Profiling N6-methyladenosine (m6A) methylation-related genes in endometriosis towards a diagnostic model

Profiling N6-methyladenosine (m6A) methylation-related genes in endometriosis towards a diagnostic model | 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 Profiling N6-methyladenosine (m6A) methylation-related genes in endometriosis towards a diagnostic model ying lin, ming yuan, yufei huang, guoyun wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2742276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Endometriosis is an inflammatory disease with non-specific symptoms, including chronic pelvic pain and infertility, which affects thousands of women of reproductive age. Early diagnosis of endometriosis remains challenging. We aimed to build a diagnostic model based on m6A methylation-related genes to provide a new perspective on the clinical diagnosis of endometriosis. Two datasets from previous endometriosis studies were selected. GSE51981 was for training and GSE7305 was for validation. The expression of m6A methylation-related genes between proliferative eutopic endometrium from women with and without endometriosis was compared. Most m6A methylation-related genes were down-regulated in eutopic endometrium from women with endometriosis than those without it. The random forest classifier identified 5 significant differentially expressed genes (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC) that might be involved in the development of endometriosis by affecting miRNA maturation or immune cell infiltration. These genes were included in a logistic regression to construct a new diagnostic model for endometriosis with an area under the ROC curve of 0.852. The model was tested on another independent dataset（AUC 0.750）and not only diagnosed endometriosis well but also showed how severe it was. We also found that YTHDF2 was very good at diagnosing endometriosis on its own and was correlated with macrophage and neutrophil infiltration that may be important for endometriosis development. In conclusion, this novel diagnostic model using m6A methylation-related genes may be a new method for early non-invasive diagnosis of endometriosis. endometriosis random forest m6A methylation diagnostic model immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Endometriosis (EM) is defined as the presence of endometrioid tissue (glands and stroma) outside the uterine cavity [ 1 ]. It affects approximately 10–15% of women of childbearing age and is becoming more common [ 2 , 3 ]. EM is an estrogen-driven inflammatory disorder that can cause chronic pelvic pain and infertility, reducing quality of life [ 4 ]. Even though EM is a benign gynecological disease, it can spread, invade, and recur like a malignant tumor [ 5 , 6 ]. Early diagnosis and treatment of EM can improve fertility, slow down the disease progression, ease pain, and enhance quality of life [ 7 , 8 ]. Laparoscopy and pathological biopsy are the gold standard for the diagnosis of EM [ 9 ], but is not accepted by most patients because it is invasive, risky, and costly. Besides the difficulty of early diagnosis, the pathogenesis of EM remains unclear [ 10 ]. The most accepted explanation is Sampson’s theory of retrograde menstruation [ 11 ]. According to this classic theory, the eutopic endometrium plays a key role in this disease. Previous studies have shown that many proteins are differently expressed in the eutopic endometrium of women with or without EM. These proteins are involved in various processes such as cell proliferation, decidualization, angiogenesis, signaling pathways, endometrial receptivity, and so on [ 12 , 13 ]. Hence, it is necessary to conduct specific research on the eutopic endometrium of women with EM. Finding effective non-invasive diagnostic methods like eutopic endometrial biopsy could replace laparoscopy to some extent and reduce surgical risks and diagnostic delays. N6-methyladenosine (m6A) is one of the most abundant RNA modifications [ 14 ], and is a reversible and dynamic process regulated by methyltransferases, demethylases, and specific RNA binding proteins [ 15 ]. These proteins are called ‘writers’, ‘erasers’, and ‘readers’, respectively. Writers like METTL3, METTL14, and WTAP add m6A marks. Erasers like FTO and ALKBH5 remove them. Readers like YTHDF1/2/3, YTHDC1/2, HNRNPG, and IGF2BP 1/2/3 recognize m6A marks and affect RNA functions [ 16 , 17 ]. m6A methylation influences various aspects of RNA metabolism, including splicing, translation, and degradation. It also play a role in tumor proliferation, differentiation, apoptosis, invasion, and metastasis [ 18 – 21 ]. Emerging evidence suggests that m6A methylation may be important for EM development. Significant dysregulation of m6A regulators has been observed in EM patients. A previous study found that downregulation of METTL3 in EM attenuated pri-miR126 maturation in an m6A-dependent manner, thus enhancing the migration and invasion of endometrial stromal cells and promoting the development of EM [ 22 ]. The proteins HNRNPA2B1 and HNRNPC might affect immune responses and immune cell infiltration in EM [ 23 ]. In this study, we used the Gene Expression Omnibus (GEO) database to identify m6A-related genes that are different in EM and control samples. A clinical diagnosis model was constructed using random forest trees and logistic regression to test its value for EM diagnosis. This model could offer new insights into EM causes and new markers for early diagnosis and treatment of EM. The study flow is shown in Fig. 1 . Materials and Methods Data Download and Processing The expression profile data for the GSE51981 and GSE7305 datasets are publicly available through the GEO portal. The relevant annotation information, including platforms, probes, and ID conversions, was obtained from the GEO database. When multiple probes were associated with the same gene symbol, the average expression of multiple probes was used to represent the expression of the corresponding gene. ID conversion was conducted with the R package “org.Hs.eg.db ”(version 3.14.0). Affymetrix expression data were normalized with the “NormalizeBetweenArrays” function of the LIMMA package (version 3.50.3). All R analysis was performed in R version 4.1.3 and RStudio was used for R. Study Population Selection The training set was consisted of 64 proliferative phase eutopic endometrial samples from GSE51981. Among them, 35 samples were from patients who had no endometriosis but might have other uterine or pelvic diseases, and 29 samples came from patients with endometriosis. None of the patients had hormone therapy, malignant tumors, or major systemic diseases in the last 3 months; surgery and pathology reports confirmed uterine/pelvic abnormalities, E stage followed the revised American Fertility Association classification system; menstrual cycle stage was based on endometrial histology by 2 pathologists and serum estradiol and P4 levels [ 24 ]. Sample information on GSE51981 and GSE7305 is detailed in Table 1 . Table 1 Overview of the details of datasets used to construct and validate a diagnostic model for endometriosis. GSE51981 (Training Set) GSE7305 (Validation Set) Tissue eutopic endometrium eutopic endometrium Cycle phase proliferative proliferative Control 34 8 Endometriosis 29 8 Severity Minimal/Mild:12 Moderate/Severe:17 unknown Type unknown Ovarian endometriosis Screening for Differentially Expressed m6A Methylation-Related Genes Initially, a list of 24 m6A methylation-related genes were assembled from the available published literature and reviews [ 24 – 29 ]. Then, the expression levels of these 24 genes were systematically compared in the eutopic endometrium of women with and without EM using the Wilcoxon test in R. A p-value of < 0.05 was set as the cutoff for differentially expressed genes (DEGs). Clustering analysis was performed on the DEGs to generate a heatmap using the R package “pheatmap” (version 1.0.12). A boxplot was generated using the “ggpubr” package (version 0.4.0). Functional Enrichment and PPI Module Analysis To investigate the biological significance of m6A methylation-related DEGs in EM pathogenesis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the R package "clusterProfiler" (version 4.2.2) to identify significantly enriched GO terms and KEGG pathways with p-values < 0.05 [ 30 ]. The online database STRING-DB (version 11.5) was used to retrieve protein–protein interaction (PPI) information. The minimum required interaction score in the PPI network was set to “medium confidence” (0.400). The degree of nodes was calculated using the Cyto-Hubba plug-in software, and nodes with higher degrees were identified as core proteins [ 31 ]. Finally, Cytoscape (v3.9.0) was used to visualize the hub gene network. Co-expression levels of m6A methylation-related DEGs in the eutopic endometrium were visualized using the “corrplot” package (version 0.92) and the Spearman