{"paper_id":"65f9b959-819f-4d8b-a970-fdfca90aa9b8","body_text":"Combined with bioinformatics and machine learning, the diagnostic model, Immunological features and subtypes of stage IV endometriosis with infertility were analyzed | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Combined with bioinformatics and machine learning, the diagnostic model, Immunological features and subtypes of stage IV endometriosis with infertility were analyzed Yong Lin, Yan Long, Jin He, Qinqin Yi, Jiao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4747993/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 Many studies have shown that endometriosis can lead to infertility in women of reproductive age, but the mechanism is unknown. Our study aims to explore the pathogenesis of stage IV endometriosis with infertility and the role of characteristic genes in this condition. Methods Gene expression profiles were obtained from the GSE120103 dataset retrieved from the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify key modules. Subsequently, minimum absolute contraction, selection operator (LASSO), and random forest machine learning algorithms were employed to screen the characteristic genes of stage IV endometriosis complicated with infertility. The ROC curve and diagnostic model were generated to evaluate the diagnostic efficacy. CIBERSORT was utilized to estimate immune cell infiltration and quantify immune checkpoints. Additionally, we constructed the regulatory network of miRNA and transcription factors.GSEA was utilized to explore the signaling pathways associated with characteristic genes, and potential small molecule compounds were identified through screening the CTD database. Samples from individuals with infertility in stage IV endometriosis were categorized using the consensus clustering method, followed by an examination of the expression and immunological features of different subtypes. Results We identified five characteristic genes (CDY2A, KRT6B, SLC2A2, SRY, MYH7) that predict infertility in stage IV endometriosis. When compared to women of childbearing age with stage IV endometriosis, the immunological features of stage IV endometriosis combined with infertility show significant differences, which are clearly linked to the characteristic genes. Patients can benefit from a gene-based characteristic nomogram. Our study reveals that multiple signaling pathways are strongly associated with infertility in stage IV endometriosis. Furthermore, several small molecule compounds were predicted based on the characteristic genes, and relevant regulatory networks of miRNA and TF were constructed. Stage IV endometriosis combined with infertility is categorized into three subtypes, each showing significantly different immunological characteristics of the characteristic genes. Conclusion This study enhances our understanding of the pathogenesis and immune mechanisms of stage IV endometriosis with infertility. It identifies effective characteristic genes and subtypes, offering valuable insights for treatment. Nevertheless, additional prospective studies and experiments are necessary to validate our findings. endometriosis infertility immunological characteristics characteristic genes subtype Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Endometriosis is a condition where the endometrial tissue (glands and stroma) appears in areas outside the uterine body. Ectopic endometrial tissue can invade any part of the body, such as the umbilical cord, bladder, pleura, breast, arms, thighs, the majority is found in pelvic organs and the peritoneum wall. The most common sites are the ovaries, uterine sacral ligament, followed by the uterus, and other areas like the peritoneum, vaginal rectal septum, among others, leading to pelvic endometriosis[1,2]. And endometriosis is a hormone-dependent condition[3]. The incidence of endometriosis in women of childbearing age is up to 10%, and it is increasing every year [4, 5]. Some studies have shown that the clinical manifestations of endometriosis include abnormal menstruation, dysmenorrhea, and painful intercourse, which are closely related to infertility [6,7]. Endometriosis is the primary cause of infertility, affecting 0.5% to 5% of women of childbearing age and 25% to 40% of infertile women. The optimal treatment option for endometriosis-related infertility remains poorly defined. A specific combination of drugs, surgery, and psychotherapy can improve the quality of life of women with endometriosis. The benefits of these treatments have not been fully demonstrated, particularly in terms of women's expectations of their own fertility, and may delay further fertility treatment.[8,9] Although the close association between endometriosis and infertility is well established, the exact mechanism by which this occurs is unclear, and many processes may be at play. A review offers a comprehensive overview of the multifactorial occurrence of endometriosis and its impact on fertility[10]. The uterus itself, and the inflammatory environment associated with endometriosis (EM) in the ovary, peritoneum, and pelvis, chronic inflammation of the peritoneal cavity leading to obvious adhesions and fundamental anatomical changes, abnormal expression of cells and secreted immune mediators in peritoneal fluid (PF) and plasma of patients with endometriosis, and abnormal expression [11–18] in proinflammatory cytokines such as TNF-α, IL-1, IL-1β, IL-6, and IL-8 in PF of women with endometriosis, all affect the successful conception rate. Furthermore, the intraperitoneal immune cell status was altered in women with endometriosis-related infertility when compared to women with unexplained infertility [19]. The pathogenesis of endometriosis is not only complex and unknown, but the pathogenesis of infertility associated with endometriosis remains elusive. This is the impetus that prompted us to explore the underlying mechanism of infertility in endometriosis. In this paper, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithm to identify the characteristic genes associated with infertility in stage IV endometriosis. The analysis aimed to predict the correlation between stage IV endometriosis and infertility. Furthermore, we classified stage IV endometriosis with infertility and assessed the enriched signaling pathways of these characteristic genes, along with their impact on immune cell infiltration. These findings aim to offer novel perspectives for clinicians in the diagnosis and treatment of this condition. Methods Data sources In the present study, inclusion criteria were as follows: expression profiling by array ; Stage IV endometriosis with infertility or without infertility; samples are endometrial samples from patients with Stage IV endometriosis with infertility or normal fertility. A comprehensive search was conducted in the Gene Expression Omnibus(GEO)(http://www.ncbi.nlm.nih.gov/geo/) database, from the date of library completion to June 2024. We search for a GEO dataset, namely GSE120103 [Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version)]. We extracted the samples from stage IV endometriosis in women of reproductive age, including 9 samples from stage IV endometriosis with infertility and 9 samples from stage IV endometriosis with normal fertility. Identification of differentially expressed genes( DEGs) All data were analyzed using the limma package of R software[20]. The data were then analyzed for differentially expressed genes (DEGs) between stage IV endometriosis with infertility and control groups based on the following criteria: adjusted p-value < 0.05 and |log fold change (FC)| > 1. Heat maps and volcano plots were generated from the screened DEGs. Functional enrichment analysis The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the clusterProfiler software package in R [21]. For the GO analysis, three categories were identified: biological processes (BP), cellular components (CC), and molecular functions (MF), which helped to explore the biological processes of these DEGs. The potential signaling pathways were explored using KEGG analysis. Network analysis of the weighted gene co-expression network was conducted based on the scale-free topology criterion. The GSE120103 dataset was analyzed using weighted gene co-expression network analysis (WGCNA)[22] . Weighted gene co-expression network (WGCNA) analysis is beneficial for gene expression research as it helps identify potentially important aberrant expression values. A co-expression network was constructed using an automated network system, employing hierarchical clustering and dynamic tree cutting function detection. Finally, gene modules with a minimum size of 100 were obtained. We then identified the association between the gene module and the genetic significance (GS) and the module membership (MM) values, and finally identified the key modules. Identification and validation of characteristic genes We identified candidate hub genes by intersecting DGEs and key module genes. Subsequently, we utilized LASSO regression analysis and the random forest algorithm to identify characteristic genes. The LASSO regression analysis was conducted for 10-fold cross-validation using the glmnet package with penalized parameters. The minimum λ value was considered optimal and outperformed the regression analysis method when evaluating high-dimensional data[23]. We calculated the error rate from 1 to 500 trees and determined the best number of trees based on the lowest error rate. After establishing the above parameters, a random forest tree model was created[24]. Subsequently, the importance score for each candidate hub gene was determined, and genes with a significance value greater than or equal to 0.04 were selected. The genes that were common to both machine learning algorithms were considered as signature genes for mild endometriosis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic efficiency of these feature genes. An AUC exceeding 0.7 indicates good diagnostic performance. Gene Set Enrichment Analysis To identify the links between eigenes and signaling pathways, we conducted gene set enrichment analysis (GSEA) for eigenes.[25] Immune Cell Infiltration The study investigated the variances in 22 human immune cells among individuals with stage IV endometriosis with infertility using CIBERSORT analysis[26]. Subsequently, immune cells showing significant differences in immune infiltration were identified, and the correlation between immune cells and characteristic genes was analyzed using the Spearman method. Exploration of transcription factors and miRNAs of characteristic genes Transcription factors (TFs) and microRNAs (miRNAs) regulating the characteristic genes were obtained from the JASPAR database and the TarBase (v8.0) database, respectively[27]. The interaction networks of TF-gene and miRNA-gene were visualized using Cytoscape. The intersection networks was analyzed through a Venn diagram. Prediction of potential drugs for characteristic genes We obtained a catalog of small molecule compounds interacting with characteristic genes from the CTD database(http://ctdbase.org/). This data was visualized using Cytoscape and analyzed for intersections using Venn diagrams. Subsequently, potential drugs related to the characteristic genes were identified. Consensus clustering analysis Based on the expression profile of stage IV endometriosis with infertility, the consensus clustering method through the ConsensusClusterPlus package (100 iterations and 80% threshold) was used to quantitatively estimate the number of unsupervised categories of stage IV endometriosis with infertility [28]. The consistency matrix map, the consistency cumulative distribution function (CDF) map, the relative change of the area under the CDF curve, and the tracking map were utilized to determine the optimal number of clusters. Principal component analysis (PCA) was employed to identify differences in the expression of