Diagnostic gene biomarkers for predicting immune infiltration in endometriosis

Diagnostic gene biomarkers for predicting immune infiltration in endometriosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Diagnostic gene biomarkers for predicting immune infiltration in endometriosis Chengmao Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1305846/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 Objective To determine the potential diagnostic markers and extent of immune cell infiltration in endometriosis (EMS). Methods From the Gene Expression Omnibus database (GEO), we downloaded two published profiles (GSE7305 and GSE25628 datasets) of human EMS and endometrial specimens. Differential genes between 17 EMS and 19 endometrial samples were compared. Candidate biomarkers were identified by support vector machine recursive feature elimination analysis and a Lasso regression model. The area under the receiver operating characteristic curve value represented the discriminatory biomarkers. The diagnostic value and expression levels of biomarkers in EMS were verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting, then further validated in the GSE5108 dataset that included 11 eutopic and 11 ectopic endometria. On the basis of the merged cohorts, we used CIBERSORT to estimate the composition pattern of immune cell components in EMS. Results Fifty-three genes were identified in cells from benign neoplasms, polycystic ovary syndrome, gallbladder carcinomas, and adenomas. Gene sets related to arachidonic acid metabolism, cytokine–cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and systemic lupus erythematosus were differentially activated in EMS compared with endometrial samples. Aquaporin 1 (AQP1) and ZW10 binding protein (ZWINT) were identified as diagnostic markers of EMS, which were verified using qRT-PCR and western blotting and validated in the GSE5108 dataset. Immune cell infiltrate analysis showed that AQP1 and ZWINT were correlated with M2 macrophages, NK cells, activated dendritic cells, T follicular helper cells, regulatory T cells, memory B cells, activated mast cells, and plasma cells. Conclusion AQP1 and ZWINT can be regarded as diagnostic markers of EMS and may provide a new direction for the study of EMS pathogenesis in the future. endometriosis GEO immune infiltration CIBERSORT biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Endometriosis (EMS) is defined as the presence of ectopic endometrial glands and stroma outside of the uterine cavity and affects 6–10% of reproductive-aged women. Women with EMS can have symptoms of dyspareunia, dysmenorrhea, irregular uterine bleeding, and chronic pelvic pain [ 1 – 3 ]. Although medical therapies can relieve symptoms in up to 50–80% of cases, residual symptoms are still present in at least 20% of patients [ 4 – 7 ]. Decreased quality of life, increased surgical intervention, and increased use of assisted reproductive technology caused by EMS result in high social costs [ 8 ]. Thus, EMS has become a critical social problem that needs to be addressed. EMS is similar to malignancies in certain respects. Both show estrogen-dependent growth, invasive growth and progression, and recurrence, and both have a tendency to metastasize [ 9 , 10 ]. EMS can be divided into four disease stages on the basis of the amount, severity, location, and depth or size of growths: minimal disease (stage I), mild disease (stage II), moderate disease (stage III), and severe disease (stage IV) [ 11 , 12 ]. Furthermore, EMS infiltration of more than 5 mm under the peritoneum is defined as deep EMS (DE) [ 13 ]. However, this classification cannot be used to predict clinical outcomes, symptomatology, or pain [ 14 ]. Medical professionals dealing with EMS face many issues regarding the diagnosis, treatment, and follow-up of patients, and EMS has the highest incidence rate among benign gynecological disorders in premenopausal women [ 15 , 16 ]. External endometrial lesions are common in the pelvic peritoneum and ovary, and can also be found in abdominal scars, bladder, ureter, intestines, and appendix, but are rare in the brain and eye [ 4 , 5 ]. DE is a nodular form that can coexist in the pelvis[ 17 , 18 ]. Therefore, it is very difficult to treat when EMS invades the surrounding organs such as the bladder or rectum. Most deep rectovaginal lesions are thought to originate from the posterior part of the cervix, followed by infiltration into the anterior wall of the rectum [ 19 – 21 ]. The invasion process dominated by collective cell migration is considered the most invasive form of DE [ 22 , 23 ]. Adenomyosis of cervix may be the cause of DE, which has been verified from the invasion of the cervix (lesion center) to the rectum (lesion front) [ 24 ]. In fact, collective cell migration and epithelial–mesenchymal transformation may be closely related to the pathogenesis of endometriotic nodules and adenomyosis [ 25 ]. Although there are increasing numbers of studies on EMS immune regulation, its specific mechanism remains unclear. In our paper, two EMS microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged them into a meta-data cohort. A differential expression gene (DEG) screen was performed comparing EMS with control (normal endometrium) data. Diagnostic biomarkers of EMS were filtered and identified with machine-learning algorithms. Another cohort was used to identify and validate candidate genes that were closely related to immune cell infiltration, and then the diagnostic prediction model was constructed by a regression method. Our study is the first to use CIBERSORT to quantify the proportions of immune cells in EMS or endometrial tissues on the basis of their gene microarray data. In addition, the correlation between the infiltrating immune cells and identified biomarkers was discussed for its potential contribution to future studies. Materials And Methods Microarray data We downloaded GSE7305 (GPL570, Affymetrix Human Genome U133 Plus 2.0 Array) and GSE25628 (GPL571, Affymetrix Human Genome U133 Plus 2.0 Array) datasets from the GEO database. The GSE7305 dataset included 10 ectopic and 10 eutopic endometria, and the GSE25628 dataset included 7 ectopic and 9 eutopic endometria. Eutopic and ectopic endometria were obtained from the same patients, and for each patient the eutopic endometrium from the uterus served as the control for their ectopic endometrial sample. According to each dataset probe annotation file, the probes were changed into gene symbols. Because one gene symbol corresponded to multiple probes, the final expression value of the gene was considered according to the average value of the probe. The batch effect was removed with the combat function of the surrogate variable analysis package of R software [26]. Furthermore, the GSE5108 dataset (Illumina HumanWG-6 v3.0 expression beadchip) contained 11 eutopic and 11 ectopic endometria and was used as the validation cohort. DEG screening and data processing GSE7305 and GSE25628 were merged into one meta-data cohort, while batch effects were preprocessed and removed with the combat function of the surrogate variable analysis package. The background correction, endometrial uniformity, and differential expression analysis between arrays was performed with the limma package of R. P 2 was regarded as the critical cutoffs for DEGs. Functional enrichment analysis Disease Ontology (DO), Gene Ontology (GO) and KEGG pathway enrichment analyses were executed with the clusterProfiler, org.Hs.eg.db, DOSE, and enrichplot packages in R. Significant functional terms between EMS and control samples were identified with the gene set enrichment analysis (GSEA). c2.cp. kegg. v7.4. symbols. gmt was used as the reference gene set. P < 0.05 and a false discovery rate < 0.025 were regarded as significantly enriched. Candidate diagnostic biomarker screening We used two machine-learning algorithms to identify significant prognostic variables. Least absolute shrinkage and selection operator (LASSO) is a regression analysis algorithm that was performed by the glmnet package in R to identify genes that could significantly distinguish eutopic and ectopic endometria. Another machine-learning technique that was used in our study was the support vector machine (SVM), which was widely used for classification or regression. A recursive feature elimination (RFE) algorithm was used to select the appropriate genes to avoid overfitting [27]. Then, SVM-RFE was applied to select features that identified a set of genes with the highest discriminatory power. Candidate gene expression levels were validated in the GSE5108 dataset using the two algorithms. Diagnostic value of featured biomarkers