The NLRP3 activation-related signature predict the diagnosis and indicate immune characteristics in endometriosis

The NLRP3 activation-related signature predict the diagnosis and indicate immune characteristics in endometriosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The NLRP3 activation-related signature predict the diagnosis and indicate immune characteristics in endometriosis Weihua Nong, Huimei Wei, Sheng Dou, Liqiao He, Tianlong Li, Luping Lin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2830815/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 (EMS) is a common gynecological disease leading to chronic pelvic pain and infertility in women of reproductive age, but its underlying pathogenic genes and effective treatment are still unclear. To date, abnormal expression of NLRP3 activation-related genes has been identified in EMS patients and mouse models. Therefore, this study sought to identify the key genes that could affect the diagnosis and treatment of EMS. The GSE7307 dataset was downloaded from the Gene Expression Omnibus (GEO) database, including 18 EMS samples and 23 control samples. 14 differential genes related to NLRP3 activation and EMS were obtained from the endometrial samples of GSE7307 by differential analysis. GO and KEGG analysis showed that these genes were mainly involved in the production and regulation of the cytokine IL-1β, and the NOD-like receptor signaling pathway. Random Forest (RF) and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to select four diagnostic markers related to NLRP3 activation (NLRP3, IL-1β, LY96 and PDIA3) to construct the EMS diagnostic model. The four diagnostic markers were verified using western blotting and validated in the GSE7305 and GSE23339 datasets. The AUC values showed that the model had a good diagnostic performance. In addition, the infiltration of immune cells in the samples and the correlation between different immune factors and diagnostic markers were further discussed. These results suggest that four diagnostic markers may also play an important role in the immunity of EMS. Finally, 10 drugs targeting to four diagnostic markers were retrieved from the DrugBank database, of which niclosamide proved useful for treating EMS. Overall, we identified four key diagnostic genes for EMS. In addition, large-scale and multicenter prospective cohort studies are necessary to confirm whether these four genes also have valid diagnostic value in blood samples from EMS patients. Endometriosis GEO Diagnostic marker Nomogram Immune cells infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Endometriosis (EMS) is a chronic inflammatory disease defined as the presence of endometrial tissue outside the uterus, causing pelvic pain and infertility, affecting 5–10% of women of reproductive age worldwide[ 1 , 2 ]. The most prevalent theory of the origin of endometriosis, advanced by Sampson (1928), holds that during menstruation, endometrial cells and tissue fragments return through the fallopian tubes, survive, attach and invade pelvic structures[ 3 ]. Depending on the location of the disease, EMS is classified as superficial (or peritoneal), ovarian (endometrioma), and deep (defined as invasive lesions greater than 5 mm in depth). The diagnosis of EMS is made primarily through laparoscopy, based on an empirically guided clinical decision based on history, symptoms, clinical examination, and imaging, whereas biochemical markers lack sensitivity and specificity[ 4 , 5 ]. Therefore, it is necessary to explore the underlying molecular mechanism and find new markers to provide more meaningful and reliable diagnostic information for EMS treatment, so as to promote early diagnosis and individualized treatment. Nod-like receptor protein 3 (NLRP3) is a component of the inflammasome that activates caspase1, promoting the processing of IL-1β and IL-18 into biologically active mature forms and inducing pyroptosis[ 6 , 7 ]. Furthermore, NLRP3 inflammasome-mediated pyroptosis has been reported to play a crucial role in the pathogenesis of inflammatory diseases[ 8 , 9 ]. EMS is also a chronic inflammatory disease, and previous studies have also reported an association between NLRP3 inflammasome and the progression of EMS[ 10 , 11 ]. A study has reported that targeted inhibition of NLRP3 can inhibit the production of IL-1β and cell proliferation in EMS, thereby improving ovarian function in EMS[ 12 ]. Estrogen receptor(ER)β has been reported to interact with components of the inflammasome in endometriotic cells, increasing interleukin-1β to enhance their cell adhesion and proliferative properties, thereby increasing the invasive activity of endometrial tissues to establish ectopic lesions[ 13 ]. Due to the non-negligible relationship between NLRP3 activation and EMS, it is necessary to explore the specific role of NLRP3 activation-related features in EMS and identify more diagnostic biomarkers. In this study, we conducted a systematic study of NLRP3 activation-related genes to explore the differential expression of these genes in EMS patients and normal controls and to construct an effective diagnostic nomogram model for clinical application. Inflammation is a vital pathological feature of EMS, which is closely related to immune function. Therefore, we further explored the immune penetration of these samples and the correlation between different immune factors and these diagnostic biomarker candidates. In addition, ten drugs targeting these diagnostic biomarker candidates were retrieved from the DSigDB database, which has implications for the pharmacotherapy of EMS. Therefore, this study provides potential targets for the diagnosis and treatment of EMS. The workflow of this study is shown in Fig. 1 . Materials and Methods Data acquisition First, the three datasets, GSE7307 (including 18 EMS samples and 23 normal samples) , GSE7305 (including 10 EMS samples and 10 normal samples), and GSE23339 (including 10 EMS samples and 9 normal samples ) were downloaded from Gene Expression Omnibus (GEO) database. The data processing steps are as follows: (1) match multiple probes to the same gene, (2) remove the null probes, (3) first match probes to genes, and (4) log2 transformation of the obtained data set. The number of bits used to normalize signal strength. DEGs in EMS Gene expression difference data between EMS samples and normal samples were examined by using a limma package in R and using a threshold with a modified P value of 0.05 or less. Functional enrichment analysis of the DEGs To explore the biological mechanism of differentially expressed genes (DEGs) affecting EMS, a functional enrichment analysis was carried out. First, the “clusterProfiler” and “ggplot2” packages were employed for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for enrichment analyses of DEGs[14]. Adjusted P < 0.05 had significance in statistics. Subsequently, Gene Set Enrichment Analysis (GSEA) that used the ‘Clusterprofiler’ R package to perform revealed the respective function of each key gene. The samples were divided into low-expression groups and high-expression groups according to median values of the expression levels in key genes. The genes were sorted from highest to lowest by logFC, and the first 5 and last 5 GSEA results were taken after sorting. These gene sets were downloaded from the Molecular Signature Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/ index.jsp). The final result is displayed in the "enrichplot" package of the R software. P -values < 0.05 was considered to be statistically significant. Screening for candidate diagnostic biomarkers Diagnostic markers of EMS were classified using random forest (RF) and support vector machine recursive feature elimination (SVM-RFE) arithmetic methods. The RF algorithm randomizes a single decision tree overfitting reduction algorithm and improves model accuracy based on many related decision trees in the training set[15]. SVM-RFE as a support vector monitoring machine learning method identifies optimal variables via the deletion of the SVM-produced eigenvectors[16]. Markers selected for EMS diagnosis were classified and analyzed using the SVM classifier in the R package e1071. Verification of the diagnostic biomarker results Endometrium from endometriosis patients or matched control patients came from the Affiliated Hospital of Youjiang Medical College for Nationalities, Baise, China. We collected 12 cases diagnosed as ovarian endometriosis cysts (n=6) and non-endometriosis (n=6) with inclusion conditions: ageg between 26 and 46 years, BMI of 21-27 kg/m 2 , non-vegetarian patients, no contraindications for surgery; The exclusion criteria were endocrine diseases such as diabetes, and severe gastrointestinal, heart, lung, and liver diseases. All the donors did not take drugs and hormones before surgery, and underwent combined uterine and abdominal surgery at the same time due to abnormal uterine bleeding. All tissue samples were obtained from tissue resected during surgery and were approved and informed by the patient and the ethics review committee of our hospital. Protein preparation and western blotting were performed as described previously[17]. Endometrial tissue lysates (500 μL in RIPA lysis buffer PMSF= 94:6) were processed for western blotting, where protein concentrations were determined with BCA Protein Assay Kit. Then, proteins were denatured by boiling at 100 °C for 10 min. SDS-PAGE was conducted with denatured protein samples of approximately 20 μg loaded onto a 10% Tris–acetate gel, run at 120 V for 2 h. Proteins in the gel were transferred onto polyvinylidene fluoride membranes at 300 mA for 50 min, and blocked in PBST containing 5% skim milk powder for 2 h. After washing with PBST, the membranes were respectively incubated with antibodies against NLRP3 (1:1000 dilution, bs-10021R; Bioss, Beijing, China), LY96 (1:1000 dilution, 11784-1-AP; proteintech, Wuhan, China), IL-1β (1:3000 dilution, 26048-1-AP; proteintech), PDIA3 (1:8000 dilution, 15967-1-AP; proteintech), and TUBULIN (1:20000 dilution, 66031-1-Ig; proteintech) overnight at 4 °C, and washed with PBST three times for 10 min each. It was then incubated for 2 h at room temperature with a goat anti-rabbit IgG (1:5000 dilution, SA00001-2; proteintech) and a goat anti-mouse IgG (1:5000 dilution, SA00001-1; proteintech) horseradish peroxidase (HRP)-conjugated secondary antibody. After three 10-min washes of the membrane in PBST, eECL was added, and the Tanon-5500 Chemiluminescence Imaging System was used to detect the chemiluminescence of protein bands. Diagnostic value of the key genes in EMS The generalized linear model (GLM), support vector machine model (SVM), extreme gradient boosting model (XGB), and random forest model (RF) were performed based on the combined training dataset. The four models were analyzed using the interpretation function of DALEX package in R, and the residual distribution was performed to obtain the optimal model. We constructed ROC curves to examine the predictive utility of the proposed biomarkers by using mRNA expression data from 23 normal samples and 18 EMS samples. EMS and control samples were separated by ROC analysis and further validated in GSE7305 and GSE23339 