relevant genes between subtypes. The PCA plot was generated using the ggplot2 package[29]. Statistical analysis All statistical analyses in this study were performed using the R software (version 4.3.3). Unless otherwise stated, p < 0.05 was considered statistically significant, and all p-values were two-tailed. The flow chart of this study is shown in Figure 1. Results Determination of the DEGs The endometriums of stage IV endometriosis with infertility and control groups were analyzed using the \"limma\" software package. Nine samples from each group were analyzed, and differentially expressed genes (DEGs) were identified. A total of 7102 genes were selected through the differential expression analysis, with 3540 upregulated and 3562 down-regulated genes (Fig. 2 A). The heatmap shows the top 100 and bottom 100 genes in terms of expression levels between stage IV endometriosis with infertility and control groups (Fig. 2 B). Functional enrichment analysis The GO analysis includes three categories (Fig. 3 A): biological process (BP), cellular component (CC), and molecular function (MF). In stage IV endometriosis with infertility, the BP analysis showed upregulation of trans-synaptic signaling, modulation of chemical synaptic transmission, and signal release, synapse organization, regulation of membrane potential, regulation of monoatomic ion, transmembrane transport, organic anion transport, neurotransmitter transport, regulation of neurotransmitter levels and postsynaptic membrane potential, the regulation had been significantly enriched. The CC analysis revealed neuronal cell bodies, the apical part of the cell, and the synaptic membrane, external side of plasma membrane, glutamatergic synapse, monoatomic ion channel complex, postsynaptic membrane, The regulation of cation channel complex, presynaptic membrane and postsynaptic specialization membrane were significantly enriched. In addition, MF analysis revealed DNA − binding transcription activator activity, DNA − binding transcription activator activity, RNA Polymerase II-specific metal ion transmembrane transporter activity, monoatomic ion channel activity, monoatomic ion-gated channel activity, gated channel activity, voltage-gated monoatomic ion channel activity, voltage-gated channel activity, ligand-gated monoatomic ion channel activity, neurotransmitter receptor activity were significantly enriched. Moreover, as shown in KEGG's analysis, the top eight enrichment pathways include neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, MAPK signaling pathway, calcium signaling pathway, ras signaling pathway, and rap1 signaling pathway, JAK-STAT signaling pathway (Fig. 3 B). Construction of a weighted gene co-expression network The WGCNA software package in R was utilized to analyze the endometrium of stage IV endometriosis with infertility and control groups. A scale-free co-expression network was established with a soft threshold power of 12, a scale-free index of 0.5, and a relatively favorable mean connectivity (Fig. 4 A,B). The clustering tree diagram is shown in Fig. 4 C. Finally, the data were categorized into seven modules (Fig. 4 D). The correlation between each module and stage IV endometriosis with infertility was computed. The results indicated that MEblue module (cor = 0.88, p = 1e-06) was significantly associated with infertility in stage IV endometriosis. This module includes 304 genes and is identified as a key module associated with infertility in stage IV endometriosis. The overlap between genes in the module and DEGs is candidate hub genes, shown in Fig. 4 E. Selection of characteristic genes by machine algorithms. Two machine algorithms were used to identify the characteristic genes from the candidate hub genes in stage IV endometriosis with infertility. Through LASSO analysis, 5 characteristic genes were selected (Fig. 5 C,D), while random forest analysis identified 16 characteristic genes with relative importance greater than or equal to 0.04 (Fig. 5 A,B). Ultimately, five characteristic genes were identified: CDY2A, KRT6B, SLC2A2, SRY, and MYH7 (Fig. 5 E). Diagnostic efficacy of characteristic genes in predicting stage VI endometriosis with infertility Compared with the control group, the expression levels of the screened characteristic genes were significantly different in patients with stage VI endometriosis with infertility, suggesting that these genes may play a potential role in infertility caused by endometriosis (Fig. 6 A-E). In addition, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) for these feature genes was as follows: AUC = 1 for SRY, AUC = 1 for CDY2A, AUC = 1 for MHY7, AUC = 1 for SLC2A2, and AUC = 1 for KRT6B7 (Fig. 6 F-J). These results indicate that the selected characteristic genes had significant diagnostic efficiency in predicting stage IV endometriosis with infertility. The performance of the characteristic genes in GSE20103. A–E The expression of characteristic genes between stage IV endometriosis with infertility and control group. (F–J) ROC showed the diagnostic performance of the characteristic genes. Predictive power of characteristic genes We utilized the RMS package in R to analyze the nomogram model of five characteristic genes. The analysis revealed that the nomogram model could effectively predict stage IV endometriosis with infertility. The Decision Curve Analysis (DCA) demonstrated that the curve of the characteristic genes significantly surpassed the gray line, which indicates that the nomogram model has high accuracy. Additionally, a calibration curve was constructed to assess the predictive power of the nomogram model. The calibration curve indicated minimal variance between the actual and predicted abilities of the characteristic genes in predicting stage IV endometriosis with infertility, indicating the high accuracy of the characteristic genes' prediction.These results are shown in Fig. 7 . Gene Set Enrichment Analysis (GSEA) We evaluated the signaling pathways associated with the characteristic genes. Through GSEA analysis, the 30 major signaling pathways were shown in Fig. 8 . AGE-RAGE signaling pathway in diabetic complications, mitophagy - animal, herpes simplex virus 1 infection, shigellosis, cellular senescence, olfactory transduction, efferocytosis, focal adhesion, lysosome, spliceosome, human immunodeficiency virus 1 infection, endocytosis, ubiquitin-mediated proteolysis, morphine addiction, autophagy - animal, apoptosis, cytokine-cytokine receptor interaction, salmonella infection, neuroactive ligand-receptor interaction, nicotine addiction, yersinia infection, taste transduction are associated with CDY2A, KRT6B, MYH7, SLC2A2, and SRY. Adherens junction, polycomb repressive complex intersected with four characteristic genes: CDY2A, KRT6B, MYH7 and SLC2A2.Nucleocytoplasmic transport, sphingolipid signaling pathway are associated with MYH7, SLC2A2 and SRY.Fc gamma R-mediated phagocytosis, ATP-dependent chromatin remodeling are associated with CDY2A, KRT6B and SRY. TNF signaling pathway is associated with KRT6B, MYH7 and SRY. These results are shown in Fig. 8 . Immune cell infiltration Immunoinfiltration was assessed based on immune cell infiltration. Compared to the normal group, Stage IV endometriosis with infertility has higher levels of mast cells resting, T cells follicular helper, NK cells activated, plasma cells, macrophages M1, macrophages M2, mast cells activated, T cells regulatory (Tregs). SRY was positively correlated with NK cells resting, and mast cells resting. NK cells activated were negatively correlated. CDY2A was positively correlated with Eosinophils and dendritic cells resting. SLC2A2 was negatively correlated with Mast cells activated. KRT6B was positively correlated with dendritic cells activation. These results are shown in Fig. 9 , 10 . Identification of transcription factors and miRNAs for characteristic genes In order to determine the regulatory effects of miRNAs and TFs on characteristic genes' expression at the transcriptional level, the interaction networks of miRNAs or TFs and characteristic genes were obtained and visualized using cytoscape software. The intersection between characteristic genes was analyzed using venn diagrams. There were 2, 12, 41, and 6 miRNAs interacting with SRY, SLC2A2, MYH7, and KRT6B, respectively. Hsa-miR-98-5p and hsa-miR-210-3p interact with MYH7 and SLC2A2. Hsa-miR-155-5p, hsa-miR-20a-5p, and hsa-miR-10b-5p interact with KRT6B and MYH7, while hsa-miR-200b-3p interacts with SLC2A2 and SRY. These results suggest that these miRNAs may have a closer interaction with the characteristic genes. We did not find miRNAs for CDY2A in the TarBase (v8.0) database. We only found 14 transcription factors related to SRY in the JASPAR database. These results are shown in supplementary Fig. 1. Supplementary Fig. 1 The plot about regulatory network of characteristic genes. A Networks of miRNAs about characteristic genes. B The interaction of miRNAs about characteristic genes. C Networks of TFs about SRY. Identifying of potentially effective drugs To explore potentially effective drugs that target characteristic genes, we obtained chemicals from the CTD database that can reduce levels of characteristic gene expression. Subsequently, we visualized the gene-chemical interaction network map using cytoscape software. Dietary fats and KRT6B, MYH7, SLC2A2, SRY have crossovers. Calcitriol, valproic acid, and estradiol had crossovers with KRT6B, MYH7, and SLC2A2. Progesterone and KRT6B, SRY had crossovers. Bisphenol A and MYH7, SRY had crossovers. Diethylstilbestrol, troglitazone, ethano and KRT6B, SLC2A2 had crossovers. Triptonide and KRT6B, MYH7 had crossovers. Resveratrol, rosiglitazone, triptolide, triptolide, triptolide, curcumin, flavonoids, metformin, ethinyl and MYH7, SLC2A2 had crossovers. These results are shown in supplementary Fig. 2. Supplementary Fig. 2 The plot of potentially effective drugs. A Gene-chemical interaction network map. B The interaction of potentially effective drugs about characteristic genes. Construction of subtypes for stage IV endometriosis with infertility A consensus clustering method was used to cluster 9 stage IV endometriosis samples with infertility. The optimal number of subtypes was 3, determined through consensus matrix plots, CDF plots, relative changes in area under CDF curves, and tracking plots. The three subtypes were named cluster 1, cluster 2, and cluster 3. PCA demonstrated significant differences between the subtypes. It was found that there were no significant differences in the expression of KRT6B among subtypes of stage IV endometriosis with infertility. A comparative analysis of all subtypes of immune checkpoints indicated significant differences in T cells CD4 memory activated, NK cells activated, and monocytes. Immunocell infiltration analysis of stage IV endometriosis was performed based on the expression profiles of infertility and immune-related genes. Activated CD8 cells, activated dendritic cells, immature B cells, immature dendritic cells, monocytes, mast cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, natural killer T cells, T follicular helper cells, and type 17 T helper cells showed significant differences among subtypes.These results are shown in supplementary Fig. 3,4. Supplementary Fig. 3 Construction of subtypes about stage IV endometriosis with infertility. A Consensus matrix heatmap when k = 3. B Consensus CDF when k = 2–5. C Relative alterations in the area under CDF curve. D Tracking plot showing the sample classification when k = 2–5. E PCA plots demonstrating that atherosclerotic plaque specimens are categorized as three subtypes in stage IV endometriosis with infertility. F Heatmap showing the expression of stage IV endometriosis with infertility in three subtypes. Supplementary Fig. 4 Three subtypes characterized by different immunological features and molecular mechanisms. A Box plots of the mRNA expression of characteristic genes in three subtypes. B Box plots of the mRNA expression of immune checkpoints in three subtypes. C The box plots about the infiltration levels of immune cell components in three subtypes. *p < 0.05; **p < 0.01; and ***p < 0.001. Discussion Numerous studies have shown that endometriosis is closely related to infertility, yet the mechanism by which endometriosis causes infertility remains unclear.