We generated a receiver operating characteristic (ROC) curve using the data from 17 ectopic and 19 eutopic endometria to test the predictive value of the identified biomarkers. The diagnostic effectiveness was determined by the area under the ROC curve (AUC) value in discriminating EMS from endometria and was further validated in the GSE5108 dataset. Verification of the diagnostic biomarker results Endometriosis or matched control endometrium from the same patients were obtained from Beijing Obstetrics and Gynecology Hospital, Beijing, China. All donors had not taken drugs and hormones before surgery. All tissue samples were taken from tissues discarded during surgery after being approved and informed by the ethics review committee of our hospital. Protein preparation, western blotting, RNA isolation, and qRT-PCR were performed as described previously [28]. Rabbit monoclonal anti-human aquaporin 1 ( AQP1 ) (ab168387; Abcam) and ZW10 binding protein ( ZWINT ) (ab252950; Abcam) antibodies were used in western blotting. The following primers were used for qRT-PCR: AQP1 , sense 5′-AGAGGGACCCACCTT GCTAA-3′ and anti-sense 5′-GCACAA AGCAATCACCGAGG-3′; ZWINT , sense 5′-AACTCCGGG AAGCCTTTGAG-3′ and anti-sense 5′-TTCTGGACTGCTCTGC GTTT-3′. AQP1 and ZWINT were expressed relative to GAPDH . Discovery of immune cell subtypes Immune cell infiltration was calculated by CIBERSORT to quantify the relative proportion of infiltrating immune cells in EMS. The R software package corrplot was used to analyze and visualize 21 types of invasive immune cells, and the R software package vioplot was used to construct a violin diagram to visualize the differences in immune cell infiltration between the two groups. Correlation between identified genes and infiltrating immune cells We used the Spearman’s rank correlation analysis in R to explore the correlations between the identified gene biomarkers and the levels of infiltrating immune cells. The chart technique with the ggplot2 package was used to visualize the identified associations. Statistical analysis R 4.1.1 was used to conduct the statistical analyses. We used Student’s t-test or Mann–Whitney U-test to undertake group comparisons for continuous variables of endometrium-distributed variables.SVM algorithm was performed by the e1071 package in R, while the LASSO regression analysis was performed by the glmnet package. The diagnostic efficacy of the biomarkers was determined by ROC curve analysis. We used Spearman’s correlation to analyze the correlation between infiltrating immune cells and gene biomarkers. Two-tailed tests with P < 0.05 were regarded as statistically significant. Results Identification DEGs for EMS The data of 17 EMS and 19 endometria from GSE7305 and GSE25628 were retrospectively analyzed in our study. Limma package was used to analyze the differences between the EMS and endometria and the DEGs from the meta-data after the batch effects had been removed. Finally, 53 DEGs were obtained: 28 significantly upregulated genes and 25 significantly downregulated genes (Fig. 1). Analysis of functional correlations The function of the DEGs was investigated by GO, KEGG, and DO pathway enrichment analyses. We found that GO enriched by DEGs was mainly associated with lung development, respiratory tube development, complement activation, alternative pathway, and regulation of humoral immune response of the biological process (BP), blood microparticles, myosin filament of the cellular component (CC), tubulin binding, and peptidase regulator activity of the molecular function (MF) (Fig.2A). Enriched KEGG pathways were mainly associated with drug metabolism-cytochrome P450, tyrosine metabolism, and complement and coagulation cascades (Fig. 2B). Moreover, the enriched diseases were mainly associated with benign neoplasm, polycystic ovary syndrome, gallbladder carcinoma, and adenoma cell types (Fig. 2C). The GSEA-enriched pathways were mainly associated with arachidonic acid metabolism, cytokine–cytokine receptor interactions, chemokine signaling pathways, complement and coagulation cascades, and systemic lupus erythematosus (Fig. 2D). The above results show that the immune response is an essential part of the pathogenesis of EMS. Identification and validation of diagnostic biomarkers Potential biomarkers of EMS were screened by two different algorithms. Seven diagnostic biomarkers were identified by the LASSO regression algorithm of the DEGs for EMS (Fig. 3A). We determined four features among the DEGs using the SVM-RFE algorithm (Fig. 3B). The overlapping region obtained by the two calculation methods included the two screened genes ( AQP1 and ZWINT ) (Fig. 3C). Then, the levels of the two features were verified in the GSE5108 dataset. The level of AQP1 in EMS tissues was notably higher than that in the control group, while the level of ZWINT was the opposite (all P < 0.05; Fig. 4A, B). Finally, a logistic regression algorithm was used to establish a diagnostic model with the two identified genes. Verification of differential gene expression Results We verified the results of the screened differential genes at the mRNA and protein level. The verification results revealed that the mRNA (Fig. 5A) and protein (Fig. 5B, C) levels of AQP1 in EMS were higher than those in the control group, while the results of ZWINT were the opposite, consistent with the Gene Chip data. Diagnostic effect of characteristic EMS biomarkers The diagnostic ability of the two biomarkers in discriminating EMS demonstrated a favorable diagnostic efficiency, with an AUC of 0.941 (95% confidence interval (CI) 0.824–1.000) for AQP1 and an AUC of 0.954 (95% CI 0.864–1.000) for ZWINT (Fig. 6A, B). In addition, a powerful discriminatory ability was demonstrated in the GSE5108 dataset with an AUC of 0.785 (95% CI 0.570–0.950) for AQP1 and an AUC of 0.860 (95% CI 0.636–1.000) for ZWINT (Fig. 6C, D). The above results indicated that the featured biomarkers had a high diagnostic ability in EMS. Immune cell infiltration First, we studied the composition of immune cells in the EMS and control groups. The proportions of T follicular helper cells (P = 0.001), regulatory T cells (Tregs) (P = 0.028), activated NK cells (P < 0.001), resting natural killer (NK) cells (P = 0.018), M2 macrophages (P < 0.001), activated dendritic cells (P = 0.012), and activated mast cells (P = 0.001) were significantly lower in EMS tissues than in endometrial tissues. Furthermore, the proportion of memory B cells (P < 0.001) and plasma cells (P < 0.001) in EMS was evidently higher than that in endometrial tissues (Fig. 7A). The correlations of 21 types of immune cells were analyzed (Fig. 7B). Eosinophils were positively correlated with activated NK cells, T follicular helper cells, and M1 macrophages, but negatively correlated with M2 macrophages, activated dendritic cells, and memory B cells. T follicular helper cells were positively correlated with Tregs and activated NK cells but negatively correlated with M2 macrophages. Activated NK cells were positively correlated with T follicular helper cells but negatively correlated with plasma cells, M2 macrophages, and memory B cells. Activated memory CD4 T cells were positively correlated with resting activated NK cells but negatively correlated with M2 macrophages and monocytes. Resting mast cells were positively correlated with activated NK cells but negatively correlated with activated mast cells and M0 macrophages. Resting NK cells were positively correlated with activated dendritic cells, Tregs, and activated memory CD4 T cells but negatively correlated with M2 macrophages. Activated dendritic cells were significantly positively correlated with activated memory CD4 T cells and resting NK cells but significantly negatively correlated with plasma cells, resting dendritic cells, monocytes, and M1 macrophages. Naive B cells were positively correlated with Tregs, activated NK cells, and T follicular helper cells but negatively correlated with memory B cells and plasma cells. Neutrophils were significantly positively correlated with activated M1 macrophages, M0 macrophages, and gamma-delta T cells but negatively correlated with resting T follicular helper cells and mast cells. Correlations between the biomarkers and infiltrating immune cells AQP1 was positively correlated with activated mast cells (r = 0.6, P = 0.00025), M2 macrophages (r = 0.52, P = 0.0027), memory B cells (r = 0.51, P = 0.0031), and plasma cells (r = 0.49, P = 0.0045), and negatively correlated with follicular helper T cells (r = −0.58, P = 0.00061), activated NK cells (r = −0.51, P = 0.003), Tregs (r = −0.42, P =0.016), and activated dendritic cells (r = −0.39, P = 0.028) (Figure 8A). ZWINT