method. Correlations between genes were established at a cut-off of P ≤ 0.05, and correlation heatmaps were generated. Screening for Disease Signature Genes Using a Random Forest Classifier The R package “randomForest” (version 4.7–1.1) was used to construct a random forest (RF) model to screen for disease signature genes [ 32 ]. In the random forest classifier, the number of random seeds and decision trees were initially set to 12345 and 500, respectively. The final number of decision trees was set to 397, which resulted in a high accuracy and stable model error for the constructed model. The Gini coefficient method was used to calculate the dimensional importance value of all variables in the constructed random forest model. The top 5 DEGs were identified as significant genes for EM, and were used for subsequent model construction and validation. Construction and Validation of the Nomogram Model The GSE51981 dataset was selected as the training set to build the nomogram model. We used logistic regression with R package “rms” (version 6.3-0) to make the nomogram [ 33 ]. A diagnostic index was calculated by summing the risk points for each of the weighted covariates in the nomogram. The index was then used to classify people by their EM risk. The c-index ROC curve, calibration curve, and decision curve, were used to validate the nomogram. ROC curves measured the nomogram’s predictive accuracy. Calibration curves compared the agreement between the probabilities predicted by the nomogram and observed outcomes. Decision curve analysis showed the nomogram’s clinical effects and importance. An additional dataset, GSE7305, was used to check the accuracy of our nomogram model for EM diagnosis. The R package “pROC” (version 1.18.0) was used to make ROC curves for the validation dataset and to get AUC value to validate the model’s discrimination efficiency. Single Sample Gene Set Enrichment Analysis The immune cell levels in the eutopic endometrium were estimated using single sample gene set enrichment analysis (ssGSEA) in the “gsva” R package (version 1.42.0). ssGSEA identified immune cell populations in each endometrium sample based on gene expression data [ 34 ]. Results were visualized using the R package “vioplot” (version 0.4.0). P < 0.05 was considered as significant. In addition, Spearman correlation coefficients were calculated to assess the correlation between 24 m6a methylation-related genes and immune cell infiltration in eutopic endometrium samples. Results m6A Methylation-Related DEGs The expression of m6A methylation-related genes and the differential expression analysis results are shown in Fig. 2 a and 2 b, respectively. There was one significantly upregulated gene (ALKBH5) and twelve significantly downregulated genes (ALKBH3, FTO, YTHDF3, CBLL1, ZCCHC4, NKAP, YTHDF2, KIAA1429, HNRNPC, FMR1, METTL14 and YTHDC2) in the samples with EM compared to those without EM. Functional Enrichment Analysis of DEGs and Construction of a PPI Network GO enrichment analysis revealed that these m6A methylation-related DEGs were mainly involved in the regulation of mRNA metabolic process, the regulation of mRNA stability, and RNA modification (Fig. 3 a). In terms of cellular components, these genes were mainly associated with the methyltransferase complex, nuclear specks, and ribonucleoprotein granules (Fig. 3 a). With regard to molecular function, these genes were mainly enriched in catalytic activity (acting on RNA), demethylase activity, poly(U) RNA binding, ferrous iron binding, oxidoreductase activity and RNA methyltransferase activity (Fig. 3 a). The KEGG pathway enrichment analysis revealed that these m6A methylation-related genes were significantly associated with the p53 signaling pathway and the IL-17 signaling pathway (Fig. 3 b). Figure 3 c shows the hub genes selected based on the PPI network. And we identified 10 hub genes with the highest confidence scores (Fig. 3 d). Subsequent gene co-expression analysis revealed that, in the eutopic endometrium of endometriosis patients, METTL3, METTL14 and YTHDC2 exhibited a positive correlation with other genes, while ALKBH5 exhibited a negative correlation with other genes (Fig. 3 e). Constructing a Random Forest Model to Screen for Disease Signature Genes To screen disease signature genes, we added the m6A methylation-related DEGs into a random forest classifier with 12345 random seeds. We chose 397 trees as the parameter of the random forest model based on the relationship between the model error and the number of decision trees (Fig. 4 a), which generated a stable error in the model. The Gini coefficient was used to measure the importance of all variables based on the decreased mean squared error and the model accuracy (Fig. 4 b). Finally, top 5 DEGs were selected as disease signature genes for subsequent analysis (YTHDF2, NKAP, FTO, ZCCHC4, HNRNPC ). These five hub genes were significantly downregulated in the eutopic endometrium of patients with endometriosis compared to controls (Fig. 4 c). Establishment and Validation of a Nomogram Model A nomogram model was created to predict the probability of endometriosis based on the expression of the identified DEGs (Fig. 4 d). The expression of 5 target genes in the eutopic endometrium was first detected, and then the points corresponding to each gene was obtained according to the nomogram, and the sum of the 5 gene points was the total points. Risk of disease was estimated based on the total points, and when the risk of disease was > 0.4, we considered that the patient had a high risk of endometriosis. The C-index of the nomogram model was 0.852 (95% CI, 0.756–0.949), indicating good discrimination (Fig. 4 e). The calibration plot (Fig. 4 f) indicates that the nomogram is well-calibrated, with predictions made by the nomogram close to the actual outcomes. Decision curve analysis was applied to assess the clinical utility of the diagnostic nomogram model. As shown in Fig. 4 g, regardless of the threshold probability, patients with endometriosis would always benefit more from using this diagnostic nomogram model than from the ‘treating all’ or ‘treating none’ scenarios. Besides, all five genes involved in the model construction have good diagnostic value for endometriosis (Fig. 4 h). In the validation dataset (GSE7305), YTHDF2, ZCCHC4, and HNRNPC expression was reduced in the endometriosis group, consistent with the training set (Fig. 5 a). All eutopic endometrium samples from patients with and without endometriosis could be well distinguished by the nomogram model (Fig. 5 b). And the five hub genes also showed good diagnostic value in the validation set (Fig. 5 c). This external validation indicates that the nomogram model is accurate. The nomogram model and disease severity The boxplot shows the expression differences of 24 m6A methylation-related genes between eutopic endometrium of patients with minimal/mild and moderate/severe endometriosis (Fig. 5 e). Most genes have increased expression levels in severe patients. Taking minimal/mild as the control group and moderate/severe as the experimental group, the area under the ROC curve for judging disease severity using the constructed nomogram model is 0.716, which indicates that the model also has good ability to distinguish disease stage (Fig. 5 d). Immune Infiltration Analysis We used RNA-seq data from 64 samples from GSE51985 to study how m6A methylation-related genes affect immunity in EM. Macrophages (P = 0.035) and Neutrophil (P = 0.030) were significantly more abundant in the eutopic endometrium of EM patients (Fig. 6 a). Many methylation-related genes had strong correlations with immune cell levels. For example, YTHDF2 was negatively linked to macrophages and neutrophil cells (correlation coefficient − 0.89 and − 0.57), while ALKBH5 and macrophages were positively linked (correlation coefficient 0.79) (Fig. 6 b). Discussion Endometriosis can cause several non-specific symptoms, including chronic pelvic pain and infertility [ 35 ]. These non-specific symptoms make early EM diagnosis particularly challenging. The development of non-invasive diagnostic methods with high accuracy is essential for the successful long-term management of EM. In the present study, we generated a model based on m6A methylation-related gene expression in the eutopic endometrium for diagnosing EM with good accuracy. Based on an RF classifier, we found five key DEGs (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC). The GSE51981 dataset was used to construct a nomogram model for EM, and the diagnostic efficacy of the model was tested in GSE51981and GSE7305. The c-index of the model was 0.852.The calibration curves and DCA curves showed that the predictions made by the model were close to the actual results, and that the use of the model to diagnose EM resulted in better clinical treatment gains. The model was accurate in both training (GSE51981) and validation datasets (GSE7305), with AUCs of 0.852 and 0.750, respectively. In addition to being able to identify patients with endometriosis well, the model could also predict EM severity well (AUC is 0.716). We made a 10-hub-gene-based PPI network, and three genes (YTHDF2, FTO, and HNRNPC) from the model were among the 10 hub genes, which confirmed their importance in endometriosis. YTHDF2 was especially notable, as it had high diagnostic value for endometriosis in both sets, with AUC = 0.829 in training and AUC = 1.000 in validation. In short, our model could offer new insights into EM causes and a new non-invasive way for early EM diagnosis. And we found YTHDC2 is an interesting disease signature gene for endometriosis. RNA modification, like m6A, affects many biological functions and diseases. In our study, promoted methylation-related genes (METTL3, METTL14, KIAA1429, ZCCHC4 ) were downregulated in endometriosis, suggesting a general trend of hypomethylation in these samples. This result is consistent with previous studies. For example, it had been reported that m6A levels were decreased in patients with EM, and the down-regulation of methyltransferase-like 3 (METTL3) accounted for decreased m6A levels [ 22 ]. METTL3 is the core active component of the m6A methyltransferase complex, and has multiple functions in various types of cancer, including regulation of cancer stem cell pluripotency, cancer proliferation, cancer metastasis, and tumor immunity [ 19 , 36 ]. Another study showed that lower METTL3 reduced the m6A levels of pri-miR126, thereby attenuating DGCR8-mediated maturation of pri-miR126 to miR126 [ 22 ]. This enhanced the migration and invasion of endometrial stromal cells, thus promoting EM development [ 22 ]. METTL14 was catalytically inactive, but it could stabilize METTL3 and make it work better by forming a complex with it. The complex ultimately provided a platform for the recognition and binding of substrate RNA [ 22 ]. In an endometriosis model, METTL14 cooperates with METTL3 to mediate the promotion of cell proliferation and invasion [ 38 ]. In this study, we found five important genes (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC) that may play an important role in the pathogenesis of EM. YTHDF2 is a ‘reader’ protein that promotes the decay of its target mRNAs, and YTHDF3 helps YTHDF2 during the decay process of methylated mRNA [ 39 ]. In endometrial cancer, knockdown of YTHDF2 impeded the decay of mTORC2, thus activating the AKT pathway [ 40 ] which is also triggered in EM[ 41 ]. Our analysis found that YTHDF2 was significantly downregulated in the eutopic endometrium of endometriosis. Therefore, it is reasonable to speculate that downregulated YTHDF2 promotes endometriosis progression by stopping YTHDF2 from making mTORC2 decay and turning on the AKT pathway. In addition, this study found that YTHDF2 could tell apart patients with and without endometriosis well and YTHDF2 expression was negatively correlated with macrophages and neutrophil cells, suggesting that YTHDF2 is a gene with great research potential in endometriosis. In addition to YTHDF2, a recent study demonstrated that FTO, a key demethylase for RNA m6A modification, inhibited the process of EMs through the ATG5/PKM2 axis [ 42 ]. Over expression of FTO could raise autophagy level through m6A modification of ATG5, which could lower glycolysis level by suppressing PKM2 expression [ 42 ]. Knockdown of ATG5 not only hindered the inhibitory effect of FTO on PKM2 but also stopped FTO from blocking glycolysis, proliferation, and invasion of EESCs [ 42 ]. Therefore, the lower FTO expression in eutopic endometrium may play an important role in EM progression. ZCCHC4 is a highly conserved m6A RNA methyltransferase that methylates the m6A4220 site in 28S rRNA, thereby enhancing ribosome assembly and translation and affecting cell proliferation and tumor growth [ 43 , 44 ]. However, the structural mechanism of how ZCCHC4 recognizes and modifies RNA substrates is unclear. HNRNPC and NKAP are associated with abnormal immune responses and may serve as useful m6A-related biomarkers for endometriosis diagnosis [ 23 , 45 ]. No studies have yet explored the role of ZCCHC4, HNRNPC and NKAP in EM, although the results of the present study suggest that this research is warranted, and may uncover important information on the pathogenesis of EM. Immune system abnormalities are closely associated with the development of EM. We found that Macrophages (P = 0.035) and Neutrophil (P = 0.030) were significantly increased in the eutopic endometrium of patients with EM. Macrophages can be classified into two phenotypes: M1 and M2. M1 macrophages produce pro-inflammatory cytokines (TNFα, IL-1, IL-6) and reactive oxygen and nitrogen species that have anti-proliferative and cytotoxic effects [ 46 ]. M2 macrophages produce anti-inflammatory cytokines (IL-4, IL-10, IL-13), VEGF, and transforming growth factor β. M2 macrophages promote tissue repair, angiogenesis, neurogenesis, and tumor growth [ 46 ]. More M2 macrophages in the peritoneal cavity cause fibrosis and angiogenesis that support EM development [ 46 ]. Some studies showed that endometriosis patients have elevated neutrophils in their blood and peritoneal fluid [ 47 , 48 ]. This may happen because endometriotic tissue makes factors that attract and activate neutrophils [ 49 ]. Neutrophils may also cause tissue damage, pain, inflammation and infertility in endometriosis by releasing reactive oxygen species, proteases and cytokines [ 47 ]. These findings highlight the role of immune cells in EM development. Many methylation-related genes are highly correlated with immune cell infiltration. Ectopic endometrial stromal cell-derived lactate induces M2 macrophage polarization through the METTL3/Trib1/ERK/STAT3 signaling pathway, and promotes EM stromal cell invasion both in vitro and in vivo [ 50 ]. In our study, YTHDF2 expression was negatively correlated with macrophages and neutrophil (correlation coefficient − 0.89 and − 0.57, respectively), which suggested that low expression of YTHDF2 in the eutopic endometrium may be an important factor in endometriosis development and progression. In the present study, we developed a novel diagnostic model for EM based on random forest and logistic regression algorithms. It could supplement existing diagnostic methods, and could be an alternative marker panel for early EM screening. However, our study has some limits. First, the diagnostic model was build and validated on two public datasets. Second, the data extracted from the GEO database was for mRNA levels in EM tissue samples, and this should also be further validated using a new cohort and including both mRNA and protein levels. Third, it is necessary to conduct further research in a larger sample population to evaluate the diagnostic efficiency of the proposed early non-invasive diagnostic markers for EM. Overall, our study suggests that these possible biomarkers could facilitate the early screening of EM, and thus improve patient outcomes and life quality. Declarations Author Contributions All authors participated in this research, including conception and design (YL, MY, YFH and GYW), data acquisition (YL), data analysis and interpretation (YL), study supervision (GYW, YL, MY and YFH), as well as drafting the article or critically revising (GYW, YL, MY, and YFH). The final version ensured and approved by all authors. GYW provided funding and support for the research. Funding This study was supported by the National Key R&D Program of China (No.2022YFC2704002), the Major Program of Shandong Provincial Natural Science Foundation [ZR2021ZD34] and the National Natural Science Foundation of China [grant numbers 82071621and 81901458]. Acknowledgments: This study was supported by the National Key R&D Program of China (No.2022YFC2704002), the Major Program of Shandong Provincial Natural Science Foundation [ZR2021ZD34] and the National Natural Science Foundation of China [grant numbers 82071621and 81901458]. Conflicts of interest ： The authors have no conflicts of interest to declare. Ethics approval ： Our study was approved by the Medical Ethics Committee of Medical Integration and Practice Center, Cheeloo College of Medicine of Shandong University, number SDULCLL2022-1-21. Consent to participate: Not applicable. Consent for publication: All authors agree to publish this article. Availability of data and material: Publicly available datasets were analyzed in this study. This data can be found here: The data of this study were downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo/). Code availability: Codes generated or used during the study are available from the corresponding author by request. References Chapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15(11):666-682. 10.1038/s41574-019-0245-z. Parasar P, Ozcan P, Terry KL. Endometriosis: Epidemiology, Diagnosis and Clinical Management. Curr Obstet Gynecol Rep. 2017;6(1):34-41. 