zhang, Y., Shen, L., Han, W., Shen, L., Ding, J., Shao, F. Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells. SCIENCE, 368,6494 (2020).\u003c/li\u003e\n\u003cli\u003eXi, G., Gao, J., Wan, B., Zhan, P., Xu, W., Lv, T., Song, Y. GSDMD is required for effector CD8\u0026thinsp;+\u0026thinsp;T cell responses to lung cancer cells. INT IMMUNOPHARMACOL, 74 105713,(2019).\u003c/li\u003e\n\u003cli\u003eDavis, S., and Meltzer, P. S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 23, 1846\u0026ndash;1847 (2007).\u003c/li\u003e\n\u003cli\u003eTamaresis, J. S., Irwin, J. C., Goldfien, G. A., Rabban, J. T., Burney, R. O., Nezhat, C. et al. Molecular classification of endometriosis and disease stage using high-dimensional genomic data. Endocrinology. 155, 4986\u0026ndash;4999 (2014).\u003c/li\u003e\n\u003cli\u003eAghajanova, L., Tatsumi, K., Horcajadas, J. A., Zamah, A. M., Esteban, F. J., Herndon, C. N. et al. Unique transcriptome, pathways, and networks in the human endometrial fibroblast response to progesterone in endometriosis. Biol. Reprod. 84, 801\u0026ndash;815 (2011).\u003c/li\u003e\n\u003cli\u003eCarlson, M. hgu133plus2.Db: Affymetrix Human Genome U133 Plus 2.0 Array Annotation Data (Chip hgu133plus2). R Package Version 3.2.3 (2016).\u003c/li\u003e\n\u003cli\u003eLangkilde A, Olsen LC, S\u0026aelig;trom P, Drabl\u0026oslash;s F, Besenbacher S, Raaby L, Johansen C, Iversen L. Pathway analysis of skin from psoriasis patients after adalimumab treatment reveals new early events in the anti-inflammatory mechanism of Anti-TNF-\u0026alpha;. PLOS ONE. 11, e0167437(2016).\u003c/li\u003e\n\u003cli\u003eTweedie, S., Braschi, B., Gray, K., Jones, T., Seal, R. L., Yates, B., \u0026amp; Bruford, E. A. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 49, D939-D946(2021).\u003c/li\u003e\n\u003cli\u003eXia, X., Wang, X., Cheng, Z., Qin, W., Lei, L., Jiang, J., \u0026amp; Hu, J. The role of pyroptosis in cancer: pro-cancer or pro-\u0026lsquo;host\u0026rsquo;? Cell Death Dis. 10, 650 (2019).\u003c/li\u003e\n\u003cli\u003eYao, S.and Liu, T. Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database. Oncol. Lett. 15, 8319\u0026ndash;8324 (2018).\u003c/li\u003e\n\u003cli\u003eRitchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., \u0026amp; Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47(2015).\u003c/li\u003e\n\u003cli\u003eZhou, J., Zhu, Y., Ma, S., Li, Y., Liu, K., Xu, S., Li, X., Li, L., Hu, J., \u0026amp; Liu, Y. Bioinformatics analysis identifies DYNC1I1 as prognosis marker in male patients with liver hepatocellular carcinoma. PLOS ONE. 16, e0258797(2021).\u003c/li\u003e\n\u003cli\u003eParvathi, A., Catena, M., Jasna, V., Phadke, N., and Gogate, N. Influence of hydrological factors on bacterial community structure in a tropical monsoonal estuary in India. Environ. Sci. Pollut. Res. Int. 28, 50579\u0026ndash;50592(2021).\u003c/li\u003e\n\u003cli\u003evon Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., and Snel, B. String: a database of predicted functional associations between proteins. Nucleic Acids Res. 31, 258\u0026ndash;261 (2003).\u003c/li\u003e\n\u003cli\u003eShannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., \u0026amp; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498\u0026ndash;2504(2003).\u003c/li\u003e\n\u003cli\u003eDeng, J. L., Xu, Y. H., and Wang, G. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front. Genet. 10, 695 (2019).\u003c/li\u003e\n\u003cli\u003eTang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556-W560 (2019).\u003c/li\u003e\n\u003cli\u003eGao, J., Aksoy, B. A., Dogrusoz, U., Dresdner, G., Gross, B., Sumer, S. O., Sun, Y., Jacobsen, A., Sinha, R., Larsson, E., Cerami, E., Sander, C., \u0026amp; Schultz, N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, p11(2013).\u003c/li\u003e\n\u003cli\u003eWilkerson, M. D. and Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 26, 1572\u0026ndash;1573(2010).\u003c/li\u003e\n\u003cli\u003eWu, X., Sui, Z., Zhang, H., Wang, Y., and Yu, Z. Integrated analysis of lncRNA-mediated ceRNA network in lung adenocarcinoma. Front. Oncol. 10, 554759(2020).\u003c/li\u003e\n\u003cli\u003eThe Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330-D338 (2019).\u003c/li\u003e\n\u003cli\u003eKanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., and Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017).\u003c/li\u003e\n\u003cli\u003eZhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., \u0026amp; Chanda, S. K. Metascape provides a biologist-oriented resource for the analysis of systEM-level datasets. Nat. Commun. 10, 1523(2019).\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann, S., Castelo, R., and Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 14, 7 (2013).\u003c/li\u003e\n\u003cli\u003eChin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., and Lin, C. Y. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8 (Suppl 4), S11(2014).\u003c/li\u003e\n\u003cli\u003eChen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., and Alizadeh, A. A. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711, 243\u0026ndash;259(2018).\u003c/li\u003e\n\u003cli\u003eBaciu, C., Pasini, E., Angeli, M., Schwenger, K., Afrin, J., Humar, A., Fischer, S., Patel, K., Allard, J., \u0026amp; Bhat, M. Systematic integrative analysis of gene expression identifies HNF4A as the central gene in pathogenesis of non-alcoholic steatohepatitis. PLOS ONE. 12, e0189223 (2017).\u003c/li\u003e\n\u003cli\u003eJuge, P. A., Lee, J. S., Lau, J., Kawano-Dourado, L., Rojas Serrano, J., Sebastiani, M., Koduri, G., Matteson, E., Bonfiglioli, K., Sawamura, M., Kairalla, R., Cavagna, L., Bozzalla Cassione, E., Manfredi, A., Mejia, M., Rodr\u0026iacute;guez-Henriquez, P., Gonz\u0026aacute;lez-P\u0026eacute;rez, M. I., Falf\u0026aacute;n-Valencia, R., Buendia-Rold\u0026aacute;n, I., P\u0026eacute;rez-Rubio, G., \u0026hellip; Dieud\u0026eacute;, P. Methotrexate and rheumatoid arthritis associated interstitial lung disease. The European respiratory journal, 57(2), 2000337 (2021).\u003c/li\u003e\n\u003cli\u003eRobin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., \u0026amp; M\u0026uuml;ller, M. pROC: an Open-Source Package for R and S\u0026thinsp;+\u0026thinsp;to Analyze and Compare ROC Curves. BMC Bioinformatics. 12, 77(2011).\u003c/li\u003e\n\u003cli\u003eRobles-Jimenez, L. E., Aranda-Aguirre, E., Castelan-Ortega, O. A., Shettino-Bermudez, B. S., Ortiz-Salinas, R., Miranda, M., Li, X., Angeles-Hernandez, J. C., Vargas-Bello-P\u0026eacute;rez, E., \u0026amp; Gonzalez-Ronquillo, M. Worldwide traceability of antibiotic residues from livestock in wastewater and soil: A systematic review. Animals (Basel). 12, 60(2021).\u003c/li\u003e\n\u003cli\u003eMosbah, A., Nabiel, Y., and Khashaba, E. Interleukin-6, intracellular adhesion molecule-1, and glycodelin A levels in serum and peritoneal fluid as biomarkers for endometriosis. Int. J. Gynaecol. Obstet. 134, 247\u0026ndash;251 (2016).\u003c/li\u003e\n\u003cli\u003eIrungu, S, Mavrelos, D, Worthington, J, Blyuss, O, Saridogan, E, Timms, JF. Discovery of non-invasive biomarkers for the diagnosis of endometriosis. CLIN PROTEOM, 16 CLIN PROTEOM(2019).\u003c/li\u003e\n\u003cli\u003eJan\u0026scaron;a, V., Klančič, T., Pu\u0026scaron;ić, M., Klein, M., Vrtačnik Bokal, E., Ban Frangež, H., and Rižner, T. L. Proteomic analysis of peritoneal fluid identified COMP and TGFBI as new candidate biomarkers for endometriosis. Scientific reports, 11(1), 20870 (2021).\u003c/li\u003e\n\u003cli\u003eLiao, T. L., Lee, Y. C., Tzeng, C. R., Wang, Y. P., Chang, H. Y., Lin, Y. F., \u0026amp; Kao, S. H. Mitochondrial translocation of estrogen receptor \u0026beta; affords resistance to oxidative insult-induced apoptosis and contributes to the pathogenesis of endometriosis. Free Radic. Biol. Med. 134, 359\u0026ndash;373 (2019).\u003c/li\u003e\n\u003cli\u003eSikora, J., Smycz-Kubańska, M., Mielczarek-Palacz, A., Bednarek, I., and Kondera-Anasz, Z. The involvement of multifunctional TGF-\u0026beta; and related cytokines in pathogenesis of endometriosis. Immunol. Lett. 201, 31\u0026ndash;37 (2018).\u003c/li\u003e\n\u003cli\u003eHang, Y., Tan, L., Chen, Q., Liu, Q., and Jin, Y. E3 ubiquitin ligase TRIM24 deficiency promotes NLRP3/caspase-1/IL-1\u0026beta;-mediated pyroptosis in endometriosis. Cell Biol. Int. 45, 1561\u0026ndash;1570(2021).\u003c/li\u003e\n\u003cli\u003eAnderson, G. Endometriosis pathoetiology and pathophysiology: roles of vitamin A, estrogen, immunity, adipocytes, gut microbiome and melatonergic pathway on mitochondria regulation. Biomol. Concepts. 10, 133\u0026ndash;149(2019).\u003c/li\u003e\n\u003cli\u003eYoung, V. J., Ahmad, S. F., Duncan, W. C., and Horne, A. W. The role of TGF-\u0026beta; in the pathophysiology of peritoneal endometriosis. Hum. Reprod. Update. 23, 548\u0026ndash;559(2017).\u003c/li\u003e\n\u003cli\u003eHou, X. X., Wang, X. Q., Zhou, W. J., and Li, D. J. Regulatory T cells induce polarization of pro-repair macrophages by secreting sFGL2 into the endometriotic milieu. Commun. Biol. 4, 499 (2021).\u003c/li\u003e\n\u003cli\u003eBj\u0026ouml;rk, E., Vinnars, M. T., Nagaev, I., Nagaeva, O., Lundin, E., Ottander, U., \u0026amp; Mincheva-Nilsson, L. Enhanced local and systemic inflammatory cytokine mRNA expression in women with endometriosis evokes compensatory adaptive regulatory mRNA response that mediates immune suppression and impairs cytotoxicity. Am. J. Reprod. Immunol. 4, 84, e13298 (2020).\u003c/li\u003e\n\u003cli\u003eShi, J., Gao, W., and Shao, F. Pyroptosis: gasdermin-mediated programmed necrotic cell death. Trends Biochem. Sci. 42, 245\u0026ndash;254 (2017).\u003c/li\u003e\n\u003cli\u003eAkino, K., Toyota, M., Suzuki, H., Imai, T., Maruyama, R., Kusano, M., Nishikawa, N., Watanabe, Y., Sasaki, Y., Abe, T., Yamamoto, E., Tarasawa, I., Sonoda, T., Mori, M., Imai, K., Shinomura, Y., \u0026amp; Tokino, T. Identification of DFNA5 as a target of epigenetic inactivation in gastric cancer. Cancer Sci. 98, 88\u0026ndash;95(2007).