{"paper_id":"46944417-fe19-4a06-b1bd-df5a67859544","body_text":"Cross-Talk Between n6-Methyladenosine and Their Related RNAs Defined a Signature and Confirmed m6A Regulators for Diagnosis of 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 Cross-Talk Between n6-Methyladenosine and Their Related RNAs Defined a Signature and Confirmed m6A Regulators for Diagnosis of Endometriosis Xiaotong Wang, Xibo Zhao, Han Wu, Jing Wang, Yan Cheng, Qiuyan Guo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2266490/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 Background An RNA modification known as n6-methyladenosine (m6A) interacts with a range of coding and non-coding RNAs. The majority of research focused on identifying m6A regulators that are differentially expressed in endometriosis but ignored their mechanisms which derived from the alterations of modifications among RNAs, affecting the disease progression primarily. Here, we aimed to investigate the potential roles of m6A regulators in the diagnostic potency, immune microenvironment, and clinicopathological features in endometriosis through interacting genes. Results A thorough investigation of the m6A modification patterns in the GEO database was carried out, based on mRNAs and lncRNAs related to these m6A regulators. Two molecular subtypes were identified with different infiltration levels of immune microenvironment cells and clinical features using unsupervised clustering analysis. We identified two m6A regulators, named METTL3 and YTHDF2, as diagnostic targets of endometriosis following the usage of overlapping genes to construct a diagnostic m6A signature of endometriosis. Finally, we found that m6A alterations might be one of the important reasons for the progression of endometriosis, especially with significant down-expressions of METTL3 and YTHDF2. Conclusion M6A modification patterns play significant effects on the diversity and complexity of the progression and immune microenvironment and might be key diagnostic markers for endometriosis. endometriosis m6A regulators network diagnosis immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Endometriosis(EMs) is a common condition from which women suffer [ 1 – 2 ]. Typically, it is described as the presence of functional endometrial tissues implanted in areas other than the uterine body, such as the ovaries, peritonea, and deep infiltrations. Although categorized as benign, EMs exhibits numerous biological behaviors similar to malignancies, including the invasion of adjacent tissues and the induction of tissue remodeling[ 3 – 4 ]. The concept of \"EMs-associated infertility\" was set up years back [ 5 ], emphasizing that occurrence among EMs patients with infertility was significantly higher than that of the non-disease population, while some connections between EMs and infertility have been proved, as EMs possibly causing infertility or spontaneous abortion by interfering with several pregnancy-related processes, and vice versa [ 6 ]. Since the early signs of EMs are always non-typical, research based on the etiology and early diagnostic indicators of EMs has aroused hot spots in recent years. Epigenetics often refers to DNA methylations, histone modifications, and non-coding RNA-mediated regulations of widespread regulatory mechanisms that modify the biological phenotype without affecting DNA sequences[ 7 , 8 ]. Histone alterations and DNA methylations have proved the linkage to both the pathophysiology and progression of EMs recently [ 9 ]. Post-transcriptional RNA modifications include N6-methyladenosine (m6A), cytosine hydroxylation (m5C), and N1-methyladenosine (m1A) [ 10 ], where m6A is known as the methylation of adenosine (A) at the sixth N position. The m6A methylation process could be catalyzed and stimulated by \"writers\", such as METTL3, METTL14, and WTAP, and could be ceased by \"erasers\", such as ALKBH5 and FTO, resulting in a dynamic and reversible modification, while \"readers\", such as the YTHDF family and YTHDC family, recognize and bind the m6A modification sites in RNAs further. Numerous studies have revealed that m6A alterations play different regulatory functions to a great extent in various types of malignancies and autoimmune or infectious diseases through their involvement in cell proliferation[ 11 ], resistance to chemotherapy and radiotherapy[ 12 ], as well as the immune response[ 13 ]. However, less research has been referred on the function of RNA methylation in EMs, yet known as a chronic inflammatory disease. To completely elucidate the regulatory network of m6A regulators impacted in EMs, urgent needs are required to understand the cross-talk changes between m6A regulators and these interacting genes. Modifications of m6As by lncRNAs and mRNAs could contribute to the formation of crucial and intricate networks of cellular modulations. Information on these networks might provide essential insights into prospective mechanisms of EMs development and present novel therapeutic options for EMs. In the present study, genomic alterations were explored in healthy and EMs samples from the Gene Expression Omnibus (GEO) dataset for a comprehensive assessment of m6A-associated RNAs. Two distinct molecular isoforms that could be used to predict clinicopathological characteristics and the activities of the immune microenvironment were identified. A diagnostic risk model was further developed for EMs patients by integrating the m6A regulators associated with lncRNAs and mRNAs. These results suggest that m6A regulators might be important diagnostic markers and provide new insights into potential mechanisms during the development of EMs. Materials And Methods 1. Data pre-processing The data used in this study were obtained from the GEO database under the series ID GSE141549, followed by GSE86534 and GSE105764, for subsequent validation. The quantile method was used to normalize the EMs-related data of GSE141549, in which samples were grouped into the endometrium, EMs, and peritoneum lesions, followed by annotating gene symbols with gene types obtained from GENCODE(version 38). The expression values with duplicate gene symbols were calculated as arithmetic means. Analysis of similarities was applied for three extracted groups, based on permutation test and rank sum test to determine whether the differences among groups are wider than those within groups, thus verifying notable groupings, and p < 0.05 was considered as significant sampling units. 2. Landscape Of Alteration In M6a Regulators In Ems The m6A regulators investigated in this study were derived from previous findings[ 14 – 15 ]. A landscape of the expression and correlation of 23 m6A regulators, containing 8 writers, 13 readers, and 2 erasers, were assessed in EMs lesions, in-situ endometrium, and normal endometrium, while the expressed differences of these regulators were compared comprehensively. The protein-protein interaction(PPI) network of m6A regulators was obtained from the STRING database ( https://string-db.org/ ). 3. Alteration Of Differentially Expressed Mrnas Associated With M6a Regulators Biological databases with a wide variety of human protein interaction networks are emerging as a result of ongoing research on the roles of human proteins. Here, an aggregation of five databases, which provide a more comprehensive view of human-protein interactions verified by different essays and experimental methods, named HPRD (Human Protein Reference Database, http://hprd.org/index_html ), BIND(the Biomolecular Interaction Network Database, http://bind.ca/ ), MINT (the Molecular INteraction database, http://mint.bio.uniroma2.it/mint/ ), IntAct (IntAct Molecular Interaction Database, http://www.ebi.ac.uk/intact/index.html ) and DIP (the Database of Interacting Proteins, http://dip.doe-mbi.ucla.edu/ ), were applied. Based on these background networks, the nearest neighbor networks comprised of m6A regulators and the mRNAs that were confirmed to interact with m6As(m6A_PPI) were filtered out, of which differential expression was extracted as the key m6A-related mRNAs in EMs. Pearson correlation analysis was performed on these key mRNAs and m6As, and each mRNA-m6A pair with an absolute correlated coefficient value > 0.3 and p < 0.05 were selected to construct the mRNA and m6A co-expression network(MACN), and the m6A nodes overlapping with m6A_PPI were labeled as mRNA_related_m6As. Moreover, the functional roles of related mRNA were integrated as enriched terms or pathways. 4. Consensus Clustering Analysis And Immune Microenvironment Characteristics In Ems Firstly, based on the expression of the m6A_PPI screened, an unsupervised clustering method was performed to identify heterogeneous patterns of mRNA modifications, conducted by the ConsensusClusterPlus package on all EMs samples, while the number of clusters was evaluated through iterations to ensure the robust classification. The determined optimal number of clusters was based on a cumulative distribution function, and variations of clinical characteristics across subtypes were assessed further. Then, the relationship between unsupervised classification and infiltrated immune cells was explored using samples with empirical CIBERSORT p-value < 0.05. Additionally, the scores of different subtypes were assessed using an R package estimate to evaluate the abundance of immune or stromal infiltration in tissues, as well as estimate scores tested by the Wilcoxon-rank sum test. 5. Identification Of M6a-related Lncrnas Using the reference genome GRCh38, which contains 17,944 lncRNAs, an lncRNA expression matrix for EMs was constructed, and all lncRNAs were examined for differential expression among different conditions, with FDR < 0.05 considered as a significant threshold. Next, a co-expression network (lncRNAs and m6As co-expression network, LACN) composed of differentially expressed lncRNAs in the previous step was created based on Pearson correlation analysis, setting criteria as the absolute correlated coefficient value greater than 0.3 and p < 0.05. An edge could be joined up from a significant lncRNA to m6A pair thus a network was cross-linked. The key lncRNA-associated m6As(labeled lncRNA_related_m6As) for regulating EMs with a more remarkable score than the mean of all node scores were chosen using the PageRank method based on the calculation of the igraph package. 6. Establishment And Validation Of M6a Diagnostic Model For Ems For the lncRNA-related m6As and mRNA-related m6As explored above, their common parts were extracted as training parameters. The least absolute shrinkage and selection operator(LASSO) regression-based method was used by ten-fold cross-validation for continuous shrinkage among input variables and the selected features were considered as diagnostic indicators for patients with EMs using the glmnet package, which were further confirmed with the most significant impact on EMs prediction by receiver operating characteristic(ROC) curves. A stepwise method with a ‘both’ mode was then performed and verified by testing multicollinearity for a more compact model. The rms package was used to perform and validate the model and the effect of these key m6As was visualized by the nomogram plot. And then, the diagnostic model was validated using independent GSE86534 and GSE105764 cohorts. 7. Clinical Sample Collection Patients with ovarian EMs who underwent surgery at the Second Affiliated Hospital of Harbin Medical University from July 2021 to July 2022 were enrolled, all classified as stage III-IV according to the revised American Fertility Society (AFS-r). The eutopic endometrium tissues(EU) and ovarian endometriosis tissues(EC) were collected in a total of 12 cases, of which the paired specimens were exactly matched. Besides, 12 cases of normal control endometrium tissues(NM) diagnosed as cervical lesions were collected. Specimens in all 3 groups were detected as the proliferative phase in the menstrual cycle by postoperative pathology, excluding hormone treatment for nearly six months. All tissues were verified by two independent experienced histopathologists. 8. Primary Cell Extraction The tissue specimens were rinsed with saline and twice with PBS, then cut into paste and transferred to a culture dish with collagenase type IV(1 mg/ml). The culture dish was placed in a 37°C incubator for 2 h with gentle shaking, and then the tissue debris and other cells, such as endometrial epithelial cells, were removed with a 40-mm sieve and the filter placed in a centrifuge for 10 min at 1000 r/min, the supernatant was aspirated to obtain cell precipitates. The cells were resuspended by adding complete DMEM/F12 of culture medium to the centrifuge tube and transferred to culture flasks. 