Discovery of N6-methyladenosine modification regulators and their related mRNAs in endometriosis

Discovery of N6-methyladenosine modification regulators and their related mRNAs in endometriosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Discovery of N6-methyladenosine modification regulators and their related mRNAs in endometriosis Chengcheng Ren, Yanan He, Dejun Wang, Jixin Li, Liyuan Sun, Guangmei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3003927/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 N6-methyladenosine(m6A) modification regulates the processes of RNA splicing, subcellular localization, translation and stability by changing the RNA structure and the interaction between RNA and RNA-binding proteins to ensure the timely and accurate expression of genes. In this study, we investigated m6A regulators and m6A-related genes and for the first time explored effective prevention and treatment targets in endometriosis (EM). Methods By incorporating the Gene Expression Omnibus (GEO) database, biological information analysis technologies, and validation of other databases, aberrant m6A-methylated genes and m6A-related genes were uncovered, as well as efficient therapeutic drugs. Results METTL3, RMB15B, FTO, YTHDF1, and YTHDF2 might be vital m6A regulators, and GGT5 and CAMK1D may be essential m6A-related genes of EM. A few crucial small-molecule agents supply new views for the treatment of EM. Conclusion These results demonstrated novel insights into m6A methylation of EM and revealed potential biomarkers and precision medicine strategies for EM. endometriosis m6A regulators differentially expressed genes immune infiltration therapeutic drugs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Backgroud Endometriosis (EM) refers to the appearance of endometrial tissue (glands and stroma) outside the body of the uterus[ 1 ]. Although EM is a benign disease, it has many malignant characteristics, such as infiltration, metastasis, and recurrence[ 2 ]. Therefore, EM is also known as a neoplastic disease. Since its pathogenesis has not been elucidated, the diagnosis and treatment of EM are complex issues[ 3 ]. There are some theories about the etiology of EM, including immunity and inflammation[ 4 ], menstrual blood reflux[ 5 ], and epigenetic factors[ 6 ], but none of them can completely elucidate the pathogenesis of EM. Therefore, an in-depth investigation of the pathogenesis of EM and the identification of effective prevention and treatment targets are urgent issues to be addressed. N6-methyladenosine (m6A) methylation is a dynamically reversible posttranscriptional modification[ 7 ]. As early as the 1970s, m6A modification of mRNA has attracted the attention of scientists[ 8 ]. However, due to the limited amount of technological tools at that time, researchers did not achieve many breakthroughs. With advances in molecular biology and sequencing technology, m6A methylation research has become a hot spot in basic research and clinical research[ 9 ]. M6A methylation is mainly regulated by three enzymes: m6A methyltransferases, m6A demethylases, and m6A reader proteins[ 10 ]. M6A methylation governs the stability of mRNA at the posttranscriptional level and plays a vital role in various tumors[ 11 ]. Previous studies have revealed that the methyltransferase METTL3 promotes the migration and invasion of endometrial mesenchymal cells[ 12 ]. The m6A methylated reader protein YTHDF2 can induce target cells to secrete inflammatory factors by regulating the NF-κB signaling pathway and aggravating the inflammatory response[ 13 , 14 ]. However, the functions of m6A methylation in EM have rarely been reported. In this article, data from the GSE25628 and GSE7305 datasets were analyzed and integrated to screen out abnormal m6A regulators and m6A-related biomarkers through the GEO2R online software, Weighted Gene Co-Expression Network Analysis (WGCNA), Lasso analysis, Receiver Operating Characteristic curve (ROC), immune infiltration evaluation, and enrichment analysis. A protein‒protein interaction (PPI) network was established to identify vital nodes. In addition, the GSE23339 database and Turku Endometriosis Database were used to validate the key genes. Finally, we evaluated drugs for EM and provided new ideas for diagnostic and therapeutic targets. Materials and Methods Data processing The GSE25628 and GSE7305 microarray data were obtained from the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/ )[ 36 ]. The GSE25628 dataset, which includes seven ectopic endometrium samples, six normal endometrium samples, and nine eutopic endometrium samples, was derived on the GPL571 platform. The GSE7305 dataset, which consists of ten ectopic endometrium samples and ten normal endometrium samples, was extracted on the GPL 570 platform. Identification of differentially expressed genes GEO2R online software was applied to analyze the GSE25628 and