10.1007/s13669-017-0187-1. Dai Y, Zhang JJ, Lang JH, Zhou YF, Guo HY, Zhang XM, et al. [A convenience sampling questionnaire survey of the current status of diagnosis and treatment of endometriosis in China in 2018]. Zhonghua Fu Chan Ke Za Zhi. 2020;55(6):402-407. 10.3760/cma.j.cn112141-20191213-00669. 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Ageing hallmarks exhibit organ-specific temporal signatures. Nature. 2020;583(7817):596-602. 10.1038/s41586-020-2499-y. Zhang H, Guo L, Zhang Z, Sun Y, Kang H, Song C, et al. Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival. J Cancer. 2019;10(16):3706-3716. 10.7150/jca.32092. She J, Su D, Diao R, Wang L. A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis. Front Genet. 2022;13:848116. 10.3389/fgene.2022.848116. O HB, Gransar H, Callister T, Shaw LJ, Schulman-Marcus J, Stuijfzand WJ, et al. Development and Validation of a Simple-to-Use Nomogram for Predicting 5-, 10-, and 15-Year Survival in Asymptomatic Adults Undergoing Coronary Artery Calcium Scoring. JACC Cardiovasc Imaging. 2018;11(3):450-458. 10.1016/j.jcmg.2017.03.018. Chong W, Shang L, Liu J, Fang Z, Du F, Wu H, et al. m(6)A regulator-based methylation modification patterns characterized by distinct tumor microenvironment immune profiles in colon cancer. Theranostics. 2021;11(5):2201-2217. 10.7150/thno.52717. Bulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, et al. Endometriosis. Endocr Rev. 2019;40(4):1048-1079. 10.1210/er.2018-00242. Pan Y, Ma P, Liu Y, Li W, Shu Y. Multiple functions of m(6)A RNA methylation in cancer. J Hematol Oncol. 2018;11(1):48. 10.1186/s13045-018-0590-8. Wu B, Li L, Huang Y, Ma J, Min J. Readers, writers and erasers of N(6)-methylated adenosine modification. Curr Opin Struct Biol. 2017;47:67-76. 10.1016/j.sbi.2017.05.011. Shen L, Zhang C, Zhang Y, Yang Y. METTL3 and METTL14-mediated N(6)-methyladenosine modification promotes cell proliferation and invasion in a model of endometriosis. Reprod Biomed Online. 2023;46(2):255-265. 10.1016/j.rbmo.2022.10.010. Shi H, Wang X, Lu Z, Zhao BS, Ma H, Hsu PJ, et al. YTHDF3 facilitates translation and decay of N(6)-methyladenosine-modified RNA. Cell Res. 2017;27(3):315-328. 10.1038/cr.2017.15. Kim TH, Yu Y, Luo L, Lydon JP, Jeong JW, Kim JJ. Activated AKT pathway promotes establishment of endometriosis. Endocrinology. 2014;155(5):1921-30. 10.1210/en.2013-1951. Eaton JL, Unno K, Caraveo M, Lu Z, Kim JJ. Increased AKT or MEK1/2 activity influences progesterone receptor levels and localization in endometriosis. J Clin Endocrinol Metab. 2013;98(12):E1871-9. 10.1210/jc.2013-1661. Wang H, Liang Z, Gou Y, Li Z, Cao Y, Jiao N, et al. FTO-dependent N(6)-Methyladenosine regulates the progression of endometriosis via the ATG5/PKM2 Axis. Cell Signal. 2022;98:110406. 10.1016/j.cellsig.2022.110406. Ma H, Wang X, Cai J, Dai Q, Natchiar SK, Lv R, et al. N(6-)Methyladenosine methyltransferase ZCCHC4 mediates ribosomal RNA methylation. Nat Chem Biol. 2019;15(1):88-94. 10.1038/s41589-018-0184-3. van Tran N, Ernst F, Hawley BR, Zorbas C, Ulryck N, Hackert P, et al. The human 18S rRNA m6A methyltransferase METTL5 is stabilized by TRMT112. Nucleic Acids Res. 2019;47(15):7719-7733. 10.1093/nar/gkz619. Cao X, Geng Q, Fan D, Wang Q, Wang X, Zhang M, et al. m(6)A methylation: a process reshaping the tumour immune microenvironment and regulating immune evasion. Mol Cancer. 2023;22(1):42. 10.1186/s12943-022-01704-8. Li Q, Yuan M, Jiao X, Huang Y, Li J, Li D, et al. M1 Macrophage-Derived Nanovesicles Repolarize M2 Macrophages for Inhibiting the Development of Endometriosis. Front Immunol. 2021;12:707784. 10.3389/fimmu.2021.707784. Symons LK, Miller JE, Tyryshkin K, Monsanto SP, Marks RM, Lingegowda H, et al. Neutrophil recruitment and function in endometriosis patients and a syngeneic murine model. Faseb J. 2020;34(1):1558-1575. 10.1096/fj.201902272R. Riley CF, Moen MH, Videm V. Inflammatory markers in endometriosis: reduced peritoneal neutrophil response in minimal endometriosis. Acta Obstet Gynecol Scand. 2007;86(7):877-81. 10.1080/00016340701417398. Lamceva J, Uljanovs R, Strumfa I. The Main Theories on the Pathogenesis of Endometriosis. Int J Mol Sci. 2023;24(5):4254. 10.3390/ijms24054254. Gou Y, Wang H, Wang T, Wang H, Wang B, Jiao N, et al. Ectopic endometriotic stromal cells-derived lactate induces M2 macrophage polarization via Mettl3/Trib1/ERK/STAT3 signalling pathway in endometriosis. Immunology. 2022. 10.1111/imm.13574. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2742276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":196326777,"identity":"cb5ef15d-fcf0-4fdc-be3e-942d6526574d","order_by":0,"name":"ying lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIie2QsWrDMBCGTwjkRU3WEwa7j3BBEPw48pIphEKXFEorMPgZGgh5hk6ZVQTuEujaQgYngdKtfoAW6o5ZbI+F6oNbjv/j5w4gEPiL8HZqgkREhXO4xCQdpBgCPZZVXme7TE/skCYDkK8e5ppuymUOridNzxf+w1zt2aMzU3zboGGWH46vXYofzTJD75ycm6nVFhcRCK3nnYqckiEv6MlWsdriNbNSxEMUSZ6V8fcac+v6FV23CqqSc1J2gKLalvbJnsZSsBor1JOi55bRy043zZe/L9PPxuHtXZJGxeHUpVw6EHi+4h3xX1ILvOnJBAKBwH/nBx4MT0I2wM/NAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0152-2436","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"ying","middleName":"","lastName":"lin","suffix":""},{"id":196326778,"identity":"397bbce5-5be9-4aa6-950d-f47409e80460","order_by":1,"name":"ming yuan","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"ming","middleName":"","lastName":"yuan","suffix":""},{"id":196326779,"identity":"9132c7dc-bd55-4dc9-bf08-0c4940d4a807","order_by":2,"name":"yufei huang","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"yufei","middleName":"","lastName":"huang","suffix":""},{"id":196326780,"identity":"7c04c82a-b1b5-412f-9b34-f65cb720a45d","order_by":3,"name":"guoyun wang","email":"","orcid":"https://orcid.org/0000-0003-4023-084X","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"guoyun","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2023-03-27 13:18:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2742276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2742276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36583067,"identity":"113d7bfa-4fd4-495a-89de-cfa94c1e67fb","added_by":"auto","created_at":"2023-05-03 15:28:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/da189618f9adaf3c3089605b.png"},{"id":36582822,"identity":"4ec6c0d3-b765-41cb-8db1-177d87cd99b2","added_by":"auto","created_at":"2023-05-03 15:20:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214152,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression landscape of 24 m6A genes in endometriosis \u003cstrong\u003ea\u003c/strong\u003e The heatmap plot showed the expression level of 24 m6A genes in control and endometriosis samples.\u003cstrong\u003e b \u003c/strong\u003eThe box plot showed the expression level of 24 m6A genes in control and endometriosis samples. Control=patients without endometriosis. Wilcoxon test was utilized to perform differential expression analysis. \u0026nbsp;*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001 indicated the statistical significance of data.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/17dab40ddb2a0817ee94fab4.png"},{"id":36582821,"identity":"b73fd0bd-2e01-43eb-b164-3cd71d496513","added_by":"auto","created_at":"2023-05-03 15:20:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464907,"visible":true,"origin":"","legend":"\u003cp\u003ePPI networks, GO enrichment and KEGG pathway analysis. \u003cstrong\u003e\u0026nbsp;a \u003c/strong\u003eThe top 10 GO terms of differentially expressed genes. \u003cstrong\u003eb \u003c/strong\u003eThe KEGG pathways of differentially expressed genes. \u003cstrong\u003ec \u003c/strong\u003eSignificant hub proteins extracted by MCC algorithm from differentially expressed genes. \u003cstrong\u003ed \u003c/strong\u003eTop 10 genes calculated by MCC algorithm. \u003cstrong\u003ee \u003c/strong\u003eThe correlation between the 24 m6A gene expressions in eutopic endometrium samples. Red represents positive correlation, blue represents negative correlation, size and colour shades represent the degree of correlation, while a cross represents no significant correlation.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/a823224b7c20af5ec5055de9.png"},{"id":36582825,"identity":"94c53f49-a1f3-4e51-bf05-16f4fa662dce","added_by":"auto","created_at":"2023-05-03 15:20:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":256553,"visible":true,"origin":"","legend":"\u003cp\u003eScreen important genes throngh Random Forest classifier and construct endometriosis diagnostic model.