9. Reverse Transcription And Qrt-pcr Total RNA from ovarian EMs of patients and endometrial stromal cells was extracted by TRIzol reagent (Ambion, USA) and converted to complementary DNA by a PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). qRT-PCR was performed with a TB Green® Premix Ex Taq™ (Takara, Japan). The settings were as follows: 40 cycles of 15 min at 37°C, 5 s at 60°C, and 30 s at 72°C. All relative mRNA expression levels were analyzed using the 2 −ΔΔCt method. The primers used are listed in Table S1. 10. M6a Quantity Assay The m6A relative levels were measured by an m6A RNA Methylation Quantification Kit(Colorimetric) (Epigentek, USA). RNA was extracted using the TRIzol method as mentioned before and added into the 96 well plate as the manufacturers's instructions. Following the instructions, RNAs were well-bonded to strip at 37℃ for 90 min with binding solution. After adding capture and detect solution, the m6A levels were read at a wavelength of 450 nm. The data were calculated using relative quantification with three repeat wells obtained from each reaction. 11. Western Blotting 11. Western blotting The harvested cells were washed with cold PBS and then lysed with RIPA buffer which added PMSF on ice for 30 min. The lysate was centrifuged at 12,000 rpm for 10 min at 4°C, and the supernatant was collected. Total protein concentration was determined using a BCA protein assay kit (meilunbio). A moderate amount of protein (20 µg) of each sample was separated by SDS-PAGE and transferred to PVDF membranes. After being closed with fast closure solution for half an hour, membranes were incubated with anti-METTL3 (1:2000, Abcam), YTHDF2 (1:5000, Proteintech), and GAPDH (1:5000, Proteintech) at 4°C overnight. The membranes were then washed 3 times with TBST and incubated with horseradish peroxidase (HRP)-labeled goat anti-rabbit secondary antibody (1:5000, Bioss) for 1 h at room temperature. The results of the strips were observed using the Enhanced Chemiluminescence Detection Kit (Meilunbio, China). 12. Statistical Analysis Comparisons were analyzed using the Wilcoxon rank sum test for two groups or the Kruskal-Wallis test for more than two groups in bioinformatics research. The false discovery rate(FDR) correction method was applied to the p-values in differentially expressed analysis. Here, log 2 FC > 0 was considered an upregulated gene as described previously, while log 2 FC < 0 was a down-regulated one[ 16 – 17 ]. All experiments were repeated three times or more and all statistical and visualization work by GraphPad Prism 9.0 and SPSS software. The results were represented using the mean ± standard deviation. Comparison of all experimental results between two groups was performed by Student t-test and one-way Avona among three or more groups. The difference was considered significant at p < 0.05(ns, p ≥ 0.05;*, p < 0.05༛ **, p < 0.01༛ ***, p < 0.001, ****, p < 0.0001). Results 1. Summaries of data in silico The entire workflow was displayed in Fig. 1 . After removing the non-compliant samples, the data which contained 43 normal endometrial samples, 104 eutopic endometrial samples, and 198 EMs samples termed ectopic ones including peritoneal endometriosis lesions, deep infiltrating endometriosis lesions, sacrouterine ligament lesions, rectovaginal lesions, and ovarian endometrioma, was reserved. 345 samples with detailed distribution such as the menstrual cycle phase were shown in Table 1 . Table 1 Clinical characteristics in GSE141549 (n = 345). Ectopic Eutopic Normal N = 198 N = 104 N = 43 Cycle phase: medication 96 (48.5%) 43 (41.3%) 10 (23.3%) menstruation 10 (5.05%) 7 (6.73%) 0 (0.00%) proliferative 29 (14.6%) 17 (16.3%) 7 (16.3%) secretory 41 (20.7%) 27 (26.0%) 17 (39.5%) unknown 22 (11.1%) 10 (9.62%) 9 (20.9%) Tissue: Deep infiltrating endometriosis lesion 42 (21.2%) 0 (0.00%) 0 (0.00%) Endometrium 0 (0.00%) 104 (100%) 43 (100%) Ovarian endometrioma 28 (14.1%) 0 (0.00%) 0 (0.00%) Peritoneal endometriosis lesion 79 (39.9%) 0 (0.00%) 0 (0.00%) Rectovaginal lesion 22 (11.1%) 0 (0.00%) 0 (0.00%) Sacrouterine ligament lesion 27 (13.6%) 0 (0.00%) 0 (0.00%) Stage: 1 20 (10.1%) 15 (14.4%) 0 (0.00%) 2 26 (13.1%) 14 (13.5%) 0 (0.00%) 3 47 (23.7%) 22 (21.2%) 1 (2.33%) 4 101 (51.0%) 52 (50.0%) 0 (0.00%) Healthy 0 (0.00%) 0 (0.00%) 42 (97.7%) unknown 4 (2.02%) 1 (0.96%) 0 (0.00%) Age: <35 135 (68.2%) 68 (65.4%) 5 (11.6%) >=35 63 (31.8%) 36 (34.6%) 38 (88.4%) 2. The Landscape Of Expression And Diversity Of M6a Regulators Among Healthy And Disease Samples To determine whether m6A alterations are relevant to endometriosis, we conducted the differentially expressed analysis of gene expression in all samples. A total of 11 m6A regulators with altered patterns were identified from the landscape of expression among different sample groups(Figs. 2 A, B), including 6 readers, 4 writers, and 1 eraser. YTHDF2 and HNRNPA2B1 stood out among the readers as possessing the most significant alteration, and both had reduced expressions considerably in the ectopic lesions. When compared to the other two writers, METTL3 and METTL16 have higher expressions with more notable significance than METTL14 and ZC3H13. In erasers, FTO showed a significantly increased expression trend, whereas ALKBH5 didn`t. We also discovered that in the ectopic group, erasers had higher expression while writers had significantly downregulated expression compared to the normal group, indicating that the downregulation of m6As may be a key factor in the development of EMs. The regulatory interactions of these m6A regulators could manifest as an intricate PPI network(Fig. 2 C), suggesting multiple cross-links in EMs. Additionally, the close transcriptome correlations among writers, readers, and erasers were investigated in EC, EU, and NM groups, meaning multiple effects were altered in EMs(Fig. 2 D). 3. The M6as And Their Relative Mrnas Recognized Patterns Of Co-alteration In Ems 410 mRNAs interacting with 19 m6As were discovered as one-step neighbors in 5 public databases(Fig. 3 A), with 374 mRNAs included here for analyzing their expressed alteration separately. Three gene groups were obtained, that is, 31 mRNAs in the eutopic vs normal group(Fig. 3 B), 239 mRNAs in the ectopic vs normal group(Fig. 3 C), and 255 mRNAs in the ectopic vs eutopic group(Fig. 3 D), with each FDR < 0.05. SCG2, CTSG, and GPC3 possessed the highest modified extent in either 3 compared groups. The intersection of these three groups was 25 mRNAs(Fig. 3 E), meaning a co-alteration of development in EMs. Based on the expression levels of all the m6As and the mRNAs(Figs. 3 F), Pearson correlation coefficients for each mRNA-m6A pair were calculated to construct MACN, and the higher the absolute values, the darker the edges would be(Figs. 3 G). Finally, a co-expressed network in the NM group was comprised of 20 m6As and 21 mRNAs, of which 19 m6As and 24 mRNAs, 19 m6As and 22 mRNAs in the EU and the EC group respectively. 4. Consensus Clustering Analysis For M6a-related Mrnas Unveiled The Heterogeneity In Ems To characterize the influence of m6As-related mRNAs in the MACN on the development of EMs, we performed unsupervised k-means clustering analysis and calculated the Euclidean distances based on the expression levels of m6A-related mRNAs. The value of k = 2 was assessed as the most appropriate number of clusters for further analysis according to the delta area plot and matrix heatmap and then referred them as cluster1 and cluster2 respectively(Figs. 4 A-B, Supplementary Fig. 1, Supplementary Fig. 2). Further investigations of these two subtypes revealed different clinicopathological characteristics and expression patterns of patients in EMs. As shown(Fig. 4 C), cluster2 pointed to a younger age (p-value < 0.05) and a more diverse endothelial ectopic pathology type (p-value < 0.05) with a higher stage (p-value < 0.05) compared to cluster1. However, no significance was shown in the cycle phase, which seems hormone factors might not be dominant over others such as genetic alterations. In conclusion, the clustered subtypes could provide a more comprehensive opinion that unveiled a significant association with the heterogeneity of EMs. 5. Characteristics Of The Immune Microenvironment In Ems Subtypes As emerging immunological evidence participated in EMs, we deconvoluted the mRNA profiles and the roles of the immune microenvironment were investigated, typically the relationship between these two EMs subtypes and infiltrating immune cell subpopulations. These results showed different categories of infiltrated immune cells notably between two subtypes, where total lymphocytes and total dendritic cells were proportionally upregulated in cluster1(Supplementary Fig. 3), leaving opposite trends shown in macrophages and mast cells. Amongst, the proportion of B cells memory, plasma cells, T cells CD4 memory resting, T cells CD4 memory activated, gamma delta T cells, T regulatory cells, NK cells resting, NK cells activated, monocytes, M1 macrophages, M2 macrophages, and resting mast cells are significantly higher in cluster2 than cluster1(Fig. 4 D), underlying the intricate mechanisms forming EMs lesions that could be aroused for more intensive investigations. In addition, three scores(stromal score, immune score, and estimate score) for both subtypes were evaluated, all indicating higher scores in cluster2(Fig. 4 E), representing a higher relative content of stromal cells or immune cells in the immune microenvironment, which might promote heterogeneity and accelerate the progression in EMs by stimulating immunologic reaction and restoring the anatomical relations. 6. The M6as And Their Relative Lncrnas Recognized Patterns Of Co-alteration In Ems Next, 767 lncRNAs were screened from the reference genome for differentially expressed analysis, and 33 lncRNAs with FDR < 0.05 were obtained in the eutopic vs normal group(Fig. 5 A), as well as 170 lncRNAs in the ectopic vs normal group(Fig. 5 B), and 186 lncRNAs in the ectopic vs eutopic group(Fig. 5 C). Linc02381 possessed the largest fold change in both the ectopic-normal and ectopic-eutopic groups, while linc00578 was downregulated significantly in the eutopic group. 28 overlapped lncRNAs were selected and differentially expressed in these three groups(Figs. 5 D-E), and Pearson correlation analysis was performed based on the expression levels of lncRNA-m6A pairs, forming the LACN that satisfied the threshold, shown as described before(Fig. 5 F). Finally, 9 m6As and 9 lncRNAs co-expressed were involved in the NM group, then 17 m6As and 22 lncRNAs in the EU group, 17 m6As and 20 lncRNAs in the EC group. Furthermore, the random walk algorithm was applied to determine the key m6As and the mean score of all nodes were 0.00877193, 0.005347594, and 0.007042254 in NM, EU, and EC groups respectively(Fig. 5 G). Different nodes found in each group meant dynamic changes among m6A regulators in EMs. Also, METTL3, as a vital regulator, scored more remarkable in all three groups, hence might be a potential indicator in EMs. 7. Construction And Validation Of An M6a-related Diagnostic Signature To explore the diagnostic efficacy of m6A regulators in EMs, key m6A regulators from MACN and key m6A regulators from LACN were obtained, and an intersection part with differentially expressed analysis respectively was extracted, leaving 14 key m6A regulators(Fig. 6 A, Supplementary Fig. 4A). These m6As were integrated based on LASSO binomial analysis with 10-fold cross-validation(Fig. 6 B). A total of 11 m6A regulators with non-zero coefficients were screened, namely HNRNPA2B1, METTL3, ZC3H13, RBM15, ELAVL1, LRPPRC, YTHDC2, YTHDF2, FTO, YTHDC1, YTHDF1, with the minimum lambda value being 0.007609331(Fig. 6 C, Supplementary Fig. 4B). Subsequently, multivariate logistic regression was applied including all genes from LASSO results in the model. The ROC value showed a precise performance in predicting the efficacy of EMs(Fig. 6 D), indicating the vital roles of these m6A regulators in the progression of EMs. Aiming for a more concise model, the stepwise regression method was further chosen for screening variables, remaining METTL3, ELAVL1, LRPPRC, YTHDC2, YTHDF2, YTHDC1, and FTO, followed by confirming each value of multicollinearity less than 5(Fig. 6 E, Supplementary Fig. 4C). The AUC value could remain at 0.900. The model was further validated using two independent cohorts additionally, and AUC could remain at 0.875 and 0.984, representing an excellent generalization(Fig. 6 F). Then, the predicted accuracies of seven m6A regulators were evaluated separately, and the results suggested that METTL3 had the highest AUC value among all writers and YTHDF2 had the highest AUC value among all readers(Fig. 6 G, Supplementary Figs. 4C-H), and also, METTL3 was highly correlated with many other regulators in EC group, as METTL3 and YTHDC2 being the most relevant regulators(Fig. 6 H, Supplementary Fig. 4I). Finally, a nomogram was constructed for risk assessment(Fig. 6 I). The results showed that YTHDF2 possessed the highest risk weight followed by METTL3, suggesting that the METTL3-m6A-mRNA/lncRNA-YTHDF2 axis might play a vital role in the progression of EMs. 8. Enrichment Analysis Of Mettl3-related Modification Patterns To investigate the biological responses in the METTL3-m6A modification pattern, the GO and KEGG pathways were explored(Fig. 7 A). Besides, using the Hallmark in MSigDB as the gene background, the activation status of biological pathways was assessed by GSEA(Fig. 7 B, Supplementary Table 2). The results pointed out that several classical immune pathways, such as the TNF-α signaling via NF-kB, inflammatory response, and the IL6-JAK-STAT3 signaling-mediated passages were significantly enriched, suggesting METTL3-related modification could facilitate the immune regulation. The results on the graph show that genes co-expressed with METTL3 are significantly enriched in the signature E2F target, the marker G2M checkpoint, and the MYC targets, and these could speculate that METTL3 might play a role in regulating the cell cycle which affects the proliferation of EMs cells significantly. 