\u003c/li\u003e\n\u003cli\u003eKim, M. S., Chang, X., Yamashita, K., Nagpal, J. K., Baek, J. H., Wu, G., Trink, B., Ratovitski, E. A., Mori, M., \u0026amp; Sidransky, D. Aberrant promoter methylation and tumor suppressive activity of the DFNA5 gene in colorectal carcinoma. Oncogene. 27, 3624\u0026ndash;3634 (2008).\u003c/li\u003e\n\u003cli\u003eVan Laer, L., Huizing, E. H., Verstreken, M., van Zuijlen, D., Wauters, J. G., Bossuyt, P. J. et al. Nonsyndromic hearing impairment is associated with a mutation in DFNA5. Nat. Genet. 20, 194\u0026ndash;197(1998).\u003c/li\u003e\n\u003cli\u003eWang, Y., Gao, W., Shi, X., Ding, J., Liu, W., He, H., Wang, K., \u0026amp; Shao, F. Chemotherapy drugs induce pyroptosis through caspase-3 cleavage of a gasdermin. Nature. 547, 99\u0026ndash;103 (2017).\u003c/li\u003e\n\u003cli\u003eRogers, C., Fernandes-Alnemri, T., Mayes, L., Alnemri, D., Cingolani, G., and Alnemri, E. S. Cleavage of DFNA5 by caspase-3 during apoptosis mediates progression to secondary necrotic/pyroptotic cell death. Nat. Commun. 8, 14128(2017).\u003c/li\u003e\n\u003cli\u003eMai, F. Y., He, P., Ye, J. Z., Xu, L. H., Ouyang, D. Y., Li, C. G., Zeng, Q. Z., Zeng, C. Y., Zhang, C. C., He, X. H., \u0026amp; Hu, B. Caspase-3-mediated GSDME activation contributes to cisplatin- and doxorubicin-induced secondary necrosis in mouse macrophages. Cell Prolif. 52, e12663(2019).\u003c/li\u003e\n\u003cli\u003eWang, B., Bhattacharya, M., Roy, S., Tian, Y., and Yin, Q. Immunobiology and structural biology of AIM2 inflammasome. Mol. Aspects Med. 76, 100869 (2020).\u003c/li\u003e\n\u003cli\u003eKumari, P, Russo, AJ, Shivcharan, S, Rathinam, VA. AIM2 in health and disease: Inflammasome and beyond. IMMUNOL REV, 297 IMMUNOL REV (2020).\u003c/li\u003e\n\u003cli\u003eChoubey, D. Absent in melanoma 2 proteins in the development of cancer. CMLS-CELL MOL LIFE S, 73 CMLS-CELL MOL LIFE S (2016).\u003c/li\u003e\n\u003cli\u003eYe, Y., Dai, Q., and Qi, H. A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer. Cell death discovery, 7(1), 71 (2021).\u003c/li\u003e\n\u003cli\u003eLozano-Ruiz, B., Tzoumpa, A., Mart\u0026iacute;nez-Cardona, C., Moreno, D., Aransay, A. M., Cortazar, A. R., Pic\u0026oacute;, J., Peir\u0026oacute;, G., Lozano, J., Zapater, P., Franc\u0026eacute;s, R., \u0026amp; Gonz\u0026aacute;lez-Navajas, J. M. Absent in melanoma 2 (AIM2) regulates the stability of regulatory T cells. Int. J. Mol. Sci. 23, 2230(2022).\u003c/li\u003e\n\u003cli\u003ePan, J., Lu, L., Wang, X., Liu, D., Tian, J., Liu, H., Zhang, M., Xu, F., \u0026amp; An, F. AIM2 regulates vascular smooth muscle cell migration in atherosclerosis. Biochem. Biophys. Res. Commun. 497, 401\u0026ndash;409(2018).\u003c/li\u003e\n\u003cli\u003eChao, B., Jiang, F., Bai, H., Meng, P., Wang, L., and Wang, F. Predicting the prognosis of glioma by pyroptosis-related signature. Journal of cellular and molecular medicine, 26(1), 133\u0026ndash;143 (2022).\u003c/li\u003e\n\u003cli\u003eLiu, Z., Wang, C., Rathkey, J. K., Yang, J., Dubyak, G. R., Abbott, D. W., \u0026amp; Xiao, T. S. Structures of the gasdermin D C-terminal domains reveal mechanisms of autoinhibition. Structure. 26, 778\u0026ndash;784.e3 (2018).\u003c/li\u003e\n\u003cli\u003eAglietti, R. A., Estevez, A., Gupta, A., Ramirez, M. G., Liu, P. S., Kayagaki, N., Ciferri, C., Dixit, V. M., \u0026amp; Dueber, E. C. GsdmD p30 elicited by caspase-11 during pyroptosis forms pores in membranes. Proc. Natl Acad. Sci. U. S. A. 113, 7858\u0026ndash;7863 (2016).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Related information of dataset Platform (Affymetrix Human Genome U133 Plus 2.0 Array和Affymetrix Human Gene 1.0 ST Array [transcript (gene) version])\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003eDataset\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003ePatient\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003eControl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003ePMID\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003eGSE51981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e25243856\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003eGSE35287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"189\"\u003e\n\u003cp\u003e20864642\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable2: The 32 original pyroptosis-related genes that were used in this study.