\u003cstrong\u003e a \u003c/strong\u003eThe relationship between the number of decision tree and the model error. When the number of decision trees is nearly 397, the error rate of the constructed model is relatively stable. \u003cstrong\u003eb\u003c/strong\u003e The importance of all variables in the random forest classifier through the Gini coefficient method. \u003cstrong\u003ec \u003c/strong\u003eBox plot of the difference in expression of 5 hub genes. \u003cstrong\u003ed\u003c/strong\u003e Construction of clinical diagnostic nomogram based on 5 genes. \u003cstrong\u003ee\u003c/strong\u003e ROC curves of the diagnostic nomogram model. AUC = area under the curve. \u003cstrong\u003ef \u003c/strong\u003eThe calibration curve showed relatively high accuracy in predicting endometriosis samples. \u003cstrong\u003eg\u003c/strong\u003e Decision curve analyses confirmed the benefit of this diagnostic model. \u003cstrong\u003eh\u003c/strong\u003e ROC curves of 5 hub genes.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/17aaacf7110c2ae5d66902ff.png"},{"id":36583331,"identity":"2d0a5348-9e24-45c9-a7b9-76b4a1e598e0","added_by":"auto","created_at":"2023-05-03 15:36:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238567,"visible":true,"origin":"","legend":"\u003cp\u003eExternal validation of the endometriosis diagnostic model. \u003cstrong\u003ea \u003c/strong\u003eDifferences in the expression of 5 key genes in GSE7305 dataset. \u003cstrong\u003eb,c\u003c/strong\u003e ROC curves to validate the diagnostic model and unique diagnostic value of 5 hub genes in GSE7305 dataset. AUC = area under the curve. \u003cstrong\u003ed\u003c/strong\u003e The ability of the diagnostic model to distinguish disease severity. \u003cstrong\u003ee\u003c/strong\u003e Differences in the expression of 24 m6A methylation-related genes in the eutopic endometrium of patients with different severity of endometriosis.\u003c/p\u003e","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/ebcaf39511fb3a50d34249ed.png"},{"id":36582826,"identity":"dfdafadf-e88b-4cfd-a208-79b8040f7b3d","added_by":"auto","created_at":"2023-05-03 15:20:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":765853,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape of immune cell subsets infiltrated in endometriosis eutopic endometrium tissue.\u003cstrong\u003e a \u003c/strong\u003eViolin plot of the 24 immune cells infiltrated in endometriosis and control tissues. \u003cstrong\u003eb \u003c/strong\u003eSpearman correlation analysis of the 24 m6A methylation-associated genes and 23 immune cells infiltration in eutopic endometrium samples. In the correlation heatmap, red represents positive correlation, blue represents negative correlation. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001 indicated the statistical significance of data.\u003c/p\u003e","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/3da74cfd3e281c7e5567f1cc.png"},{"id":38565772,"identity":"90d6276c-f018-4edf-b5a3-622da5d4ca5f","added_by":"auto","created_at":"2023-06-14 23:31:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895392,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2742276/v1/27315cc9-6338-4738-b435-362d3b8dd300.pdf"}],"financialInterests":"","formattedTitle":"Profiling N6-methyladenosine (m6A) methylation-related genes in endometriosis towards a diagnostic model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis (EM) is defined as the presence of endometrioid tissue (glands and stroma) outside the uterine cavity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It affects approximately 10\u0026ndash;15% of women of childbearing age and is becoming more common [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EM is an estrogen-driven inflammatory disorder that can cause chronic pelvic pain and infertility, reducing quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Even though EM is a benign gynecological disease, it can spread, invade, and recur like a malignant tumor [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Early diagnosis and treatment of EM can improve fertility, slow down the disease progression, ease pain, and enhance quality of life [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Laparoscopy and pathological biopsy are the gold standard for the diagnosis of EM [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but is not accepted by most patients because it is invasive, risky, and costly. Besides the difficulty of early diagnosis, the pathogenesis of EM remains unclear [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The most accepted explanation is Sampson\u0026rsquo;s theory of retrograde menstruation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to this classic theory, the eutopic endometrium plays a key role in this disease. Previous studies have shown that many proteins are differently expressed in the eutopic endometrium of women with or without EM. These proteins are involved in various processes such as cell proliferation, decidualization, angiogenesis, signaling pathways, endometrial receptivity, and so on [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, it is necessary to conduct specific research on the eutopic endometrium of women with EM. Finding effective non-invasive diagnostic methods like eutopic endometrial biopsy could replace laparoscopy to some extent and reduce surgical risks and diagnostic delays.\u003c/p\u003e \u003cp\u003eN6-methyladenosine (m6A) is one of the most abundant RNA modifications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and is a reversible and dynamic process regulated by methyltransferases, demethylases, and specific RNA binding proteins [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These proteins are called \u0026lsquo;writers\u0026rsquo;, \u0026lsquo;erasers\u0026rsquo;, and \u0026lsquo;readers\u0026rsquo;, respectively. Writers like METTL3, METTL14, and WTAP add m6A marks. Erasers like FTO and ALKBH5 remove them. Readers like YTHDF1/2/3, YTHDC1/2, HNRNPG, and IGF2BP 1/2/3 recognize m6A marks and affect RNA functions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. m6A methylation influences various aspects of RNA metabolism, including splicing, translation, and degradation. It also play a role in tumor proliferation, differentiation, apoptosis, invasion, and metastasis [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that m6A methylation may be important for EM development. Significant dysregulation of m6A regulators has been observed in EM patients. A previous study found that downregulation of METTL3 in EM attenuated pri-miR126 maturation in an m6A-dependent manner, thus enhancing the migration and invasion of endometrial stromal cells and promoting the development of EM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The proteins HNRNPA2B1 and HNRNPC might affect immune responses and immune cell infiltration in EM [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, we used the Gene Expression Omnibus (GEO) database to identify m6A-related genes that are different in EM and control samples. A clinical diagnosis model was constructed using random forest trees and logistic regression to test its value for EM diagnosis. This model could offer new insights into EM causes and new markers for early diagnosis and treatment of EM. The study flow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eData Download and Processing\u003c/p\u003e \u003cp\u003eThe expression profile data for the GSE51981 and GSE7305 datasets are publicly available through the GEO portal. The relevant annotation information, including platforms, probes, and ID conversions, was obtained from the GEO database. When multiple probes were associated with the same gene symbol, the average expression of multiple probes was used to represent the expression of the corresponding gene. ID conversion was conducted with the R package \u0026ldquo;org.Hs.eg.db \u0026rdquo;(version 3.14.0). Affymetrix expression data were normalized with the \u0026ldquo;NormalizeBetweenArrays\u0026rdquo; function of the LIMMA package (version 3.50.3). All R analysis was performed in R version 4.1.3 and RStudio was used for R.