datasets. Distribution of the immune cells The CIBERSORT (http://cibersortx.stanford.edu) algorithm was used to calculate the relative proportions of the 22 immune cell types in the training dataset samples. Analysis of genes identified and immune cells infiltrated Spearman rank correlation analysis was used to determine the correlation between the four key diagnostic genes and the number of infiltrating immune cells. The graph method of the ggplot2 package is used to show the correlation of results. Construction of nomogram and diagnostic score model First, a nomogram model for predicting the occurrence of EMS was constructed using the rms package (version 5.1-2)[18]. Then, the calibration curve was constructed to assess the diagnostic power of the nomogram model. Finally, the clinical value of the nomogram model was evaluated by using decision curve analysis (DCA). Prediction of potential drug compounds The Drug Signature Database (DSigDB) was used to screen potential drug molecules with gene-based interactions. As a public web-based database derived from an enrichment web server, DSigDB provides information about drugs and their target genes for GSEA[19]. Based on a statistical threshold of adjusted p -value < 0.05, we identified protein-drug interactions in the EMS-pyroptosis interaction that may affect important central genes. Statistical analysis. All analyses were performed in R program 4.0.2. The t -test and the Mann-Whitney U-test were chosen based on whether the data fit a normal distribution. The Spearman correlation method was used to study the relationship between the expressions of diagnostic genes and infiltration immunocytes. P values < 0.05 was considered statistically significant. Results Determination of DEGs in EMS To investigate DEGs of EMS, we examined the GSE7307 dataset and identified 14 DEGs in EMS, including 11 up-regulated genes (CASP1, IL10, IL17RA, IL1B, IL1R1, IL6, LY96, NLRP3, P2RX7, PIK3CA and TLR4) and 3 down-regulated genes (EIF2AK3, HMGB1 and PDIA3) (Fig. 2A and 2B). Enrichment analysis of biological process and pathway GO analysis results showed that DEGs were mainly rich in positive regulation of cytokine production, cellular response to biotic stimulus, cellular response to lipopolysaccharide, cellular response to molecule of bacterial origin, response to lipopolysaccharide, interleukin-1 production, regulation of interleukin-1 production, positive regulation of interleukin-1 beta production, positive regulation of interleukin-1 production, and regulation of cytokine production involved in the immune response (Fig. 3A). KEGG assays revealed that DEGs were mainly enriched in the NOD-like receptor signaling pathway (Fig. 3B). Detection and verification of diagnostic genes in EMS Two machine learning methods were applied in selecting potential markers of EMS: SVM-RFE and RF. We first screened 14 DEGs with SVM-RFE analysis and identified 10 diagnostic biomarkers (Fig. 4A). RF analysis identified 4 diagnostic biomarkers from 14 DEGs (Fig. 4B and 4C). After integrating the characteristic genes from SVM-RFE and RF, four characteristic genes closely related to pyroptosis were obtained: NLRP3, IL-1β, LY96, and PDIA3 (Fig. 4D). The expression of NLRP3, IL-1Β, and LY96 was distinctly upregulated in EMS, and the expression of PDIA3 was distinctly upregulated in EMS (Fig. 5A). The diagnostic value of four overlapping genes was shown by the use of ROC assays (Fig. 5B). We verified the results of the screened differential genes at the protein level. The verification results revealed that the protein (Fig. 6A and 6B) levels of NLRP3 ( P <0.05), LY96 ( P <0.01) and IL-1β ( P 0.05) were the opposite, consistent with the Gene Chip data with multiple exposures. Moreover, we further determined the diagnostic value of NLRP3, IL-1β, LY96 and PDIA3 in the GSE7305 and GSE23339 datasets, and the results are shown in Fig. 7. We established the RF, SVM, XGB, and GLM models to choose NLRP3 activation-related genes to predict the incidence of EMS. Both “Reverse Cumulative Distribution of Residual” (Fig. 8A) and “Boxplots of Residual” (Fig. 8B) indicate that the GLM model has minimal residuals. The ROC curve was drawn to evaluate the model, and the AUC value of the ROC curve also indicated that the GLM model had higher accuracy compared with the other three models (Fig. 8C). Finally, the ROC curve also demonstrated the good diagnostic performance of the four genetic biomarker combinations based on the four classification models in both validation datasets (GSE7305 and GSE23339) for normal women and EMS patients. (Fig. 8D and 8E). Analysis of functional correlations Gene set enrichment analysis (GSEA) was performed for the four key genes based on the median expression profile. GSEA expression analysis of four genes in NLRP3 showed that the gene expressions of malaria, pertussis, systemic lupus erythematosus immune-related diseases and biological processes of complement and coagulation cascades and taurine and hypotaurine metabolism were significantly increased in the high expression group, while base excision repair, DNA replication, Fanconi anemia pathway, homologous recombination, and mismatch repair in the low expression group (Fig. 9A). The IL-1β in the high expression group were significantly enriched in the legionellosis, malaria, systemic lupus erythematosus, complement and coagulation cascades, and viral protein interaction with cytokine and cytokine receptor, whereas those in the low-expression groups were significantly enriched in aminoacyl−tRNA biosynthesis, base excision repair, DNA replication, homologous recombination, and mismatch repair (Fig. 9B). The LY96 in the high-expression group was significantly enriched in asthma, legionellosis, malaria, systemic lupus erythematosus, and complement and coagulation cascades. On the other hand, LY96 in the low expression groups was significantly enriched in base excision repair, DNA replication, Fanconi anemia pathway, homologous recombination, and mismatch repair (Fig. 9C). Ultimately, the PDIA3 in the high expression group was significantly enriched in DNA replication, Fanconi anemia pathway, homologous recombination, mismatch repair, and protein export, nevertheless, those in the low expression group were significantly enriched in asthma, malaria, pertussis, systemic lupus erythematosus, and taurine and hypotaurine metabolism (Fig. 9D). Correlation between four diagnostic genes and EMS immune permeation level In this study, the proportion of 21 immune cells in 18 EMS samples and 23 Normal samples was estimated with the CIBERSORT algorithm, which can be seen in the histograms. The abundance of B cells memory, Plasma cells, T cells CD4 memory resting, Macrophages M2, Mast cells activated, and Eosinophils were higher in EMS samples than that in normal samples, while the infiltration abundance of B cells naïve, NK cells activated, and Mast cells resting in EMS samples were lower (Fig. 10A and 10B). Four biomarkers (NLRP3, IL-1β, LY96 and PDIA3) were associated with 6 significantly different immune cells in 21 immune cell types, including Dendritic cells activated, Dendritic cells resting, Eosinophils, Macrophages M1, Monocytes, and Plasma cells (Fig. 10C and 10D). As shown in Figure 9D, NLRP3 was negatively correlated with B cells naïve only. IL-1β was positively correlated with Plasma cells but negatively correlated with Mast cells resting and T cells CD4 naïve. LY96 was related to Plasma cells, Macrophages M2, B cells mEMSory, T cells gamma delta, and Eosinophils in a positive way, while negatively related with B cells naïve, NK cells activated, Dendritic cells activated, Mast cells resting, T cells CD8, and T cells follicular helper. PDIA3 has positively correlated with T cells CD4 memory activated when negatively correlated with B cells memory and Macrophages M2. Construction and assessment of the diagnostic nomogram model A nomogram was created to validate the diagnostic power of the four EMS biomarkers (Fig. 11A). The calibration curve showed a small error between the actual EMS risk and the predicted risk, indicating that the nomogram model had a high accuracy in predicting EMS (Fig. 11B). DCA showed that the nomogram curve was higher than the gray critical line and the four biomarker curves, indicating that the nomogram model had the best clinical benefit (Fig. 11C). Identification of candidate pharmacologic agents Four well-validated diagnostic genes (NLRP3, IL-1β, LY96, and PDIA3) were used as genetically linked targets to facilitate further studies in therapeutic development by a drug target enrichment analysis was performed (Table 1). We have listed the top 10 candidate enrichment agents targeting gene links, which may be targeted for the development of co-onset therapeutic strategies. Studies have shown that niclosamide can target and inhibit macrophage-induced endometriosis inflammatory microenvironment, thereby improving reproductive function[20–22]. In addition, our analysis of the results based on Table 1 indicates that niclosamide is considered a candidate molecule for the treatment of EMS because of its high odds ratio and high composite score. Discussion Endometriosis, a gynecological disease characterized by the implantation of endometrial tissue outside the uterus, has attracted increasing attention worldwide recently. However, despite its prevalence, diagnosis is often delayed by years, misdiagnosis is common, clinical presentations are varied, and the presence of pelvic lesions is heterogeneous[ 1 ]. Currently, EMS is usually treated with a combination of hormone therapy and surgery to remove the lesion[ 23 ]. Despite recent advances in the medical treatment of endometriosis-related pelvic pain, there are still no drugs available to treat the condition[ 24 ]. Therefore, the identification of novel molecular factors and the unraveling of the underlying mechanisms of EMS will facilitate early diagnosis and the development of effective therapies. In the present study, we identified DEGs across the GSE7307 dataset including 18 EMS samples and 23 normal samples. These DEGs included 11 upregulated and 3 downregulated genes, respectively. GO and KEGG analyses revealed that these DEGs were involved in regulating immune-related signaling pathways, such as the production and regulation of the cytokine IL-1β, as well as the NOD-like receptor signaling pathway. We applied the machine learning methods of SVM-RFE and RF to integrate the feature genes and obtained four feature genes: NLRP3, IL-1β, LY96 and PDIA3. Then, we further confirmed their diagnostic using GSE7305 and GSE23339 datasets, and further demonstrated NLRP3, IL-1β, LY96 and PDIA3 as critical biomarkers for EMS based on the results of ROC assays. The inflammasome is newly discovered and plays an important role in innate immunity. The most typical is the NLRP3 inflammasome, the so-called NLRP3 protein because the NLRP3 protein in this complex belongs to the family of nucleotide-binding and oligomerization domain-like receptors (NLRs), also known as "pyrin domain-containing protein 3"[ 25 ]. The expression of NLRP3 in EMS tissues and serum of EMS patients was higher than that of normal controls, which contributed to the invasion