[ 30 – 33 ] Therefore, it is important to develop diagnostic markers for endometriosis with infertility and explore its mechanisms. From the database GSE120103, we acquired 9 samples related to stage IV endometriosis with infertility and identified 3540 up-regulated and 3562 down-regulated genes associated with infertility in stage IV endometriosis compared to the control group. Utilizing two machine learning algorithms, we identified five characteristic genes (CDY2A, KRT6B, SLC2A2, SRY, MYH7). The DCA analysis and ROC indicated that these selected characteristic genes exhibited significant diagnostic efficacy in predicting infertility in stage IV endometriosis. We studied the pathogenesis of endometriosis and its association with infertility. A study on the cellular senescence pathway has shown that excessive reactive oxygen species (ROS) induces senescence of ovarian granulosa cumulus cells in endometriosis by triggering endoplasmic reticulum stress, eventually leading to endometriosis-associated infertility[ 34 ]. In a study about the focal adhesion pathway, researchers observed that adhesion spot kinase (FAK) and monocyte chemotactic protein-1 are co-upregulated in endometriosis tissues compared to normal endometrial tissues. The FAK-mediated sequential development of endometriosis, which includes inflammatory responses and tissue fibrosis, could potentially serve as a novel therapeutic target for endometriosis.[ 35 ] We found that the number of lysosomes in the ectopic endometrial tissue of patients with endometriosis was significantly larger than the number in endometrioma.[ 36 ] Studies have shown that the degradation of the p27KIP1 gene regulates the endometrial cell cycle through the ubiquitin-mediated proteolysis pathway and influences the development of endometriosis.[ 37 ] A review about apoptosis revealed that the endometrium of women with endometriosis shows increased expression of anti-apoptotic factors and decreased expression of pro-apoptotic factors compared to the endometrium of healthy women. These differences may contribute to the survival of endometrial cells that reflux into the peritoneal cavity, leading to the development of endometriosis. Increased apoptosis of fas-carrying immune cells in the peritoneal cavity may result in reduced scavenger activity, ultimately prolonging the survival of ectopic endometrial cells in women with endometriosis.[ 38 ] Regarding the NF-kappa B signaling pathway, TNF-α induces dysregulation of microRNA associated with the NF-kB signaling pathway, thereby contributing to the pathogenesis of endometriosis[ 39 ]. The TNF-α signaling pathway is also involved in promoting the development of endometriosis[ 40 ]. Activation of PI3K/AKT signaling pathway in endometriosis significantly increased the level of cellular pyroptosis and inflammatory factors. [ 41 ] Abnormal MAPK activation in migration, implantation, growth, invasion into the pelvic structures, proliferation, and apoptosis leads to the form of endometriosis and to worsen the condition in patients with endometriosis[ 42 ]. In endometriosis, the ras signaling pathway plays a role in the regulation of cell proliferation and migration[ 43 ]. Our study found that multiple pathways are involved in the formation of endometriosis with infertility. Identifying which pathways are specifically linked to endometriosis with infertility will aid in further research on the pathogenesis of this condition. Additionally, several small molecular compounds, including resveratrol, rosiglitazone, triptolide, and curcumin, were selected based on their impact on atherosclerotic plaque progression and immune-related genes. However, experimental validation is required to evaluate the therapeutic potential of these compounds in mitigating atherosclerosis. Studies have shown a significant relationship between immune cells and infertility in endometriosis. In total endometriosis, cytokine and autoantibody levels are elevated, indicating a role for inflammation and immune cell dysregulation in infertility associated with endometriosis[ 44 , 45 ]. This study demonstrates that natural killer (NK) cells are significantly expressed in stage IV endometriosis with infertility. Other studies and analyses indicate that the cytotoxicity of NK cells in endometriosis is diminished due to alterations in the expression of NKp46 on NK cells and the activation receptor co-expressed with NKp46. Consequently, NK cells are unable to eradicate endometrial cells in the abdominal cavity, leading to the secretion of TNF-α and IFN-γ[ 46 ]. Plasma cells were found to be highly expressed in our analysis, and a previous study also demonstrated an increase in plasma cells in 12.9% of the endometriosis group (62 patients). Analyzing uterine plasma cells may offer a novel approach to identifying biomarkers for endometriosis. The elevated number of plasma cells in patients with endometriosis may be attributed to bacterial infections resulting from retrograde menstrual flow. Nevertheless, extensive studies are required to validate the link between endometriosis and plasma cells, and further research is necessary to comprehend the potential causal relationship between the two conditions[ 47 ]. Selectively activated (M2) peritoneal macrophages have been shown to contribute to the development of peritoneal injury by promoting extracellular matrix remodeling, new blood vessel formation in damaged areas, and the growth of endometriosis lesions[ 48 ]. Macrophages are roughly divided into pro-inflammatory M1 macrophages and M2 macrophages, which have selective anti-inflammatory and pro-fibrotic activities and are capable of inducing immune tolerance and angiogenesis. For each patient, we collected biological samples of cysts during laparoscopy (case endometrioma of the ovary, control group functional cyst of the ovary). We found that the number of M1 and M2 macrophages was significantly higher in the endometriosis group than in the control group, regardless of stage (p < .0001, each stage compared to the control group). In addition, our data analysis showed that M1 macrophages showed a gradually decreasing trend from stage I to stage IV; conversely, M2 macrophages showed a mirrored trend compared to M1 macrophages, showing an increasing trend from stage I to stage IV. This may contribute to the pro-inflammatory microenvironment in the early stages of the disease, as well as pro-fibrotic activity in the later stages[ 49 ]. The increase in activated and degranulated mast cells in deep infiltrating endometriosis (the most painful lesions), as well as the close histological relationship between mast cells and nerves, strongly suggests that mast cells may contribute to the development of pain and hyperalgesia in endometriosis through their direct effect on nerve structure[ 50 ]. Dysregulation of T cells regulatory, which leads to increased systemic and local inflammation of the ectopic and in-situ endometrium, appears in women who eventually develop endometriosis[ 51 ]. In early endometriosis, activated eosinophils accumulate in the peritoneal fluid and play an important role in the pathogenesis of the disease[ 52 ]. We constructed three subtypes of stage IV endometriosis with infertility. Significant differences were observed in the immune microenvironment and immune cells among the subtypes. This may aid in the early diagnosis and intervention of stage IV endometriosis with infertility. Nevertheless, some limitations of this paper should be pointed out. Although we identified the characteristic genes of stage IV endometriosis with infertility based on machine learning algorithms, we did not find similar external datasets to validate their diagnostic efficacy. However, prospective studies will be conducted to further explore the potential of these genes in predicting stage IV endometriosis with infertility. Furthermore, the underlying mechanisms of the characteristic genes will be further elucidated through experimental research. The data for this study were obtained from public databases and involved a small sample size, which may introduce selective bias. Conclusion This study enhances our understanding of the pathogenesis and immune mechanisms of stage IV endometriosis with infertility. It identifies effective characteristic genes and subtypes, offering valuable insights for treatment. Nevertheless, additional prospective studies and experiments are necessary to validate our findings. Abbreviations GEO, Gene Expression Omnibus; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process CC, cellularcomponent; MF, molecular function; WGCNA, weighted gene co-expression networkanalysis; TOM, topological overlap matrix; GS, gene significance; MM, module membership; LASSO, least absolute shrinkage and selection operator; GSEA, gene set enrichment analysis; AUC, area under curve ROC, receiver operating characteristic curve; MACSO,MinimumAbsolute Contraction and Selection Operator; DCA,Decision Curve Analysis; DCs,dendriticcells ; PF,peritoneal fluid ; EM, endometriosis ; GSEA, gene set enrichment analysis; miRNAs,microRNAs; TFs, Transcription factors; CDF, cumulative distribution function; PCA, principal component analysi; ROS, reactive oxygen species. Declarations Data availability statement All data comes from the database GEO. The original contributions presented in the study are included in the article. Author contributions LY and LY designed the study and drafted the manuscript. YQ and WJ analyzed data and typeset figures. LY and HJ revised the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgments The authors also thank the GEO database for the open access to the data . Conflict of interest The authors have no conflict of interest to declare. Ethical approval Not needed. Funding There was no Funding in the article. References Amro B, Ramirez Aristondo ME, Alsuwaidi S, Almaamari B, Hakim Z, Tahlak M, Wattiez A, Koninckx PR. New Understanding of Diagnosis, Treatment and Prevention of Endometriosis. Int J Environ Res Public Health. 2022 May 31;19(11):6725. Wang PH, Yang ST, Chang WH, Liu CH, Lee FK, Lee WL. Endometriosis: Part I. Basic concept. 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Punnonen J, Teisala K, Ranta H, Bennett B, Punnonen R. Increased levels of interleukin-6 and interleukin-10 in the peritoneal fluid of patients with endometriosis. Am J Obstet Gynecol. 1996;174:1522–6. Bersinger NA, von Roten S, Wunder DM, Raio L, Dreher E, Mueller MD. PAPP-A and osteoprotegerin, together with interleukin-8 and RANTES, are elevated in the peritoneal fluid of women with endometriosis. Am J Obstet Gynecol. 2006;195:103–8. Yoshino O, Osuga Y, Koga K, Hirota Y, Tsutsumi O, Yano T, Morita Y, Momoeda M, Fujiwara T, Kugu K, Taketani Y. Concentrations of Interferon-gamma-Induced Protein-10 (IP-10), an Antiangiogenic Substance, are Decreased in Peritoneal Fluid of Women with Advanced Endometriosis. Am J Reprod Immunol. 2003;50:60–5. Kats R, Collette T, Metz CN, Akoum A. Marked elevation of macrophage migration inhibitory factor in the peritoneal fluid of women with endometriosis. Fertil Steril. 2002;78:69–76. Tariverdian N, Siedentopf F, Rücke M, Blois SM, Klapp BF, Kentenich H, Arck PC. Intraperitoneal immune cell status in infertile women with and without endometriosis. J Reprod Immunol. 2009;80:80–90. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012 May;16(5):284-7. Yang L, Ma TJ, Zhang YB, Wang H, An RH. Construction and Analysis of lncRNA-miRNA-mRNA ceRNA Network Identify an Eight-Gene Signature as a Potential Prognostic Factor in Kidney Renal Papillary Cell Carcinoma (KIRP). Altern Ther Health Med. 2022 Sep;28(6):42-51. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997 Feb 28;16(4):385-95. Izmirlian G. Application of the random forest classification algorithm to a SELDI-TOF proteomics study in the setting of a cancer prevention trial. Ann N Y Acad Sci. 2004 May;1020:154-74. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015 May;12(5):453-7. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 2015;10:823–844. Wilkerson M. D., Hayes D. N. (2010). ConsensusClusterPlus: a Class Discovery Tool with Confidence Assessments and Item Tracking. Bioinformatics 26 (12), 1572–1573. Ito K., Murphy D. (2013). Application of Ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst. Pharmacol. 2 (10), e79. ] Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am. 2012 Dec;39(4):535-49. Mathieu d'Argent E, Cohen J, Chauffour C, Pouly JL, Boujenah J, Poncelet C, Decanter C, Santulli P. Endométriose profonde et infertilité, RPC Endométriose CNGOF-HAS [Deeply infiltrating endometriosis and infertility: CNGOF-HAS Endometriosis Guidelines]. Gynecol Obstet Fertil Senol. 2018 Mar;46(3):357-367. French. Munshi H, Khan T, Khan S, DasMahapatra P, Balakrishnan S, Nirmala C, Das V, Kulkarni K, John BM, Majumdar A, Sowmini CV, Srivastava A, Khade K, Gajbhiye RK. Determinants of Conception and Adverse Pregnancy Outcomes in Women with Endometriosis: A Longitudinal Study. Reprod Sci. 2024 Jun;31(6):1757-1762. Tanbo T, Fedorcsak P. Endometriosis-associated infertility: aspects of pathophysiological mechanisms and treatment options. Acta Obstet Gynecol Scand. 2017 Jun;96(6):659-667. Lin X, Dai Y, Tong X, Xu W, Huang Q, Jin X, Li C, Zhou F, Zhou H, Lin X, Huang D, Zhang S. Excessive oxidative stress in cumulus granulosa cells induced cell senescence contributes to endometriosis-associated infertility. Redox Biol. 2020 Feb;30:101431. Nagai T, Ishida C, Nakamura T, Iwase A, Mori M, Murase T, Bayasula, Osuka S, Takikawa S, Goto M, Kotani T, Kikkawa F. Focal Adhesion Kinase-Mediated Sequences, Including Cell Adhesion, Inflammatory Response, and Fibrosis, as a Therapeutic Target in Endometriosis. Reprod Sci. 2020 Jul;27(7):1400-1410. von Adamek EV, Simões MJ, Freitas V, Patriarca MT, Soares JM Jr, Baracat EC. Lysosomal evaluation of endometrioma capsule epithelium and endometrium of patients with or without endometriosis. Clin Exp Obstet Gynecol. 2005;32(1):27-30. Li B, Wang S, Duan H, Wang Y, Guo Z. Discovery of gene module acting on ubiquitin-mediated proteolysis pathway by co-expression network analysis for endometriosis. Reprod Biomed Online. 2021 Feb;42(2):429-441. Taniguchi F, Kaponis A, Izawa M, Kiyama T, Deura I, Ito M, Iwabe T, Adonakis G, Terakawa N, Harada T. Apoptosis and endometriosis. Front Biosci (Elite Ed). 2011 Jan 1;3(2):648-62. doi: 10.2741/e277. PMID: 21196342. Banerjee S, Xu W, Doctor A, Driss A, Nezhat C, Sidell N, Taylor RN, Thompson WE, Chowdhury I. TNFα-Induced Altered miRNA Expression Links to NF-κB Signaling Pathway in Endometriosis. Inflammation. 2023 Dec;46(6):2055-2070. Jiang L, Wang S, Xia X, Zhang T, Wang X, Zeng F, Ma J, Fang X. Novel Diagnostic Biomarker BST2 Identified by Integrated Transcriptomics Promotes the Development of Endometriosis via the TNF-α/NF-κB Signaling Pathway. Biochem Genet. 2024 Mar 5. An M, Fu X, Meng X, Liu H, Ma Y, Li Y, Li Q, Chen J. PI3K/AKT signaling pathway associates with pyroptosis and inflammation in patients with endometriosis. J Reprod Immunol. 2024 Mar;162:104213. Yotova IY, Quan P, Leditznig N, Beer U, Wenzl R, Tschugguel W. Abnormal activation of Ras/Raf/MAPK and RhoA/ROCKII signalling pathways in eutopic endometrial stromal cells of patients with endometriosis. Hum Reprod. 2011 Apr;26(4):885-97. Zhang D, Wang L, Guo HL, Zhang ZW, Wang C, Chian RC, Zhang ZF. MicroRNA‑202 inhibits endometrial stromal cell migration and invasion by suppressing the K‑Ras/Raf1/MEK/ERK signaling pathway. Int J Mol Med. 2020 Dec;46(6):2078-2088. Kolanska K, Alijotas-Reig J, Cohen J, Cheloufi M, Selleret L, d'Argent E, Kayem G, Valverde EE, Fain O, Bornes M, Darai E, Mekinian A. Endometriosis with infertility: A comprehensive review on the role of immune deregulation and immunomodulation therapy. Am J Reprod Immunol. 2021 Mar;85(3):e13384. Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am. 2012 Dec;39(4):535-49. Saeki S, Fukui A, Mai C, Takeyama R, Yamaya A, Shibahara H. Co-expression of activating and inhibitory receptors on peritoneal fluid NK cells in women with endometriosis. J Reprod Immunol. 2023 Feb;155:103765. Freitag N, Pour SJ, Fehm TN, Toth B, Markert UR, Weber M, Togawa R, Kruessel JS, Baston-Buest DM, Bielfeld AP. Are uterine natural killer and plasma cells in infertility patients associated with endometriosis, repeated implantation failure, or recurrent pregnancy loss? Arch Gynecol Obstet. 2020 Dec;302(6):1487-1494. Miller JE, Ahn SH, Marks RM, Monsanto SP, Fazleabas AT, Koti M, Tayade C. IL-17A Modulates Peritoneal Macrophage Recruitment and M2 Polarization in Endometriosis. Front Immunol. 2020 Feb 14;11:108. Laganà 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 May;36(5):441-444. Anaf V, Chapron C, El Nakadi I, De Moor V, Simonart T, Noël JC. Pain, mast cells, and nerves in peritoneal, ovarian, and deep infiltrating endometriosis. Fertil Steril. 2006 Nov;86(5):1336-43. Knez J, Kovačič B, Goropevšek A. The role of regulatory T-cells in the development of endometriosis. Hum Reprod. 2024 May 16:deae103. 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(6):590-4. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4747993\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":332263057,\"identity\":\"3bc73a78-c2f9-4900-b61c-d8cbab344f24\",\"order_by\":0,\"name\":\"Yong Lin\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACfmaG5Icf/9jIybM3HyBOi2R7wzNjyYY0Y8OeYwnEaTE4c/CBBG/D4cSGGzkGRLrsRnKCgeSOw4yNDTkfb7xhsJPTbSCgg3FGWsKDwjPpzOwMZzdbzmFINjY7QEALs0ROgoEEmzUbY2PvNmkehgOJ2whpYZPI/yDBw8bMw3CY5xlxWnh4DiRI8LY5SzAc42EjTosEOzCAJc6kGRj2sBlbzjEgwi/2h4FR+aHCpn6+/OOHN95U2MkR1IJqJQ+xUYOkhVQdo2AUjIJRMCIAAHZYRCic73zIAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yong\",\"middleName\":\"\",\"lastName\":\"Lin\",\"suffix\":\"\"},{\"id\":332263058,\"identity\":\"c456a80c-ee02-4b9d-932a-04eeb9cbe0e0\",\"order_by\":1,\"name\":\"Yan Long\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yan\",\"middleName\":\"\",\"lastName\":\"Long\",\"suffix\":\"\"},{\"id\":332263059,\"identity\":\"b30dde73-bba7-4d69-808b-3cb8336ab4ee\",\"order_by\":2,\"name\":\"Jin He\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jin\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"},{\"id\":332263060,\"identity\":\"8c3bfb47-1a50-4632-a462-c8457b800fed\",\"order_by\":3,\"name\":\"Qinqin Yi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qinqin\",\"middleName\":\"\",\"lastName\":\"Yi\",\"suffix\":\"\"},{\"id\":332263061,\"identity\":\"fd3e8813-ec85-4853-a817-dc5c5800deb2\",\"order_by\":4,\"name\":\"Jiao Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"LUZHOU MATERNAL AND CHILD HEALTH HOSPITAL (LUZHOU SECOND PEOPLE'S HOSPITAL)\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiao\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-07-16 07:52:58\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4747993/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4747993/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":62734887,\"identity\":\"d54d113d-a910-4761-aa9f-d9d9bbf63440\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:18:19\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":79978,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA flow chart of the study\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/fdaf5c54f1c09201787b796f.png\"},{\"id\":62734353,\"identity\":\"7656ecbc-5406-420a-88a2-69026e14fa48\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":971967,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIdentification of DEGs. A.Volcano shows DEGs expression between infertility in stage IV endometriosis and control groups.B.The heat map shows the 100 genes with the most difference and the 100 genes with the least difference\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/b9b5ed0ed1b2e284cc30f43f.png\"},{\"id\":62734357,\"identity\":\"d7999ad2-561a-492e-8fd8-9a0f5e5d7ccf\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":496589,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePlot of functional enrichment analysis\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/6106222229a439ba0326232d.png\"},{\"id\":62734355,\"identity\":\"cf02e107-216d-4737-a296-57478287c93a\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":436083,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePlot of weighted gene co-expression network\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/2700fd06b88698c9d3bab4b7.png\"},{\"id\":62734352,\"identity\":\"5d1d557d-67e4-40f1-a82e-6aafbfb21ce2\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":416022,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe machine algorithms for characteristic genes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/ec9425c9fec9ad6271a565a4.png\"},{\"id\":62734358,\"identity\":\"ab7d27f8-8beb-4b28-994d-3d7a2d85f3f3\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":127545,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe performance of the \\u003cstrong\\u003echaracteristic\\u003c/strong\\u003e genes in GSE20103. A–E The expression of characteristic genes between stage IV endometriosis with infertility and control group. (F–J) ROC showed the diagnostic performance of the characteristic genes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/0266101f9c95230e7ab1dd1d.png\"},{\"id\":62734356,\"identity\":\"835b48cb-219b-4623-a703-235347593461\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:19\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":857555,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredictive power of characteristic genes for stage IV endometriosis with infertility. A,D,G,J,M: Nomogram is used to predict the occurrence of stage IV endometriosis with infertility; B,E,H,K,N: DCA curves, C,F,I,L,O: calibration curve.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/257dd8b77444f4f593000484.png\"},{\"id\":62734359,\"identity\":\"178c4710-33df-42ea-abe7-f959a6210663\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:20\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1199277,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe plot about GSEA. A-E signaling pathways of the characteristic genes. F The intersection of the signaling pathways about characteristic genes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/8d275d9e40655e77c8327693.png\"},{\"id\":62734360,\"identity\":\"2fac5e6c-4b42-4754-a175-61d9d883ed8e\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:20\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":282385,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImmunoinfiltration about stage IV endometriosis with infertility\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/4d8f190d408c8d6e9e6a61a6.png\"},{\"id\":62734361,\"identity\":\"f04af9bc-9107-448c-8c01-b3773223a937\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:10:20\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":780831,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eImmunoinfiltration about the characteristic genes\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/d178e7ff291a4af1ad9a4732.png\"},{\"id\":91821691,\"identity\":\"db7c2d38-7132-4c3d-bd56-5d9ed8c90512\",\"added_by\":\"auto\",\"created_at\":\"2025-09-22 07:31:33\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6218424,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/05bf5432-0641-4d72-bcfc-3fe367a6cacb.pdf\"},{\"id\":62734888,\"identity\":\"219dc77c-baa3-4842-83d1-20aa6b7c4bc5\",\"added_by\":\"auto\",\"created_at\":\"2024-08-19 00:18:19\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":4042219,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFigure.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4747993/v1/1fb086bcd42e8799b24eb391.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Combined with bioinformatics and machine learning, the diagnostic model, Immunological features and subtypes of stage IV endometriosis with infertility were analyzed\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEndometriosis is a condition where the endometrial tissue (glands and stroma) appears in areas outside the uterine body. Ectopic endometrial tissue can invade any part of the body, such as the umbilical cord, bladder, pleura, breast, arms, thighs, the majority is found in pelvic organs and the peritoneum wall. The most common sites are the ovaries, uterine sacral ligament, followed by the uterus, and other areas like the peritoneum, vaginal rectal septum, among others, leading to pelvic endometriosis[1,2]. And endometriosis is a hormone-dependent condition[3].\\u003c/p\\u003e\\n\\u003cp\\u003eThe incidence of endometriosis in women of childbearing age is up to 10%, and it is increasing every year [4, 5].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Some studies have shown that the clinical manifestations of endometriosis include abnormal menstruation, dysmenorrhea, and painful intercourse, which are closely related to infertility [6,7].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Endometriosis is the primary cause of infertility, affecting 0.5% to 5% of women of childbearing age and 25% to 40% of infertile women.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The optimal treatment option for endometriosis-related infertility remains poorly defined. A specific combination of drugs, surgery, and psychotherapy can improve the quality of life of women with endometriosis. The benefits of these treatments have not been fully demonstrated, particularly in terms of women\\u0026apos;s expectations of their own fertility, and may delay further fertility treatment.[8,9]\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Although the close association between endometriosis and infertility is well established, the exact mechanism by which this occurs is unclear, and many processes may be at play. A review offers a comprehensive overview of the multifactorial occurrence of endometriosis and its impact on fertility[10].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The uterus itself, and the inflammatory environment associated with endometriosis (EM) in the ovary, peritoneum, and pelvis, chronic inflammation of the peritoneal cavity leading to obvious adhesions and fundamental anatomical changes, abnormal expression of cells and secreted immune mediators in peritoneal fluid (PF) and plasma of patients with endometriosis, and abnormal expression [11\\u0026ndash;18] in proinflammatory cytokines such as TNF-\\u0026alpha;, IL-1, IL-1\\u0026beta;, IL-6, and IL-8 in PF of women with endometriosis, all affect the successful conception rate. Furthermore, the intraperitoneal immune cell status was altered in women with endometriosis-related infertility when compared to women with unexplained infertility [19]. The pathogenesis of endometriosis is not only complex and unknown, but the pathogenesis of infertility associated with endometriosis remains elusive. This is the impetus that prompted us to explore the underlying mechanism of infertility in endometriosis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;In this paper, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and the random forest algorithm to identify the characteristic genes associated with infertility in stage IV endometriosis. The analysis aimed to predict the correlation between stage IV endometriosis and infertility. Furthermore, we classified stage IV endometriosis with infertility and assessed the enriched signaling pathways of these characteristic genes, along with their impact on immune cell infiltration. These findings aim to offer novel perspectives for clinicians in the diagnosis and treatment of this condition.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eData\\u0026nbsp;sources\\u003c/p\\u003e\\n\\u003cp\\u003eIn the present study, inclusion criteria were as follows: expression profiling \\u003ca href=\\\"https://www.ncbi.nlm.nih.gov/gds/?term=endometriosis\\\"\\u003eby array\\u003c/a\\u003e; Stage IV endometriosis with infertility or without infertility; samples\\u0026nbsp;are\\u0026nbsp;endometrial samples from patients with Stage IV endometriosis\\u0026nbsp;with\\u0026nbsp;infertility\\u0026nbsp;or\\u0026nbsp;normal fertility.\\u003c/p\\u003e\\n\\u003cp\\u003eA comprehensive search was conducted in the Gene Expression Omnibus(GEO)(http://www.ncbi.nlm.nih.gov/geo/) database,\\u0026nbsp;from the date of library completion to June 2024. We\\u0026nbsp;search for a GEO dataset, namely\\u0026nbsp;GSE120103 [Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version)]. We extracted the samples from stage IV endometriosis in women of reproductive age, including 9 samples from stage IV endometriosis with infertility\\u0026nbsp;and\\u0026nbsp;9 samples from stage IV endometriosis with normal fertility.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Identification of differentially expressed genes( DEGs)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;All data were analyzed using the limma package of R software[20]. The data were then analyzed for differentially expressed genes (DEGs) between stage IV endometriosis with infertility and control groups based on the following criteria: adjusted p-value \\u0026lt; 0.05 and |log fold change (FC)| \\u0026gt; 1. Heat maps and volcano plots were generated from the screened DEGs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Functional enrichment analysis\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the clusterProfiler software package in R [21].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;For the GO analysis, three categories were identified: biological processes (BP), cellular components (CC), and molecular functions (MF), which helped to explore the biological processes of these DEGs. The potential signaling pathways were explored using KEGG analysis. Network analysis of the weighted gene co-expression network was conducted based on the scale-free topology criterion. The GSE120103 dataset was analyzed using weighted gene co-expression network analysis (WGCNA)[22] . Weighted gene co-expression network (WGCNA) analysis is beneficial for gene expression research as it helps identify potentially important aberrant expression values. A co-expression network was constructed using an automated network system, employing hierarchical clustering and dynamic tree cutting function detection.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, gene modules with a minimum size of 100 were obtained. We then identified the association between the gene module and the genetic significance (GS) and the module membership (MM) values, and finally identified the key modules.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Identification and validation of characteristic genes\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;We identified candidate hub genes by intersecting DGEs and key module genes. Subsequently, we utilized LASSO regression analysis and the random forest algorithm to identify characteristic genes. The LASSO regression analysis was conducted for 10-fold cross-validation using the glmnet package with penalized parameters. The minimum \\u0026lambda; value was considered optimal and outperformed the regression analysis method when evaluating high-dimensional data[23].\\u003c/p\\u003e\\n\\u003cp\\u003eWe calculated the error rate from 1 to 500 trees and determined the best number of trees based on the lowest error rate. After establishing the above parameters, a random forest tree model was created[24]. Subsequently, the importance score for each candidate hub gene was determined, and genes with a significance value greater than or equal to 0.04 were selected. The genes that were common to both machine learning algorithms were considered as signature genes for mild endometriosis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic efficiency of these feature genes. An AUC exceeding 0.7 indicates good diagnostic performance.\\u003c/p\\u003e\\n\\u003cp\\u003eGene Set Enrichment Analysis\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify the links between eigenes and signaling pathways, we conducted gene set enrichment analysis (GSEA) for eigenes.[25]\\u003c/p\\u003e\\n\\u003cp\\u003eImmune Cell Infiltration\\u003c/p\\u003e\\n\\u003cp\\u003eThe study investigated the variances in 22 human immune cells among individuals with stage IV endometriosis with infertility using CIBERSORT analysis[26]. Subsequently, immune cells showing significant differences in immune infiltration were identified, and the correlation between immune cells and characteristic genes was analyzed using the Spearman method.\\u003c/p\\u003e\\n\\u003cp\\u003eExploration of transcription factors and miRNAs of characteristic genes\\u003c/p\\u003e\\n\\u003cp\\u003eTranscription factors\\u0026nbsp;(TFs) and microRNAs (miRNAs) regulating the characteristic genes were obtained from the JASPAR database and the TarBase (v8.0) database, respectively[27]. The interaction networks of TF-gene and miRNA-gene \\u0026nbsp;were visualized using Cytoscape. The intersection networks was analyzed through a Venn diagram.\\u003c/p\\u003e\\n\\u003cp\\u003ePrediction of potential drugs for characteristic genes\\u003c/p\\u003e\\n\\u003cp\\u003eWe obtained a catalog of small molecule compounds interacting with characteristic genes from the CTD database(http://ctdbase.org/). This data was visualized using Cytoscape and analyzed for intersections using Venn diagrams. Subsequently, potential drugs related to the characteristic genes were identified.\\u003c/p\\u003e\\n\\u003cp\\u003eConsensus clustering analysis\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the expression profile of stage IV endometriosis with infertility, the consensus clustering method through the ConsensusClusterPlus package (100 iterations and 80% threshold) was used to quantitatively estimate the number of unsupervised categories of stage IV endometriosis with infertility [28]. The consistency matrix map, the consistency cumulative distribution function (CDF) map, the relative change of the area under the CDF curve, and the tracking map were utilized to determine the optimal number of clusters. Principal component analysis (PCA) was employed to identify differences in the expression of relevant genes between subtypes. The PCA plot was generated using the ggplot2 package[29].\\u003c/p\\u003e\\n\\u003cp\\u003eStatistical analysis\\u003c/p\\u003e\\n\\u003cp\\u003eAll statistical analyses in this study were performed using the R software (version 4.3.3). Unless otherwise stated, p \\u0026lt; 0.05 was considered statistically significant, and all p-values were two-tailed. The flow chart of this study is shown in Figure 1.