9. The Experimental Validation Of M6a Modification In Ems After confirming the key m6As, we first employed an m6A quantitative assay to detect the m6A alteration experimentally in EMs. We observed that the quantity of m6A modification was increased significantly in NESCs while the least in EESCs(Fig. 8 A). The qRT-PCR experiment revealed that EESCs and EUSCs had considerably lower METTL3 and YTHDF2 mRNA levels than NESCs(Fig. 8 B). Additionally, the results of WB experiments revealed similar results, as the difference in METTL3 expression was the most remarkable among them(Figs. 8 C-D). These findings indicate that METTL3 and YTHDF2 are possibly crucial factors for the formation of EMs. Discussion EMs is a benign condition with a high incidence in women of reproductive age [ 18 ]. Current approaches have limitations for clinical feature-based diagnosis due to the insidious phenotype of EMs. Based on extensive research on post-transcriptional modifications, researchers are gradually recognizing the benefits of constructing epigenetic diagnostic models of disease and have confirmed the potential impact of m6A methylation which is emerging as the most common epigenetic modification and related to the occurrence and progression of the majority of cancers and other diseases[ 19 ]. The majority of m6A regulators were found to have altered expression in the current study, with \"writers\" decreased while \"erasers\" upregulated, indicating that the loss of m6A modification might be a potential contributor in EMs. Previous research has demonstrated the reverse ability between m6A \"writers\" and \"erasers\" on modifications in lncRNAs/mRNAs. M6A \"readers\", furthermore, recognize and bind to methylated lncRNAs/mRNAs and perform diverse functions. As Liu et al. revealed[ 20 ], the YTHDF1 complex with YTHDF2 can specifically recognize m6A modifications and thus regulate the stability of lncRNA THOR, impacting the proliferation, migration, and invasion of cancer cells. Moreover, the METTL3-Snail-YTHDF1 axis can promote metastasis in malignant tumors with modification of EMT-related mRNAs mediated by m6As, which leads to progression[ 21 ]. Although these convinced epigenetic research, little is understood about the function of m6A methylation in EMs. M6A regulators seldom function independently; instead, control diseases by interacting genes. Hence, it is crucial to investigate the potential alterations of m6A-interacting genes. Through Pearson analysis, we discovered 25 m6A-regulated mRNAs. Recent studies have demonstrated the impact of SCG2 on the clinical stage, and the influence of macrophage polarization to affect immunotherapy in colorectal cancer[ 22 , 23 ]. GPC3[ 24 ] has also been proven to be essential in the immune response, yet no reference has been reported in EMs. The alterations of m6A-related lncRNAs have been described in depth recently, with abnormal expression discovered typically in EMs. Huang et al. observed significant postoperative level of lncRNA-UCA1 was reduced, suggesting that it may function as a diagnostic and prognostic biomarker for EMs[ 25 ]. ALKBH5 acts as a modification switch of lncRNA SOX2OT and participates in the lncRNA-mediated competitive endogenous RNA model to enhance the molecular stability and exploit the function of SOX2OT, thus affecting the progression and drug resistance in glioma[ 26 ]. Here, considering the modification of non-coding RNAs by m6As, we analyzed lncRNAs expressed differentially among EC-EU, EU-NM, and EC-NM groups, in which LINC02381 and LINC00578 show the largest log 2 FC values. Concurring with our study, an aberrant expression of LINC02381 was confirmed in EMs previously[ 27 ], which identified a ceRNA network and verified 28 differentially expressed lncRNAs by analysis of RNA-seq data from EMs, followed by RT-qPCR results confirming that LINC02381 was significantly overexpressed in EMs tissues. The immune microenvironment plays an important part in EMs[ 28 ], especially immune cell infiltration and immune dysfunction involved in the progression of EMs, as depicted by Wang et al[ 29 ]. Significant diversities were investigated between the two subtypes here in the ratio of 22 immune cells and immune or stromal characteristics. It has been elucidated that infiltrations of T lymphocytes, B lymphocytes, and NK cells are reduced in EMs lesions[ 30 – 32 ]. Besides, previous studies suggest that ectopic endometrial tissue may hold an immunological surveillance function, leading to the formation of chronic inflammation, while a higher stromal score or immune score represents a more stromal or immune component relative in the immune milieu, facilitating inflammation as well as pelvic adhesions, and the estimated score indicates aggregation of stromal score or immune score in the immune microenvironment, all of which are consistent with our finding. To determine the critical m6A regulators, LASSO regression was used followed by further filtration by stepwise regression to screen the variables for a diagnostic model, and finally, 7 key m6A regulators were obtained, named METTL3, ELAVL1, LRPPRC, YTHDC2, YTHDF2, YTHDC1, and FTO. Besides, independent datasets were involved for external validation making the diagnostic model more robust. A cross-directional analysis revealed the AUC was highest for METTL3 in \"writers\", YTHDF2 in \"readers\", and FTO in \"erasers\" separately, meaning remarkable diagnostic efficiency. Consistent with our findings, previous research indicates that METTL3 promotes pre-miR126 maturation via m6A alteration[ 33 ], promoting the migration and invasion of endometrial stromal cells in EMs[ 34 ]. However, YTHDF2 has not been examined in EMs. Finally, we verified experimentally the aberrant down-expression of METTL3 and YTHDF2 according to gene and protein levels and found that both m6A regulators posed the highest risk of disease in the ectopic group. The possible enriched downstream targets of METTL3 obtained by enrichment analysis were E2F targets, G2M checkpoint, and MYC targets. It has been detected that METTL3 activates the G2M checkpoint of the cell cycle through CDC25B mediated by m6A modification, leading to malignant progression in neck squamous cell carcinoma[ 35 ]. Also, METTL3 could enhance the stability of c-MYC through YTHDF1-mediated m6A modification and promote tumorigenesis in oral squamous cell carcinoma[ 36 ]. The expression of oxidative phosphorylation-related gene program and reduction of immune-dependent cell cycle progression are indirectly regulated by METTL3 and YTHDF2 respectively[ 37 ], enhancing the high accuracy of the current findings. Conclusion In conclusion, with the investigating of the m6A modification patterns in Endometriosis, we explored the mRNAs and lncRNAs related to m6A regulators by the GEO database. Two molecular subtypes were identified with different infiltration levels of immune microenvironment cells which related to the clinical features. We constructed a diagnostic m6A signature of endometriosis, and detected METTL3 and YTHDF2 might be the key m6A targets of EMs by experiment. Still, the limited mechanism of METTL3-m6A-YTHDF2 in endometriosis is studied in this paper, and subsequent experiments are needed to verify our research results deeply, such as Me-rip sequencing to yield the most quantification accuracy and insightful mechanisms from plural prospectives, which is undergoing tests based on these key m6A regulators obtained here. Our analysis results might be relatively single, however, make new views for the diagnosis and treatment of EMs in the future. Declarations Acknowledgements We would like to thank for the Future Medical Laboratory of Harbin Medical University. (Harbin, China) Authors’ contributions GM Zhang and XT Wang designed the study, XT Wang and XB Zhao analysis and interpretation of data, XT Wang and H Wu wrote the manuscript, XT Wang, J Wang,Y Cheng and T Liang performed the experiments. GM Zhang and QY Guo supervised the project. All the authors approved the fnal version of manuscript. Funding This research was supported by the National Natural Science Foundation of China (81971359), the Natural Science Foundation of Hei Longjiang Province(LH2019H027), and Key research and development projects of Heilongjiang Province(GA21C008). Ethics approval All the procedures in this study were audited and confrmed by the Ethic Committee of the Second Afliated Hospital of Harbin Medical University (KY2022-155). And all experiments conducted complied with relative rules and regulations of the committees. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests References Gordts Stephan, Koninckx Philippe, Brosens Ivo, Pathogenesis of deep EMs.[J].Fertil Steril, 2017, 108: 872-885.e1. Taylor Hugh S, Kotlyar Alexander M, Flores Valerie A, EMs is a chronic systemic disease: clinical challenges and novel innovations.[J]. Lancet, 2021, 397: 839-852. bulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, Kohlmeier A, Yin P, Milad M, Wei J. EMs. Endocr Rev. 2019; 40:1048-79 . Gruber Teresa Mira,Mechsner Sylvia,Pathogenesis of EMs: The Origin of Pain and Subfertility.[J].Cells, 2021, 10: undefined. Gupta Sajal, Goldberg Jeffrey M, Aziz Nabil, et al. Pathogenic mechanisms in EMs-associated infertility.[J].Fertil Steril, 2008, 90: 247 -57. Pirtea Paul, Vulliemoz Nicolas, de Ziegler Dominique, et al. Infertility workup: identifying EMs.[J].Fertil Steril, 2022, 118: 29-33. Esfandiari Fereshteh, Favaedi Raha, Heidari-Khoei Heidar, et al. Insight into epigenetics of human EMs organoids: DNA methylation analysis of HOX genes and their cofactors.[J].Fertil Steril, 2021, 115: 125-137. Dyson Matthew T,Roqueiro Damian,Monsivais Diana et al. Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor expression in EMs.[J].PLoS Genet, 2014, 10: e1004158. Retis-Resendiz Alejandra Monserrat,González-García Ixchel Nayeli,León-Juárez Moisés et al. The role of epigenetic mechanisms in the regulation of gene expression in the cyclical endometrium.[J].Clin Epigenetics, 2021, 13: 116. Looking Back: Epigenomics.[J].Cell Stem Cell, 2017, 20: 755. Chen SL, Liu LL, Wang CH, et al. Loss of RDM1 enhances hepatocellular carcinoma progression via p53 and Ras/Raf/ERK pathways. Mol Oncol, 2019, 14:373-86. Taketo Kosuke, Konno Masamitsu, Asai Ayumu, et al. The epitranscriptome m6A writer METTL3 promotes chemo- and radioresistance in pancreatic cancer cells.[J]. International Journal of Oncology, 2018, 52(2):621-629. Yue Ben, Song Chenlong, Yang Linxi, et al. METTL3-mediated Deng Shuang,Zhang Jialiang,Su Jiachun et al. RNA mA regulates transcription via DNA demethylation and chromatin accessibility.[J] .Nat Genet, 2022, 54: 1427-1437. Livneh Ido,Moshitch-Moshkovitz Sharon,Amariglio Ninette et al. The mA epitranscriptome: transcriptome plasticity in brain development and function.[J] .Nat Rev Neurosci, 2020, 21: 36-51. Bruzas Simona,Gluz Oleg,Harbeck Nadia et al. Gene signatures in patients with early breast cancer and relapse despite pathologic complete response.[J] .NPJ Breast Cancer, 2022, 8: 42. Gadewal Nikhil,Kumar Rohit,Aher Swapnil et al. SMC1AmiRNA-mRNA Profiling Reveals Prognostic Impact of Expression in Acute Myeloid Leukemia.[J] .Oncol Res, 2020, 28: 321-330. Wang Yeh, Nicholes Kristen, Shih Ie-Ming, The Origin and Pathogenesis of EMs.[J].Annu Rev Pathol, 2020, 15: 71-95. Du Kunzhao, Zhang Longbin, Lee Trevor et al. mA RNA Methylation Controls Neural Development and Is Involved in Human Diseases.[J].Mol Neurobiol, 2019, 56: 1596-1606. Liu Hongmei, Xu Yuxin, Yao Bing et al. A novel N6-methyladenosine (m6A)-dependent fate decision for the lncRNA THOR.