GSE7305 microarrays and identify differentially expressed genes (DEGs). |logFC|>1 and p value < 0.05 were set as the cutoff criteria for DEGs. The Venn diagram tool was used to display intersecting genes. The expression of m6A regulators in EM The m6A regulators were downloaded from a previous study[ 37 ]. Using the ‘Kruskal test ’ in R Package, 17 m6A regulators were investigated in ectopic endometrium samples, normal endometrium samples, and eutopic endometrium samples, including five writers, eleven readers, and one eraser. The heatmap of these 17 m6A regulators was unraveled by ‘pheatmap’ in the R package, and the correlations between them were evaluated with Spearman’s correlation analysis (p < 0.05). Key module and gene selection from WGCNA Weighted gene co-expression network analysis (WGCNA), a systematic biology technique, was employed to elaborate gene association between samples[ 38 ]. First, the median absolute deviation (MAD) of each gene was calculated, and 50% of genes accompanied by the minimum MAD were eliminated. Secord, outlier genes, and samples were removed by the good-Samples Genes method, and a scale-free co-expression network was established. Third, an adjacency matrix was constructed by soft thresholding power. The adjacency was then transformed into a topological overlap matrix (TOM), and the gene ratio and dissimilarity were computed. Fourth, average linkage hierarchical clustering was applied to classify the genes that expressed identical profiles into gene modules according to the Tom-based different measure. Fifth, the differences in module characteristic genes were calculated, and an ideal module dendrogram was selected. Exploration of candidate m6A regulators and ROC analysis Lasso is a linear regression analysis using L1 regularization for achieving a sparsification and feature selection model[ 16 ]. ‘glmnet’ in the R package was adopted to integrate survival time, survival status, and gene expression. The lasso-cox technique was employed to construct a regression model. In the end, 3-fold cross validation was set up to harvest five optimal genes. The ROC was exhibited to appraise the diagnostic value of selected genes by ‘pROC’ in the R package. An AUC > 0.7 was conceived as having excellent diagnostic value. Immune infiltration evaluation ESTIMATE was applied to compute the immune score, stromal score, and ESTIMATE score for ectopic endometrium samples, normal endometrium samples, and eutopic endometrium samples. CIBERSORT was utilized to identify the relative diversity of 22 kinds of infiltrating immune cells in each sample. Through barplot, the proportion of infiltrating immune cells was visualized. With the vioplot, the relative proportion of different types of immune cells between the above groups was determined. Functional enrichment analysis Gene Ontology (GO) analysis is an enrichment for gene functions and gene products, including biological process (BP), cellular component (CC) and molecular function (MF)[ 39 ]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a large-scale database for the exploration of gene functions[ 40 ]. Via the R package cluster Profilter, gene enrichment analysis was conducted. P value < 0.05 and FDR of < 0.1 were set as statistically significant. Protein‒protein interaction network analysis and hub gene selection M6A regulators and 339 candidate genes were uploaded to the STRING database to establish their protein‒protein interaction (PPI) networks[ 16 ]. The minimum required interaction score was set at > 0.9, and the disconnected genes were hidden in the network. Cytoscape software was used to present the PPI network and select hub genes. A Venn diagram was applied to display the hub genes in betweenness, degree and closeness different algorithm analyses through Cytoscape. Identification of novel hub genes in other databases For other database identification, the authors sought the gene expression profile dataset GSE23339 derived from the platform GPL6102 and the Turku Endometriosis Database ( https://endometdb.utu.fi ), which was used to confirm the hub genes in EM samples[ 41 ]. Validation of small molecular therapeutic drugs The differentially expressed m6A-related mRNAs were introduced into the CMap database ( https://clue.io/ ), a publicly accessible validation of small molecular drug website[ 42 ]. PubChem ( https://pubchem.ncbi.nml.gov ) was used for the 3D structure of the established potential small molecules[ 42 ]. The STITCH database ( http://stitch.embl.de ) was applied to search for the connection between hub genes and small molecules[ 43 ]. Molecular docking analysis First, the authors downloaded the 3D structures of small molecules from the PubChem database, imported the frames into ChemBio3D Ultra 14.0, and set the minimum RMS gradient to 0.001. They then performed hydrogenation, charge calculation, charge distribution, and the rotatable key set in AutoDockTools-1.5.6. Second, they