was positively correlated with activated dendritic cells (r = 0.39, P = 0.025), activated NK cells (r = 0.54, P = 0.0019), T follicular helper cells (r = 0.5, P = 0.0043), and Tregs (r = 0.35, P = 0.05), and negatively correlated with M2 macrophages (r = −0.63, P = 0.00017), activated mast cells (r = −0.61, P = 0.00022), memory B cells (r = −0.54, P = 0.0015), and plasma cells (r = −0.5, P = 0.0044; Fig. 8B). Discussion EMS is a progressive disease that is mainly manifested over three aspects: the gradual aggravation of dysmenorrhea, the gradual increase in EMS cysts, and the gradual increase in EMS stage. It is very important to intervene and delay its progress, but diagnosis is often delayed. The internationally agreed definition of delayed diagnosis of EMS is the interval from the onset of pain symptoms to the surgical diagnosis of EMS, and the delay time of EMS diagnosis ranges 4–10 years [ 29 – 31 ]. At present, the diagnostic process in EMS is generally to visit a doctor after dysmenorrhea symptoms, undergo gynecological and auxiliary examinations, and receive a diagnosis after laparoscopic surgery and postoperative pathological examination. The clinical diagnosis of EMS (non-surgical diagnosis) has a certain value, but the gold standard for EMS diagnosis is surgery and postoperative pathological examination. The reasons for the delay in the diagnosis of EMS are controversial. Studies found that the main reasons for the delay in the diagnosis of EMS are the patients' insufficient attention to dysmenorrhea, the doctors' insufficient understanding of EMS, the lack of non-invasive diagnostic methods, and the limitations of surgical diagnosis [ 30 , 32 , 33 ]. Diagnosis delay is an important clinical problem affecting the diagnosis and treatment of patients with EMS. It will not only delay treatment and miss the best treatment opportunity, but also lead to the progression of EMS to a certain extent, increase the degree of pain and the probability of infertility, and also increase the difficulty and trauma of surgery. Thus, it is very important to identify early diagnostic markers that could play an important role in the diagnosis and treatment of EMS. In recent years, an increasing number of researchers have searched for new diagnostic biomarkers of EMS and explored the components of immune cell infiltrates in EMS, which may have a beneficial impact on the clinical results of EMS patients. Moreover, mRNA and microRNA have become promising biomarkers in EMS. Further, only a few studies have investigated the abnormal expression of gene biomarkers and endometrial immune infiltration in EMS. Therefore, the purpose of this study was to identify candidate diagnostic biomarkers for EMS and to study the effect of immune cell infiltration in EMS. As far as we know, our study is the first to identify diagnostic biomarkers associated with immune cell infiltrates in EMS by mining multiple GEO datasets. An integrated analysis of GSE7305 and GSE25628 datasets from GEO was conducted. Fifty-three DEGs were identified, comprising 28 upregulated and 25 downregulated genes. We found that GO enrichment was mainly associated with lung development, respiratory tube development, complement activation, alternative pathway, regulation of humoral immune response of the BP, blood microparticles, myosin filament of the CC, tubulin binding, and peptidase regulator activity of the molecular function. KEGG pathway enrichment was mainly associated with drug metabolism-cytochrome P450, tyrosine metabolism, and complement and coagulation cascades. The diseases enriched were mainly associated with benign neoplasm, polycystic ovary syndrome, gallbladder carcinoma, and adenoma cell types. The GSEA-enriched pathways were mainly associated with cytokine–cytokine receptor interactions, arachidonic acid metabolism, chemokine signaling pathways, systemic lupus erythematosus, and complement and coagulation cascades. All of the results indicated that the immune response plays a crucial role in EMS. We identified two diagnostic markers using two machine-learning algorithms. AQPs are a group of glycoproteins that selectively transport transmembrane molecules. They are found in the uterus, ovary, fallopian tube, and other parts of the female reproductive organs, and are involved in the ovulation of follicles, menstruation, and the occurrence and development of malignant tumors or benign gynecological diseases with malignant behavior. When the endometrium changes, the expression of AQP is abnormal [ 34 ]. At present, 13 members of the AQP family have been identified, AQP0–12, among which AQP1 and AQP5 are mostly related to disease [ 35 ]. AQP1, the first-discovered AQP, is a transmembrane tetramer composed of four monomers with a molecular weight of approximately 112 kDa. Narváez-Moreno et al. confirmed that the expression level of AQP1 in benign lesions was higher than that in malignant lesions [ 36 ]. Colombelli et al. showed that AQP1 is mainly distributed in vascular endothelial cells and that a decrease in AQP1 would lead to a decrease in microvessel density, indicating that AQP1 can promote angiogenesis and be used as a marker of EMS invasion [ 37 ]. ZWINT is a component of a known centromeric complex that is composed of 278 amino acids. It can specifically bind to 80 amino acid residues at the N-terminal of the ZW10 protein, and it plays an important role in regulating mitosis and chromosome movement as well as regulating the cell cycle [ 38 , 39 ]. Studies have shown that ZWINT is necessary for the assembly of spindle assembly checkpoint, and this checkpoint protein is a complex formed by a variety of binding proteins connecting chromosome centromeres and spindle tubulin, which plays an important role in accurate chromosome allocation in progeny cells [ 40 , 41 ]. If the checkpoint protein is defective, it can lead to chromosome aneuploidy separation and even carcinogenesis [ 42 ]. EMS is a benign disease with malignant tumor characteristics, and the levels of ZWINT in EMS are lower than those in the endometrium. Thus, we consider that ZWINT may play a crucial role in the pathogenesis of EMS. We used CIBERSORT to evaluate the type of immune cell infiltrates in EMS and endometrial samples. The results show that many immune cell subtypes are closely related to the biological process of EMS. An increased infiltration of M2 macrophages, activated mast cells, memory B cells, and T follicular helper cells, as well as a decreased infiltration of T follicular helper cells, activated dendritic cells, activated NK cells, and Tregs, were shown to be potentially associated with the pathogenesis of EMS. And, the results showed that AQP1 and ZWINT were correlated with memory B cells, activated mast cells, M2 macrophages, T follicular helper cells, activated dendritic cells, Tregs, and activated NK cells. In fact, the various immune cells in the abdominal cavity environment improve the invasive and adhesive abilities of endometrial cells, including dendritic cells, macrophages, mast cells, NK cells, and T cells, which can lead to ectopic endometrium flowing back into the pelvic and abdominal cavities with menstrual blood [ 43 ]. During the menstrual cycle, endometrial-like tissue can spread outside its endometrial location [ 44 , 45 ]. These lesions attract cytotoxic T cells, macrophages, and NK cells [ 46 , 47 ]. Subsequently, the activation of the inflammatory response promotes the secretion of cytokines and chemokines in the abdominal cavity to create a microenvironment and induce the development of ectopic endometrial tissue by promoting local angiogenesis and destroying the process of endometrial apoptosis [ 48 ]. The large amount of evidence mentioned above as well as our current results show that several types of invasive immune cells have a crucial role in EMS and should be investigated further in future studies. While, this research also has some limitations. First, because of the retrospective nature of our research, we could not obtain the clinical information associated with the samples. Second, the biomarker and immune cell profiles of the tissues were collected from two different datasets, and hence it is important to further validate their reproducibility. Third, the total cases in the GSE5108 validation cohort were small, thus contributing to less robust results. Finally, the function of the two biomarkers and the role of immune cell infiltrates in EMS were inferred through bioinformatics analyses, and therefore a prospective study with a larger sample size should be performed to confirm our findings. Declarations Acknowledgments We thank Mark Abramovitz, PhD, and H. Nikki March, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the language of a draft of this manuscript. Authors’ contributions ZHL : Data curation, Writing- Original draft preparation. YL , LGJ : Resources, Data Curation. CL : Visualization. CMX : Conceptualization, Methodology, Writing- Reviewing and Editing. All authors read and approved the final manuscript. F unding None D ata Availability Statement Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are derived from the public GEO data portal (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE7305, GSE25628, and GSE5018). Declarations Ethics approval and consent to participate All procedures were approved by the ethics review committee of Beijing Obstetrics and Gynecology Hospital. Consent for publication All data are presented at a group level and participants consented to the publication of their anonymous results. Competing interests The authors declare that they have no competing interests. References 1. Burney RO, Giudice LC: Pathogenesis and pathophysiology of endometriosis . Fertil Steril 2012, 98 (3):511-519. 