[J].Cell Death Dis, 2020, 11: 613. Lin Xinyao, Chai Guoshi, Wu Yingmin, et al. RNA mA methylation regulates the epithelial-mesenchymal transition of cancer cells and translation of Snail.[J].Nat Commun, 2019, 10: 2065. Szukiewicz Dariusz, Epigenetic regulation and T-cell responses in EMs - something other than autoimmunity.[J].Front Immunol, 2022, 13: 943839. Weng Siyuan, Liu Zaoqu, Ren Xiaofeng et al. SCG2: A Prognostic Marker That Pinpoints Chemotherapy and Immunotherapy in Colorectal Cancer.[J].Front Immunol, 2022, 13: 873871. Wang Hao, Yin Jinwen, Hong Yuntian, et al. SCG2 is a Prognostic Biomarker Associated With Immune Infiltration and Macrophage Polarization in Colorectal Cancer.[J].Front Cell Dev Biol, 2021, 9: 795133. Huang Huan, Zhu Zhengyan, Song Yu, Downregulation of lncRNA uca1 as a diagnostic and prognostic biomarker for ovarian EMs.[J].Rev Assoc Med Bras (1992), 2019, 65: 336-341. Liu Boyang, Zhou Jian, Wang Chenyang, et al. LncRNA SOX2OT promotes temozolomide resistance by elevating SOX2 expression via ALKBH5-mediated epigenetic regulation in glioblastoma.[J].Cell Death Dis, 2020, 11: 384. Yin Meichen, Zhai Lingyun, Wang Jianzhang, et al. Comprehensive Analysis of RNA-Seq in EMs Reveals Competing Endogenous RNA Network Composed of circRNA, lncRNA and mRNA.[J].Front Genet, 2022, 13: 828238. Cheung Peggie, Schaffert Steven, Chang Sarah E, et al. Repression of CTSG, ELANE and PRTN3-mediated histone H3 proteolytic cleavage promotes monocyte- to-macrophage differentiation.[J].Nat Immunol, 2021, 22: 711-722. N6-methyladenosine modification is critical for epithelial-mesenchymal transition and metastasis of gastric cancer.[J]. Molecular Cancer, 2019, 18(1):142. Khan Khaleque N, Yamamoto Kazuo, Fujishita Akira, et al. Differential Levels of Regulatory T Cells and T-Helper-17 Cells in Women With Early and Advanced EMs.[J] .J Clin Endocrinol Metab, 2019, 104: 4715-4729. Riccio L G C, Jeljeli M, Santulli P, et al. B lymphocyte inactivation by Ibrutinib limits EMs progression in mice.[J].Hum Reprod, 2019, 34: 1225-1234. Freitag Nadine, Baston-Buest Dunja M, Kruessel Jan-Steffen, et al. Eutopic endometrial immune profile of infertility-patients with and without EMs.[J] .J Reprod Immunol, 2022, 150: 103489. Li Xiaoou, Xiong Wenqian, Long Xuefeng, et al. Inhibition of METTL3/m6A/miR126 promotes the migration and invasion of endometrial stromal cells in EMs†. [J].Biol Reprod, 2021, 105: 1221-1233. Wang Han, Liang Zongwen, Gou Yanling, et al. FTO-dependent N(6)-Methyladenosine regulates the progression of EMs via the ATG5/PKM2 Axis. [J].Cell Signal, 2022, 98: 110406. Guo Yu-Qing, Wang Qiang, Wang Jun-Guo, et al. METTL3 modulates m6A modification of CDC25B and promotes head and neck squamous cell carcinoma malignant Progression.[J].Exp Hematol Oncol, 2022, 11: 14. Zhao Wei, Cui Yameng, Liu Lina, et al. METTL3 Facilitates Oral Squamous Cell Carcinoma Tumorigenesis by Enhancing c-Myc Stability via YTHDF1- Mediated mA Modification.[J].Mol Ther Nucleic Acids, 2020, 20: 1-12. Grenov Amalie C,Moss Lihee,Edelheit Sarit et al. The germinal center reaction depends on RNA methylation and divergent functions of specific methyl readers.[J]. J Exp Med, 2021, 218: undefined. Additional Declarations No competing interests reported. Supplementary Files FIGURES1.pdf Supplementary Figure 1Consensus clustering analysis showed additional consensus matrix and tracking plot from k = 3 to k =10. FIGURES2.pdf Supplementary Figure 2Consensus clustering analysis showed all consensus k values FIGURES3.pdf Supplementary Figure 3The composition of total lymphocytes, total dendritic cell, total macrophage, and total mast cell between two consensus subtypes tested by Wilcoxon rank sum analysis. FIGURES4.pdf Supplementary Figure 4(A)The sankey plot showed interactions among 14 key m6A regulators(middle column) and lncRNAs(left column) and mRNAs(right column). Width of each alluvial stripes represents pairwise Pearson's correlation. (B)Extracting parameters using LASSO. (C)The DCA plot of m6A diagnostic model in GSE141549. The roc plots for FTO(D), LRPPRC(E), YTHDC2(F), YTHDC1(G), and ELAVL1(H). (I)The mutual relationship among the composition of the diagnostic model in circos plot in eutopic samples. LASSO, least absolute shrinkage and selection operator. ROC, receiver operating characteristic; AUC, area under curve. Supplementarytable1.pdf Supplementary Table 1 Primers sequences Table supplementarytable2.xlsx Supplementary Table2 The entire results of GSEA. 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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-2266490\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":151830229,\"identity\":\"1c8c7038-d1d6-4780-89ca-eb41b6ff2284\",\"order_by\":0,\"name\":\"Xiaotong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Afliated Hospital of Harbin Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaotong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":151830230,\"identity\":\"0520c7d9-13d9-466b-9347-e1f6f0f2176c\",\"order_by\":1,\"name\":\"Xibo 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study.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/711c976cc7ba87610680fc6b.png\"},{\"id\":29154984,\"identity\":\"e718a973-1646-463a-b0b7-8a08cb9c5a25\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:57:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":869219,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe transcriptome expression status of all m6A regulators between normal， eutopic, and ectopic samples by heatmap (\\u003cstrong\\u003eA\\u003c/strong\\u003e) and boxplot (\\u003cstrong\\u003eB\\u003c/strong\\u003e). (\\u003cstrong\\u003eC\\u003c/strong\\u003e)The protein-protein interactions among differential expressed m6A regulators. Three frames from top to bottom represent erasers, writers, and readers, respectively. (\\u003cstrong\\u003eD\\u003c/strong\\u003e)The correlation analysis of m6A regulators in normal(top), eutopic(median), and ectopic(bottom) groups. ns， p≥0.05；*， p\\u0026lt;0.05； **，p\\u0026lt;0.01； ***，p\\u0026lt;0.001， ****，p\\u0026lt;0.0001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/fa36a199c9e8e3274e16230b.png\"},{\"id\":29154630,\"identity\":\"a818e307-1277-4add-ac4b-6f7b09544a6b\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1139154,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnalysis of m6A regulators-related mRNAs. (\\u003cstrong\\u003eA\\u003c/strong\\u003e)A comprehensive network was extracted from m6A regulators and their nearest neighbor mRNAs in five experimentally validated databases. (\\u003cstrong\\u003eB-D\\u003c/strong\\u003e)The volcano plots showed significant variations in these related mRNAs in eutopic vs normal, ectopic vs normal, and ectopic vs eutopic groups. The overlapping mRNAs among 3 differential results were exhibited in the venn plot(\\u003cstrong\\u003eE\\u003c/strong\\u003e), and their expression in the heatmap(\\u003cstrong\\u003eF\\u003c/strong\\u003e). (\\u003cstrong\\u003eG\\u003c/strong\\u003e)A robust MACN consisted of m6A regulators with interacting mRNAs in normal, eutopic and ectopic groups. Red lines represent positive correlations between mRNA and m6A pairs, while blue lines represent negative correlations. A darker edge means a higher coefficient value.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE3N.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/de6d57eeb61fde75fa2eb192.png\"},{\"id\":29154631,\"identity\":\"9b9a1abf-605d-4ac7-a8be-abfd063e918f\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":589408,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeterogeneity patterns and immune characteristics of m6A-related mRNAs in EMs. (\\u003cstrong\\u003eA, B\\u003c/strong\\u003e)According to the clustergram and delta area plot, the best clustering number was set up to 2. (\\u003cstrong\\u003eC\\u003c/strong\\u003e)Differences in clinicopathologic features and the expression levels of m6A-related mRNAs between the two distinct clusters. (\\u003cstrong\\u003eD\\u003c/strong\\u003e)The infiltration levels of 22 immune cells in the two clusters. (\\u003cstrong\\u003eE\\u003c/strong\\u003e)Different scores(stromal score, immune score, and estimate score) distinguished the configuration and function evidently from clusters. ***，p\\u0026lt;0.001， ****，p\\u0026lt;0.0001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/dbb0383adab85e9873abb599.png\"},{\"id\":29155430,\"identity\":\"119d2361-b5eb-40c1-9ac9-a2ccf566808c\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 20:05:22\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":998159,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAnalysis of m6A regulators-related lncRNAs. (\\u003cstrong\\u003eA-C\\u003c/strong\\u003e) The volcano plots showed significant variations in these related lncRNAs in eutopic vs normal, ectopic vs normal, and ectopic vs eutopic groups. The overlapping lncRNAs among 3 differential results were exhibited in the venn plot(\\u003cstrong\\u003eD\\u003c/strong\\u003e), and their expression in the heatmap(\\u003cstrong\\u003eE\\u003c/strong\\u003e). (\\u003cstrong\\u003eF\\u003c/strong\\u003e)A robust LACN consisted of m6A regulators with interacting lncRNAs in normal, eutopic and ectopic groups. Red lines represent positive correlations between lncRNA and m6A pairs, while blue lines represent negative correlations. A darker edge means a higher coefficient value. (\\u003cstrong\\u003eG\\u003c/strong\\u003e)Random walk algorithm determined key nodes in normal, eutopic and ectopic groups respectively using Rose Charts.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/c7a23dcc0c85b38d307be921.png\"},{\"id\":29154632,\"identity\":\"67548441-2e3f-4d1b-92d5-dad7ab4bb162\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":443156,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEstablishment and validation of m6A diagnostic model. (\\u003cstrong\\u003eA\\u003c/strong\\u003e)The key m6A regulators overlapping in two networks were extracted for model training. 10-fold cross-validation for shrinking parameters(\\u003cstrong\\u003eB\\u003c/strong\\u003e) and extracting non-zero coefficients(\\u003cstrong\\u003eC\\u003c/strong\\u003e). (\\u003cstrong\\u003eD\\u003c/strong\\u003e)A diagnostic model was constructed by 14 m6As for EMs. (\\u003cstrong\\u003eE\\u003c/strong\\u003e)A concise model was further constructed by 7 m6As with AUC remaing high. (\\u003cstrong\\u003eF\\u003c/strong\\u003e)The roc plots for validation of the diagnostic model using GSE86534 and GSE105764. (\\u003cstrong\\u003eG\\u003c/strong\\u003e)The roc plots for METTL3 and YTHDF2, with the most prognostic potentialities among writers and readers, respectively. (\\u003cstrong\\u003eH\\u003c/strong\\u003e)The mutual relationship among the composition of the diagnostic model in circos plot in ectopic samples. (\\u003cstrong\\u003eI\\u003c/strong\\u003e)The nomogram for risk assessment in EMs. ROC, receiver operating characteristic; AUC, area under curve.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/c62738d09923066d12b856f5.png\"},{\"id\":29154985,\"identity\":\"73119f07-8bec-4b64-a2f6-f74dd446611c\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:57:22\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1112304,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe biological function of the modification patterns of METTL3. (\\u003cstrong\\u003eA\\u003c/strong\\u003e)Top results of enrichment analysis. The gene ontology(GO) terms were under three categories, named biological processes(BP), cell components(CC), molecular functions(MF). KEGG, Kyoto Encyclopedia of Genes and Genomes KEGG pathways. (\\u003cstrong\\u003eB\\u003c/strong\\u003e)The top 3 of significantly positive and negative HALLMARK terms and normalized enrichment scores(NES) in the METTL3-m6A modification pattern in GSEA. FDR, false discovery rate., GSEA, gene set enrichment analysis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/bc803d5de122b0f149e51e98.png\"},{\"id\":29154635,\"identity\":\"eb773eec-a13b-4458-80a6-d26ab6f17f4a\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":170900,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eExperimental validation of key m6A regulators in tissues. (\\u003cstrong\\u003eA\\u003c/strong\\u003e)Each m6A levels detected by m6A quantity assay in EC, EU and NC groups. (\\u003cstrong\\u003eB\\u003c/strong\\u003e)The mRNA expression levels of METTL3 and YTHDF2 by qRT-PCR in EC, EU and NC groups. (\\u003cstrong\\u003eC, D\\u003c/strong\\u003e)The protein expression levels of METTL3 and YTHDF2 by WB in EC, EU and NC groups. These results were presented as the mean ± SDs. *， p\\u0026lt;0.05； **，p\\u0026lt;0.01； ***，p\\u0026lt;0.001， ****，p\\u0026lt;0.0001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURE8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/83d5885fa44cb9d869897998.png\"},{\"id\":30227596,\"identity\":\"d1a0e75f-9f4f-4c4f-9289-933081e7e779\",\"added_by\":\"auto\",\"created_at\":\"2022-12-12 20:59:28\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4649387,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/ec551b2e-6d22-4aed-822b-d2fa633ba126.pdf\"},{\"id\":29154643,\"identity\":\"490f290b-7a23-400a-8dba-3677f45077be\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:23\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":8227102,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Figure 1\\u003c/strong\\u003eConsensus clustering analysis showed additional consensus matrix and tracking plot from k = 3 to k =10.