of EMS[ 26 ]. Since EMS is a gynecologic disorder associated with estrogen, higher estrogen receptor (ER)-β levels and enhanced ER-β activity are detected in endometriotic tissues. ER-β is known to interact with NALP3, and the activation of NALP3 increases interleukin IL-1β and IL-18, thereby enhancing cell adhesion and proliferation[ 27 ]. The NLRP3 inhibitor (MCC950) has recently been reported to significantly reduce the co-localization of NLRP3 and IL-1β in cyst-derived stromal cells (CSCs) and IL-1β concentration in the supernatant of CSCs. Thereby contributing to Ovarian endometriosis (OE) suppression and improving ovarian function in endometriosis[ 12 ]. However, the specific mechanism of NLRP3 in EMS is still unclear. IL-1β plays a considerable role in inflammatory diseases and the main sources of its secretion are macrophages and monocytes, dendritic cells (DC), B lymphocytes, neutrophils and natural killer (NK) cells and non-immune cells such as keratinocytes[ 28 ]. The maturation of IL-1β and IL-18 induces pyroptosis, a form of cell death[ 29 ]. The inflammatory factor IL-1β has been reported to be significantly increased in ectopic endometrial tissue of patients with EMS[ 30 ], which is consistent with our results. Chronic pelvic pain is one of the main clinical symptoms of female endometriosis, but its specific mechanism is unclear. IL-1β is closely related to inflammatory pain by sending inflammatory pain signals to the hypothalamus and participating in afferent pain response[ 31 ]. A retrospective study of peritoneal fluid samples obtained by laparoscopy found an increase in IL-1β concentration compared with normal controls that were statistically significant only in women with endometriosis who reported pelvic pain[ 32 ]. In addition, some studies have revealed the mechanism of deep dyspareunia in endometriosis, that is, IL-1β stimulates the expression of nerve growth factor (NGF), promotes local neurogenesis around endometriosis, and then leads to tenderness in the pelvic anatomy, leading to deep dyspareunia[ 33 ]. Because IL-1β can induce NGF expression and increase NGF bioactivity, dienogest, a selective P-receptor (PR) agonist, Inhibition of NGF expression by PR-A and PR-B in human endometrial epithelial cells (hEECs) may contribute to the relief of endometriosis pain[ 34 ]. Sulforaphane alleviates pain caused by sciatic nerve endometriosis, which is mediated by the inhibition of inflammatory cytokines IL6, IL-1β, and TNF-α[ 35 ]. Overall, pharmacological targeting of IL-1β/NGF may also have the potential for the treatment of endometriosis-related pain. LY96 plays a crucial role in inflammation-related and immune-related diseases, such as Crohn's disease, rheumatoid arthritis, and inflammatory diabetic cardiomyopathy[ 36 ]. Key protein disulfide isomerase 3 (PDIA3) is a kind of chaperone, that can adjust the folding of the new synthesis glycoprotein, the heterogeneous and REDOX enzyme activity, and immune activation marks, cancer immune cells and immune modulators significantly related, is a powerful cancer prognosis biomarkers, which can effectively predict the immune response to treatment[ 37 , 38 ]. Similarly, the expression and function of LY96 and PDIA3 in EMS remain are largely unknown. Furthermore, we also evaluated the degree of infiltration of 22 immune cells in EMS and normal samples. Compared with normal samples, EMS samples had distinctly higher levels of B cells memory, Plasma cells, T cells CD4 memory resting, Macrophages M2, Mast cells activated, and Eosinophils. It is well known that endometriosis lesions are characterized by the presence of numerous plasma cells and activated macrophages[ 39 ]. One study found that the relative abundance of resting CD4 + memory T cells and even less abundant memory B cells were significantly higher in patients with stage III/IV endometriosis, which was strongly associated with endometrial receptivity[ 40 ]. Activated macrophages are generally divided into two groups, M1-like macrophages and M2-like macrophages, both of which are closely related to the inflammatory response[ 41 ]. Another study found a significant increase in total macrophages in ectopic endometrial tissue (EC) and a syngeneic mouse model of endometriosis, and M2 macrophages were the predominant macrophages in EC[ 42 , 43 ]. Of note, M2 macrophages have been found to increase gradually from stage I to stage IV in ectopic endometrial tissues of EMS patients, whereas M1 macrophages have been found to increase in contrast, which may contribute to the proinflammatory microenvironment in the early stage of the disease, as well as to the profibrotic activity in the late stage[ 44 ]. Diffuse infiltration of large numbers of mast cells (MCs) is observed in the interstitium of ectopic endometrial tissues of EMS. MCs promote the development of endometriosis through NLRP3 inflammasome activation mediated by nuclear-initiated estrogen signaling pathway and the production of mature IL-1β[ 45 , 46 ]. In addition, the preoperative pain score of EMS is closely related to the density and number of mast cells, and the use of MCs stabilizers and inhibitors can relieve endometriosis and its associated pain[ 47 , 48 ]. Eosinophils can regulate fibroblasts and stimulate collagen synthesis. Eosinophils are abundant in fibrotic areas and peritoneal fluid of endometriosis, which is associated with fibrosis of endometriosis lesions[ 49 , 50 ]. Moreover, we found that the expressions of NLRP3, IL-1β, LY96, and PDIA3 were related to the levels of many immune cells, highlighting their potential used as therapeutic targets for EMS. Hence, The differential expression of NLRP3, IL-1β, LY96, and PDIA3 between EMS and normal groups pointed out they might be crucial immune-related biomarkers to diagnose EMS. A diagnostic nomogram model based on four core genes with excellent predictive power was established, and patients would benefit from this model. Finally, based on four diagnostic genes as gene junction points, we identified niclosamide as a candidate molecule for EMS treatment due to its high odds ratio and high comprehensive score among the top 10 candidate enrichment agents by drug target enrichment analysis. Niclosamide was discovered in 1953 at the Bayer Chemotherapy Research Laboratory to treat tapeworm infections in humans and is listed on the World Health Organization's Essential Medicines List[ 51 ]. However, accumulating evidence suggests that niclosamide is a multifunctional drug capable of inhibiting or modulating multiple signaling pathways and biological processes, which has been demonstrated to treat endometriosis in vitro and in vivo experiments. Recent niclosamide was reported can effectively reduce the endometriosis of endometriosis lesions in mice (ELL) induced by peritoneal macrophages (LPM) and LPM the expression of related genes, and by reducing ELL and pelvic organs may stimulate the peripheral nerve as a medium of inflammatory factor to reduce the abnormal inflammation, may eventually reduce the pain associated[ 52 ]. In the endometriosis model in mice, chlorine nitramine can not only through targeted STAT3 and NFkB signaling pathways, reduce the growth and progress of the sample endometriosis lesions, but also can inhibit the inflammatory mechanisms, thereby reducing the macrophages induced cell activity and the secretion of cytokine/chemokine based on not affect reproductive function[ 20 – 22 ]. At present, although niclosamide has not been used in the clinical treatment of endometriosis, the results of previous studies and our results suggest that niclosamide may be a novel strategy for the targeted treatment of inflammatory dysfunction in endometriosis. Conclusion Four diagnostic genes related to NLRP3 activation and EMS were identified by bioinformatics. Although we have explored the biological processes and pathways involved in them, further experimental verification is needed to verify these functions. The nomogram based on these four genes has a good diagnostic effect on EMS, which provides a theoretical basis for clinical diagnosis. In addition, these diagnostic genes were found to be associated with different immune factors, suggesting that they may also have important roles in the immune microenvironment. Currently, drugs targeting these pyroptosis genes are predicted to alleviate EMS, but only niclosamide has the results of basic studies to support its use in the treatment of EMS, and further studies are needed to explore its specific mechanism of action. Firstly, the GSE7307 dataset was downloaded from the GEO database, including 18 EMS samples and 23 control samples. The final 14 DEGs related to pyroptosis and EMS were obtained from the endometrial samples of GSE7307 by differential analysis. GO and KEGG analysis showed that these genes were mainly involved in two major biological processes. Random Forest (RF) and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to select four genes to construct the EMS diagnostic model. The model was validated in endometrial samples of GSE7305 and GSE23339, and the AUC values showed that the model had a good diagnostic performance. In addition, the infiltration of immune cells in the samples and the correlation between different immune factors and pyroptosis genes were further discussed. These results suggest that these diagnostic genes may also play an important role in the immunity of EMS. Finally, 10 drugs targeting these four diagnostic genes were retrieved from the DrugBank database, of which niclosamide proved useful for treating EMS. Overall, we identified four key diagnostic genes for EMS. In addition, large-scale and multicenter prospective cohort studies are necessary to confirm whether these four genes also have valid diagnostic value in blood samples from EMS patients. Declarations Acknowledgements None. Authors’ contributions MD and PH directed the study; WN, HW and SD performed the experiments; LH, TL, LL and BW performed the data collection and statistical analysis; SZ edited the language; all the authors approved the manuscript for submission. Funding This research was supported by the National Natural Science Foundation of China (No. 81960274), the 2020 university-level Research Project of Youjiang Medical University for Nationalities (No. yy2020gcky039), and the 2021 Scientific Research and Technology Development Program of Baise City (No. 20212351). Availability of data and materials Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are derived from the public GEO data portal (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE7307, GSE7305 and GSE23339). Ethics approval and consent to participate All procedures were approved by the ethics review committee of Affiliated Hospital of Youjiang Medical College for Nationalities. All methods were carried out in accordance with relevant guidelines and regulations of the ethics review committee of Affiliated Hospital of Youjiang Medical College for Nationalities, and informed consent was obtained from all subjects and/or their legal guardian(s). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021;397:839–52. Chapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15:666–82. Vallvé-Juanico J, Houshdaran S, Giudice LC. The endometrial immune environment of women with endometriosis. 