2. Johnson NP, Hummelshoj L, Adamson GD, Keckstein J, Taylor HS, Abrao MS, Bush D, Kiesel L, Tamimi R, Sharpe-Timms KL et al : World Endometriosis Society consensus on the classification of endometriosis . Hum Reprod 2017, 32 (2):315-324. 3. 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Mihalyi A, Gevaert O, Kyama CM, Simsa P, Pochet N, De Smet F, De Moor B, Meuleman C, Billen J, Blanckaert N et al : Non-invasive diagnosis of endometriosis based on a combined analysis of six plasma biomarkers . Hum Reprod 2010, 25 (3):654-664. 34. Zhu C, Jiang Z, Bazer FW, Johnson GA, Burghardt RC, Wu G: Aquaporins in the female reproductive system of mammals . Front Biosci (Landmark Ed) 2015, 20 :838-871. 35. Shen Q, Lin W, Luo H, Zhao C, Cheng H, Jiang W, Zhu X: Differential Expression of Aquaporins in Cervical Precursor Lesions and Invasive Cervical Cancer . Reprod Sci 2016, 23 (11):1551-1558. 36. Narváez-Moreno B, Sendín-Martín M, Jiménez-Thomas G, Sánchez-Silva R, Suárez-Luna N, Echevarría M, Bernabeu-Wittel J: Expression patterns of aquaporin 1 in vascular tumours . Eur J Dermatol 2019, 29 (4):366-370. 37. Colombelli KT, Santos S, Camargo A, Constantino FB, Barquilha CN, Rinaldi JC, Felisbino SL, Justulin LA: Impairment of microvascular angiogenesis is associated with delay in prostatic development in rat offspring of maternal protein malnutrition . Gen Comp Endocrinol 2017, 246 :258-269. 38. Woo Seo D, Yeop You S, Chung WJ, Cho DH, Kim JS, Su Oh J: Zwint-1 is required for spindle assembly checkpoint function and kinetochore-microtubule attachment during oocyte meiosis . Sci Rep 2015, 5 :15431. 39. Wang H, Hu X, Ding X, Dou Z, Yang Z, Shaw AW, Teng M, Cleveland DW, Goldberg ML, Niu L et al : Human Zwint-1 specifies localization of Zeste White 10 to kinetochores and is essential for mitotic checkpoint signaling . J Biol Chem 2004, 279 (52):54590-54598. 40. Famulski JK, Vos L, Sun X, Chan G: Stable hZW10 kinetochore residency, mediated by hZwint-1 interaction, is essential for the mitotic checkpoint . J Cell Biol 2008, 180 (3):507-520. 41. Alfieri C, Chang L, Barford D: Mechanism for remodelling of the cell cycle checkpoint protein MAD2 by the ATPase TRIP13 . Nature 2018, 559 (7713):274-278. 42. Yang Q, Cao W, Wang Z, Zhang B, Liu J: Regulation of cancer immune escape: The roles of miRNAs in immune checkpoint proteins . Cancer Lett 2018, 431 :73-84. 43. Symons LK, Miller JE, Kay VR, Marks RM, Liblik K, Koti M, Tayade C: The Immunopathophysiology of Endometriosis . Trends Mol Med 2018, 24 (9):748-762. 44. Hansen, Keith A: Endometriosis . Clinical Obstetrics & Gynecology 2010 . 45. Sampson JA: Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation . American Journal of Pathology 1927, 3 (2):93-110. 46. Christodoulakos G, Augoulea A, Lambrinoudaki I, Sioulas V, Creatsas G: Pathogenesis of endometriosis: the role of defective 'immunosurveillance' . Eur J Contracept Reprod Health Care 2007, 12 (3):194-202. 47. Dmowski WP, Ding J, Shen J, Rana N, Fernandez BB, Braun DP: Apoptosis in endometrial glandular and stromal cells in women with and without endometriosis . Hum Reprod 2001, 16 (9):1802-1808. 48. Mantovani A, Allavena P, Sica A, Balkwill F: Cancer-related inflammation . Nature 2008, 454 (7203):436-444. 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-1305846","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":79847890,"identity":"f8b83276-f336-4c19-8eea-f5c7988fb2ca","order_by":0,"name":"Chengmao Xie","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Chengmao","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2022-01-28 10:35:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1305846/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1305846/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17813338,"identity":"d1d6632b-dd45-436e-aa2b-b61c68e39157","added_by":"auto","created_at":"2022-01-31 17:28:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":527977,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs between EMS and endometrial samples. A. Heat map. B. Volcano map.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/608d4cf4738e4d0893d4f415.jpeg"},{"id":17813345,"identity":"1f1c65af-9f85-4012-843e-e4c1a60c0a55","added_by":"auto","created_at":"2022-01-31 17:28:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":301219,"visible":true,"origin":"","legend":"\u003cp\u003eGO, KEGG, DO, and GSEA pathway enrichment. (A) GO enrichment analysis. (B) KEGG enrichment analysis. (A) DO enrichment analysis. (D) GSEA enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/1e041ea49a9331b5fcc1f051.jpeg"},{"id":17813399,"identity":"3435087f-17e3-48fe-ae63-75840c6d1e7f","added_by":"auto","created_at":"2022-01-31 17:31:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119261,"visible":true,"origin":"","legend":"\u003cp\u003eThe process of screening diagnostic biomarker candidates for endometriosis. (A) Results of the LASSO regression algorithm of the DEGs for EMS. (B) Results of the SVM-RFE algorithm among the DEGs. (C) Venn diagram of the two different algorithms.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/55b260ccb67fb0f8e44053ca.jpeg"},{"id":17813342,"identity":"b13e52fd-092f-4769-98ce-cfd9314d33fe","added_by":"auto","created_at":"2022-01-31 17:28:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61211,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of the two biomarkers in the GSE5108 dataset. (A) \u003cem\u003eAQP1\u003c/em\u003e. (B) \u003cem\u003eZWINT\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/a3172d3480056c1b87ed9156.jpeg"},{"id":17813341,"identity":"09daef15-d49d-4b33-98e7-0731ffd032eb","added_by":"auto","created_at":"2022-01-31 17:28:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66831,"visible":true,"origin":"","legend":"\u003cp\u003eResults of differential gene expression of AQP1 and ZWINT at the mRNA and protein level between EMS and control samples. A. RT-PCR results. B. Western blotting results. C. Densitometry of the western blot.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/129fbb56fd34a4821b211a5c.jpeg"},{"id":17813344,"identity":"e31e46ca-76c4-4bc7-8204-eb79f66a2c5a","added_by":"auto","created_at":"2022-01-31 17:28:27","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115875,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagnostic effectiveness of the two markers was represented by the receiver operating characteristic (ROC) curve. (A) \u003cem\u003eAQP1\u003c/em\u003e. (B) \u003cem\u003eZWINT\u003c/em\u003e. (C) \u003cem\u003eAQP1\u003c/em\u003e in the GSE5108 dataset. (D) \u003cem\u003eZWINT\u003c/em\u003e in the GSE5108 dataset.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/cff1a710bef4d24b36e1296c.jpeg"},{"id":17813533,"identity":"2577bd0a-a047-47ad-8fdf-74850235068a","added_by":"auto","created_at":"2022-01-31 17:34:27","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":323116,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization and distribution of immune cell infiltrates. (A) Composition of immune cells in the EMS and control groups. Blue and red colors represent control and EMS samples. (B) Correlation matrix results of the immune cells.