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURES1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/cf0733b4d1ea9ca72aa0690e.pdf\"},{\"id\":29154634,\"identity\":\"42ad9218-e170-47b0-91a9-58d4998fc5ea\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"pdf\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1596776,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Figure 2\\u003c/strong\\u003eConsensus clustering analysis showed all consensus k values\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURES2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/e233ffdf18f6410e739901a4.pdf\"},{\"id\":29154638,\"identity\":\"6852e38a-e8b4-4972-8251-88715003a055\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"pdf\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":145554,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Figure 3\\u003c/strong\\u003eThe composition of total lymphocytes, total dendritic cell, total macrophage, and total mast cell between two consensus subtypes tested by Wilcoxon rank sum analysis.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURES3.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/d9b872ef32d69213bebe283f.pdf\"},{\"id\":29154640,\"identity\":\"4f188c75-aca2-4f9e-8b4d-c3a482dff824\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"pdf\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":943712,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Figure 4\\u003c/strong\\u003e(\\u003cstrong\\u003eA\\u003c/strong\\u003e)The sankey plot showed interactions among 14 key m6A regulators(middle column) and lncRNAs(left column) and mRNAs(right column). Width of each alluvial stripes represents pairwise Pearson's correlation. (\\u003cstrong\\u003eB\\u003c/strong\\u003e)Extracting parameters using LASSO. (\\u003cstrong\\u003eC\\u003c/strong\\u003e)The DCA plot of m6A diagnostic model in GSE141549. The roc plots for FTO(\\u003cstrong\\u003eD\\u003c/strong\\u003e), LRPPRC(\\u003cstrong\\u003eE\\u003c/strong\\u003e), YTHDC2(\\u003cstrong\\u003eF\\u003c/strong\\u003e), YTHDC1(\\u003cstrong\\u003eG\\u003c/strong\\u003e), and ELAVL1(\\u003cstrong\\u003eH\\u003c/strong\\u003e). (I)The mutual relationship among the composition of the diagnostic model in circos plot in eutopic samples. LASSO, least absolute shrinkage and selection operator. ROC, receiver operating characteristic; AUC, area under curve.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"FIGURES4.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/e40b424867c85da8e3ad2e83.pdf\"},{\"id\":29154641,\"identity\":\"97bf74b9-4166-4be5-b82f-6e337b14403f\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:49:22\",\"extension\":\"pdf\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":49806,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Table 1 Primers sequences Table\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarytable1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/ef59e1a98a5a241b42eb680f.pdf\"},{\"id\":29154982,\"identity\":\"bb7a17f4-a4eb-4130-ae92-65a740d8bf99\",\"added_by\":\"auto\",\"created_at\":\"2022-11-16 19:57:22\",\"extension\":\"xlsx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17091,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Table2 \\u003c/strong\\u003e\\u0026nbsp;The entire results of GSEA.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"supplementarytable2.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2266490/v1/030940146c0b261b78767d27.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Cross-Talk Between n6-Methyladenosine and Their Related RNAs Defined a Signature and Confirmed m6A Regulators for Diagnosis of Endometriosis\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eEndometriosis(EMs) is a common condition from which women suffer [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Typically, it is described as the presence of functional endometrial tissues implanted in areas other than the uterine body, such as the ovaries, peritonea, and deep infiltrations. Although categorized as benign, EMs exhibits numerous biological behaviors similar to malignancies, including the invasion of adjacent tissues and the induction of tissue remodeling[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. The concept of \\\"EMs-associated infertility\\\" was set up years back [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], emphasizing that occurrence among EMs patients with infertility was significantly higher than that of the non-disease population, while some connections between EMs and infertility have been proved, as EMs possibly causing infertility or spontaneous abortion by interfering with several pregnancy-related processes, and vice versa [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Since the early signs of EMs are always non-typical, research based on the etiology and early diagnostic indicators of EMs has aroused hot spots in recent years.\\u003c/p\\u003e \\u003cp\\u003eEpigenetics often refers to DNA methylations, histone modifications, and non-coding RNA-mediated regulations of widespread regulatory mechanisms that modify the biological phenotype without affecting DNA sequences[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Histone alterations and DNA methylations have proved the linkage to both the pathophysiology and progression of EMs recently [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Post-transcriptional RNA modifications include N6-methyladenosine (m6A), cytosine hydroxylation (m5C), and N1-methyladenosine (m1A) [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], where m6A is known as the methylation of adenosine (A) at the sixth N position. The m6A methylation process could be catalyzed and stimulated by \\\"writers\\\", such as METTL3, METTL14, and WTAP, and could be ceased by \\\"erasers\\\", such as ALKBH5 and FTO, resulting in a dynamic and reversible modification, while \\\"readers\\\", such as the YTHDF family and YTHDC family, recognize and bind the m6A modification sites in RNAs further. Numerous studies have revealed that m6A alterations play different regulatory functions to a great extent in various types of malignancies and autoimmune or infectious diseases through their involvement in cell proliferation[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], resistance to chemotherapy and radiotherapy[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], as well as the immune response[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. However, less research has been referred on the function of RNA methylation in EMs, yet known as a chronic inflammatory disease.\\u003c/p\\u003e \\u003cp\\u003eTo completely elucidate the regulatory network of m6A regulators impacted in EMs, urgent needs are required to understand the cross-talk changes between m6A regulators and these interacting genes. Modifications of m6As by lncRNAs and mRNAs could contribute to the formation of crucial and intricate networks of cellular modulations. Information on these networks might provide essential insights into prospective mechanisms of EMs development and present novel therapeutic options for EMs. In the present study, genomic alterations were explored in healthy and EMs samples from the Gene Expression Omnibus (GEO) dataset for a comprehensive assessment of m6A-associated RNAs. Two distinct molecular isoforms that could be used to predict clinicopathological characteristics and the activities of the immune microenvironment were identified. A diagnostic risk model was further developed for EMs patients by integrating the m6A regulators associated with lncRNAs and mRNAs. These results suggest that m6A regulators might be important diagnostic markers and provide new insights into potential mechanisms during the development of EMs.\\u003c/p\\u003e\"},{\"header\":\"Materials And Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1. Data pre-processing\\u003c/h2\\u003e \\u003cp\\u003eThe data used in this study were obtained from the GEO database under the series ID GSE141549, followed by GSE86534 and GSE105764, for subsequent validation. The quantile method was used to normalize the EMs-related data of GSE141549, in which samples were grouped into the endometrium, EMs, and peritoneum lesions, followed by annotating gene symbols with gene types obtained from GENCODE(version 38). The expression values with duplicate gene symbols were calculated as arithmetic means. Analysis of similarities was applied for three extracted groups, based on permutation test and rank sum test to determine whether the differences among groups are wider than those within groups, thus verifying notable groupings, and p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered as significant sampling units.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e2. Landscape Of Alteration In M6a Regulators In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eThe m6A regulators investigated in this study were derived from previous findings[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. A landscape of the expression and correlation of 23 m6A regulators, containing 8 writers, 13 readers, and 2 erasers, were assessed in EMs lesions, in-situ endometrium, and normal endometrium, while the expressed differences of these regulators were compared comprehensively. The protein-protein interaction(PPI) network of m6A regulators was obtained from the STRING database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://string-db.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://string-db.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003e3. Alteration Of Differentially Expressed Mrnas Associated With M6a Regulators\\u003c/h3\\u003e\\n\\u003cp\\u003eBiological databases with a wide variety of human protein interaction networks are emerging as a result of ongoing research on the roles of human proteins. Here, an aggregation of five databases, which provide a more comprehensive view of human-protein interactions verified by different essays and experimental methods, named HPRD (Human Protein Reference Database, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://hprd.org/index_html\\u003c/span\\u003e\\u003cspan address=\\\"http://hprd.org/index_html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), BIND(the Biomolecular Interaction Network Database, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://bind.ca/\\u003c/span\\u003e\\u003cspan address=\\\"http://bind.ca/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), MINT (the Molecular INteraction database, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://mint.bio.uniroma2.it/mint/\\u003c/span\\u003e\\u003cspan address=\\\"http://mint.bio.uniroma2.it/mint/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), IntAct (IntAct Molecular Interaction Database, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.ebi.ac.uk/intact/index.html\\u003c/span\\u003e\\u003cspan address=\\\"http://www.ebi.ac.uk/intact/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and DIP (the Database of Interacting Proteins, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://dip.doe-mbi.ucla.edu/\\u003c/span\\u003e\\u003cspan address=\\\"http://dip.doe-mbi.ucla.edu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), were applied. Based on these background networks, the nearest neighbor networks comprised of m6A regulators and the mRNAs that were confirmed to interact with m6As(m6A_PPI) were filtered out, of which differential expression was extracted as the key m6A-related mRNAs in EMs. Pearson correlation analysis was performed on these key mRNAs and m6As, and each mRNA-m6A pair with an absolute correlated coefficient value\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.3 and p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were selected to construct the mRNA and m6A co-expression network(MACN), and the m6A nodes overlapping with m6A_PPI were labeled as mRNA_related_m6As. Moreover, the functional roles of related mRNA were integrated as enriched terms or pathways.