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eDetermination of the DEGs\\u003c/p\\u003e \\u003cp\\u003eThe endometriums of stage IV endometriosis with infertility and control groups were analyzed using the \\\"limma\\\" software package. Nine samples from each group were analyzed, and differentially expressed genes (DEGs) were identified. A total of 7102 genes were selected through the differential expression analysis, with 3540 upregulated and 3562 down-regulated genes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). The heatmap shows the top 100 and bottom 100 genes in terms of expression levels between stage IV endometriosis with infertility and control groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFunctional enrichment analysis\\u003c/p\\u003e \\u003cp\\u003eThe GO analysis includes three categories (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA): biological process (BP), cellular component (CC), and molecular function (MF). In stage IV endometriosis with infertility, the BP analysis showed upregulation of trans-synaptic signaling, modulation of chemical synaptic transmission, and signal release, synapse organization, regulation of membrane potential, regulation of monoatomic ion, transmembrane transport, organic anion transport, neurotransmitter transport, regulation of neurotransmitter levels and postsynaptic membrane potential, the regulation had been significantly enriched. The CC analysis revealed neuronal cell bodies, the apical part of the cell, and the synaptic membrane, external side of plasma membrane, glutamatergic synapse, monoatomic ion channel complex, postsynaptic membrane, The regulation of cation channel complex, presynaptic membrane and postsynaptic specialization membrane were significantly enriched. In addition, MF analysis revealed DNA − binding transcription activator activity, DNA − binding transcription activator activity, RNA Polymerase II-specific metal ion transmembrane transporter activity, monoatomic ion channel activity, monoatomic ion-gated channel activity, gated channel activity, voltage-gated monoatomic ion channel activity, voltage-gated channel activity, ligand-gated monoatomic ion channel activity, neurotransmitter receptor activity were significantly enriched.\\u003c/p\\u003e \\u003cp\\u003eMoreover, as shown in KEGG's analysis, the top eight enrichment pathways include neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, MAPK signaling pathway, calcium signaling pathway, ras signaling pathway, and rap1 signaling pathway, JAK-STAT signaling pathway (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eConstruction of a weighted gene co-expression network\\u003c/p\\u003e \\u003cp\\u003eThe WGCNA software package in R was utilized to analyze the endometrium of stage IV endometriosis with infertility and control groups. A scale-free co-expression network was established with a soft threshold power of 12, a scale-free index of 0.5, and a relatively favorable mean connectivity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA,B). The clustering tree diagram is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC. Finally, the data were categorized into seven modules (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD). The correlation between each module and stage IV endometriosis with infertility was computed. The results indicated that MEblue module (cor = 0.88, p = 1e-06) was significantly associated with infertility in stage IV endometriosis. This module includes 304 genes and is identified as a key module associated with infertility in stage IV endometriosis. The overlap between genes in the module and DEGs is candidate hub genes, shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSelection of characteristic genes by machine algorithms.\\u003c/p\\u003e \\u003cp\\u003eTwo machine algorithms were used to identify the characteristic genes from the candidate hub genes in stage IV endometriosis with infertility. Through LASSO analysis, 5 characteristic genes were selected (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC,D), while random forest analysis identified 16 characteristic genes with relative importance greater than or equal to 0.04 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA,B). Ultimately, five characteristic genes were identified: CDY2A, KRT6B, SLC2A2, SRY, and MYH7 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDiagnostic efficacy of characteristic genes in predicting stage VI endometriosis with infertility\\u003c/p\\u003e \\u003cp\\u003eCompared with the control group, the expression levels of the screened characteristic genes were significantly different in patients with stage VI endometriosis with infertility, suggesting that these genes may play a potential role in infertility caused by endometriosis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA-E). In addition, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) for these feature genes was as follows: AUC = 1 for SRY, AUC = 1 for CDY2A, AUC = 1 for MHY7, AUC = 1 for SLC2A2, and AUC = 1 for KRT6B7 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eF-J). These results indicate that the selected characteristic genes had significant diagnostic efficiency in predicting stage IV endometriosis with infertility.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe performance of the \\u003cb\\u003echaracteristic\\u003c/b\\u003e genes in GSE20103. A–E The expression of characteristic genes between stage IV endometriosis with infertility and control group. (F–J) ROC showed the diagnostic performance of the characteristic genes.\\u003c/p\\u003e \\u003cp\\u003ePredictive power of characteristic genes\\u003c/p\\u003e \\u003cp\\u003eWe utilized the RMS package in R to analyze the nomogram model of five characteristic genes. The analysis revealed that the nomogram model could effectively predict stage IV endometriosis with infertility. The Decision Curve Analysis (DCA) demonstrated that the curve of the characteristic genes significantly surpassed the gray line, which indicates that the nomogram model has high accuracy. Additionally, a calibration curve was constructed to assess the predictive power of the nomogram model. The calibration curve indicated minimal variance between the actual and predicted abilities of the characteristic genes in predicting stage IV endometriosis with infertility, indicating the high accuracy of the characteristic genes' prediction.These results are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eGene Set Enrichment Analysis (GSEA)\\u003c/p\\u003e \\u003cp\\u003eWe evaluated the signaling pathways associated with the characteristic genes. Through GSEA analysis, the 30 major signaling pathways were shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e. AGE-RAGE signaling pathway in diabetic complications, mitophagy - animal, herpes simplex virus 1 infection, shigellosis, cellular senescence, olfactory transduction, efferocytosis, focal adhesion, lysosome, spliceosome, human immunodeficiency virus 1 infection, endocytosis, ubiquitin-mediated proteolysis, morphine addiction, autophagy - animal, apoptosis, cytokine-cytokine receptor interaction, salmonella infection, neuroactive ligand-receptor interaction, nicotine addiction, yersinia infection, taste transduction are associated with CDY2A, KRT6B, MYH7, SLC2A2, and SRY. Adherens junction, polycomb repressive complex intersected with four characteristic genes: CDY2A, KRT6B, MYH7 and SLC2A2.Nucleocytoplasmic transport, sphingolipid signaling pathway are associated with MYH7, SLC2A2 and SRY.Fc gamma R-mediated phagocytosis, ATP-dependent chromatin remodeling are associated with CDY2A, KRT6B and SRY. TNF signaling pathway is associated with KRT6B, MYH7 and SRY. These results are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eImmune cell infiltration\\u003c/p\\u003e \\u003cp\\u003eImmunoinfiltration was assessed based on immune cell infiltration. Compared to the normal group, Stage IV endometriosis with infertility has higher levels of mast cells resting, T cells follicular helper, NK cells activated, plasma cells, macrophages M1, macrophages M2, mast cells activated, T cells regulatory (Tregs). SRY was positively correlated with NK cells resting, and mast cells resting. NK cells activated were negatively correlated. CDY2A was positively correlated with Eosinophils and dendritic cells resting. SLC2A2 was negatively correlated with Mast cells activated. KRT6B was positively correlated with dendritic cells activation. These results are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIdentification of transcription factors and miRNAs for characteristic genes\\u003c/p\\u003e \\u003cp\\u003eIn order to determine the regulatory effects of miRNAs and TFs on characteristic genes' expression at the transcriptional level, the interaction networks of miRNAs or TFs and characteristic genes were obtained and visualized using cytoscape software. The intersection between characteristic genes was analyzed using venn diagrams. There were 2, 12, 41, and 6 miRNAs interacting with SRY, SLC2A2, MYH7, and KRT6B, respectively. Hsa-miR-98-5p and hsa-miR-210-3p interact with MYH7 and SLC2A2. Hsa-miR-155-5p, hsa-miR-20a-5p, and hsa-miR-10b-5p interact with KRT6B and MYH7, while hsa-miR-200b-3p interacts with SLC2A2 and SRY. These results suggest that these miRNAs may have a closer interaction with the characteristic genes.\\u003c/p\\u003e \\u003cp\\u003eWe did not find miRNAs for CDY2A in the TarBase (v8.0) database. We only found 14 transcription factors related to SRY in the JASPAR database. These results are shown in supplementary Fig.\\u0026nbsp;1.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSupplementary Fig.\\u0026nbsp;1 The plot about regulatory network of characteristic genes. A Networks of miRNAs about characteristic genes. B The interaction of miRNAs about characteristic genes. C Networks of TFs about SRY.\\u003c/p\\u003e \\u003cp\\u003eIdentifying of potentially effective drugs\\u003c/p\\u003e \\u003cp\\u003eTo explore potentially effective drugs that target characteristic genes, we obtained chemicals from the CTD database that can reduce levels of characteristic gene expression. Subsequently, we visualized the gene-chemical interaction network map using cytoscape software. Dietary fats and KRT6B, MYH7, SLC2A2, SRY have crossovers. Calcitriol, valproic acid, and estradiol had crossovers with KRT6B, MYH7, and SLC2A2. Progesterone and KRT6B, SRY had crossovers. Bisphenol A and MYH7, SRY had crossovers. Diethylstilbestrol, troglitazone, ethano and KRT6B, SLC2A2 had crossovers. Triptonide and KRT6B, MYH7 had crossovers. Resveratrol, rosiglitazone, triptolide, triptolide, triptolide, curcumin, flavonoids, metformin, ethinyl and MYH7, SLC2A2 had crossovers. These results are shown in supplementary Fig.\\u0026nbsp;2.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSupplementary Fig.\\u0026nbsp;2 The plot of potentially effective drugs. A Gene-chemical interaction network map. B The interaction of potentially effective drugs about characteristic genes.\\u003c/p\\u003e \\u003cp\\u003eConstruction of subtypes for stage IV endometriosis with infertility\\u003c/p\\u003e \\u003cp\\u003eA consensus clustering method was used to cluster 9 stage IV endometriosis samples with infertility. The optimal number of subtypes was 3, determined through consensus matrix plots, CDF plots, relative changes in area under CDF curves, and tracking plots. The three subtypes were named cluster 1, cluster 2, and cluster 3. PCA demonstrated significant differences between the subtypes. It was found that there were no significant differences in the expression of KRT6B among subtypes of stage IV endometriosis with infertility. A comparative analysis of all subtypes of immune checkpoints indicated significant differences in T cells CD4 memory activated, NK cells activated, and monocytes.\\u003c/p\\u003e \\u003cp\\u003eImmunocell infiltration analysis of stage IV endometriosis was performed based on the expression profiles of infertility and immune-related genes. Activated CD8 cells, activated dendritic cells, immature B cells, immature dendritic cells, monocytes, mast cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, natural killer T cells, T follicular helper cells, and type 17 T helper cells showed significant differences among subtypes.These results are shown in supplementary Fig.\\u0026nbsp;3,4.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSupplementary Fig.\\u0026nbsp;3 Construction of subtypes about stage IV endometriosis with infertility. A Consensus matrix heatmap when k = 3. B Consensus CDF when k = 2–5. C Relative alterations in the area under CDF curve. D Tracking plot showing the sample classification when k = 2–5. E PCA plots demonstrating that atherosclerotic plaque specimens are categorized as three subtypes in stage IV endometriosis with infertility. F Heatmap showing the expression of stage IV endometriosis with infertility in three subtypes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSupplementary Fig.