downloaded protein structures from the PDB database, used PyMOL2.3.0 to remove protein crystal water and primitive ligands, and then carried the protein structures to AutoDockTools-1.5.6. Third, PyMOL2.3.0 was applied to estimate the interaction mode of the docking results[ 44 ]. Statistical analyses A comparison of all laboratory results between the two sets was carried out through Student’s t test. GraphPad Prism 7.0 software was used for statistical data analysis. A P value < 0.05 was considered statistically significant (ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001). Results Determination of DEGs in EM Figure 1 illustrates the experimental protocol. A total of 3208 DEGs were screened from GSE25628 and GSE7305 using the online method GEO2R. As shown in Fig. 2A, B, the expression of the DEGs was displayed via a volcano plot. There were 6257 and 6842 DEGs from the comparison of ectopic endometrium samples and normal endometrium samples in GSE25628 and GSE7305, respectively. Based on the Venn diagram, 3208 intersection genes were visualized (Fig. 2C). Table 1 displays information about the database, including the type, sample groups, numbers, and platform. Table 1 Information of GEO database applied in the article GSE series Type Samples Platform EC NE EU GSE25628 mRNA 7 6 9 GPL571 GSE7305 mRNA 10 10 0 GPL570 GEO, gene expression omnibus; EC, Ectopic endometrium; NE, Normal endometrium; EU, Eutopic endometrium. The expression of m6A regulators in EM Altogether, 17 m6A regulators were selected among the ectopic, normal and eutopic groups, including eleven readers, five writers, and one eraser (Fig. 3A). When compared to the NE and EU groups, ten m6A regulators were remarkably downregulated in the EC group, namely, METTL3, WTAP, RBM15, RBM15B, YTHDC1, YTHDF2, YTHDF3, FMR1, LRPPRC, and HNRNPA2B. In contrast, YTHDF1 and FTO were obviously upregulated in the EC group compared with the NE group (Fig. 3B). The interaction network of m6A regulators is shown in Fig. 3C. Of note, LRPPRC was hidden because it was not connected to the others. The correlation coefficient among these m6A regulators was investigated in EC samples (Fig. 3D). The three pairs with the highest positive values were METTL3 and RBM15, LRPPRC and RBM15, and YTHDC1 and HNRNPA2B1 (r = 0.96, p < 0.05). Key module and gene selection from WGCNA WGCNA was utilized to substantiate the construction of a gene-correlated module in EM. The scale independence (ẞ=8, scale-free R 2 = 0.9) and the mean connectivity network were revealed (Fig. 4A, B). Figure 4C presents the clustering dendrogram in the EC, NE, and EU groups. Based on this power, 21 gene modules were produced, which are depicted in Fig. 4D, E. The correlation between EM and the 21 gene modules is displayed in Fig. 3F, and the dark olive-green module (451 genes) indicated the highest connectivity with the EC group. As shown in Fig. 4G, a dramatic positive correlation was acquired between gene significance and module membership in the dark olive-green module for the EC group. Exploration of candidate m6A regulators and ROC analysis Lasso analysis was employed to screen candidate m6A regulators. With this method, five m6A regulators were found, namely, YTHDF1, RBM15B, FTO, METTL3, and YTHDF2, by lambda = 0.09781592 (Fig. 5A) and 3-fold cross-validation for shrinking parameters (Fig. 5B). Established by the Lasso analysis, the ROC curve was used to evaluate the sensitivity and specificity of the five m6A regulators, and their AUCs were 0.86, 1.00, 1.00, 0.86, and 0.93, respectively (Fig. 5C). The results demonstrated that the five m6A genes possess obvious diagnostic value for EM. Immune infiltration evaluation Previous studies have certified that immune factors are involved in the development of EM, and whether m6A regulators participate in the immune process needs to be explored. Immune cell infiltration analysis demonstrated that m6A regulators were associated with the immune system. In the barplot visualization, the relative percent of 22 types of immune cells in each tissue is shown (Fig. 6A). Via the vioplot, EC patients had a higher degree of CD8 T cells and monocytes and a lower degree of follicular helper T cells and activated dendritic cells (Fig. 6B). The correlation of 22 kinds of immune cells indicated that gamma data T cells were most relevant to memory resting CD4 T cells (r = 0.83) (Fig. 6C). Furthermore, as shown in Fig. 6D, the stromal score and ESTIMATE score were highest in the EC group, indicating that the immune microenvironment had a stronger association with EM. Functional enrichment analysis To investigate genes more closely associated with EM, 339 candidate genes were confirmed between the DEGs from GEO2R and the essential genes from WGCNA by a Venn diagram (Fig. 7A). The top results of KEGG analysis showed that candidate genes were mainly associated with “cell adhesion molecules” and “vascular