\u003c/p\u003e \u003cp\u003eStudy Population Selection\u003c/p\u003e \u003cp\u003eThe training set was consisted of 64 proliferative phase eutopic endometrial samples from GSE51981. Among them, 35 samples were from patients who had no endometriosis but might have other uterine or pelvic diseases, and 29 samples came from patients with endometriosis. None of the patients had hormone therapy, malignant tumors, or major systemic diseases in the last 3 months; surgery and pathology reports confirmed uterine/pelvic abnormalities, E stage followed the revised American Fertility Association classification system; menstrual cycle stage was based on endometrial histology by 2 pathologists and serum estradiol and P4 levels [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Sample information on GSE51981 and GSE7305 is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the details of datasets used to construct and validate a diagnostic model for endometriosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE51981\u003c/p\u003e \u003cp\u003e(Training Set)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE7305\u003c/p\u003e \u003cp\u003e(Validation Set)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTissue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeutopic endometrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eeutopic endometrium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCycle phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eproliferative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproliferative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEndometriosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal/Mild:12\u003c/p\u003e \u003cp\u003eModerate/Severe:17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOvarian endometriosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eScreening for Differentially Expressed m6A Methylation-Related Genes\u003c/p\u003e \u003cp\u003eInitially, a list of 24 m6A methylation-related genes were assembled from the available published literature and reviews [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Then, the expression levels of these 24 genes were systematically compared in the eutopic endometrium of women with and without EM using the Wilcoxon test in R. A p-value of \u0026lt;\u0026thinsp;0.05 was set as the cutoff for differentially expressed genes (DEGs). Clustering analysis was performed on the DEGs to generate a heatmap using the R package \u0026ldquo;pheatmap\u0026rdquo; (version 1.0.12). A boxplot was generated using the \u0026ldquo;ggpubr\u0026rdquo; package (version 0.4.0).\u003c/p\u003e \u003cp\u003eFunctional Enrichment and PPI Module Analysis\u003c/p\u003e \u003cp\u003eTo investigate the biological significance of m6A methylation-related DEGs in EM pathogenesis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the R package \"clusterProfiler\" (version 4.2.2) to identify significantly enriched GO terms and KEGG pathways with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The online database STRING-DB (version 11.5) was used to retrieve protein\u0026ndash;protein interaction (PPI) information. The minimum required interaction score in the PPI network was set to \u0026ldquo;medium confidence\u0026rdquo; (0.400). The degree of nodes was calculated using the Cyto-Hubba plug-in software, and nodes with higher degrees were identified as core proteins [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Finally, Cytoscape (v3.9.0) was used to visualize the hub gene network. Co-expression levels of m6A methylation-related DEGs in the eutopic endometrium were visualized using the \u0026ldquo;corrplot\u0026rdquo; package (version 0.92) and the Spearman method. Correlations between genes were established at a cut-off of P\u0026thinsp;\u0026le;\u0026thinsp;0.05, and correlation heatmaps were generated.\u003c/p\u003e \u003cp\u003eScreening for Disease Signature Genes Using a Random Forest Classifier\u003c/p\u003e \u003cp\u003eThe R package \u0026ldquo;randomForest\u0026rdquo; (version 4.7\u0026ndash;1.1) was used to construct a random forest (RF) model to screen for disease signature genes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the random forest classifier, the number of random seeds and decision trees were initially set to 12345 and 500, respectively. The final number of decision trees was set to 397, which resulted in a high accuracy and stable model error for the constructed model. The Gini coefficient method was used to calculate the dimensional importance value of all variables in the constructed random forest model. The top 5 DEGs were identified as significant genes for EM, and were used for subsequent model construction and validation.\u003c/p\u003e \u003cp\u003eConstruction and Validation of the Nomogram Model\u003c/p\u003e \u003cp\u003eThe GSE51981 dataset was selected as the training set to build the nomogram model. We used logistic regression with R package \u0026ldquo;rms\u0026rdquo; (version 6.3-0) to make the nomogram [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A diagnostic index was calculated by summing the risk points for each of the weighted covariates in the nomogram. The index was then used to classify people by their EM risk. The c-index ROC curve, calibration curve, and decision curve, were used to validate the nomogram. ROC curves measured the nomogram\u0026rsquo;s predictive accuracy. Calibration curves compared the agreement between the probabilities predicted by the nomogram and observed outcomes. Decision curve analysis showed the nomogram\u0026rsquo;s clinical effects and importance. An additional dataset, GSE7305, was used to check the accuracy of our nomogram model for EM diagnosis. The R package \u0026ldquo;pROC\u0026rdquo; (version 1.18.0) was used to make ROC curves for the validation dataset and to get AUC value to validate the model\u0026rsquo;s discrimination efficiency.\u003c/p\u003e \u003cp\u003eSingle Sample Gene Set Enrichment Analysis\u003c/p\u003e \u003cp\u003eThe immune cell levels in the eutopic endometrium were estimated using single sample gene set enrichment analysis (ssGSEA) in the \u0026ldquo;gsva\u0026rdquo; R package (version 1.42.0). ssGSEA identified immune cell populations in each endometrium sample based on gene expression data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Results were visualized using the R package \u0026ldquo;vioplot\u0026rdquo; (version 0.4.0). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant. In addition, Spearman correlation coefficients were calculated to assess the correlation between 24 m6a methylation-related genes and immune cell infiltration in eutopic endometrium samples.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003em6A Methylation-Related DEGs\u003c/h2\u003e \u003cp\u003eThe expression of m6A methylation-related genes and the differential expression analysis results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, respectively. There was one significantly upregulated gene (ALKBH5) and twelve significantly downregulated genes (ALKBH3, FTO, YTHDF3, CBLL1, ZCCHC4, NKAP, YTHDF2, KIAA1429, HNRNPC, FMR1, METTL14 and YTHDC2) in the samples with EM compared to those without EM.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional Enrichment Analysis of DEGs and Construction of a PPI Network\u003c/h3\u003e\n\u003cp\u003eGO enrichment analysis revealed that these m6A methylation-related DEGs were mainly involved in the regulation of mRNA metabolic process, the regulation of mRNA stability, and RNA modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of cellular components, these genes were mainly associated with the methyltransferase complex, nuclear specks, and ribonucleoprotein granules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). With regard to molecular function, these genes were mainly enriched in catalytic activity (acting on RNA), demethylase activity, poly(U) RNA binding, ferrous iron binding, oxidoreductase activity and RNA methyltransferase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The KEGG pathway enrichment analysis revealed that these m6A methylation-related genes were significantly associated with the p53 signaling pathway and the IL-17 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec shows the hub genes selected based on the PPI network. And we identified 10 hub genes with the highest confidence scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Subsequent gene co-expression analysis revealed that, in the eutopic endometrium of endometriosis patients, METTL3, METTL14 and YTHDC2 exhibited a positive correlation with other genes, while ALKBH5 exhibited a negative correlation with other genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e\n\u003ch3\u003eConstructing a Random Forest Model to Screen for Disease Signature Genes\u003c/h3\u003e\n\u003cp\u003eTo screen disease signature genes, we added the m6A methylation-related DEGs into a random forest classifier with 12345 random seeds. We chose 397 trees as the parameter of the random forest model based on the relationship between the model error and the number of decision trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), which generated a stable error in the model. The Gini coefficient was used to measure the importance of all variables based on the decreased mean squared error and the model accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Finally, top 5 DEGs were selected as disease signature genes for subsequent analysis (YTHDF2, NKAP, FTO, ZCCHC4, HNRNPC ). These five hub genes were significantly downregulated in the eutopic endometrium of patients with endometriosis compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\n\u003ch3\u003eEstablishment and Validation of a Nomogram Model\u003c/h3\u003e\n\u003cp\u003eA nomogram model was created to predict the probability of endometriosis based on the expression of the identified DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The expression of 5 target genes in the eutopic endometrium was first detected, and