\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eAIM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eAbsent in melanoma 2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eCASP9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ecysteine-aspartic acid protease-9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eELANE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eelastase, neutrophil expressed\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGPX4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eglutathione peroxidase 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGSDMB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003egasdermin B\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGSDMC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003egasdermin C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGSDMD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003egasdermin D\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eGSDME\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003egasdermin E\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eIL1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003einterleukin 1 beta\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eIL6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003einterleukin 6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eIL18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003einterleukin 18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family CARD domain containing 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRP1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family pyrin domain containing 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRP2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family pyrin domain containing 2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRP3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family pyrin domain containing 3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRP6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family pyrin domain containing 6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNLRP7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eNLR family pyrin domain containing 7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNOD1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003enucleotide binding oligomerization domain containing 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eNOD2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003enucleotide binding oligomerization domain containing 2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003ePJVK\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003epejvakin/deafness, autosomal recessive 59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003ePLCG1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ephospholipase C gamma 1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003ePRKACA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eprotein kinase cAMP-activated catalytic subunit alpha\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003ePYCARD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003ePYD and CARD domain containing\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eSCAF11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eSR-related CTD associated factor 11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eTIRAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003eTIR domain containing adaptor protein\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"180\"\u003e\n\u003cp\u003eTNF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"518\"\u003e\n\u003cp\u003etumor necrosis factor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable3: GO功能富集分析\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003eLogP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0010942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003epositive regulation of cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-6.795237394\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0043065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003epositive regulation of apoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-6.725948242\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0043068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003epositive regulation of programmed cell death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-6.468562417\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:1901216\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003epositive regulation of neuron death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.102600975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:1901214\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eregulation of neuron death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.074072761\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0043525\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003epositive regulation of neuron apoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.96232387\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0043523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eregulation of neuron apoptotic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.113727913\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0044283\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003esmall molecule biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-5.803088323\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0046394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003ecarboxylic