smooth muscle contraction” (Fig. 7B). The top results of GO analysis revealed that candidate genes were primarily enriched in “biological adhesion,” “circulatory system development,” and “tube development” in BP terminology, “external encapsulating structure,” “anchoring junction,” and “supramolecular polymer” in CC terminology, and “cytoskeletal protein binding” and “protein-containing complex binding” in MF terminology (Fig. 7C). Details of the GO and KEGG analyses are listed in Supplementary Table S1 . Protein‒protein interaction network analysis and hub gene selection. Five m6A regulators and 339 candidate genes were uploaded to the STRING database to explore their connections. Of the 339 candidate genes, only ten interacted with m6A regulators: CAMK1D, RBM47, GGT5, COIL, RC3H2, G3BP2, DDX3X, ADAM19, DNAJC15, and LTC4S. (Fig. 8A). The PPI network of m6A-related genes consisted of 15 nodes and 21 edges (Fig. 8B). The cytoHubba plug-in was applied to select intersecting genes by three algorithms – degree, betweenness, and closeness. The hub genes were RC3H2, GGT5, G3BP2, and CAMK1D, overlapping three other features among m6A-related mRNA-expressed genes (Fig. 8C), which might have pivotal roles in m6A methylation in EM. Identification of novel hub genes in other databases To verify the findings, the authors searched the Turku Endometriosis Database to identify the expression of the hub genes (Fig. 9A). The four gene expression trends between the two groups were consistent with the analysis results, although it was not known whether the differences were statistically significant. Moreover, the GSE23339 database was applied to confirm the expression level of these hub genes (Fig. 9B). Among the four genes, GGT5 and CAMK1D were selected for follow-up analysis because of their high statistical significance. Validation of small molecular therapeutic drugs By retrieving the CMAP database, abundant and remarkable small-molecule agents targeting EM were screened. The top five small-module agents with high correlations with EM were fananserin, scopolamine, buphenine, rucaparib, and ruxolitinib. The 3D structures of the above five chemicals that may be selected for potential treatment options were acquired from PubChem and are shown in Fig. 10. Hunting the connection between hub genes and drugs Through the analysis of two hub genes in the STITCH database, the authors confirmed that the 2 genes are most likely to play a vital role in EM. In the next step, they aimed to find chemical compounds acting on GGT5 and CAMK1D to observe new treatments for EM. In the compound-protein interaction network, the compounds leukotriene C4, leukotriene D4, and glutamic acid interacted with GGT5 and GSK3 inhibitor. XIII interacting with CAMK1D were identified for subsequent analysis (Fig. 11A). Molecular docking analysis Molecular docking analysis validated the GSK3 inhibitor. XIII to CAMK1D binding fractions of -9.2 kcal/mol, which indicated that there was a compelling binding effect between them. GSK3 inhibitors interact with CAMK1D mainly through hydrogen bonding and hydrophobic forces. Guanosine forms hydrogen bonds with GLN-99 and VAL-101, and the lengths of the hydrogen bonds were 2.1 Å, 2.0 Å and 2.1 Å, respectively. GSK inhibitor. XIII had hydrophobic effects with VAL-82, LEU-151, GLY-30, ALA-309, VAL-37, LEU-29, and SER-164 (Fig. 11B). The same method validated glutamic acid, leukotriene C4, and leukotriene D4 to GGT5 binding fractions of -5.3 kcal/mol, -6.6 kcal/mol, and − 7.0 kcal/mol, respectively (Fig. 11C, D, E). Discussion EM is a benign disease in which endometrial cells colonize and grow outside the uterine cavity, but it has the biological behavior of a malignant disease. Common symptoms of EM include dysmenorrhea, pelvic mass, and infertility, which seriously affect the quality of life of patients[ 1 , 3 ]. The pathogenesis of EM is still unclear. The classic theory comes from the menstrual flow reflux theory proposed by Sampson in 1927[ 15 ]. Works in recent years have verified that EM is considered to be a disease related to immune factors[ 16 ]. However, none of the theories can thoroughly explain the diversity of clinical manifestations and the complexity of the treatment of EM, and the core molecular mechanism has yet to be described. M6A modifications are generally found in eukaryotes, and occur on transcripts, such as mRNAs, tRNAs, and rRNAs[ 11 ]. In this experiment, five m6A regulators associated with EM were screened, including the methyltransferases METTL3 and RMB15B, the demethyltransferase FTO, and the reader proteins YTHDF1 and YTHDF2. The expression of METTL3 is upregulated in maternal hepatic tumors, which can activate the Wnt/β-catenin signaling pathway and promote the proliferation, migration, and invasion of liver cancer cells[ 17 ]. In the presence of p53, the tumor suppressor gene RDM1 can inhibit the phosphorylation of Raf and