then the points corresponding to each gene was obtained according to the nomogram, and the sum of the 5 gene points was the total points. Risk of disease was estimated based on the total points, and when the risk of disease was \u0026gt;\u0026thinsp;0.4, we considered that the patient had a high risk of endometriosis. The C-index of the nomogram model was 0.852 (95% CI, 0.756\u0026ndash;0.949), indicating good discrimination (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The calibration plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef) indicates that the nomogram is well-calibrated, with predictions made by the nomogram close to the actual outcomes. Decision curve analysis was applied to assess the clinical utility of the diagnostic nomogram model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, regardless of the threshold probability, patients with endometriosis would always benefit more from using this diagnostic nomogram model than from the \u0026lsquo;treating all\u0026rsquo; or \u0026lsquo;treating none\u0026rsquo; scenarios. Besides, all five genes involved in the model construction have good diagnostic value for endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh). In the validation dataset (GSE7305), YTHDF2, ZCCHC4, and HNRNPC expression was reduced in the endometriosis group, consistent with the training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). All eutopic endometrium samples from patients with and without endometriosis could be well distinguished by the nomogram model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). And the five hub genes also showed good diagnostic value in the validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). This external validation indicates that the nomogram model is accurate.\u003c/p\u003e\n\u003ch3\u003eThe nomogram model and disease severity\u003c/h3\u003e\n\u003cp\u003eThe boxplot shows the expression differences of 24 m6A methylation-related genes between eutopic endometrium of patients with minimal/mild and moderate/severe endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Most genes have increased expression levels in severe patients. Taking minimal/mild as the control group and moderate/severe as the experimental group, the area under the ROC curve for judging disease severity using the constructed nomogram model is 0.716, which indicates that the model also has good ability to distinguish disease stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e\n\u003ch3\u003eImmune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eWe used RNA-seq data from 64 samples from GSE51985 to study how m6A methylation-related genes affect immunity in EM. Macrophages (P\u0026thinsp;=\u0026thinsp;0.035) and Neutrophil (P\u0026thinsp;=\u0026thinsp;0.030) were significantly more abundant in the eutopic endometrium of EM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Many methylation-related genes had strong correlations with immune cell levels. For example, YTHDF2 was negatively linked to macrophages and neutrophil cells (correlation coefficient \u0026minus;\u0026thinsp;0.89 and \u0026minus;\u0026thinsp;0.57), while ALKBH5 and macrophages were positively linked (correlation coefficient 0.79) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometriosis can cause several non-specific symptoms, including chronic pelvic pain and infertility [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These non-specific symptoms make early EM diagnosis particularly challenging. The development of non-invasive diagnostic methods with high accuracy is essential for the successful long-term management of EM. In the present study, we generated a model based on m6A methylation-related gene expression in the eutopic endometrium for diagnosing EM with good accuracy.\u003c/p\u003e \u003cp\u003eBased on an RF classifier, we found five key DEGs (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC). The GSE51981 dataset was used to construct a nomogram model for EM, and the diagnostic efficacy of the model was tested in GSE51981and GSE7305. The c-index of the model was 0.852.The calibration curves and DCA curves showed that the predictions made by the model were close to the actual results, and that the use of the model to diagnose EM resulted in better clinical treatment gains. The model was accurate in both training (GSE51981) and validation datasets (GSE7305), with AUCs of 0.852 and 0.750, respectively. In addition to being able to identify patients with endometriosis well, the model could also predict EM severity well (AUC is 0.716). We made a 10-hub-gene-based PPI network, and three genes (YTHDF2, FTO, and HNRNPC) from the model were among the 10 hub genes, which confirmed their importance in endometriosis. YTHDF2 was especially notable, as it had high diagnostic value for endometriosis in both sets, with AUC\u0026thinsp;=\u0026thinsp;0.829 in training and AUC\u0026thinsp;=\u0026thinsp;1.000 in validation. In short, our model could offer new insights into EM causes and a new non-invasive way for early EM diagnosis. And we found YTHDC2 is an interesting disease signature gene for endometriosis.\u003c/p\u003e \u003cp\u003eRNA modification, like m6A, affects many biological functions and diseases. In our study, promoted methylation-related genes (METTL3, METTL14, KIAA1429, ZCCHC4 ) were downregulated in endometriosis, suggesting a general trend of hypomethylation in these samples. This result is consistent with previous studies. For example, it had been reported that m6A levels were decreased in patients with EM, and the down-regulation of methyltransferase-like 3 (METTL3) accounted for decreased m6A levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. METTL3 is the core active component of the m6A methyltransferase complex, and has multiple functions in various types of cancer, including regulation of cancer stem cell pluripotency, cancer proliferation, cancer metastasis, and tumor immunity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Another study showed that lower METTL3 reduced the m6A levels of pri-miR126, thereby attenuating DGCR8-mediated maturation of pri-miR126 to miR126 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This enhanced the migration and invasion of endometrial stromal cells, thus promoting EM development [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. METTL14 was catalytically inactive, but it could stabilize METTL3 and make it work better by forming a complex with it. The complex ultimately provided a platform for the recognition and binding of substrate RNA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In an endometriosis model, METTL14 cooperates with METTL3 to mediate the promotion of cell proliferation and invasion [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we found five important genes (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC) that may play an important role in the pathogenesis of EM. YTHDF2 is a \u0026lsquo;reader\u0026rsquo; protein that promotes the decay of its target mRNAs, and YTHDF3 helps YTHDF2 during the decay process of methylated mRNA [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In endometrial cancer, knockdown of YTHDF2 impeded the decay of mTORC2, thus activating the AKT pathway [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] which is also triggered in EM[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our analysis found that YTHDF2 was significantly downregulated in the eutopic endometrium of endometriosis. Therefore, it is reasonable to speculate that downregulated YTHDF2 promotes endometriosis progression by stopping YTHDF2 from making mTORC2 decay and turning on the AKT pathway. In addition, this study found that YTHDF2 could tell apart patients with and without endometriosis well and YTHDF2 expression was negatively correlated with macrophages and neutrophil cells, suggesting that YTHDF2 is a gene with great research potential in endometriosis. In addition to YTHDF2, a recent study demonstrated that FTO, a key demethylase for RNA m6A modification, inhibited the process of EMs through the ATG5/PKM2 axis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Over expression of FTO could raise autophagy level through m6A modification of ATG5, which could lower glycolysis level by suppressing PKM2 expression [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Knockdown of ATG5 not only hindered the inhibitory effect of FTO on PKM2 but also stopped FTO from blocking glycolysis, proliferation, and invasion of EESCs [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, the lower FTO expression in eutopic endometrium may play an important role in EM progression. ZCCHC4 is a highly conserved m6A RNA methyltransferase that methylates the m6A4220 site in 28S rRNA, thereby enhancing ribosome assembly and translation and affecting cell proliferation and tumor growth [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, the structural mechanism of how ZCCHC4 recognizes and modifies RNA substrates is unclear. HNRNPC and NKAP are associated with abnormal immune responses and may serve as useful m6A-related biomarkers for endometriosis diagnosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. No studies have yet explored the role of ZCCHC4, HNRNPC and NKAP in EM, although the results of the present study suggest that this research is warranted, and may uncover important information on the pathogenesis of EM.