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Increase of activated mast cells in human endometriosis. Am J Reprod Immunol. 2005;53:120–5. Guo X, Xu X, Li T, Yu Q, Wang J, Chen Y, et al. NLRP3 Inflammasome Activation of Mast Cells by Estrogen via the Nuclear-Initiated Signaling Pathway Contributes to the Development of Endometriosis. Front Immunol. 2021;12:749979. Anaf V, Chapron C, El Nakadi I, De Moor V, Simonart T, Noël J-C. Pain, mast cells, and nerves in peritoneal, ovarian, and deep infiltrating endometriosis. Fertil Steril. 2006;86:1336–43. Binda MM, Donnez J, Dolmans M-M. Targeting mast cells: a new way to treat endometriosis. Expert Opin Ther Targets. 2017;21:67–75. Blumenthal RD, Samoszuk M, Taylor AP, Brown G, Alisauskas R, Goldenberg DM. Degranulating eosinophils in human endometriosis. Am J Pathol. 2000;156:1581–8. Eidukaite A, Tamosiunas V. Activity of eosinophils and immunoglobulin E concentration in the peritoneal fluid of women with endometriosis. Clin Chem Lab Med. 2004;42:590–4. Chen W, Mook RA, Premont RT, Wang J. Niclosamide: Beyond an antihelminthic drug. Cell Signal. 2018;41:89–96. Shi M, Sekulovski N, Whorton AE, MacLean JA, Greaves E, Hayashi K. Efficacy of niclosamide on the intra‐abdominal inflammatory environment in endometriosis. FASEB j [Internet]. 2021 [cited 2022 Oct 20];35. Table 1 Table 1. Predictive top 10 pharmacologic agent candidates for EMS with NLRP3 activation. Drug name P adj Odds Ratio Combined Score Genes Triclocarban CTD 00000497 0.00 713.14 8021.85910 IL1B; NLRP3 chlorhexidine CTD 00005633 0.00 498.90 5271.69962 IL1B; NLRP3 uric acid CTD 00006967 0.00 498.90 5271.69962 IL1B; NLRP3 GNF-Pf-4325 BOSS 0.00 486.71 5119.715195 IL1B; NLRP3 niclosamide BOSS 0.00 464.02 4838.479209 IL1B; NLRP3 9-Methoxyellipticine TTD 00001373 0.00 464.02 4838.479209 IL1B; NLRP3 niclosamide 0.00 415.58 4245.007066 IL1B; NLRP3 chlorhexidine 0.00 415.58 4245.007066 IL1B; NLRP3 5H-quinolino[8,7-c][1,2]benzothiazine 6,6-dioxide TTD 00001179 0.00 398.92 4043.246313 IL1B; NLRP3 thimerosal BOSS 0.00 332.27 3249.969098 IL1B; NLRP3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2830815","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":196157945,"identity":"319179a2-f2b4-44df-8b18-64959c40ffba","order_by":0,"name":"Weihua Nong","email":"","orcid":"","institution":"The Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Nong","suffix":""},{"id":196157947,"identity":"f624cf93-d696-44a1-9332-3291a3fa5634","order_by":1,"name":"Huimei Wei","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huimei","middleName":"","lastName":"Wei","suffix":""},{"id":196157949,"identity":"ba928f77-c270-4639-9ee9-69c5a2f5a9db","order_by":2,"name":"Sheng Dou","email":"","orcid":"","institution":"Guangxi Medical and health key discipline construction project of the Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Dou","suffix":""},{"id":196157951,"identity":"8d8786c1-6aea-4a7b-b4e0-efc194cfa0d7","order_by":3,"name":"Liqiao He","email":"","orcid":"","institution":"The Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":false,"prefix":"","firstName":"Liqiao","middleName":"","lastName":"He","suffix":""},{"id":196157953,"identity":"294570bc-f7c4-4513-9054-6c7964c0202d","order_by":4,"name":"Tianlong Li","email":"","orcid":"","institution":"University of South 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University","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Zhang","suffix":""},{"id":196157959,"identity":"1aa4b3ea-b9b0-4c1c-a80e-c68a5e6927cb","order_by":8,"name":"Peng Huo","email":"","orcid":"","institution":"Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Huo","suffix":""},{"id":196157960,"identity":"e2918d63-c5c8-4e5a-ac18-04ffed0e6e02","order_by":9,"name":"Mingyou Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYHACAyC2gbLZiNeSRrqWwyRo0Z2RvPFxwa/ziWtn5B5g+FB2mIF/dgN+LWY30oqNZ/bdTtx2Iy+Bcca5wwwSdw4Q0pJjJs3bA9KSY8DM23aYwUAigSgt5yBa/hKthefHAYgWRqK0nHlWbMzbkGy87cwbg4M959J5JG4Q0nIcGGI8f+xktx3PMXzwo8xajn8GAS0MAkAFjG0Mjg1A9gEg5iGgHgj4Qer+MNgTVjkKRsEoGAUjFgAAzvZJPEs1Bs4AAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Medical and health key discipline construction project of the Affiliated Hospital of Youjiang Medical University for Nationalities","correspondingAuthor":true,"prefix":"","firstName":"Mingyou","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2023-04-18 08:44:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2830815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2830815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36581163,"identity":"47df220c-8e5d-413d-97ee-4972a0e4ca87","added_by":"auto","created_at":"2023-05-03 14:59:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161652,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of the analysis\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/dbbefb6f9df0f9d80938f4b1.png"},{"id":36582720,"identity":"410f1b19-ac01-4fe2-93b1-d6c43a7a80b1","added_by":"auto","created_at":"2023-05-03 15:15:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":193186,"visible":true,"origin":"","legend":"\u003cp\u003eThe dysregulated genes were shown in the \u003cstrong\u003e(A)\u003c/strong\u003e boxplot and \u003cstrong\u003e(B)\u003c/strong\u003e heat map via analyzing GSE7307 datasets. 14 DEGs were identified between EMS samples and normal samples.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/d45c8ddb34e8ce19b40f41f6.png"},{"id":36582718,"identity":"0b959c5b-cae5-45b5-827f-35e22058839a","added_by":"auto","created_at":"2023-05-03 15:15:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":247768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e GO and \u003cstrong\u003e(B)\u003c/strong\u003e KEGG pathway analyses of 14 DEGs in EMS.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/1f77b2d4f746247d8201a3d2.png"},{"id":36581164,"identity":"abc20fb8-3a34-4c07-afbe-8f2c4e8bf6ab","added_by":"auto","created_at":"2023-05-03 14:59:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219923,"visible":true,"origin":"","legend":"\u003cp\u003eSelection procedure of diagnostic markers for EMS diagnoses. \u0026nbsp;\u003cstrong\u003e(A)\u003c/strong\u003e An illustration of biological marker screening through the SVM-RFE arithmetic. \u003cstrong\u003e(B)\u003c/strong\u003eTuning feature selection in the random forest model.\u003cstrong\u003e (C) \u003c/strong\u003eAnalyze the meaning of NLRP3 activation-related genes using the scores returned by the random forest model. \u003cstrong\u003e(D) \u003c/strong\u003eVenn graph presenting 4 diagnostic biomarkers shared by the random forest and SVM-RFE arithmetic methods.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/2ff27ecb1b5a25c65b7cfc00.png"},{"id":36581672,"identity":"37c64c68-e181-4d26-8d5c-11e609edfb1b","added_by":"auto","created_at":"2023-05-03 15:07:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167229,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of four biomarkers in EMS and the diagnostic value of four biomarkers in GSE7307 were studied by ROC analysis. \u003cstrong\u003e(A)\u003c/strong\u003e NLRP3, IL-1Β, and LY96 were highly expressed in EMS. PDIA3 was lowly expressed in EMS. \u003cstrong\u003e(B)\u003c/strong\u003eThe diagnostic value of the 4 biomarkers was studied using ROC assays in GSE7307.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/5aaaf7cdfccec44aec128f9b.png"},{"id":36581667,"identity":"2aeae7ca-548d-4300-869d-1b007ff4839d","added_by":"auto","created_at":"2023-05-03 15:07:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":92903,"visible":true,"origin":"","legend":"\u003cp\u003eResults of differential gene expression of NLRP3, LY96, IL-1β and PDIA3 at the protein level between EMS and control samples. \u003cstrong\u003e(A) \u003c/strong\u003eWestern blotting results. \u003cstrong\u003e(B) \u003c/strong\u003eDensitometry of the western blot. N=6 each group. \u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and \u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/5cc11f3714f14f1919da7e14.png"},{"id":36581668,"identity":"5be1c02c-ad97-4ee8-bb3f-19a1a1bb0dd4","added_by":"auto","created_at":"2023-05-03 15:07:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":133783,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of the model EMS in the validation datasets (GSE7305 and GSE23339).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/aee795b2be929ee7958ba8ee.png"},{"id":36582991,"identity":"32b0ca85-bb70-4fb6-9a0a-f59b755744a7","added_by":"auto","created_at":"2023-05-03 15:23:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":129026,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of NLRP3 activation-related genes. \u003cstrong\u003e(A) \u003c/strong\u003eBoxplots of the residuals of the sample. Red dot stands for root mean square of residuals.\u003cstrong\u003e (B)\u003c/strong\u003e Cumulative residual distribution map of the sample. \u003cstrong\u003e(C) \u003c/strong\u003eThe AUC value of the ROC curve indicated that the accuracy of GLM model (0.933) was the highest among the four models. \u003cstrong\u003e(D) \u003c/strong\u003eThe ROC curve assesses the accuracy of the four models in the validation datasets (GSE7305 and GSE23339).\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/f10f3d64fdf541f39a0b06cc.png"},{"id":36581172,"identity":"b7957744-5693-418e-acc6-433d94655210","added_by":"auto","created_at":"2023-05-03 14:59:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1233314,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA revealed the enriched pathways of four biomarkers. \u003cstrong\u003e(A)\u003c/strong\u003eNLRP3,\u003cstrong\u003e (B)\u003c/strong\u003e IL-1Β,\u003cstrong\u003e (C)\u003c/strong\u003e LY96, and \u003cstrong\u003e(D)\u003c/strong\u003ePDIA3.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/5a1406e16cfc8d16cc46414a.png"},{"id":36581670,"identity":"107e2355-b774-4522-b1fe-64eb8e5427d8","added_by":"auto","created_at":"2023-05-03 15:07:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":730623,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration and immune-related factors.\u003cstrong\u003e (A)\u003c/strong\u003eThe number of immune cells in each EMS sample is represented by a different color. \u003cstrong\u003e(B) \u003c/strong\u003eDifferences of the infiltrate immune cells between the normal (blue) and EMS (red) groups. \u003cstrong\u003e(C)\u003c/strong\u003e Heat map of correlation of four biomarkers (NLRP3, IL-1B, LY96, and PDIA3) with 22 immune cell subsets. \u003cstrong\u003e(D)\u003c/strong\u003e Correlation between NLRP3, IL-1B, LY96, and PDIA3 and infiltrating immune cells in EMS.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/0f1392193885c924ee0ff699.png"},{"id":36581168,"identity":"5bed43a0-7524-4ec2-b13c-6a22e91aa228","added_by":"auto","created_at":"2023-05-03 14:59:54","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":139745,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and assessment of a nomogram model for EMS diagnosis based on the training set. \u003cstrong\u003e(A) \u003c/strong\u003eNomogram to predict the occurrence of EMS. \u003cstrong\u003e(B)\u003c/strong\u003eCalibration curve to assess the predictive power of the nomogram model. \u003cstrong\u003e(C) \u003c/strong\u003eDecision curve analysis (DCA) to evaluate the clinical value of the nomogram model.