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/36d9fdf74d6c9f15b737339e.jpeg"},{"id":17813343,"identity":"c41116fe-92f7-4ec1-ab7c-32490eadee47","added_by":"auto","created_at":"2022-01-31 17:28:27","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":337380,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the two biomarkers and infiltrating immune cells in endometriosis. (A) \u003cem\u003eAQP1\u003c/em\u003e. (B) \u003cem\u003eZWINT\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/624afcb31157cd415cc98646.jpeg"},{"id":17813534,"identity":"b3cc65d7-81d9-4be1-aad4-4447b51e7a9d","added_by":"auto","created_at":"2022-01-31 17:34:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2215703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1305846/v1/fb4eeb11-7329-4393-8ff0-b581fd3f78e7.pdf"}],"financialInterests":"","formattedTitle":"Diagnostic gene biomarkers for predicting immune infiltration in endometriosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis (EMS) is defined as the presence of ectopic endometrial glands and stroma outside of the uterine cavity and affects 6\u0026ndash;10% of reproductive-aged women. Women with EMS can have symptoms of dyspareunia, dysmenorrhea, irregular uterine bleeding, and chronic pelvic pain [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although medical therapies can relieve symptoms in up to 50\u0026ndash;80% of cases, residual symptoms are still present in at least 20% of patients [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Decreased quality of life, increased surgical intervention, and increased use of assisted reproductive technology caused by EMS result in high social costs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, EMS has become a critical social problem that needs to be addressed.\u003c/p\u003e \u003cp\u003eEMS is similar to malignancies in certain respects. Both show estrogen-dependent growth, invasive growth and progression, and recurrence, and both have a tendency to metastasize [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. EMS can be divided into four disease stages on the basis of the amount, severity, location, and depth or size of growths: minimal disease (stage I), mild disease (stage II), moderate disease (stage III), and severe disease (stage IV) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, EMS infiltration of more than 5 mm under the peritoneum is defined as deep EMS (DE) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, this classification cannot be used to predict clinical outcomes, symptomatology, or pain [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Medical professionals dealing with EMS face many issues regarding the diagnosis, treatment, and follow-up of patients, and EMS has the highest incidence rate among benign gynecological disorders in premenopausal women [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. External endometrial lesions are common in the pelvic peritoneum and ovary, and can also be found in abdominal scars, bladder, ureter, intestines, and appendix, but are rare in the brain and eye [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. DE is a nodular form that can coexist in the pelvis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, it is very difficult to treat when EMS invades the surrounding organs such as the bladder or rectum. Most deep rectovaginal lesions are thought to originate from the posterior part of the cervix, followed by infiltration into the anterior wall of the rectum [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The invasion process dominated by collective cell migration is considered the most invasive form of DE [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Adenomyosis of cervix may be the cause of DE, which has been verified from the invasion of the cervix (lesion center) to the rectum (lesion front) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In fact, collective cell migration and epithelial\u0026ndash;mesenchymal transformation may be closely related to the pathogenesis of endometriotic nodules and adenomyosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough there are increasing numbers of studies on EMS immune regulation, its specific mechanism remains unclear. In our paper, two EMS microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged them into a meta-data cohort. A differential expression gene (DEG) screen was performed comparing EMS with control (normal endometrium) data. Diagnostic biomarkers of EMS were filtered and identified with machine-learning algorithms. Another cohort was used to identify and validate candidate genes that were closely related to immune cell infiltration, and then the diagnostic prediction model was constructed by a regression method. Our study is the first to use CIBERSORT to quantify the proportions of immune cells in EMS or endometrial tissues on the basis of their gene microarray data. In addition, the correlation between the infiltrating immune cells and identified biomarkers was discussed for its potential contribution to future studies.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eMicroarray data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded GSE7305 (GPL570, Affymetrix Human Genome U133 Plus 2.0 Array) and GSE25628 (GPL571, Affymetrix Human Genome U133 Plus 2.0 Array) datasets from the GEO database. The GSE7305 dataset included 10 ectopic and 10 eutopic endometria, and the GSE25628 dataset included 7 ectopic and 9 eutopic endometria. Eutopic and ectopic endometria were obtained from the same patients, and for each patient the eutopic endometrium from the uterus served as the control for their ectopic endometrial sample. According to each dataset probe annotation file, the probes were changed into gene symbols. Because one gene symbol corresponded to multiple probes, the final expression value of the gene was considered according to the average value of the probe. The batch effect was removed with the combat function of the surrogate variable analysis package of R software [26]. Furthermore, the GSE5108 dataset (Illumina HumanWG-6 v3.0 expression beadchip) contained 11 eutopic and 11 ectopic endometria and was used as the validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEG screening and data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSE7305 and GSE25628 were merged into one meta-data cohort, while batch effects were preprocessed and removed with the combat function of the surrogate variable analysis package. The background correction, endometrial uniformity, and differential expression analysis between arrays was performed with the limma package of R. P \u0026lt; 0.05 and log fold change \u0026gt;2 was regarded as the critical cutoffs for DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisease Ontology (DO), Gene Ontology (GO) and KEGG pathway enrichment analyses were executed with the clusterProfiler, org.Hs.eg.db, DOSE, and enrichplot packages in R. Significant functional terms between EMS and control samples were identified with the gene set enrichment analysis (GSEA). c2.cp. kegg. v7.4. symbols. gmt was used as the reference gene set. P \u0026lt; 0.05 and a false discovery rate \u0026lt; 0.025 were regarded as significantly enriched.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCandidate diagnostic biomarker screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used two machine-learning algorithms to identify significant prognostic variables. Least absolute shrinkage and selection operator (LASSO) is a regression analysis algorithm that was performed by the glmnet package in R to identify genes that could significantly distinguish eutopic and ectopic endometria. Another machine-learning technique that was used in our study was the support vector machine (SVM), which was widely used for classification or regression. A recursive feature elimination (RFE) algorithm was used to select the appropriate genes to avoid overfitting [27]. Then, SVM-RFE was applied to select features that identified a set of genes with the highest discriminatory power. Candidate gene expression levels were validated in the GSE5108 dataset using the two algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic value of featured biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe generated a receiver operating characteristic (ROC) curve using the data from 17 ectopic and 19 eutopic endometria to test the predictive value of the identified biomarkers. The diagnostic effectiveness was determined by the area under the ROC curve (AUC) value in discriminating EMS from endometria and was further validated in the GSE5108 dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVerification of the diagnostic biomarker results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndometriosis or matched control endometrium from the same patients were obtained from Beijing Obstetrics and Gynecology Hospital, Beijing, China. All donors had not taken drugs and hormones before surgery. All tissue samples were taken from tissues discarded during surgery after being approved and informed by the ethics review committee of our hospital.