\\u0026nbsp;4 Three subtypes characterized by different immunological features and molecular mechanisms. A Box plots of the mRNA expression of characteristic genes in three subtypes. B Box plots of the mRNA expression of immune checkpoints in three subtypes. C The box plots about the infiltration levels of immune cell components in three subtypes. *p \\u0026lt; 0.05; **p \\u0026lt; 0.01; and ***p \\u0026lt; 0.001.\\u003c/p\\u003e \"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eNumerous studies have shown that endometriosis is closely related to infertility, yet the mechanism by which endometriosis causes infertility remains unclear.[\\u003cspan additionalcitationids=\\\"CR31 CR32\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e–\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eTherefore, it is important to develop diagnostic markers for endometriosis with infertility and explore its mechanisms.\\u003c/p\\u003e\\u003cp\\u003eFrom the database GSE120103, we acquired 9 samples related to stage IV endometriosis with infertility and identified 3540 up-regulated and 3562 down-regulated genes associated with infertility in stage IV endometriosis compared to the control group. Utilizing two machine learning algorithms, we identified five characteristic genes (CDY2A, KRT6B, SLC2A2, SRY, MYH7). The DCA analysis and ROC indicated that these selected characteristic genes exhibited significant diagnostic efficacy in predicting infertility in stage IV endometriosis.\\u003c/p\\u003e\\u003cp\\u003eWe studied the pathogenesis of endometriosis and its association with infertility.\\u003c/p\\u003e\\u003cp\\u003eA study on the cellular senescence pathway has shown that excessive reactive oxygen species (ROS) induces senescence of ovarian granulosa cumulus cells in endometriosis by triggering endoplasmic reticulum stress, eventually leading to endometriosis-associated infertility[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn a study about the focal adhesion pathway, researchers observed that adhesion spot kinase (FAK) and monocyte chemotactic protein-1 are co-upregulated in endometriosis tissues compared to normal endometrial tissues. The FAK-mediated sequential development of endometriosis, which includes inflammatory responses and tissue fibrosis, could potentially serve as a novel therapeutic target for endometriosis.[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eWe found that the number of lysosomes in the ectopic endometrial tissue of patients with endometriosis was significantly larger than the number in endometrioma.[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eStudies have shown that the degradation of the p27KIP1 gene regulates the endometrial cell cycle through the ubiquitin-mediated proteolysis pathway and influences the development of endometriosis.[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eA review about apoptosis revealed that the endometrium of women with endometriosis shows increased expression of anti-apoptotic factors and decreased expression of pro-apoptotic factors compared to the endometrium of healthy women. These differences may contribute to the survival of endometrial cells that reflux into the peritoneal cavity, leading to the development of endometriosis. Increased apoptosis of fas-carrying immune cells in the peritoneal cavity may result in reduced scavenger activity, ultimately prolonging the survival of ectopic endometrial cells in women with endometriosis.[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eRegarding the NF-kappa B signaling pathway, TNF-α induces dysregulation of microRNA associated with the NF-kB signaling pathway, thereby contributing to the pathogenesis of endometriosis[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe TNF-α signaling pathway is also involved in promoting the development of endometriosis[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eActivation of PI3K/AKT signaling pathway in endometriosis significantly increased the level of cellular pyroptosis and inflammatory factors. [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/p\\u003e\\u003cp\\u003eAbnormal MAPK activation in migration, implantation, growth, invasion into the pelvic structures, proliferation, and apoptosis leads to the form of endometriosis and to worsen the condition in patients with endometriosis[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn endometriosis, the ras signaling pathway plays a role in the regulation of cell proliferation and migration[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eOur study found that multiple pathways are involved in the formation of endometriosis with infertility. Identifying which pathways are specifically linked to endometriosis with infertility will aid in further research on the pathogenesis of this condition. Additionally, several small molecular compounds, including resveratrol, rosiglitazone, triptolide, and curcumin, were selected based on their impact on atherosclerotic plaque progression and immune-related genes. However, experimental validation is required to evaluate the therapeutic potential of these compounds in mitigating atherosclerosis.\\u003c/p\\u003e\\u003cp\\u003eStudies have shown a significant relationship between immune cells and infertility in endometriosis. In total endometriosis, cytokine and autoantibody levels are elevated, indicating a role for inflammation and immune cell dysregulation in infertility associated with endometriosis[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThis study demonstrates that natural killer (NK) cells are significantly expressed in stage IV endometriosis with infertility. Other studies and analyses indicate that the cytotoxicity of NK cells in endometriosis is diminished due to alterations in the expression of NKp46 on NK cells and the activation receptor co-expressed with NKp46. Consequently, NK cells are unable to eradicate endometrial cells in the abdominal cavity, leading to the secretion of TNF-α and IFN-γ[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003ePlasma cells were found to be highly expressed in our analysis, and a previous study also demonstrated an increase in plasma cells in 12.9% of the endometriosis group (62 patients). Analyzing uterine plasma cells may offer a novel approach to identifying biomarkers for endometriosis. The elevated number of plasma cells in patients with endometriosis may be attributed to bacterial infections resulting from retrograde menstrual flow. Nevertheless, extensive studies are required to validate the link between endometriosis and plasma cells, and further research is necessary to comprehend the potential causal relationship between the two conditions[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eSelectively activated (M2) peritoneal macrophages have been shown to contribute to the development of peritoneal injury by promoting extracellular matrix remodeling, new blood vessel formation in damaged areas, and the growth of endometriosis lesions[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eMacrophages are roughly divided into pro-inflammatory M1 macrophages and M2 macrophages, which have selective anti-inflammatory and pro-fibrotic activities and are capable of inducing immune tolerance and angiogenesis. For each patient, we collected biological samples of cysts during laparoscopy (case endometrioma of the ovary, control group functional cyst of the ovary). We found that the number of M1 and M2 macrophages was significantly higher in the endometriosis group than in the control group, regardless of stage (p \\u0026lt; .0001, each stage compared to the control group). In addition, our data analysis showed that M1 macrophages showed a gradually decreasing trend from stage I to stage IV; conversely, M2 macrophages showed a mirrored trend compared to M1 macrophages, showing an increasing trend from stage I to stage IV. This may contribute to the pro-inflammatory microenvironment in the early stages of the disease, as well as pro-fibrotic activity in the later stages[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe increase in activated and degranulated mast cells in deep infiltrating endometriosis (the most painful lesions), as well as the close histological relationship between mast cells and nerves, strongly suggests that mast cells may contribute to the development of pain and hyperalgesia in endometriosis through their direct effect on nerve structure[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eDysregulation of T cells regulatory, which leads to increased systemic and local inflammation of the ectopic and in-situ endometrium, appears in women who eventually develop endometriosis[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn early endometriosis, activated eosinophils accumulate in the peritoneal fluid and play an important role in the pathogenesis of the disease[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eWe constructed three subtypes of stage IV endometriosis with infertility. Significant differences were observed in the immune microenvironment and immune cells among the subtypes. This may aid in the early diagnosis and intervention of stage IV endometriosis with infertility.\\u003c/p\\u003e\\u003cp\\u003eNevertheless, some limitations of this paper should be pointed out. Although we identified the characteristic genes of stage IV endometriosis with infertility based on machine learning algorithms, we did not find similar external datasets to validate their diagnostic efficacy. However, prospective studies will be conducted to further explore the potential of these genes in predicting stage IV endometriosis with infertility. Furthermore, the underlying mechanisms of the characteristic genes will be further elucidated through experimental research. The data for this study were obtained from public databases and involved a small sample size, which may introduce selective bias.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study enhances our understanding of the pathogenesis and immune mechanisms of stage IV endometriosis with infertility. It identifies effective characteristic genes and subtypes, offering valuable insights for treatment. Nevertheless, additional prospective studies and experiments are necessary to validate our findings.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eGEO, Gene Expression Omnibus; DEGs, differentially expressed genes; GO, Gene \\u0026nbsp;Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process\\u003c/p\\u003e\\n\\u003cp\\u003eCC, cellularcomponent; MF, molecular function; WGCNA, weighted gene co-expression networkanalysis; TOM, topological overlap matrix; GS, gene significance; MM, module membership; LASSO, least absolute shrinkage and selection operator; GSEA, gene set enrichment analysis; AUC, area under curve\\u003c/p\\u003e\\n\\u003cp\\u003eROC, receiver operating characteristic curve; MACSO,MinimumAbsolute Contraction and Selection Operator; DCA,Decision Curve Analysis; DCs,dendriticcells ; PF,peritoneal fluid ; EM, endometriosis ; GSEA, gene set enrichment analysis; miRNAs,microRNAs; TFs, Transcription factors; CDF, cumulative distribution function; PCA, principal component analysi; ROS, reactive oxygen species.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eData availability statement\\u003c/p\\u003e\\n\\u003cp\\u003eAll data comes from the database GEO. The original contributions presented in the study are included in the article.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor contributions\\u003c/p\\u003e\\n\\u003cp\\u003eLY and LY designed the study and drafted the manuscript. YQ and WJ analyzed data and typeset figures. LY and HJ revised the manuscript. All authors contributed to the article and approved the submitted version.