\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/69652a66760859a04bfacd14.png"},{"id":41173515,"identity":"0c8172e3-631d-4bf7-9020-f8b94dd93fcc","added_by":"auto","created_at":"2023-08-07 12:22:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2378371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2830815/v1/52499802-d98c-42fd-a321-e0dcb6fd3357.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The NLRP3 activation-related signature predict the diagnosis and indicate immune characteristics in endometriosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis (EMS) is a chronic inflammatory disease defined as the presence of endometrial tissue outside the uterus, causing pelvic pain and infertility, affecting 5\u0026ndash;10% of women of reproductive age worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most prevalent theory of the origin of endometriosis, advanced by Sampson (1928), holds that during menstruation, endometrial cells and tissue fragments return through the fallopian tubes, survive, attach and invade pelvic structures[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Depending on the location of the disease, EMS is classified as superficial (or peritoneal), ovarian (endometrioma), and deep (defined as invasive lesions greater than 5 mm in depth). The diagnosis of EMS is made primarily through laparoscopy, based on an empirically guided clinical decision based on history, symptoms, clinical examination, and imaging, whereas biochemical markers lack sensitivity and specificity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, it is necessary to explore the underlying molecular mechanism and find new markers to provide more meaningful and reliable diagnostic information for EMS treatment, so as to promote early diagnosis and individualized treatment.\u003c/p\u003e \u003cp\u003eNod-like receptor protein 3 (NLRP3) is a component of the inflammasome that activates caspase1, promoting the processing of IL-1β and IL-18 into biologically active mature forms and inducing pyroptosis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, NLRP3 inflammasome-mediated pyroptosis has been reported to play a crucial role in the pathogenesis of inflammatory diseases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. EMS is also a chronic inflammatory disease, and previous studies have also reported an association between NLRP3 inflammasome and the progression of EMS[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study has reported that targeted inhibition of NLRP3 can inhibit the production of IL-1β and cell proliferation in EMS, thereby improving ovarian function in EMS[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Estrogen receptor(ER)β has been reported to interact with components of the inflammasome in endometriotic cells, increasing interleukin-1β to enhance their cell adhesion and proliferative properties, thereby increasing the invasive activity of endometrial tissues to establish ectopic lesions[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Due to the non-negligible relationship between NLRP3 activation and EMS, it is necessary to explore the specific role of NLRP3 activation-related features in EMS and identify more diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a systematic study of NLRP3 activation-related genes to explore the differential expression of these genes in EMS patients and normal controls and to construct an effective diagnostic nomogram model for clinical application. Inflammation is a vital pathological feature of EMS, which is closely related to immune function. Therefore, we further explored the immune penetration of these samples and the correlation between different immune factors and these diagnostic biomarker candidates. In addition, ten drugs targeting these diagnostic biomarker candidates were retrieved from the DSigDB database, which has implications for the pharmacotherapy of EMS. Therefore, this study provides potential targets for the diagnosis and treatment of EMS. The workflow of this study is shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eData acquisition\u003c/h2\u003e\n\u003cp\u003eFirst, the three datasets, GSE7307 (including 18 EMS samples and 23 normal samples) , \u0026nbsp;GSE7305 (including 10 EMS samples and 10 normal samples), and GSE23339 (including 10 EMS samples and 9 normal samples ) were downloaded from Gene Expression Omnibus (GEO) database. The data processing steps are as follows: (1) match multiple probes to the same gene, (2) remove the null probes, (3) first match probes to genes, and (4) log2 transformation of the obtained data set. The number of bits used to normalize signal strength.\u003c/p\u003e\n\u003ch2\u003eDEGs in EMS\u003c/h2\u003e\n\u003cp\u003eGene expression difference data between EMS samples and normal samples were examined by using a limma package in R and using a threshold with a modified \u003cem\u003eP\u003c/em\u003e value of 0.05 or less.\u003c/p\u003e\n\u003ch2\u003eFunctional enrichment analysis of the DEGs\u003c/h2\u003e\n\u003cp\u003eTo explore the biological mechanism of differentially expressed genes (DEGs)\u0026nbsp;affecting EMS, a functional enrichment analysis was carried out. First, the \u0026ldquo;clusterProfiler\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; packages were employed for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for enrichment analyses of DEGs[14]. Adjusted P \u0026lt; 0.05 had significance in statistics.\u0026nbsp;Subsequently, Gene Set Enrichment Analysis (GSEA) that used the \u0026lsquo;Clusterprofiler\u0026rsquo; R package to perform revealed the respective function of each key gene. The samples were divided into low-expression groups and high-expression groups according to median values of the expression levels in key\u0026nbsp;genes. The genes were sorted from highest to lowest by logFC, and the first 5 and last 5 GSEA results were taken after sorting. These gene sets were downloaded from the Molecular Signature Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/ index.jsp). The final result is displayed in the \u0026quot;enrichplot\u0026quot; package of the R software. \u003cem\u003eP\u003c/em\u003e-values \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e\n\u003ch2\u003eScreening for candidate diagnostic biomarkers\u003c/h2\u003e\n\u003cp\u003eDiagnostic markers of EMS were classified using random forest (RF) and support vector machine recursive feature elimination (SVM-RFE) arithmetic methods. The \u0026nbsp;RF algorithm randomizes a single decision tree overfitting reduction algorithm and improves model accuracy based on many related decision trees in the training set[15]. SVM-RFE as a support vector monitoring machine learning method identifies optimal variables via the deletion of the SVM-produced eigenvectors[16]. Markers selected for EMS diagnosis were classified and analyzed using the SVM classifier in the R package e1071.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of the diagnostic biomarker results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndometrium from endometriosis patients or matched control patients came from the Affiliated Hospital of Youjiang Medical College for Nationalities, Baise, China. We collected 12 cases diagnosed as ovarian endometriosis cysts (n=6) and non-endometriosis (n=6) with inclusion conditions: ageg between 26 and 46 years, BMI of 21-27 kg/m\u003csup\u003e2\u003c/sup\u003e, non-vegetarian patients, no contraindications for surgery; The exclusion criteria were endocrine diseases such as diabetes, and severe gastrointestinal, heart, lung, and liver diseases. All the donors did not take drugs and hormones before surgery, and underwent combined uterine and abdominal surgery at the same time due to abnormal uterine bleeding. All tissue samples were obtained from tissue resected during surgery and were approved and informed by the patient and the ethics review committee of our hospital. Protein preparation and western blotting were performed as described previously[17]. Endometrial tissue lysates (500 \u0026mu;L in RIPA lysis buffer \u0026nbsp;PMSF= 94:6) were processed for western blotting, where protein concentrations were determined with BCA Protein Assay Kit. Then, proteins were denatured by boiling at 100 \u0026deg;C for 10 min. SDS-PAGE was conducted with denatured protein samples of approximately 20 \u0026mu;g loaded onto a 10% Tris\u0026ndash;acetate gel, run at 120 V for 2 h. Proteins in the gel were transferred onto polyvinylidene fluoride membranes at 300 mA for 50 min, and blocked in PBST containing 5% skim milk powder for 2 h. After washing with PBST, the membranes were respectively incubated with antibodies against NLRP3 (1:1000 dilution, bs-10021R; Bioss, Beijing, China), LY96 (1:1000 dilution, 11784-1-AP;\u0026nbsp;proteintech, Wuhan, China), IL-1\u0026beta;\u0026nbsp;(1:3000 dilution, 26048-1-AP;\u0026nbsp;proteintech), PDIA3 (1:8000 dilution, 15967-1-AP;\u0026nbsp;proteintech), and\u0026nbsp;TUBULIN\u0026nbsp;(1:20000 dilution, 66031-1-Ig; proteintech) overnight at 4 \u0026deg;C, and washed with PBST three times for 10 \u0026nbsp;min each. It was then incubated for 2 h at room temperature with a goat anti-rabbit IgG (1:5000\u0026nbsp;dilution, SA00001-2;\u0026nbsp;proteintech) and a goat anti-mouse IgG (1:5000\u0026nbsp;dilution, SA00001-1;\u0026nbsp;proteintech) horseradish peroxidase (HRP)-conjugated secondary antibody. After three 10-min washes of the membrane in PBST, eECL was added, and the Tanon-5500 Chemiluminescence Imaging System was used to detect the chemiluminescence of protein bands.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDiagnostic\u0026nbsp;value of the key genes in EMS\u003c/h2\u003e\n\u003cp\u003eThe generalized linear model (GLM), support vector machine model (SVM), extreme gradient boosting model (XGB), and random forest model (RF) were performed based on the combined training dataset. The four models were analyzed using the interpretation function of DALEX package in R, and the residual distribution was performed to obtain the optimal model. We constructed ROC curves to examine the predictive utility of the proposed biomarkers by using mRNA expression data from 23 normal samples and 18 EMS samples. EMS and control samples were separated by ROC analysis and further validated in GSE7305 and GSE23339 datasets.\u003c/p\u003e\n\u003ch2\u003eDistribution of the immune cells\u003c/h2\u003e\n\u003cp\u003eThe CIBERSORT (http://cibersortx.stanford.edu) algorithm was used to calculate the relative proportions of the 22 immune cell types in the training dataset samples.\u003c/p\u003e\n\u003ch2\u003eAnalysis\u0026nbsp;of genes identified and immune cells infiltrated\u003c/h2\u003e\n\u003cp\u003eSpearman rank correlation analysis was used to determine the correlation between the four key diagnostic genes and the number of infiltrating immune cells. The graph method of the ggplot2 package is used to show the correlation of results.