\u0026nbsp;Protein preparation, western blotting, RNA isolation, and qRT-PCR were performed as described previously\u0026nbsp;[28]. Rabbit monoclonal anti-human aquaporin 1 (\u003cem\u003eAQP1\u003c/em\u003e) (ab168387; Abcam) and ZW10 binding protein (\u003cem\u003eZWINT\u003c/em\u003e) (ab252950; Abcam) antibodies were used in western blotting. The following primers were used for qRT-PCR: \u003cem\u003eAQP1\u003c/em\u003e, sense 5\u0026prime;-AGAGGGACCCACCTT GCTAA-3\u0026prime; and anti-sense 5\u0026prime;-GCACAA AGCAATCACCGAGG-3\u0026prime;; \u003cem\u003eZWINT\u003c/em\u003e, sense 5\u0026prime;-AACTCCGGG AAGCCTTTGAG-3\u0026prime; and anti-sense 5\u0026prime;-TTCTGGACTGCTCTGC GTTT-3\u0026prime;. \u003cem\u003eAQP1\u003c/em\u003e and \u003cem\u003eZWINT\u003c/em\u003e were expressed relative to \u003cem\u003eGAPDH\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscovery of immune cell subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration was calculated by CIBERSORT to quantify the relative proportion of infiltrating immune cells in EMS. The R software package corrplot was used to analyze and visualize 21 types of invasive immune cells, and the R software package vioplot was used to construct a violin diagram to visualize the differences in immune cell infiltration between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between identified genes and infiltrating immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the Spearman\u0026rsquo;s rank correlation analysis in R to explore the correlations between the identified gene biomarkers and the levels of infiltrating immune cells. The chart technique with the ggplot2 package was used to visualize the identified associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR 4.1.1 was used to conduct the statistical analyses. We used Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U-test to undertake group comparisons for continuous variables of endometrium-distributed variables.SVM algorithm was performed by the e1071 package in R, while the LASSO regression analysis was performed by the glmnet package. The diagnostic efficacy of the biomarkers was determined by ROC curve analysis. We used Spearman\u0026rsquo;s correlation to analyze the correlation between infiltrating immune cells and gene biomarkers. Two-tailed tests with P \u0026lt; 0.05 were regarded as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification DEGs for EMS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data of 17 EMS and 19 endometria from GSE7305 and GSE25628 were retrospectively analyzed in our study. Limma package was used to analyze the differences between the EMS and endometria and the DEGs from the meta-data after the batch effects had been removed. Finally, 53 DEGs were obtained: 28 significantly upregulated genes and 25 significantly downregulated genes (Fig. 1).\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eAnalysis of functional correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe function of the DEGs was investigated by GO, KEGG, and DO pathway enrichment analyses. We found that GO enriched by DEGs was mainly associated with lung development, respiratory tube development, complement activation, alternative pathway, and regulation of humoral immune response of the biological process (BP), blood microparticles, myosin filament of the cellular component (CC), tubulin binding, and peptidase regulator activity of the molecular function (MF) (Fig.2A). Enriched KEGG pathways were mainly associated with drug metabolism-cytochrome P450, tyrosine metabolism, and complement and coagulation cascades (Fig. 2B). Moreover, the enriched diseases were mainly associated with benign neoplasm, polycystic ovary syndrome, gallbladder carcinoma, and adenoma cell types (Fig. 2C). The GSEA-enriched pathways were mainly associated with arachidonic acid metabolism, cytokine\u0026ndash;cytokine receptor interactions, chemokine signaling pathways, complement and coagulation cascades, and systemic lupus erythematosus (Fig. 2D). The above results show that the immune response is an essential part of the pathogenesis of EMS.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eIdentification and validation of diagnostic biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential biomarkers of EMS were screened by two different algorithms. Seven diagnostic biomarkers were identified by the LASSO regression algorithm of the DEGs for EMS (Fig. 3A). We determined four features among the DEGs using the SVM-RFE algorithm (Fig. 3B). The overlapping region obtained by the two calculation methods included the two screened genes (\u003cem\u003eAQP1\u003c/em\u003e and \u003cem\u003eZWINT\u003c/em\u003e) (Fig. 3C). Then, the levels of the two features were verified in the GSE5108 dataset. The level of \u003cem\u003eAQP1\u003c/em\u003e in EMS tissues was notably higher than that in the control group, while the level of \u003cem\u003eZWINT\u003c/em\u003e was the opposite (all P \u0026lt; 0.05; Fig. 4A, B). Finally, a logistic regression algorithm was used to establish a diagnostic model with the two identified genes.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eVerification of differential gene expression Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe verified the results of the screened differential genes at the mRNA and protein level. The verification results revealed that the mRNA (Fig. 5A) and protein (Fig. 5B, C) levels of AQP1 in EMS were higher than those in the control group, while the results of ZWINT were the opposite, consistent with the Gene Chip data.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eDiagnostic effect of characteristic EMS biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic ability of the two biomarkers in discriminating EMS demonstrated a favorable diagnostic efficiency, with an AUC of 0.941 (95% confidence interval (CI) 0.824\u0026ndash;1.000) for \u003cem\u003eAQP1\u003c/em\u003e and an AUC of 0.954 (95% CI 0.864\u0026ndash;1.000) for \u003cem\u003eZWINT\u003c/em\u003e (Fig. 6A, B). In addition, a powerful discriminatory ability was demonstrated in the GSE5108 dataset with an AUC of 0.785 (95% CI 0.570\u0026ndash;0.950) for \u003cem\u003eAQP1\u003c/em\u003e and an AUC of 0.860 (95% CI 0.636\u0026ndash;1.000) for \u003cem\u003eZWINT\u003c/em\u003e (Fig. 6C, D). The above results indicated that the featured biomarkers had a high diagnostic ability in EMS.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eImmune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we studied the composition of immune cells in the EMS and control groups. The proportions of T follicular helper cells (P = 0.001), regulatory T cells (Tregs) (P = 0.028), activated NK cells (P \u0026lt; 0.001), resting natural killer (NK) cells (P = 0.018), M2 macrophages (P \u0026lt; 0.001), activated dendritic cells (P = 0.012), and activated mast cells (P = 0.001) were significantly lower in EMS tissues than in endometrial tissues. Furthermore, the proportion of memory B cells (P \u0026lt; 0.001) and plasma cells (P \u0026lt; 0.001) in EMS was evidently higher than that in endometrial tissues (Fig. 7A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlations of 21 types of immune cells were analyzed (Fig. 7B). Eosinophils were positively correlated with activated NK cells, T follicular helper cells, and M1 macrophages, but negatively correlated with M2 macrophages, activated dendritic cells, and memory B cells. T follicular helper cells were positively correlated with Tregs and activated NK cells but negatively correlated with M2 macrophages. Activated NK cells were positively correlated with T follicular helper cells but negatively correlated with plasma cells, M2 macrophages, and memory B cells. Activated memory CD4 T cells were positively correlated with resting activated NK cells but negatively correlated with M2 macrophages and monocytes. Resting mast cells were positively correlated with activated NK cells but negatively correlated with activated mast cells and M0 macrophages. Resting NK cells were positively correlated with activated dendritic cells, Tregs, and activated memory CD4 T cells but negatively correlated with M2 macrophages. Activated dendritic cells were significantly positively correlated with activated memory CD4 T cells and resting NK cells but significantly negatively correlated with plasma cells, resting dendritic cells, monocytes, and M1 macrophages. Naive B cells were positively correlated with Tregs, activated NK cells, and T follicular helper cells but negatively correlated with memory B cells and plasma cells. Neutrophils were significantly positively correlated with activated M1 macrophages, M0 macrophages, and gamma-delta T cells but negatively correlated with resting T follicular helper cells and mast cells.