\u003c/p\u003e \u003cp\u003eImmune system abnormalities are closely associated with the development of EM. We found that Macrophages (P\u0026thinsp;=\u0026thinsp;0.035) and Neutrophil (P\u0026thinsp;=\u0026thinsp;0.030) were significantly increased in the eutopic endometrium of patients with EM. Macrophages can be classified into two phenotypes: M1 and M2. M1 macrophages produce pro-inflammatory cytokines (TNFα, IL-1, IL-6) and reactive oxygen and nitrogen species that have anti-proliferative and cytotoxic effects [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. M2 macrophages produce anti-inflammatory cytokines (IL-4, IL-10, IL-13), VEGF, and transforming growth factor β. M2 macrophages promote tissue repair, angiogenesis, neurogenesis, and tumor growth [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. More M2 macrophages in the peritoneal cavity cause fibrosis and angiogenesis that support EM development [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Some studies showed that endometriosis patients have elevated neutrophils in their blood and peritoneal fluid [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This may happen because endometriotic tissue makes factors that attract and activate neutrophils [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Neutrophils may also cause tissue damage, pain, inflammation and infertility in endometriosis by releasing reactive oxygen species, proteases and cytokines [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These findings highlight the role of immune cells in EM development. Many methylation-related genes are highly correlated with immune cell infiltration. Ectopic endometrial stromal cell-derived lactate induces M2 macrophage polarization through the METTL3/Trib1/ERK/STAT3 signaling pathway, and promotes EM stromal cell invasion both in vitro and in vivo [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In our study, YTHDF2 expression was negatively correlated with macrophages and neutrophil (correlation coefficient \u0026minus;\u0026thinsp;0.89 and \u0026minus;\u0026thinsp;0.57, respectively), which suggested that low expression of YTHDF2 in the eutopic endometrium may be an important factor in endometriosis development and progression.\u003c/p\u003e \u003cp\u003eIn the present study, we developed a novel diagnostic model for EM based on random forest and logistic regression algorithms. It could supplement existing diagnostic methods, and could be an alternative marker panel for early EM screening. However, our study has some limits. First, the diagnostic model was build and validated on two public datasets. Second, the data extracted from the GEO database was for mRNA levels in EM tissue samples, and this should also be further validated using a new cohort and including both mRNA and protein levels. Third, it is necessary to conduct further research in a larger sample population to evaluate the diagnostic efficiency of the proposed early non-invasive diagnostic markers for EM. Overall, our study suggests that these possible biomarkers could facilitate the early screening of EM, and thus improve patient outcomes and life quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAuthor Contributions\u003c/h1\u003e\n\u003cp\u003eAll authors participated in this research, including conception and design (YL, MY, YFH and GYW), data acquisition (YL), data analysis and interpretation (YL), study supervision (GYW, YL, MY and YFH), as well as drafting the article or critically revising (GYW, YL, MY, and YFH). The final version ensured and approved by all authors. GYW provided funding and support for the research.\u003c/p\u003e\n\u003ch1\u003eFunding\u003c/h1\u003e\n\u003cp\u003eThis study was supported by the National Key R\u0026amp;D Program of China (No.2022YFC2704002), the Major Program of Shandong Provincial Natural Science Foundation [ZR2021ZD34] and the National Natural Science Foundation of China [grant numbers 82071621and 81901458].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Key R\u0026amp;D Program of China (No.2022YFC2704002), the Major Program of Shandong Provincial Natural Science Foundation [ZR2021ZD34] and the National Natural Science Foundation of China [grant numbers 82071621and 81901458].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eOur study was approved by the Medical Ethics Committee of Medical Integration and Practice Center, Cheeloo College of Medicine of Shandong University, number SDULCLL2022-1-21.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003eAll authors agree to publish this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003ePublicly available datasets were analyzed in this study. This data can be found here: The data of this study were downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eCodes generated or used during the study are available from the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChapron C, Marcellin L, Borghese B, Santulli P. Rethinking mechanisms, diagnosis and management of endometriosis. Nat Rev Endocrinol. 2019;15(11):666-682. 10.1038/s41574-019-0245-z.\u003c/li\u003e\n\u003cli\u003eParasar P, Ozcan P, Terry KL. Endometriosis: Epidemiology, Diagnosis and Clinical Management. Curr Obstet Gynecol Rep. 2017;6(1):34-41. 10.1007/s13669-017-0187-1.\u003c/li\u003e\n\u003cli\u003eDai Y, Zhang JJ, Lang JH, Zhou YF, Guo HY, Zhang XM, et al. [A convenience sampling questionnaire survey of the current status of diagnosis and treatment of endometriosis in China in 2018]. 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Ectopic endometriotic stromal cells-derived lactate induces M2 macrophage polarization via Mettl3/Trib1/ERK/STAT3 signalling pathway in endometriosis. Immunology. 2022. 10.1111/imm.13574.\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":"[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, random forest, m6A methylation, diagnostic model, immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-2742276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2742276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis is an inflammatory disease with non-specific symptoms, including chronic pelvic pain and infertility, which affects thousands of women of reproductive age. Early diagnosis of endometriosis remains challenging. We aimed to build a diagnostic model based on m6A methylation-related genes to provide a new perspective on the clinical diagnosis of endometriosis.\u003cstrong\u003e \u003c/strong\u003eTwo datasets from previous endometriosis studies were selected. GSE51981 was for training and GSE7305 was for validation. The expression of m6A methylation-related genes between proliferative eutopic endometrium from women with and without endometriosis was compared. Most m6A methylation-related genes were down-regulated in eutopic endometrium from women with endometriosis than those without it. The random forest classifier identified 5 significant differentially expressed genes (YTHDF2, NKAP, FTO, ZCCHC4 and HNRNPC) that might be involved in the development of endometriosis by affecting miRNA maturation or immune cell infiltration. These genes were included in a logistic regression to construct a new diagnostic model for endometriosis with an area under the ROC curve of 0.852. The model was tested on another independent dataset（AUC 0.750）and not only diagnosed endometriosis well but also showed how severe it was. We also found that YTHDF2 was very good at diagnosing endometriosis on its own and was correlated with macrophage and neutrophil infiltration that may be important for endometriosis development. In conclusion, this novel diagnostic model using m6A methylation-related genes may be a new method for early non-invasive diagnosis of endometriosis.\u003c/p\u003e","manuscriptTitle":"Profiling N6-methyladenosine (m6A) methylation-related genes in endometriosis towards a diagnostic model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-03 15:20:17","doi":"10.21203/rs.3.rs-2742276/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":"601d8f87-7d23-425e-ba73-a10aa61fb4b6","owner":[],"postedDate":"May 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-14T23:31:22+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-03 15:20:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2742276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2742276","identity":"rs-2742276","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}