\\u003c/p\\u003e\\n\\u003ch3\\u003e4. Consensus Clustering Analysis And Immune Microenvironment Characteristics In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eFirstly, based on the expression of the m6A_PPI screened, an unsupervised clustering method was performed to identify heterogeneous patterns of mRNA modifications, conducted by the ConsensusClusterPlus package on all EMs samples, while the number of clusters was evaluated through iterations to ensure the robust classification. The determined optimal number of clusters was based on a cumulative distribution function, and variations of clinical characteristics across subtypes were assessed further. Then, the relationship between unsupervised classification and infiltrated immune cells was explored using samples with empirical CIBERSORT p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. Additionally, the scores of different subtypes were assessed using an R package estimate to evaluate the abundance of immune or stromal infiltration in tissues, as well as estimate scores tested by the Wilcoxon-rank sum test.\\u003c/p\\u003e\\n\\u003ch3\\u003e5. Identification Of M6a-related Lncrnas\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing the reference genome GRCh38, which contains 17,944 lncRNAs, an lncRNA expression matrix for EMs was constructed, and all lncRNAs were examined for differential expression among different conditions, with FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered as a significant threshold. Next, a co-expression network (lncRNAs and m6As co-expression network, LACN) composed of differentially expressed lncRNAs in the previous step was created based on Pearson correlation analysis, setting criteria as the absolute correlated coefficient value greater than 0.3 and p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. An edge could be joined up from a significant lncRNA to m6A pair thus a network was cross-linked. The key lncRNA-associated m6As(labeled lncRNA_related_m6As) for regulating EMs with a more remarkable score than the mean of all node scores were chosen using the PageRank method based on the calculation of the igraph package.\\u003c/p\\u003e\\n\\u003ch3\\u003e6. Establishment And Validation Of M6a Diagnostic Model For Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eFor the lncRNA-related m6As and mRNA-related m6As explored above, their common parts were extracted as training parameters. The least absolute shrinkage and selection operator(LASSO) regression-based method was used by ten-fold cross-validation for continuous shrinkage among input variables and the selected features were considered as diagnostic indicators for patients with EMs using the glmnet package, which were further confirmed with the most significant impact on EMs prediction by receiver operating characteristic(ROC) curves. A stepwise method with a \\u0026lsquo;both\\u0026rsquo; mode was then performed and verified by testing multicollinearity for a more compact model. The rms package was used to perform and validate the model and the effect of these key m6As was visualized by the nomogram plot. And then, the diagnostic model was validated using independent GSE86534 and GSE105764 cohorts.\\u003c/p\\u003e\\n\\u003ch3\\u003e7. Clinical Sample Collection\\u003c/h3\\u003e\\n\\u003cp\\u003e Patients with ovarian EMs who underwent surgery at the Second Affiliated Hospital of Harbin Medical University from July 2021 to July 2022 were enrolled, all classified as stage III-IV according to the revised American Fertility Society (AFS-r). The eutopic endometrium tissues(EU) and ovarian endometriosis tissues(EC) were collected in a total of 12 cases, of which the paired specimens were exactly matched. Besides, 12 cases of normal control endometrium tissues(NM) diagnosed as cervical lesions were collected. Specimens in all 3 groups were detected as the proliferative phase in the menstrual cycle by postoperative pathology, excluding hormone treatment for nearly six months. All tissues were verified by two independent experienced histopathologists.\\u003c/p\\u003e\\n\\u003ch3\\u003e8. Primary Cell Extraction\\u003c/h3\\u003e\\n\\u003cp\\u003eThe tissue specimens were rinsed with saline and twice with PBS, then cut into paste and transferred to a culture dish with collagenase type IV(1 mg/ml). The culture dish was placed in a 37\\u0026deg;C incubator for 2 h with gentle shaking, and then the tissue debris and other cells, such as endometrial epithelial cells, were removed with a 40-mm sieve and the filter placed in a centrifuge for 10 min at 1000 r/min, the supernatant was aspirated to obtain cell precipitates. The cells were resuspended by adding complete DMEM/F12 of culture medium to the centrifuge tube and transferred to culture flasks.\\u003c/p\\u003e\\n\\u003ch3\\u003e9. Reverse Transcription And Qrt-pcr\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA from ovarian EMs of patients and endometrial stromal cells was extracted by TRIzol reagent (Ambion, USA) and converted to complementary DNA by a PrimeScript\\u0026trade; RT reagent Kit with gDNA Eraser (Takara, Japan). qRT-PCR was performed with a TB Green\\u0026reg; Premix Ex Taq\\u0026trade; (Takara, Japan). The settings were as follows: 40 cycles of 15 min at 37\\u0026deg;C, 5 s at 60\\u0026deg;C, and 30 s at 72\\u0026deg;C. All relative mRNA expression levels were analyzed using the 2\\u003csup\\u003e\\u0026minus;ΔΔCt\\u003c/sup\\u003e method.\\u003c/p\\u003e \\u003cp\\u003eThe primers used are listed in Table S1.\\u003c/p\\u003e\\n\\u003ch3\\u003e10. M6a Quantity Assay\\u003c/h3\\u003e\\n\\u003cp\\u003eThe m6A relative levels were measured by an m6A RNA Methylation Quantification Kit(Colorimetric) (Epigentek, USA). RNA was extracted using the TRIzol method as mentioned before and added into the 96 well plate as the manufacturers's instructions. Following the instructions, RNAs were well-bonded to strip at 37℃ for 90 min with binding solution. After adding capture and detect solution, the m6A levels were read at a wavelength of 450 nm. The data were calculated using relative quantification with three repeat wells obtained from each reaction.\\u003c/p\\u003e\\n\\u003ch3\\u003e11. Western Blotting\\u003c/h3\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003e11. Western blotting\\u003c/div\\u003e \\u003cp\\u003eThe harvested cells were washed with cold PBS and then lysed with RIPA buffer which added PMSF on ice for 30 min. The lysate was centrifuged at 12,000 rpm for 10 min at 4\\u0026deg;C, and the supernatant was collected. Total protein concentration was determined using a BCA protein assay kit (meilunbio). A moderate amount of protein (20 \\u0026micro;g) of each sample was separated by SDS-PAGE and transferred to PVDF membranes. After being closed with fast closure solution for half an hour, membranes were incubated with anti-METTL3 (1:2000, Abcam), YTHDF2 (1:5000, Proteintech), and GAPDH (1:5000, Proteintech) at 4\\u0026deg;C overnight. The membranes were then washed 3 times with TBST and incubated with horseradish peroxidase (HRP)-labeled goat anti-rabbit secondary antibody (1:5000, Bioss) for 1 h at room temperature. The results of the strips were observed using the Enhanced Chemiluminescence Detection Kit (Meilunbio, China).\\u003c/p\\u003e\\n\\u003ch3\\u003e12. Statistical Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eComparisons were analyzed using the Wilcoxon rank sum test for two groups or the Kruskal-Wallis test for more than two groups in bioinformatics research. The false discovery rate(FDR) correction method was applied to the p-values in differentially expressed analysis. Here, log\\u003csub\\u003e2\\u003c/sub\\u003eFC\\u0026thinsp;\\u0026gt;\\u0026thinsp;0 was considered an upregulated gene as described previously, while log\\u003csub\\u003e2\\u003c/sub\\u003eFC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0 was a down-regulated one[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. All experiments were repeated three times or more and all statistical and visualization work by GraphPad Prism 9.0 and SPSS software. The results were represented using the mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation. Comparison of all experimental results between two groups was performed by Student t-test and one-way Avona among three or more groups. The difference was considered significant at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05(ns, p\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.05;*, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05༛ **, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01༛ ***, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, ****, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1. Summaries of data in silico\\u003c/h2\\u003e \\u003cp\\u003eThe entire workflow was displayed in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. After removing the non-compliant samples, the data which contained 43 normal endometrial samples, 104 eutopic endometrial samples, and 198 EMs samples termed ectopic ones including peritoneal endometriosis lesions, deep infiltrating endometriosis lesions, sacrouterine ligament lesions, rectovaginal lesions, and ovarian endometrioma, was reserved. 345 samples with detailed distribution such as the menstrual cycle phase were shown 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\\u003eClinical characteristics in GSE141549 (n\\u0026thinsp;=\\u0026thinsp;345).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\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\\u003eEctopic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEutopic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNormal\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eN\\u0026thinsp;=\\u0026thinsp;198\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eN\\u0026thinsp;=\\u0026thinsp;104\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cspan type=\\\"BoldItalic\\\" class=\\\"BoldItalic\\\" name=\\\"Emphasis\\\"\\u003eN\\u0026thinsp;=\\u0026thinsp;43\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCycle phase:\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emedication\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e96 (48.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e43 (41.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 (23.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emenstruation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10 (5.05%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 (6.73%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eproliferative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29 (14.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17 (16.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 (16.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esecretory\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41 (20.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27 (26.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 (39.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (9.62%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 (20.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTissue:\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeep infiltrating endometriosis lesion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42 (21.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEndometrium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e43 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOvarian endometrioma\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28 (14.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeritoneal endometriosis lesion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79 (39.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRectovaginal lesion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e22 (11.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSacrouterine ligament lesion\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27 (13.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage:\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20 (10.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (14.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47 (23.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22 (21.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (2.33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e101 (51.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52 (50.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHealthy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e42 (97.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eunknown\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 (2.02%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 (0.96%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge:\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e135 (68.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e68 (65.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5 (11.