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eCorrelations between the biomarkers and infiltrating immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAQP1\u003c/em\u003e was positively correlated with activated mast cells (r = 0.6, P = 0.00025), M2 macrophages (r = 0.52, P = 0.0027), memory B cells (r = 0.51, P = 0.0031), and plasma cells (r = 0.49, P = 0.0045), and negatively correlated with follicular helper T cells (r = \u0026minus;0.58, P = 0.00061), activated NK cells (r = \u0026minus;0.51, P = 0.003), Tregs (r = \u0026minus;0.42, P =0.016), and activated dendritic cells (r = \u0026minus;0.39, P = 0.028) (Figure 8A). \u003cem\u003eZWINT\u0026nbsp;\u003c/em\u003ewas positively correlated with activated dendritic cells (r = 0.39, P = 0.025), activated NK cells (r = 0.54, P = 0.0019), T follicular helper cells (r = 0.5, P = 0.0043), and Tregs (r = 0.35, P = 0.05), and negatively correlated with M2 macrophages (r = \u0026minus;0.63, P = 0.00017), activated mast cells (r = \u0026minus;0.61, P = 0.00022), memory B cells (r = \u0026minus;0.54, P = 0.0015), and plasma cells (r = \u0026minus;0.5, P = 0.0044; Fig. 8B).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEMS is a progressive disease that is mainly manifested over three aspects: the gradual aggravation of dysmenorrhea, the gradual increase in EMS cysts, and the gradual increase in EMS stage. It is very important to intervene and delay its progress, but diagnosis is often delayed. The internationally agreed definition of delayed diagnosis of EMS is the interval from the onset of pain symptoms to the surgical diagnosis of EMS, and the delay time of EMS diagnosis ranges 4\u0026ndash;10 years [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. At present, the diagnostic process in EMS is generally to visit a doctor after dysmenorrhea symptoms, undergo gynecological and auxiliary examinations, and receive a diagnosis after laparoscopic surgery and postoperative pathological examination. The clinical diagnosis of EMS (non-surgical diagnosis) has a certain value, but the gold standard for EMS diagnosis is surgery and postoperative pathological examination. The reasons for the delay in the diagnosis of EMS are controversial. Studies found that the main reasons for the delay in the diagnosis of EMS are the patients' insufficient attention to dysmenorrhea, the doctors' insufficient understanding of EMS, the lack of non-invasive diagnostic methods, and the limitations of surgical diagnosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Diagnosis delay is an important clinical problem affecting the diagnosis and treatment of patients with EMS. It will not only delay treatment and miss the best treatment opportunity, but also lead to the progression of EMS to a certain extent, increase the degree of pain and the probability of infertility, and also increase the difficulty and trauma of surgery. Thus, it is very important to identify early diagnostic markers that could play an important role in the diagnosis and treatment of EMS. In recent years, an increasing number of researchers have searched for new diagnostic biomarkers of EMS and explored the components of immune cell infiltrates in EMS, which may have a beneficial impact on the clinical results of EMS patients. Moreover, mRNA and microRNA have become promising biomarkers in EMS. Further, only a few studies have investigated the abnormal expression of gene biomarkers and endometrial immune infiltration in EMS. Therefore, the purpose of this study was to identify candidate diagnostic biomarkers for EMS and to study the effect of immune cell infiltration in EMS.\u003c/p\u003e \u003cp\u003eAs far as we know, our study is the first to identify diagnostic biomarkers associated with immune cell infiltrates in EMS by mining multiple GEO datasets. An integrated analysis of GSE7305 and GSE25628 datasets from GEO was conducted. Fifty-three DEGs were identified, comprising 28 upregulated and 25 downregulated genes. We found that GO enrichment was mainly associated with lung development, respiratory tube development, complement activation, alternative pathway, regulation of humoral immune response of the BP, blood microparticles, myosin filament of the CC, tubulin binding, and peptidase regulator activity of the molecular function. KEGG pathway enrichment was mainly associated with drug metabolism-cytochrome P450, tyrosine metabolism, and complement and coagulation cascades. The diseases enriched were mainly associated with benign neoplasm, polycystic ovary syndrome, gallbladder carcinoma, and adenoma cell types. The GSEA-enriched pathways were mainly associated with cytokine\u0026ndash;cytokine receptor interactions, arachidonic acid metabolism, chemokine signaling pathways, systemic lupus erythematosus, and complement and coagulation cascades. All of the results indicated that the immune response plays a crucial role in EMS.\u003c/p\u003e \u003cp\u003eWe identified two diagnostic markers using two machine-learning algorithms. AQPs are a group of glycoproteins that selectively transport transmembrane molecules. They are found in the uterus, ovary, fallopian tube, and other parts of the female reproductive organs, and are involved in the ovulation of follicles, menstruation, and the occurrence and development of malignant tumors or benign gynecological diseases with malignant behavior. When the endometrium changes, the expression of AQP is abnormal [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. At present, 13 members of the AQP family have been identified, AQP0\u0026ndash;12, among which AQP1 and AQP5 are mostly related to disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. AQP1, the first-discovered AQP, is a transmembrane tetramer composed of four monomers with a molecular weight of approximately 112 kDa. Narv\u0026aacute;ez-Moreno et al. confirmed that the expression level of AQP1 in benign lesions was higher than that in malignant lesions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Colombelli et al. showed that AQP1 is mainly distributed in vascular endothelial cells and that a decrease in AQP1 would lead to a decrease in microvessel density, indicating that AQP1 can promote angiogenesis and be used as a marker of EMS invasion [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eZWINT is a component of a known centromeric complex that is composed of 278 amino acids. It can specifically bind to 80 amino acid residues at the N-terminal of the ZW10 protein, and it plays an important role in regulating mitosis and chromosome movement as well as regulating the cell cycle [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Studies have shown that ZWINT is necessary for the assembly of spindle assembly checkpoint, and this checkpoint protein is a complex formed by a variety of binding proteins connecting chromosome centromeres and spindle tubulin, which plays an important role in accurate chromosome allocation in progeny cells [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. If the checkpoint protein is defective, it can lead to chromosome aneuploidy separation and even carcinogenesis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. EMS is a benign disease with malignant tumor characteristics, and the levels of ZWINT in EMS are lower than those in the endometrium. Thus, we consider that ZWINT may play a crucial role in the pathogenesis of EMS.\u003c/p\u003e \u003cp\u003eWe used CIBERSORT to evaluate the type of immune cell infiltrates in EMS and endometrial samples. The results show that many immune cell subtypes are closely related to the biological process of EMS. An increased infiltration of M2 macrophages, activated mast cells, memory B cells, and T follicular helper cells, as well as a decreased infiltration of T follicular helper cells, activated dendritic cells, activated NK cells, and Tregs, were shown to be potentially associated with the pathogenesis of EMS. And, the results showed that AQP1 and ZWINT were correlated with memory B cells, activated mast cells, M2 macrophages, T follicular helper cells, activated dendritic cells, Tregs, and activated NK cells. In fact, the various immune cells in the abdominal cavity environment improve the invasive and adhesive abilities of endometrial cells, including dendritic cells, macrophages, mast cells, NK cells, and T cells, which can lead to ectopic endometrium flowing back into the pelvic and abdominal cavities with menstrual blood [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. During the menstrual cycle, endometrial-like tissue can spread outside its endometrial location [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These lesions attract cytotoxic T cells, macrophages, and NK cells [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Subsequently, the activation of the inflammatory response promotes the secretion of cytokines and chemokines in the abdominal cavity to create a microenvironment and induce the development of ectopic endometrial tissue by promoting local angiogenesis and destroying the process of endometrial apoptosis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The large amount of evidence mentioned above as well as our current results show that several types of invasive immune cells have a crucial role in EMS and should be investigated further in future studies.