\u003c/p\u003e\n\u003ch2\u003eConstruction of nomogram and diagnostic score model\u003c/h2\u003e\n\u003cp\u003eFirst, a nomogram model for predicting the occurrence of EMS was constructed using the rms package (version 5.1-2)[18]. Then, the calibration curve was constructed\u0026nbsp;to\u0026nbsp;assess\u0026nbsp;the diagnostic power\u0026nbsp;of the\u0026nbsp;nomogram model. Finally,\u0026nbsp;the clinical value of the nomogram model was evaluated by using\u0026nbsp;decision curve analysis\u0026nbsp;(DCA).\u003c/p\u003e\n\u003ch2\u003ePrediction of potential drug compounds\u003c/h2\u003e\n\u003cp\u003eThe Drug Signature Database (DSigDB) was used to screen potential drug molecules with gene-based interactions. As a public web-based database derived from an enrichment web server, DSigDB provides information about drugs and their target genes for GSEA[19]. Based on a statistical threshold of adjusted \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05, we identified protein-drug interactions in the EMS-pyroptosis interaction that may affect important central genes.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis.\u003c/h2\u003e\n\u003cp\u003eAll analyses were performed in R program 4.0.2. The \u003cem\u003et\u003c/em\u003e-test and\u0026nbsp;the Mann-Whitney U-test\u0026nbsp;were\u0026nbsp;chosen based on\u0026nbsp;whether the\u0026nbsp;data fit\u0026nbsp;a normal distribution. The\u0026nbsp;Spearman correlation method was used to study the relationship\u0026nbsp;between\u0026nbsp;the expressions of diagnostic\u0026nbsp;genes and\u0026nbsp;infiltration immunocytes.\u003cem\u003e\u0026nbsp;P\u003c/em\u003e values \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDetermination of DEGs in EMS\u003c/h2\u003e\n\u003cp\u003eTo investigate DEGs of EMS, we examined the GSE7307 dataset and identified 14 DEGs in EMS, including 11 up-regulated genes (CASP1, IL10, IL17RA, IL1B, IL1R1, IL6, LY96, NLRP3, P2RX7, PIK3CA and TLR4) and 3 down-regulated genes (EIF2AK3, HMGB1 and PDIA3) (Fig. 2A and 2B).\u003c/p\u003e\n\u003ch2\u003eEnrichment analysis of biological process and pathway\u003c/h2\u003e\n\u003cp\u003eGO analysis results showed that DEGs were mainly rich in positive regulation of cytokine production, cellular response to biotic stimulus, cellular response to lipopolysaccharide, cellular response to molecule of bacterial origin, response to lipopolysaccharide, interleukin-1 production, regulation of interleukin-1 production, positive regulation of interleukin-1 beta production, positive regulation of interleukin-1 production, and regulation of cytokine production involved in the immune response (Fig. 3A). KEGG assays revealed that DEGs were mainly enriched in the NOD-like receptor signaling pathway (Fig. 3B).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eDetection and verification of diagnostic genes in EMS\u003c/h2\u003e\n\u003cp\u003eTwo\u0026nbsp;machine learning\u0026nbsp;methods were applied in selecting potential markers of EMS: SVM-RFE and\u0026nbsp;RF. We\u0026nbsp;first screened 14 DEGs with\u0026nbsp;SVM-RFE analysis\u0026nbsp;and\u0026nbsp;identified\u0026nbsp;10 diagnostic biomarkers (Fig. 4A). RF analysis identified 4\u0026nbsp;diagnostic biomarkers\u0026nbsp;from 14\u0026nbsp;DEGs (Fig. 4B\u0026nbsp;and 4C). After integrating the characteristic genes from SVM-RFE and RF, four characteristic genes closely related to pyroptosis were obtained: NLRP3, IL-1\u0026beta;, LY96, and PDIA3 (Fig. 4D). The expression of NLRP3, IL-1\u0026Beta;, and LY96 was distinctly upregulated in EMS, and the expression of PDIA3 was distinctly upregulated in EMS (Fig. 5A). The diagnostic value of four overlapping genes was shown by the use of ROC assays (Fig. 5B). We verified the results of the screened differential genes at the protein level. The verification results revealed that the protein (Fig. 6A and 6B) levels of\u0026nbsp;NLRP3 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), LY96 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01)\u0026nbsp;and\u0026nbsp;IL-1\u0026beta; (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05)\u0026nbsp;in EMS were higher than those in the control group, while the results of\u0026nbsp;PDIA3 (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) were the opposite, consistent with the Gene Chip data with multiple exposures. Moreover, we further determined the diagnostic value of NLRP3, IL-1\u0026beta;, LY96 and PDIA3 in the GSE7305 and GSE23339 datasets, and the results are shown in Fig. 7. We established the RF, SVM, XGB, and GLM models to choose NLRP3\u0026nbsp;activation-related genes to predict the incidence of EMS. Both \u0026ldquo;Reverse Cumulative Distribution of Residual\u0026rdquo; (Fig.\u0026nbsp;8A) and \u0026ldquo;Boxplots of Residual\u0026rdquo; (Fig. 8B) indicate that the GLM model has minimal residuals. The ROC curve was drawn to evaluate the model, and the AUC value of the ROC curve also indicated that the GLM model had higher accuracy compared with the other three models (Fig. 8C). Finally, the ROC curve also demonstrated the good diagnostic performance of the four genetic biomarker combinations based on the four classification models in both validation datasets (GSE7305 and GSE23339) for normal women and EMS patients. (Fig. 8D and 8E).\u003c/p\u003e\n\u003ch2\u003eAnalysis of functional correlations\u003c/h2\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) was performed for the four key genes based on the median expression profile. GSEA expression analysis of four genes in NLRP3 showed that the gene expressions of malaria, pertussis, systemic lupus erythematosus immune-related diseases and biological processes of complement and coagulation cascades and taurine and hypotaurine metabolism were significantly increased in the high expression group, while base excision repair, DNA replication, Fanconi anemia pathway, homologous recombination, and mismatch repair in the low expression group (Fig. 9A). The IL-1\u0026beta; in the high expression group were significantly enriched in the legionellosis, malaria, systemic lupus erythematosus, complement and coagulation cascades, and viral protein interaction with cytokine and cytokine receptor, whereas those in the low-expression groups were significantly enriched in aminoacyl\u0026minus;tRNA biosynthesis, base excision repair, DNA replication, homologous recombination, and mismatch repair (Fig. 9B). The LY96 in the high-expression group was significantly enriched in asthma, legionellosis, malaria, systemic lupus erythematosus, and complement and coagulation cascades. On the other hand, LY96 in the low expression groups was significantly enriched in base excision repair, DNA replication, Fanconi anemia pathway, homologous recombination, and mismatch repair (Fig. 9C). Ultimately, the PDIA3 in the high expression group was significantly enriched in DNA replication, Fanconi anemia pathway, homologous recombination, mismatch repair, and protein export, nevertheless, those in the low expression group were significantly enriched in asthma, malaria, pertussis, systemic lupus erythematosus, and taurine and hypotaurine metabolism (Fig. 9D).\u003c/p\u003e\n\u003ch2\u003eCorrelation\u0026nbsp;between four diagnostic genes and EMS immune permeation level\u003c/h2\u003e\n\u003cp\u003eIn this study, the proportion of 21 immune cells in 18 EMS samples and 23 Normal samples was estimated with the CIBERSORT algorithm, which can be seen in the histograms. The abundance of B cells memory, Plasma cells, T cells CD4 memory resting, Macrophages M2, Mast cells activated, and Eosinophils were higher in EMS samples than that in normal samples, while the infiltration abundance of B cells na\u0026iuml;ve, NK cells activated, and Mast cells resting in EMS samples were lower (Fig. 10A and 10B). Four biomarkers (NLRP3, IL-1\u0026beta;, LY96 and PDIA3) were associated with 6 significantly different immune cells in 21 immune cell types, including Dendritic cells activated, Dendritic cells resting, Eosinophils, Macrophages M1, Monocytes, and Plasma cells (Fig. 10C and 10D). As shown in Figure 9D, NLRP3 was negatively correlated with B cells na\u0026iuml;ve only. IL-1\u0026beta; was positively correlated with Plasma cells but negatively correlated with Mast cells resting and T cells CD4 na\u0026iuml;ve. LY96 was related to Plasma cells, Macrophages M2, B cells mEMSory, T cells gamma delta, and Eosinophils in a positive way, while negatively related with B cells na\u0026iuml;ve, NK cells activated, Dendritic cells activated, Mast cells resting, T cells CD8, and T cells follicular helper. PDIA3 has positively correlated with T cells CD4 memory activated when negatively correlated with B cells memory and Macrophages M2. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConstruction and assessment of the diagnostic nomogram model\u003c/h2\u003e\n\u003cp\u003eA nomogram was created to validate the diagnostic power of the four EMS biomarkers (Fig. 11A). The calibration curve showed a small error between the actual EMS risk and the predicted risk, indicating that the nomogram model had a high accuracy in predicting EMS (Fig. 11B). DCA showed that the nomogram curve was higher than the gray critical line and the four biomarker curves, indicating that the nomogram model had the best clinical benefit (Fig. 11C).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eIdentification of candidate pharmacologic agents\u003c/h2\u003e\n\u003cp\u003eFour well-validated diagnostic genes (NLRP3, IL-1\u0026beta;, LY96, and PDIA3) were used as genetically linked targets to facilitate further studies in therapeutic development by a drug target enrichment analysis was performed (Table 1). We have listed the top 10 candidate enrichment agents targeting gene links, which may be targeted for the development of co-onset therapeutic strategies. Studies have shown that niclosamide can target and inhibit macrophage-induced endometriosis inflammatory microenvironment, thereby improving reproductive function[20\u0026ndash;22]. In addition, our analysis of the results based on Table 1 indicates that niclosamide is considered a candidate molecule for the treatment of EMS because of its high odds ratio and high composite score.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometriosis, a gynecological disease characterized by the implantation of endometrial tissue outside the uterus, has attracted increasing attention worldwide recently. However, despite its prevalence, diagnosis is often delayed by years, misdiagnosis is common, clinical presentations are varied, and the presence of pelvic lesions is heterogeneous[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, EMS is usually treated with a combination of hormone therapy and surgery to remove the lesion[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Despite recent advances in the medical treatment of endometriosis-related pelvic pain, there are still no drugs available to treat the condition[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the identification of novel molecular factors and the unraveling of the underlying mechanisms of EMS will facilitate early diagnosis and the development of effective therapies.