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;=35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e63 (31.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36 (34.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38 (88.4%)\\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\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e2. The Landscape Of Expression And Diversity Of M6a Regulators Among Healthy And Disease Samples\\u003c/h3\\u003e\\n\\u003cp\\u003eTo determine whether m6A alterations are relevant to endometriosis, we conducted the differentially expressed analysis of gene expression in all samples. A total of 11 m6A regulators with altered patterns were identified from the landscape of expression among different sample groups(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, B), including 6 readers, 4 writers, and 1 eraser. YTHDF2 and HNRNPA2B1 stood out among the readers as possessing the most significant alteration, and both had reduced expressions considerably in the ectopic lesions. When compared to the other two writers, METTL3 and METTL16 have higher expressions with more notable significance than METTL14 and ZC3H13. In erasers, FTO showed a significantly increased expression trend, whereas ALKBH5 didn`t. We also discovered that in the ectopic group, erasers had higher expression while writers had significantly downregulated expression compared to the normal group, indicating that the downregulation of m6As may be a key factor in the development of EMs. The regulatory interactions of these m6A regulators could manifest as an intricate PPI network(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC), suggesting multiple cross-links in EMs. Additionally, the close transcriptome correlations among writers, readers, and erasers were investigated in EC, EU, and NM groups, meaning multiple effects were altered in EMs(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3. The M6as And Their Relative Mrnas Recognized Patterns Of Co-alteration In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003e410 mRNAs interacting with 19 m6As were discovered as one-step neighbors in 5 public databases(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA), with 374 mRNAs included here for analyzing their expressed alteration separately. Three gene groups were obtained, that is, 31 mRNAs in the eutopic vs normal group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), 239 mRNAs in the ectopic vs normal group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC), and 255 mRNAs in the ectopic vs eutopic group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD), with each FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. SCG2, CTSG, and GPC3 possessed the highest modified extent in either 3 compared groups. The intersection of these three groups was 25 mRNAs(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE), meaning a co-alteration of development in EMs. Based on the expression levels of all the m6As and the mRNAs(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF), Pearson correlation coefficients for each mRNA-m6A pair were calculated to construct MACN, and the higher the absolute values, the darker the edges would be(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG). Finally, a co-expressed network in the NM group was comprised of 20 m6As and 21 mRNAs, of which 19 m6As and 24 mRNAs, 19 m6As and 22 mRNAs in the EU and the EC group respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e4. Consensus Clustering Analysis For M6a-related Mrnas Unveiled The Heterogeneity In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eTo characterize the influence of m6As-related mRNAs in the MACN on the development of EMs, we performed unsupervised k-means clustering analysis and calculated the Euclidean distances based on the expression levels of m6A-related mRNAs. The value of k\\u0026thinsp;=\\u0026thinsp;2 was assessed as the most appropriate number of clusters for further analysis according to the delta area plot and matrix heatmap and then referred them as cluster1 and cluster2 respectively(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-B, Supplementary Fig.\\u0026nbsp;1, Supplementary Fig.\\u0026nbsp;2). Further investigations of these two subtypes revealed different clinicopathological characteristics and expression patterns of patients in EMs. As shown(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC), cluster2 pointed to a younger age (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) and a more diverse endothelial ectopic pathology type (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) with a higher stage (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) compared to cluster1. However, no significance was shown in the cycle phase, which seems hormone factors might not be dominant over others such as genetic alterations. In conclusion, the clustered subtypes could provide a more comprehensive opinion that unveiled a significant association with the heterogeneity of EMs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e5. Characteristics Of The Immune Microenvironment In Ems Subtypes\\u003c/h3\\u003e\\n\\u003cp\\u003eAs emerging immunological evidence participated in EMs, we deconvoluted the mRNA profiles and the roles of the immune microenvironment were investigated, typically the relationship between these two EMs subtypes and infiltrating immune cell subpopulations. These results showed different categories of infiltrated immune cells notably between two subtypes, where total lymphocytes and total dendritic cells were proportionally upregulated in cluster1(Supplementary Fig.\\u0026nbsp;3), leaving opposite trends shown in macrophages and mast cells. Amongst, the proportion of B cells memory, plasma cells, T cells CD4 memory resting, T cells CD4 memory activated, gamma delta T cells, T regulatory cells, NK cells resting, NK cells activated, monocytes, M1 macrophages, M2 macrophages, and resting mast cells are significantly higher in cluster2 than cluster1(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD), underlying the intricate mechanisms forming EMs lesions that could be aroused for more intensive investigations. In addition, three scores(stromal score, immune score, and estimate score) for both subtypes were evaluated, all indicating higher scores in cluster2(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eE), representing a higher relative content of stromal cells or immune cells in the immune microenvironment, which might promote heterogeneity and accelerate the progression in EMs by stimulating immunologic reaction and restoring the anatomical relations.\\u003c/p\\u003e\\n\\u003ch3\\u003e6. The M6as And Their Relative Lncrnas Recognized Patterns Of Co-alteration In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eNext, 767 lncRNAs were screened from the reference genome for differentially expressed analysis, and 33 lncRNAs with FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were obtained in the eutopic vs normal group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA), as well as 170 lncRNAs in the ectopic vs normal group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB), and 186 lncRNAs in the ectopic vs eutopic group(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eC). Linc02381 possessed the largest fold change in both the ectopic-normal and ectopic-eutopic groups, while linc00578 was downregulated significantly in the eutopic group. 28 overlapped lncRNAs were selected and differentially expressed in these three groups(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD-E), and Pearson correlation analysis was performed based on the expression levels of lncRNA-m6A pairs, forming the LACN that satisfied the threshold, shown as described before(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eF). Finally, 9 m6As and 9 lncRNAs co-expressed were involved in the NM group, then 17 m6As and 22 lncRNAs in the EU group, 17 m6As and 20 lncRNAs in the EC group. Furthermore, the random walk algorithm was applied to determine the key m6As and the mean score of all nodes were 0.00877193, 0.005347594, and 0.007042254 in NM, EU, and EC groups respectively(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eG). Different nodes found in each group meant dynamic changes among m6A regulators in EMs. Also, METTL3, as a vital regulator, scored more remarkable in all three groups, hence might be a potential indicator in EMs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e7. Construction And Validation Of An M6a-related Diagnostic Signature\\u003c/h3\\u003e\\n\\u003cp\\u003eTo explore the diagnostic efficacy of m6A regulators in EMs, key m6A regulators from MACN and key m6A regulators from LACN were obtained, and an intersection part with differentially expressed analysis respectively was extracted, leaving 14 key m6A regulators(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA, Supplementary Fig.\\u0026nbsp;4A). These m6As were integrated based on LASSO binomial analysis with 10-fold cross-validation(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). A total of 11 m6A regulators with non-zero coefficients were screened, namely HNRNPA2B1, METTL3, ZC3H13, RBM15, ELAVL1, LRPPRC, YTHDC2, YTHDF2, FTO, YTHDC1, YTHDF1, with the minimum lambda value being 0.007609331(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC, Supplementary Fig.\\u0026nbsp;4B). Subsequently, multivariate logistic regression was applied including all genes from LASSO results in the model. The ROC value showed a precise performance in predicting the efficacy of EMs(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD), indicating the vital roles of these m6A regulators in the progression of EMs. Aiming for a more concise model, the stepwise regression method was further chosen for screening variables, remaining METTL3, ELAVL1, LRPPRC, YTHDC2, YTHDF2, YTHDC1, and FTO, followed by confirming each value of multicollinearity less than 5(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE, Supplementary Fig.\\u0026nbsp;4C). The AUC value could remain at 0.900. The model was further validated using two independent cohorts additionally, and AUC could remain at 0.875 and 0.984, representing an excellent generalization(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eF). Then, the predicted accuracies of seven m6A regulators were evaluated separately, and the results suggested that METTL3 had the highest AUC value among all writers and YTHDF2 had the highest AUC value among all readers(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eG, Supplementary Figs.\\u0026nbsp;4C-H), and also, METTL3 was highly correlated with many other regulators in EC group, as METTL3 and YTHDC2 being the most relevant regulators(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eH, Supplementary Fig.\\u0026nbsp;4I). Finally, a nomogram was constructed for risk assessment(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eI). The results showed that YTHDF2 possessed the highest risk weight followed by METTL3, suggesting that the METTL3-m6A-mRNA/lncRNA-YTHDF2 axis might play a vital role in the progression of EMs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e8. Enrichment Analysis Of Mettl3-related Modification Patterns\\u003c/h3\\u003e\\n\\u003cp\\u003eTo investigate the biological responses in the METTL3-m6A modification pattern, the GO and KEGG pathways were explored(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA). Besides, using the Hallmark in MSigDB as the gene background, the activation status of biological pathways was assessed by GSEA(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eB, Supplementary Table\\u0026nbsp;2). The results pointed out that several classical immune pathways, such as the TNF-α signaling via NF-kB, inflammatory response, and the IL6-JAK-STAT3 signaling-mediated passages were significantly enriched, suggesting METTL3-related modification could facilitate the immune regulation. The results on the graph show that genes co-expressed with METTL3 are significantly enriched in the signature E2F target, the marker G2M checkpoint, and the MYC targets, and these could speculate that METTL3 might play a role in regulating the cell cycle which affects the proliferation of EMs cells significantly.