acid biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-4.007782365\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Biological Processes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0016053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eorganic acid biosynthetic process\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.967401467\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0016491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eoxidoreductase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-6.431827336\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0033829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eO-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-4.941011776\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0004540\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eribonuclease activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.289962761\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0000175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003e3'-5'-exoribonuclease activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.111383633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0019901\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eprotein kinase binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.799284977\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0019900\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003ekinase binding\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.963031033\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0016616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eoxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.593933958\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0016614\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eoxidoreductase activity, acting on CH-OH group of donors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.335341726\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0016229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003esteroid dehydrogenase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.156406636\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Molecular Functions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0033764\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003esteroid dehydrogenase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-2.354071397\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0005740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial envelope\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-8.330134083\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0031966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-6.462012134\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0005759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial matrix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-5.83494067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0019866\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eorganelle inner membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-5.419918031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0005743\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial inner membrane\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-4.205371038\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0000315\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eorganellar large ribosomal subunit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.68980253\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0005762\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial large ribosomal subunit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.68980253\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0098798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial protein-containing complex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.605514366\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0000313\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003eorganellar ribosome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.31070908\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eGO Cellular Components\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003eGO:0005761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"257\"\u003e\n\u003cp\u003emitochondrial ribosome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"61\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e-3.31070908\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable4:KEGG功能富集分析\u003c/p\u003e\n\u003ctable width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e\u003cstrong\u003eLogP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa00240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003ePyrimidine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.004746308\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05168\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eHerpes