\u003c/p\u003e \u003cp\u003eIn the present study, we identified DEGs across the GSE7307 dataset including 18 EMS samples and 23 normal samples. These DEGs included 11 upregulated and 3 downregulated genes, respectively. GO and KEGG analyses revealed that these DEGs were involved in regulating immune-related signaling pathways, such as the production and regulation of the cytokine IL-1β, as well as the NOD-like receptor signaling pathway. We applied the machine learning methods of SVM-RFE and RF to integrate the feature genes and obtained four feature genes: NLRP3, IL-1β, LY96 and PDIA3. Then, we further confirmed their diagnostic using GSE7305 and GSE23339 datasets, and further demonstrated NLRP3, IL-1β, LY96 and PDIA3 as critical biomarkers for EMS based on the results of ROC assays.\u003c/p\u003e \u003cp\u003eThe inflammasome is newly discovered and plays an important role in innate immunity. The most typical is the NLRP3 inflammasome, the so-called NLRP3 protein because the NLRP3 protein in this complex belongs to the family of nucleotide-binding and oligomerization domain-like receptors (NLRs), also known as \"pyrin domain-containing protein 3\"[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The expression of NLRP3 in EMS tissues and serum of EMS patients was higher than that of normal controls, which contributed to the invasion of EMS[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Since EMS is a gynecologic disorder associated with estrogen, higher estrogen receptor (ER)-β levels and enhanced ER-β activity are detected in endometriotic tissues. ER-β is known to interact with NALP3, and the activation of NALP3 increases interleukin IL-1β and IL-18, thereby enhancing cell adhesion and proliferation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The NLRP3 inhibitor (MCC950) has recently been reported to significantly reduce the co-localization of NLRP3 and IL-1β in cyst-derived stromal cells (CSCs) and IL-1β concentration in the supernatant of CSCs. Thereby contributing to Ovarian endometriosis (OE) suppression and improving ovarian function in endometriosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the specific mechanism of NLRP3 in EMS is still unclear.\u003c/p\u003e \u003cp\u003eIL-1β plays a considerable role in inflammatory diseases and the main sources of its secretion are macrophages and monocytes, dendritic cells (DC), B lymphocytes, neutrophils and natural killer (NK) cells and non-immune cells such as keratinocytes[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The maturation of IL-1β and IL-18 induces pyroptosis, a form of cell death[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The inflammatory factor IL-1β has been reported to be significantly increased in ectopic endometrial tissue of patients with EMS[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is consistent with our results. Chronic pelvic pain is one of the main clinical symptoms of female endometriosis, but its specific mechanism is unclear. IL-1β is closely related to inflammatory pain by sending inflammatory pain signals to the hypothalamus and participating in afferent pain response[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A retrospective study of peritoneal fluid samples obtained by laparoscopy found an increase in IL-1β concentration compared with normal controls that were statistically significant only in women with endometriosis who reported pelvic pain[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, some studies have revealed the mechanism of deep dyspareunia in endometriosis, that is, IL-1β stimulates the expression of nerve growth factor (NGF), promotes local neurogenesis around endometriosis, and then leads to tenderness in the pelvic anatomy, leading to deep dyspareunia[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Because IL-1β can induce NGF expression and increase NGF bioactivity, dienogest, a selective P-receptor (PR) agonist, Inhibition of NGF expression by PR-A and PR-B in human endometrial epithelial cells (hEECs) may contribute to the relief of endometriosis pain[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Sulforaphane alleviates pain caused by sciatic nerve endometriosis, which is mediated by the inhibition of inflammatory cytokines IL6, IL-1β, and TNF-α[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Overall, pharmacological targeting of IL-1β/NGF may also have the potential for the treatment of endometriosis-related pain.\u003c/p\u003e \u003cp\u003eLY96 plays a crucial role in inflammation-related and immune-related diseases, such as Crohn's disease, rheumatoid arthritis, and inflammatory diabetic cardiomyopathy[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Key protein disulfide isomerase 3 (PDIA3) is a kind of chaperone, that can adjust the folding of the new synthesis glycoprotein, the heterogeneous and REDOX enzyme activity, and immune activation marks, cancer immune cells and immune modulators significantly related, is a powerful cancer prognosis biomarkers, which can effectively predict the immune response to treatment[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Similarly, the expression and function of LY96 and PDIA3 in EMS remain are largely unknown.\u003c/p\u003e \u003cp\u003eFurthermore, we also evaluated the degree of infiltration of 22 immune cells in EMS and normal samples. Compared with normal samples, EMS samples had distinctly higher levels of B cells memory, Plasma cells, T cells CD4 memory resting, Macrophages M2, Mast cells activated, and Eosinophils. It is well known that endometriosis lesions are characterized by the presence of numerous plasma cells and activated macrophages[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. One study found that the relative abundance of resting CD4\u0026thinsp;+\u0026thinsp;memory T cells and even less abundant memory B cells were significantly higher in patients with stage III/IV endometriosis, which was strongly associated with endometrial receptivity[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Activated macrophages are generally divided into two groups, M1-like macrophages and M2-like macrophages, both of which are closely related to the inflammatory response[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Another study found a significant increase in total macrophages in ectopic endometrial tissue (EC) and a syngeneic mouse model of endometriosis, and M2 macrophages were the predominant macrophages in EC[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Of note, M2 macrophages have been found to increase gradually from stage I to stage IV in ectopic endometrial tissues of EMS patients, whereas M1 macrophages have been found to increase in contrast, which may contribute to the proinflammatory microenvironment in the early stage of the disease, as well as to the profibrotic activity in the late stage[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Diffuse infiltration of large numbers of mast cells (MCs) is observed in the interstitium of ectopic endometrial tissues of EMS. MCs promote the development of endometriosis through NLRP3 inflammasome activation mediated by nuclear-initiated estrogen signaling pathway and the production of mature IL-1β[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, the preoperative pain score of EMS is closely related to the density and number of mast cells, and the use of MCs stabilizers and inhibitors can relieve endometriosis and its associated pain[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Eosinophils can regulate fibroblasts and stimulate collagen synthesis. Eosinophils are abundant in fibrotic areas and peritoneal fluid of endometriosis, which is associated with fibrosis of endometriosis lesions[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Moreover, we found that the expressions of NLRP3, IL-1β, LY96, and PDIA3 were related to the levels of many immune cells, highlighting their potential used as therapeutic targets for EMS. Hence, The differential expression of NLRP3, IL-1β, LY96, and PDIA3 between EMS and normal groups pointed out they might be crucial immune-related biomarkers to diagnose EMS. A diagnostic nomogram model based on four core genes with excellent predictive power was established, and patients would benefit from this model.\u003c/p\u003e \u003cp\u003eFinally, based on four diagnostic genes as gene junction points, we identified niclosamide as a candidate molecule for EMS treatment due to its high odds ratio and high comprehensive score among the top 10 candidate enrichment agents by drug target enrichment analysis. Niclosamide was discovered in 1953 at the Bayer Chemotherapy Research Laboratory to treat tapeworm infections in humans and is listed on the World Health Organization's Essential Medicines List[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. However, accumulating evidence suggests that niclosamide is a multifunctional drug capable of inhibiting or modulating multiple signaling pathways and biological processes, which has been demonstrated to treat endometriosis in vitro and in vivo experiments. Recent niclosamide was reported can effectively reduce the endometriosis of endometriosis lesions in mice (ELL) induced by peritoneal macrophages (LPM) and LPM the expression of related genes, and by reducing ELL and pelvic organs may stimulate the peripheral nerve as a medium of inflammatory factor to reduce the abnormal inflammation, may eventually reduce the pain associated[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In the endometriosis model in mice, chlorine nitramine can not only through targeted STAT3 and NFkB signaling pathways, reduce the growth and progress of the sample endometriosis lesions, but also can inhibit the inflammatory mechanisms, thereby reducing the macrophages induced cell activity and the secretion of cytokine/chemokine based on not affect reproductive function[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At present, although niclosamide has not been used in the clinical treatment of endometriosis, the results of previous studies and our results suggest that niclosamide may be a novel strategy for the targeted treatment of inflammatory dysfunction in endometriosis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFour diagnostic genes related to NLRP3 activation and EMS were identified by bioinformatics. Although we have explored the biological processes and pathways involved in them, further experimental verification is needed to verify these functions. The nomogram based on these four genes has a good diagnostic effect on EMS, which provides a theoretical basis for clinical diagnosis. In addition, these diagnostic genes were found to be associated with different immune factors, suggesting that they may also have important roles in the immune microenvironment. Currently, drugs targeting these pyroptosis genes are predicted to alleviate EMS, but only niclosamide has the results of basic studies to support its use in the treatment of EMS, and further studies are needed to explore its specific mechanism of action. Firstly, the GSE7307 dataset was downloaded from the GEO database, including 18 EMS samples and 23 control samples. The final 14 DEGs related to pyroptosis and EMS were obtained from the endometrial samples of GSE7307 by differential analysis. GO and KEGG analysis showed that these genes were mainly involved in two major biological processes. Random Forest (RF) and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to select four genes to construct the EMS diagnostic model. The model was validated in endometrial samples of GSE7305 and GSE23339, and the AUC values showed that the model had a good diagnostic performance. In addition, the infiltration of immune cells in the samples and the correlation between different immune factors and pyroptosis genes were further discussed. These results suggest that these diagnostic genes may also play an important role in the immunity of EMS. Finally, 10 drugs targeting these four diagnostic genes were retrieved from the DrugBank database, of which niclosamide proved useful for treating EMS. Overall, we identified four key diagnostic genes for EMS. In addition, large-scale and multicenter prospective cohort studies are necessary to confirm whether these four genes also have valid diagnostic value in blood samples from EMS patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eMD and PH directed the study; WN, HW and SD performed the experiments; LH, TL, LL and BW performed the data collection and statistical analysis; SZ edited the language; all the authors approved the manuscript for submission.