\u003c/p\u003e \u003cp\u003eWhile, this research also has some limitations. First, because of the retrospective nature of our research, we could not obtain the clinical information associated with the samples. Second, the biomarker and immune cell profiles of the tissues were collected from two different datasets, and hence it is important to further validate their reproducibility. Third, the total cases in the GSE5108 validation cohort were small, thus contributing to less robust results. Finally, the function of the two biomarkers and the role of immune cell infiltrates in EMS were inferred through bioinformatics analyses, and therefore a prospective study with a larger sample size should be performed to confirm our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Mark Abramovitz, PhD, and H. Nikki March, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the language of a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZHL\u003c/strong\u003e: Data curation, Writing- Original draft preparation. \u003cstrong\u003eYL\u003c/strong\u003e, \u003cstrong\u003eLGJ\u003c/strong\u003e: Resources, Data Curation. \u003cstrong\u003eCL\u003c/strong\u003e: Visualization. \u003cstrong\u003eCMX\u003c/strong\u003e: Conceptualization, Methodology, Writing- Reviewing and Editing. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eata\u0026nbsp;Availability Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are derived from the public GEO data portal (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE7305, GSE25628, and GSE5018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the ethics review committee of Beijing Obstetrics and Gynecology Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are presented at a group level and participants consented to the publication of their anonymous results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Burney RO, Giudice LC: \u003cstrong\u003ePathogenesis and pathophysiology of endometriosis\u003c/strong\u003e. \u003cem\u003eFertil Steril\u003c/em\u003e 2012, \u003cstrong\u003e98\u003c/strong\u003e(3):511-519.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Johnson NP, Hummelshoj L, Adamson GD, Keckstein J, Taylor HS, Abrao MS, Bush D, Kiesel L, Tamimi R, Sharpe-Timms KL\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eWorld Endometriosis Society consensus on the classification of endometriosis\u003c/strong\u003e. \u003cem\u003eHum Reprod\u003c/em\u003e 2017, \u003cstrong\u003e32\u003c/strong\u003e(2):315-324.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rogers PA, Adamson GD, Al-Jefout M, Becker CM, D\u0026amp;#39, Hooghe TM, Dunselman GA, Fazleabas A, Giudice LC, Horne AW\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eResearch Priorities for Endometriosis\u003c/strong\u003e. \u003cem\u003eReprod Sci\u003c/em\u003e 2017, \u003cstrong\u003e24\u003c/strong\u003e(2):202-226.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bonocher CM, Montenegro ML, Rosa E Silva JC, Ferriani RA, Meola J: \u003cstrong\u003eEndometriosis and physical exercises: a systematic review\u003c/strong\u003e. \u003cem\u003eReprod Biol Endocrinol\u003c/em\u003e 2014, \u003cstrong\u003e12\u003c/strong\u003e:4.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dunselman GA, Vermeulen N, Becker C, Calhaz-Jorge C, D\u0026amp;#39, Hooghe T, De Bie B, Heikinheimo O, Horne AW, Kiesel L\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eESHRE guideline: management of women with endometriosis\u003c/strong\u003e. \u003cem\u003eHum Reprod\u003c/em\u003e 2014, \u003cstrong\u003e29\u003c/strong\u003e(3):400-412.\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Buggio L, Barbara G, Facchin F, Frattaruolo MP, Aimi G, Berlanda N: \u003cstrong\u003eSelf-management and psychological-sexological interventions in patients with endometriosis: strategies, outcomes, and integration into clinical care\u003c/strong\u003e. \u003cem\u003eInt J Womens Health\u003c/em\u003e 2017, \u003cstrong\u003e9\u003c/strong\u003e:281-293.\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Parasar P, Ozcan P, Terry KL: \u003cstrong\u003eEndometriosis: Epidemiology, Diagnosis and Clinical Management\u003c/strong\u003e. \u003cem\u003eCurr Obstet Gynecol Rep\u003c/em\u003e 2017, \u003cstrong\u003e6\u003c/strong\u003e(1):34-41.\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Vitagliano A, Noventa M, Quaranta M, Gizzo S: \u003cstrong\u003eStatins as Targeted \u0026quot;Magical Pills\u0026quot; for the Conservative Treatment of Endometriosis: May Potential Adverse Effects on Female Fertility Represent the \u0026quot;Dark Side of the Same Coin\u0026quot;? 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\u0026nbsp; \u0026nbsp;\u0026nbsp;Mantovani A, Allavena P, Sica A, Balkwill F: \u003cstrong\u003eCancer-related inflammation\u003c/strong\u003e. \u003cem\u003eNature\u003c/em\u003e 2008, \u003cstrong\u003e454\u003c/strong\u003e(7203):436-444.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometriosis, GEO, immune infiltration, CIBERSORT, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-1305846/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1305846/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo determine the potential diagnostic markers and extent of immune cell infiltration in endometriosis (EMS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFrom the Gene Expression Omnibus database (GEO), we downloaded two published profiles (GSE7305 and GSE25628 datasets) of human EMS and endometrial specimens. Differential genes between 17 EMS and 19 endometrial samples were compared. Candidate biomarkers were identified by support vector machine recursive feature elimination analysis and a Lasso regression model. The area under the receiver operating characteristic curve value represented the discriminatory biomarkers. The diagnostic value and expression levels of biomarkers in EMS were verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting, then further validated in the GSE5108 dataset that included 11 eutopic and 11 ectopic endometria. On the basis of the merged cohorts, we used CIBERSORT to estimate the composition pattern of immune cell components in EMS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifty-three genes were identified in cells from benign neoplasms, polycystic ovary syndrome, gallbladder carcinomas, and adenomas. Gene sets related to arachidonic acid metabolism, cytokine\u0026ndash;cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and systemic lupus erythematosus were differentially activated in EMS compared with endometrial samples. Aquaporin 1 (AQP1) and ZW10 binding protein (ZWINT) were identified as diagnostic markers of EMS, which were verified using qRT-PCR and western blotting and validated in the GSE5108 dataset. Immune cell infiltrate analysis showed that \u003cem\u003eAQP1\u003c/em\u003e and \u003cem\u003eZWINT\u003c/em\u003e were correlated with M2 macrophages, NK cells, activated dendritic cells, T follicular helper cells, regulatory T cells, memory B cells, activated mast cells, and plasma cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAQP1\u003c/em\u003e and \u003cem\u003eZWINT\u003c/em\u003e can be regarded as diagnostic markers of EMS and may provide a new direction for the study of EMS pathogenesis in the future.\u003c/p\u003e","manuscriptTitle":"Diagnostic gene biomarkers for predicting immune infiltration in endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-31 17:28:25","doi":"10.21203/rs.3.rs-1305846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccc33e13-e9b9-4a0a-8c70-7997f2ec634f","owner":[],"postedDate":"January 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-01-31T17:28:25+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-31 17:28:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1305846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1305846","identity":"rs-1305846","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}