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors also thank the GEO database for the open access to the data .\\u003c/p\\u003e\\n\\u003cp\\u003eConflict of interest\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have no conflict of interest to declare.\\u003c/p\\u003e\\n\\u003cp\\u003eEthical approval\\u003c/p\\u003e\\n\\u003cp\\u003eNot needed.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThere was no Funding in the article.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAmro B, Ramirez Aristondo ME, Alsuwaidi S, Almaamari B, Hakim Z, Tahlak M, Wattiez A, Koninckx PR. New Understanding of Diagnosis, Treatment and Prevention of Endometriosis. Int J Environ Res Public Health. 2022 May 31;19(11):6725. \\u003c/li\\u003e\\n\\u003cli\\u003eWang PH, Yang ST, Chang WH, Liu CH, Lee FK, Lee WL. Endometriosis: Part I. Basic concept. Taiwan J Obstet Gynecol. 2022 Nov;61(6):927-934.\\u003c/li\\u003e\\n\\u003cli\\u003eVercellini P, Vigan\\u0026ograve; P, Somigliana E, Fedele L. Endometriosis: pathogenesis and treatment. Nat Rev Endocrinol. 2014 May;10(5):261-75. \\u003c/li\\u003e\\n\\u003cli\\u003eBrown J, Farquhar C. An overview of treatments for endometriosis. JAMA. 2015 Jan 20;313(3):296-7. \\u003c/li\\u003e\\n\\u003cli\\u003eLeone Roberti Maggiore U, Ferrero S, Mangili G, Bergamini A, Inversetti A, Giorgione V, Vigan\\u0026ograve; P, Candiani M. A systematic review on endometriosis during pregnancy: diagnosis, misdiagnosis, complications and outcomes. Hum Reprod Update. 2016 Jan-Feb;22(1):70-103. \\u003c/li\\u003e\\n\\u003cli\\u003eBlamble T, Dickerson L. Recognizing and treating endometriosis. JAAPA. 2021 Jun 1;34(6):14-19. \\u003c/li\\u003e\\n\\u003cli\\u003eParazzini F, Esposito G, Tozzi L, Noli S, Bianchi S. Epidemiology of endometriosis and its comorbidities. 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(2010). ConsensusClusterPlus: a Class Discovery Tool with Confidence Assessments and Item Tracking. Bioinformatics 26 (12), 1572\\u0026ndash;1573. \\u003c/li\\u003e\\n\\u003cli\\u003eIto K., Murphy D. (2013). Application of Ggplot2 to Pharmacometric Graphics. CPT Pharmacometrics Syst. Pharmacol. 2 (10), e79. \\u003c/li\\u003e\\n\\u003cli\\u003e] Macer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am. 2012 Dec;39(4):535-49. \\u003c/li\\u003e\\n\\u003cli\\u003eMathieu d\\u0026apos;Argent E, Cohen J, Chauffour C, Pouly JL, Boujenah J, Poncelet C, Decanter C, Santulli P. Endom\\u0026eacute;triose profonde et infertilit\\u0026eacute;, RPC Endom\\u0026eacute;triose CNGOF-HAS [Deeply infiltrating endometriosis and infertility: CNGOF-HAS Endometriosis Guidelines]. Gynecol Obstet Fertil Senol. 2018 Mar;46(3):357-367. French. \\u003c/li\\u003e\\n\\u003cli\\u003eMunshi H, Khan T, Khan S, DasMahapatra P, Balakrishnan S, Nirmala C, Das V, Kulkarni K, John BM, Majumdar A, Sowmini CV, Srivastava A, Khade K, Gajbhiye RK. Determinants of Conception and Adverse Pregnancy Outcomes in Women with Endometriosis: A Longitudinal Study. Reprod Sci. 2024 Jun;31(6):1757-1762. \\u003c/li\\u003e\\n\\u003cli\\u003eTanbo T, Fedorcsak P. Endometriosis-associated infertility: aspects of pathophysiological mechanisms and treatment options. Acta Obstet Gynecol Scand. 2017 Jun;96(6):659-667. \\u003c/li\\u003e\\n\\u003cli\\u003eLin X, Dai Y, Tong X, Xu W, Huang Q, Jin X, Li C, Zhou F, Zhou H, Lin X, Huang D, Zhang S. Excessive oxidative stress in cumulus granulosa cells induced cell senescence contributes to endometriosis-associated infertility. Redox Biol. 2020 Feb;30:101431.\\u003c/li\\u003e\\n\\u003cli\\u003eNagai T, Ishida C, Nakamura T, Iwase A, Mori M, Murase T, Bayasula, Osuka S, Takikawa S, Goto M, Kotani T, Kikkawa F. Focal Adhesion Kinase-Mediated Sequences, Including Cell Adhesion, Inflammatory Response, and Fibrosis, as a Therapeutic Target in Endometriosis. Reprod Sci. 2020 Jul;27(7):1400-1410. \\u003c/li\\u003e\\n\\u003cli\\u003evon Adamek EV, Sim\\u0026otilde;es MJ, Freitas V, Patriarca MT, Soares JM Jr, Baracat EC. Lysosomal evaluation of endometrioma capsule epithelium and endometrium of patients with or without endometriosis. Clin Exp Obstet Gynecol. 2005;32(1):27-30. \\u003c/li\\u003e\\n\\u003cli\\u003eLi B, Wang S, Duan H, Wang Y, Guo Z. Discovery of gene module acting on ubiquitin-mediated proteolysis pathway by co-expression network analysis for endometriosis. Reprod Biomed Online. 2021 Feb;42(2):429-441. \\u003c/li\\u003e\\n\\u003cli\\u003eTaniguchi F, Kaponis A, Izawa M, Kiyama T, Deura I, Ito M, Iwabe T, Adonakis G, Terakawa N, Harada T. Apoptosis and endometriosis. Front Biosci (Elite Ed). 2011 Jan 1;3(2):648-62. doi: 10.2741/e277. PMID: 21196342.\\u003c/li\\u003e\\n\\u003cli\\u003eBanerjee S, Xu W, Doctor A, Driss A, Nezhat C, Sidell N, Taylor RN, Thompson WE, Chowdhury I. TNF\\u0026alpha;-Induced Altered miRNA Expression Links to NF-\\u0026kappa;B Signaling Pathway in Endometriosis. Inflammation. 2023 Dec;46(6):2055-2070.\\u003c/li\\u003e\\n\\u003cli\\u003eJiang L, Wang S, Xia X, Zhang T, Wang X, Zeng F, Ma J, Fang X. Novel Diagnostic Biomarker BST2 Identified by Integrated Transcriptomics Promotes the Development of Endometriosis via the TNF-\\u0026alpha;/NF-\\u0026kappa;B Signaling Pathway. Biochem Genet. 2024 Mar 5. \\u003c/li\\u003e\\n\\u003cli\\u003eAn M, Fu X, Meng X, Liu H, Ma Y, Li Y, Li Q, Chen J. PI3K/AKT signaling pathway associates with pyroptosis and inflammation in patients with endometriosis. J Reprod Immunol. 2024 Mar;162:104213. \\u003c/li\\u003e\\n\\u003cli\\u003eYotova IY, Quan P, Leditznig N, Beer U, Wenzl R, Tschugguel W. Abnormal activation of Ras/Raf/MAPK and RhoA/ROCKII signalling pathways in eutopic endometrial stromal cells of patients with endometriosis. Hum Reprod. 2011 Apr;26(4):885-97. \\u003c/li\\u003e\\n\\u003cli\\u003eZhang D, Wang L, Guo HL, Zhang ZW, Wang C, Chian RC, Zhang ZF. MicroRNA‑202 inhibits endometrial stromal cell migration and invasion by suppressing the K‑Ras/Raf1/MEK/ERK signaling pathway. Int J Mol Med. 2020 Dec;46(6):2078-2088. \\u003c/li\\u003e\\n\\u003cli\\u003eKolanska K, Alijotas-Reig J, Cohen J, Cheloufi M, Selleret L, d\\u0026apos;Argent E, Kayem G, Valverde EE, Fain O, Bornes M, Darai E, Mekinian A. Endometriosis with infertility: A comprehensive review on the role of immune deregulation and immunomodulation therapy. Am J Reprod Immunol. 2021 Mar;85(3):e13384. \\u003c/li\\u003e\\n\\u003cli\\u003eMacer ML, Taylor HS. Endometriosis and infertility: a review of the pathogenesis and treatment of endometriosis-associated infertility. Obstet Gynecol Clin North Am. 2012 Dec;39(4):535-49. \\u003c/li\\u003e\\n\\u003cli\\u003eSaeki S, Fukui A, Mai C, Takeyama R, Yamaya A, Shibahara H. Co-expression of activating and inhibitory receptors on peritoneal fluid NK cells in women with endometriosis. J Reprod Immunol. 2023 Feb;155:103765.\\u003c/li\\u003e\\n\\u003cli\\u003eFreitag N, Pour SJ, Fehm TN, Toth B, Markert UR, Weber M, Togawa R, Kruessel JS, Baston-Buest DM, Bielfeld AP. Are uterine natural killer and plasma cells in infertility patients associated with endometriosis, repeated implantation failure, or recurrent pregnancy loss? Arch Gynecol Obstet. 2020 Dec;302(6):1487-1494. \\u003c/li\\u003e\\n\\u003cli\\u003eMiller JE, Ahn SH, Marks RM, Monsanto SP, Fazleabas AT, Koti M, Tayade C. IL-17A Modulates Peritoneal Macrophage Recruitment and M2 Polarization in Endometriosis. Front Immunol. 2020 Feb 14;11:108.\\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 May;36(5):441-444. \\u003c/li\\u003e\\n\\u003cli\\u003eAnaf V, Chapron C, El Nakadi I, De Moor V, Simonart T, No\\u0026euml;l JC. Pain, mast cells, and nerves in peritoneal, ovarian, and deep infiltrating endometriosis. Fertil Steril. 2006 Nov;86(5):1336-43. \\u003c/li\\u003e\\n\\u003cli\\u003eKnez J, Kovačič B, Goropev\\u0026scaron;ek A. The role of regulatory T-cells in the development of endometriosis. Hum Reprod. 2024 May 16:deae103.\\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(6):590-4. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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, infertility, immunological characteristics, characteristic genes, subtype\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4747993/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4747993/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMany studies have shown that endometriosis can lead to infertility in women of reproductive age, but the mechanism is unknown. Our study aims to explore the pathogenesis of stage IV endometriosis with infertility and the role of characteristic genes in this condition.\\u003c/p\\u003e \\u003cp\\u003eMethods\\u003c/p\\u003e \\u003cp\\u003eGene expression profiles were obtained from the GSE120103 dataset retrieved from the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify key modules. Subsequently, minimum absolute contraction, selection operator (LASSO), and random forest machine learning algorithms were employed to screen the characteristic genes of stage IV endometriosis complicated with infertility. The ROC curve and diagnostic model were generated to evaluate the diagnostic efficacy. CIBERSORT was utilized to estimate immune cell infiltration and quantify immune checkpoints. Additionally, we constructed the regulatory network of miRNA and transcription factors.GSEA was utilized to explore the signaling pathways associated with characteristic genes, and potential small molecule compounds were identified through screening the CTD database. Samples from individuals with infertility in stage IV endometriosis were categorized using the consensus clustering method, followed by an examination of the expression and immunological features of different subtypes.\\u003c/p\\u003e \\u003cp\\u003eResults\\u003c/p\\u003e \\u003cp\\u003eWe identified five characteristic genes (CDY2A, KRT6B, SLC2A2, SRY, MYH7) that predict infertility in stage IV endometriosis. When compared to women of childbearing age with stage IV endometriosis, the immunological features of stage IV endometriosis combined with infertility show significant differences, which are clearly linked to the characteristic genes. Patients can benefit from a gene-based characteristic nomogram. Our study reveals that multiple signaling pathways are strongly associated with infertility in stage IV endometriosis. Furthermore, several small molecule compounds were predicted based on the characteristic genes, and relevant regulatory networks of miRNA and TF were constructed. Stage IV endometriosis combined with infertility is categorized into three subtypes, each showing significantly different immunological characteristics of the characteristic genes.\\u003c/p\\u003e \\u003cp\\u003eConclusion\\u003c/p\\u003e \\u003cp\\u003eThis study enhances our understanding of the pathogenesis and immune mechanisms of stage IV endometriosis with infertility. It identifies effective characteristic genes and subtypes, offering valuable insights for treatment. Nevertheless, additional prospective studies and experiments are necessary to validate our findings.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Combined with bioinformatics and machine learning, the diagnostic model, Immunological features and subtypes of stage IV endometriosis with infertility were analyzed\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-08-19 00:10:14\",\"doi\":\"10.21203/rs.3.rs-4747993/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"8e12bf26-4248-4c25-b330-a4c4537ec078\",\"owner\":[],\"postedDate\":\"August 19th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-09-22T07:23:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-08-19 00:10:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4747993\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4747993\",\"identity\":\"rs-4747993\",\"version\":[\"v1\"]},\"buildId\":\"B-jG_2CBjPDmsCi4Wdhf-\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC0","license_restricted":false}