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (No. 81960274), the 2020 university-level Research Project of Youjiang Medical University for Nationalities (No. yy2020gcky039), and the 2021 Scientific Research and Technology Development Program of Baise City (No. 20212351).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are derived from the public GEO data portal (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE7307, GSE7305 and GSE23339).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the ethics review committee of Affiliated Hospital of Youjiang Medical College for Nationalities. \u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations of the ethics review committee of Affiliated Hospital of Youjiang Medical College for Nationalities, and informed consent was obtained from all subjects and/or their legal guardian(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\u003eTaylor HS, Kotlyar AM, Flores VA. 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Pan-Cancer Analysis of the Characteristics of LY96 in Prognosis and Immunotherapy Across Human Cancer. Front Mol Biosci. 2022;9:837393.\u003c/li\u003e\n \u003cli\u003eMo H-Q, Tian F-J, Ma X-L, Zhang Y-C, Zhang C-X, Zeng W-H, et al. PDIA3 regulates trophoblast apoptosis and proliferation in preeclampsia via the MDM2/p53 pathway. Reproduction. 2020;160:293\u0026ndash;305.\u003c/li\u003e\n \u003cli\u003eTu Z, Ouyang Q, Long X, Wu L, Li J, Zhu X, et al. Protein Disulfide-Isomerase A3 Is a Robust Prognostic Biomarker for Cancers and Predicts the Immunotherapy Response Effectively. Front Immunol. 2022;13:837512.\u003c/li\u003e\n \u003cli\u003eHever A, Roth RB, Hevezi P, Marin ME, Acosta JA, Acosta H, et al. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proc Natl Acad Sci U S A. 2007;104:12451\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eXiang R, Chen P, Zeng Z, Liu H, Zhou J, Zhou C, et al. Transcriptomic analysis shows that surgical treatment is likely to influence the endometrial receptivity of patients with stage III/IV endometriosis. Front Endocrinol (Lausanne). 2022;13:932339.\u003c/li\u003e\n \u003cli\u003eYunna C, Mengru H, Lei W, Weidong C. Macrophage M1/M2 polarization. Eur J Pharmacol. 2020;877:173090.\u003c/li\u003e\n \u003cli\u003eZhong Q, Yang F, Chen X, Li J, Zhong C, Chen S. Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages. Front Genet. 2021;12:631715.\u003c/li\u003e\n \u003cli\u003eJe M, Sh A, Rm M, Sp M, At F, M K, et al. IL-17A Modulates Peritoneal Macrophage Recruitment and M2 Polarization in Endometriosis. Frontiers in immunology [Internet]. Front Immunol; 2020 [cited 2022 Oct 19];11.\u003c/li\u003e\n \u003cli\u003eLagan\u0026agrave; AS, Salmeri FM, Ban Frangež H, Ghezzi F, Vrtačnik-Bokal E, Granese R. Evaluation of M1 and M2 macrophages in ovarian endometriomas from women affected by endometriosis at different stages of the disease. Gynecol Endocrinol. 2020;36:441\u0026ndash;4.\u003c/li\u003e\n \u003cli\u003eSugamata M, Ihara T, Uchiide I. Increase of activated mast cells in human endometriosis. Am J Reprod Immunol. 2005;53:120\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eGuo X, Xu X, Li T, Yu Q, Wang J, Chen Y, et al. NLRP3 Inflammasome Activation of Mast Cells by Estrogen via the Nuclear-Initiated Signaling Pathway Contributes to the Development of Endometriosis. Front Immunol. 2021;12:749979.\u003c/li\u003e\n \u003cli\u003eAnaf V, Chapron C, El Nakadi I, De Moor V, Simonart T, No\u0026euml;l J-C. Pain, mast cells, and nerves in peritoneal, ovarian, and deep infiltrating endometriosis. Fertil Steril. 2006;86:1336\u0026ndash;43.\u003c/li\u003e\n \u003cli\u003eBinda MM, Donnez J, Dolmans M-M. Targeting mast cells: a new way to treat endometriosis. Expert Opin Ther Targets. 2017;21:67\u0026ndash;75.\u003c/li\u003e\n \u003cli\u003eBlumenthal RD, Samoszuk M, Taylor AP, Brown G, Alisauskas R, Goldenberg DM. Degranulating eosinophils in human endometriosis. Am J Pathol. 2000;156:1581\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eEidukaite A, Tamosiunas V. Activity of eosinophils and immunoglobulin E concentration in the peritoneal fluid of women with endometriosis. Clin Chem Lab Med. 2004;42:590\u0026ndash;4.\u003c/li\u003e\n \u003cli\u003eChen W, Mook RA, Premont RT, Wang J. Niclosamide: Beyond an antihelminthic drug. Cell Signal. 2018;41:89\u0026ndash;96.\u003c/li\u003e\n \u003cli\u003eShi M, Sekulovski N, Whorton AE, MacLean JA, Greaves E, Hayashi K. Efficacy of niclosamide on the intra‐abdominal inflammatory environment in endometriosis. FASEB j [Internet]. 2021 [cited 2022 Oct 20];35.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePredictive top 10 pharmacologic agent candidates for EMS with NLRP3 activation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003eTriclocarban CTD 00000497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e713.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e8021.85910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003echlorhexidine CTD 00005633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e498.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e5271.69962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003euric acid CTD 00006967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e498.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e5271.69962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003eGNF-Pf-4325 BOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e486.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e5119.715195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003eniclosamide BOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e464.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e4838.479209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003e9-Methoxyellipticine TTD 00001373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e464.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e4838.479209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003eniclosamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e415.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e4245.007066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003echlorhexidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e415.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e4245.007066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003e5H-quinolino[8,7-c][1,2]benzothiazine 6,6-dioxide TTD 00001179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e398.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e4043.246313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.20900321543408%\"\u003e\n \u003cp\u003ethimerosal BOSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.112540192926046%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003e332.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.20257234726688%\"\u003e\n \u003cp\u003e3249.969098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.237942122186496%\"\u003e\n \u003cp\u003eIL1B; NLRP3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"Endometriosis, GEO, Diagnostic marker, Nomogram, Immune cells infiltration","lastPublishedDoi":"10.21203/rs.3.rs-2830815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2830815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis (EMS) is a common gynecological disease leading to chronic pelvic pain and infertility in women of reproductive age, but its underlying pathogenic genes and effective treatment are still unclear. To date, abnormal expression of NLRP3 activation-related genes has been identified in EMS patients and mouse models. Therefore, this study sought to identify the key genes that could affect the diagnosis and treatment of EMS. The GSE7307 dataset was downloaded from the Gene Expression Omnibus (GEO) database, including 18 EMS samples and 23 control samples. 14 differential genes related to NLRP3 activation and EMS were obtained from the endometrial samples of GSE7307 by differential analysis. GO and KEGG analysis showed that these genes were mainly involved in the production and regulation of the cytokine IL-1β, and the NOD-like receptor signaling pathway. Random Forest (RF) and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to select four diagnostic markers related to NLRP3 activation (NLRP3, IL-1β, LY96 and PDIA3) to construct the EMS diagnostic model. The four diagnostic markers were verified using western blotting and validated in the GSE7305 and GSE23339 datasets. The AUC values showed that the model had a good diagnostic performance. In addition, the infiltration of immune cells in the samples and the correlation between different immune factors and diagnostic markers were further discussed. These results suggest that four diagnostic markers may also play an important role in the immunity of EMS. Finally, 10 drugs targeting to four diagnostic markers were retrieved from the DrugBank database, of which niclosamide proved useful for treating EMS. Overall, we identified four key diagnostic genes for EMS. In addition, large-scale and multicenter prospective cohort studies are necessary to confirm whether these four genes also have valid diagnostic value in blood samples from EMS patients.\u003c/p\u003e","manuscriptTitle":"The NLRP3 activation-related signature predict the diagnosis and indicate immune characteristics in endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-03 14:59:49","doi":"10.21203/rs.3.rs-2830815/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":"c71bc6ea-9c59-4c40-8c70-f2b46361afb2","owner":[],"postedDate":"May 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-07T12:14:16+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-03 14:59:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2830815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2830815","identity":"rs-2830815","version":["v1"]},"buildId":"0U-iFTyB6qxOgVj8rjrZV","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}