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e9. The Experimental Validation Of M6a Modification In Ems\\u003c/h3\\u003e\\n\\u003cp\\u003eAfter confirming the key m6As, we first employed an m6A quantitative assay to detect the m6A alteration experimentally in EMs. We observed that the quantity of m6A modification was increased significantly in NESCs while the least in EESCs(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eA). The qRT-PCR experiment revealed that EESCs and EUSCs had considerably lower METTL3 and YTHDF2 mRNA levels than NESCs(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eB). Additionally, the results of WB experiments revealed similar results, as the difference in METTL3 expression was the most remarkable among them(Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eC-D). These findings indicate that METTL3 and YTHDF2 are possibly crucial factors for the formation of EMs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eEMs is a benign condition with a high incidence in women of reproductive age [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Current approaches have limitations for clinical feature-based diagnosis due to the insidious phenotype of EMs. Based on extensive research on post-transcriptional modifications, researchers are gradually recognizing the benefits of constructing epigenetic diagnostic models of disease and have confirmed the potential impact of m6A methylation which is emerging as the most common epigenetic modification and related to the occurrence and progression of the majority of cancers and other diseases[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. The majority of m6A regulators were found to have altered expression in the current study, with \\\"writers\\\" decreased while \\\"erasers\\\" upregulated, indicating that the loss of m6A modification might be a potential contributor in EMs. Previous research has demonstrated the reverse ability between m6A \\\"writers\\\" and \\\"erasers\\\" on modifications in lncRNAs/mRNAs. M6A \\\"readers\\\", furthermore, recognize and bind to methylated lncRNAs/mRNAs and perform diverse functions. As Liu et al. revealed[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e], the YTHDF1 complex with YTHDF2 can specifically recognize m6A modifications and thus regulate the stability of lncRNA THOR, impacting the proliferation, migration, and invasion of cancer cells. Moreover, the METTL3-Snail-YTHDF1 axis can promote metastasis in malignant tumors with modification of EMT-related mRNAs mediated by m6As, which leads to progression[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Although these convinced epigenetic research, little is understood about the function of m6A methylation in EMs.\\u003c/p\\u003e \\u003cp\\u003eM6A regulators seldom function independently; instead, control diseases by interacting genes. Hence, it is crucial to investigate the potential alterations of m6A-interacting genes. Through Pearson analysis, we discovered 25 m6A-regulated mRNAs. Recent studies have demonstrated the impact of SCG2 on the clinical stage, and the influence of macrophage polarization to affect immunotherapy in colorectal cancer[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. GPC3[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e] has also been proven to be essential in the immune response, yet no reference has been reported in EMs. The alterations of m6A-related lncRNAs have been described in depth recently, with abnormal expression discovered typically in EMs. Huang et al. observed significant postoperative level of lncRNA-UCA1 was reduced, suggesting that it may function as a diagnostic and prognostic biomarker for EMs[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. ALKBH5 acts as a modification switch of lncRNA SOX2OT and participates in the lncRNA-mediated competitive endogenous RNA model to enhance the molecular stability and exploit the function of SOX2OT, thus affecting the progression and drug resistance in glioma[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Here, considering the modification of non-coding RNAs by m6As, we analyzed lncRNAs expressed differentially among EC-EU, EU-NM, and EC-NM groups, in which LINC02381 and LINC00578 show the largest log\\u003csub\\u003e2\\u003c/sub\\u003eFC values. Concurring with our study, an aberrant expression of LINC02381 was confirmed in EMs previously[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e], which identified a ceRNA network and verified 28 differentially expressed lncRNAs by analysis of RNA-seq data from EMs, followed by RT-qPCR results confirming that LINC02381 was significantly overexpressed in EMs tissues.\\u003c/p\\u003e \\u003cp\\u003eThe immune microenvironment plays an important part in EMs[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], especially immune cell infiltration and immune dysfunction involved in the progression of EMs, as depicted by Wang et al[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Significant diversities were investigated between the two subtypes here in the ratio of 22 immune cells and immune or stromal characteristics. It has been elucidated that infiltrations of T lymphocytes, B lymphocytes, and NK cells are reduced in EMs lesions[\\u003cspan additionalcitationids=\\\"CR31\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Besides, previous studies suggest that ectopic endometrial tissue may hold an immunological surveillance function, leading to the formation of chronic inflammation, while a higher stromal score or immune score represents a more stromal or immune component relative in the immune milieu, facilitating inflammation as well as pelvic adhesions, and the estimated score indicates aggregation of stromal score or immune score in the immune microenvironment, all of which are consistent with our finding.\\u003c/p\\u003e \\u003cp\\u003eTo determine the critical m6A regulators, LASSO regression was used followed by further filtration by stepwise regression to screen the variables for a diagnostic model, and finally, 7 key m6A regulators were obtained, named METTL3, ELAVL1, LRPPRC, YTHDC2, YTHDF2, YTHDC1, and FTO. Besides, independent datasets were involved for external validation making the diagnostic model more robust. A cross-directional analysis revealed the AUC was highest for METTL3 in \\\"writers\\\", YTHDF2 in \\\"readers\\\", and FTO in \\\"erasers\\\" separately, meaning remarkable diagnostic efficiency. Consistent with our findings, previous research indicates that METTL3 promotes pre-miR126 maturation via m6A alteration[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e], promoting the migration and invasion of endometrial stromal cells in EMs[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. However, YTHDF2 has not been examined in EMs. Finally, we verified experimentally the aberrant down-expression of METTL3 and YTHDF2 according to gene and protein levels and found that both m6A regulators posed the highest risk of disease in the ectopic group. The possible enriched downstream targets of METTL3 obtained by enrichment analysis were E2F targets, G2M checkpoint, and MYC targets. It has been detected that METTL3 activates the G2M checkpoint of the cell cycle through CDC25B mediated by m6A modification, leading to malignant progression in neck squamous cell carcinoma[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. Also, METTL3 could enhance the stability of c-MYC through YTHDF1-mediated m6A modification and promote tumorigenesis in oral squamous cell carcinoma[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. The expression of oxidative phosphorylation-related gene program and reduction of immune-dependent cell cycle progression are indirectly regulated by METTL3 and YTHDF2 respectively[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e], enhancing the high accuracy of the current findings.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, with the investigating of the m6A modification patterns in Endometriosis, we explored the mRNAs and lncRNAs related to m6A regulators by the GEO database. Two molecular subtypes were identified with different infiltration levels of immune microenvironment cells which related to the clinical features. We constructed a diagnostic m6A signature of endometriosis, and detected METTL3 and YTHDF2 might be the key m6A targets of EMs by experiment. Still, the limited mechanism of METTL3-m6A-YTHDF2 in endometriosis is studied in this paper, and subsequent experiments are needed to verify our research results deeply, such as Me-rip sequencing to yield the most quantification accuracy and insightful mechanisms from plural prospectives, which is undergoing tests based on these key m6A regulators obtained here. Our analysis results might be relatively single, however, make new views for the diagnosis and treatment of EMs in the future.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe would like to thank for the Future Medical Laboratory of Harbin Medical University. (Harbin, China)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGM Zhang and XT Wang designed the study, XT Wang and XB Zhao analysis and interpretation of data, XT Wang and H Wu wrote the manuscript, XT Wang, J Wang,Y Cheng and T Liang performed the experiments. GM Zhang and QY Guo supervised the project. All the authors approved the fnal version of manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was supported by the National Natural Science Foundation \\u003c/p\\u003e\\n\\u003cp\\u003eof China (81971359), the Natural Science Foundation of Hei Longjiang Province(LH2019H027), and Key research and development projects of Heilongjiang Province(GA21C008).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll the procedures in this study were audited and confrmed by the Ethic Committee of the Second Afliated Hospital of Harbin Medical University (KY2022-155). And all experiments conducted complied with relative rules and regulations of the committees. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e \\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eGordts Stephan, Koninckx Philippe, Brosens Ivo, Pathogenesis of deep EMs.[J].Fertil Steril, 2017, 108: 872-885.e1.\\u003c/li\\u003e\\n\\u003cli\\u003eTaylor Hugh S, Kotlyar Alexander M, Flores Valerie A, EMs is a chronic systemic disease: clinical challenges and novel innovations.[J]. Lancet, 2021, 397: 839-852.\\u003c/li\\u003e\\n\\u003cli\\u003ebulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, Kohlmeier A, Yin P, Milad M, Wei J. 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J Exp Med, 2021, 218: undefined.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"endometriosis, m6A regulators, network, diagnosis, immune microenvironment\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2266490/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2266490/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eAn RNA modification known as n6-methyladenosine (m6A) interacts with a range of coding and non-coding RNAs. The majority of research focused on identifying m6A regulators that are differentially expressed in endometriosis but ignored their mechanisms which derived from the alterations of modifications among RNAs, affecting the disease progression primarily. Here, we aimed to investigate the potential roles of m6A regulators in the diagnostic potency, immune microenvironment, and clinicopathological features in endometriosis through interacting genes.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA thorough investigation of the m6A modification patterns in the GEO database was carried out, based on mRNAs and lncRNAs related to these m6A regulators. Two molecular subtypes were identified with different infiltration levels of immune microenvironment cells and clinical features using unsupervised clustering analysis. We identified two m6A regulators, named METTL3 and YTHDF2, as diagnostic targets of endometriosis following the usage of overlapping genes to construct a diagnostic m6A signature of endometriosis. Finally, we found that m6A alterations might be one of the important reasons for the progression of endometriosis, especially with significant down-expressions of METTL3 and YTHDF2.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eM6A modification patterns play significant effects on the diversity and complexity of the progression and immune microenvironment and might be key diagnostic markers for endometriosis.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Cross-Talk Between n6-Methyladenosine and Their Related RNAs Defined a Signature and Confirmed m6A Regulators for Diagnosis of Endometriosis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-11-16 19:49:17\",\"doi\":\"10.21203/rs.3.rs-2266490/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"10c17de5-d05b-4c8f-aad6-ea5537dde967\",\"owner\":[],\"postedDate\":\"November 16th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2022-12-12T20:59:13+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2022-11-16 19:49:17\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-2266490\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-2266490\",\"identity\":\"rs-2266490\",\"version\":[\"v1\"]},\"buildId\":\"WvIrzKhiLBfengagbw6Ux\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC0","license_restricted":false}