simplex virus 1 infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-3.366329528\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa00740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eRiboflavin metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-3.229659261\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eHuman papillomavirus infection\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-3.227120893\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa04390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eHippo signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.508029284\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05225\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.300246423\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003ePathways in cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.000779506\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa01524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003ePlatinum drug resistance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.886007438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa04210\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eApoptosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.417907689\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05142\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eChagas disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.073577812\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa04064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eNF-kappa B signaling pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.029329462\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa05164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eInfluenza A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.247064717\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa00120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003ePrimary bile acid biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.21110722\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa04360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eAxon guidance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.063595963\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\n\u003cp\u003eKEGG Pathway\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"77\"\u003e\n\u003cp\u003ehsa00532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"342\"\u003e\n\u003cp\u003eGlycosaminoglycan biosynthesis - chondroitin sulfate / dermatan sulfate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"111\"\u003e\n\u003cp\u003e-2.008477972\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\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":"pyroptosis-related genes, diagnosis, prognosis, classifier, immunity, endometriosis","lastPublishedDoi":"10.21203/rs.3.rs-1935526/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1935526/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis (EM) is a chronic inflammatory disease, affecting 10% of women and girls of reproductive ages around the globe. Pyroptosis ,a type of pro-inflammatory programmed cell death (PCD), has been associated with EM in recent studies.However,the expression of pyroptosis-related genes (PRGs) in EM and its relationship with diagnosis and prognosis are not clear.In this study,it was discovered that 32 PRGs differed in expression between EM and normal tissues, which were related to diagnosis and prognosis. Firstly, ROC analysis of a single gene was performed based on PRGs ,and then subjected to the corresponding multiomics analysis, prognostic analysis and diagnostic analysis. Secondly,the gene expression profiles of EM group dataset were consistently grouped based on PRGs by the consencesClusterPlus package. Pheatmaps were used to construct a principal component analysis (PCA) diagram of the dataset to determine the potential diagnostic value of these genes and to determine their expression patterns in different subtypes.Thirdly,The Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis. The results suggested that the risk was related to immune response. In conclusion, PRGs have an important roles in tumour immunity and can be used to predict the prognosis of EM.\u003c/p\u003e","manuscriptTitle":"Pyroptosis-Related Gene Markers Can Effectively Diagnose Endometriosis and Predict Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-12 20:03:03","doi":"10.21203/rs.3.rs-1935526/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":"23eadaf5-4f09-4af6-91cc-b41507b5a554","owner":[],"postedDate":"August 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-02T11:14:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-12 20:03:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1935526","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1935526","identity":"rs-1935526","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}