ERK. At the same time, METTL3 can significantly increase the number of cells in the G2/M phase by inhibiting the expression of RDM1 in hepatoma cells, thereby promoting the proliferation and colony formation of liver cancer cells[ 18 ]. In addition, METTL3 can also curb the expression of suppressor of cytokine signaling 2 (SOCS2) in hepatocellular carcinoma by combining with YTHDF2; knocking out METTL3 can dramatically inhibit the tumorigenicity of hepatocellular carcinoma and lung metastases in mice[ 19 ]. Some investigators have shown that METTL3 cooperates with YTHDF1 to promote the adhesion, migration, and invasion of bladder cancer cells by upregulating CDCP1[ 20 ]; METTL3 also works with YTHDF2 to promote the development and progression of bladder cancer by reducing the mRNA levels of SETD7 and KLF4[ 21 ]. Studies have found that FTO can induce the development of acute myeloid leukemia, non-small cell lung cancer, breast cancer, and bladder cancer[ 22 ]. Therefore, we wondered whether the five m6A regulators screened in this paper also play a crucial role in EM. These conjectures require a large number of subsequent experimental verifications. In this investigation, CIBERSORT was applied to analyze and evaluate the infiltration of immune cells in EM, and it was found that CD8 T cells and monocytes in the EC group had higher levels than those in the NE group. However, follicular helper T cells and activated dendritic cells had lower levels. This finding indicates that EM is influenced by immune factors. CD8 T cells are immune cells and an essential part of the tumor microenvironment. Previous research has shown that CD8 T cells, as effector cells, have an excellent predictive prognosis in various tumors, such as breast cancer, colon cancer, and gastric cancer[ 23 – 25 ]. Monocytes are derived from common myeloid progenitors (CMPs), and after being recruited into tissues, they can differentiate into macrophages or partial dendritic cells, maintain tissue homeostasis, or play multiple immunological functions in the case of infection, inflammation, tumorigenesis, etc. [ 26 ]. Follicular helper T cells belong to the CD4 + T-cell subpopulation, which can promote the maturation and differentiation of B cells and play an essential role in the pathogenesis of diverse autoimmune diseases such as systemic lupus erythematosus, rheumatoid arthritis, and primary Sjogren's syndrome[ 27 ]. Dendritic cells are antigen-presenting cells that are diffusely distributed in different body tissues. It effectively presents antigens to T lymphocytes and activates T lymphocyte cells, inducing an initial immune response. It plays a crucial role in the induction and regulation of the immune response[ 28 ]. The results of this analysis not only enrich our understanding of the immune microenvironment of EM but can also be used for the clinical application of related research to develop drugs that regulate the function of CD8 T cells, monocytes, follicular helper T cells, and dendritic cells. However, we must also realize that there are still many issues waiting to be investigated. For example, the regulation of the differentiation of monocytes in different directions in the immune microenvironment is not completely clear, and the diversity of related dendritic cells still needs to be further explored. In conclusion, immune infiltration analysis provides specific guidelines for EM immunotherapy. In the bioinformatics series, it was finally determined that GGT5 and CAMK1D were the most closely related genes to the m6A regulators. GGT5, a member of the γ-glutamyl transpeptidase gene family, is highly expressed in lung adenocarcinoma-associated fibroblasts and can increase intracellular glutathione and decrease intracellular reactive oxygen species to enhance cancer cell resistance. Downregulation of GGT5 can reduce tumor cell proliferation[ 29 ]. In addition, some studies have found that GGT5 is a poor prognostic factor for gastric cancer[ 30 ]. CAMK1D is a member of the Ca2+/CaM-dependent kinase CaMKI family and is widely expressed in human tissues[ 31 ]. CAMK1D has been confirmed to be involved in developmental differentiation and disease processes in various cells and tissues. For example, in the hematopoietic system, CAMK1D is upregulated during the differentiation of CD34 + artificial blood stem cells into neutrophils, regulating calcium-mediated functions such as granulocyte phagocytosis, migration, and adhesion[ 32 ]. In addition, CAMK1D was found to be involved in the development and progression of a variety of tumors, such as breast cancer, lung cancer, and multiple myeloma[ 33 – 35 ]. In this study, GGT5 and CAMK1D were expressed at higher levels in the EC group, demonstrating that these two genes may be involved in the pathogenesis of EM. This study indicates that m6A methylation may be the primary mechanism of abnormal gene expression in patients with EM. By exploring mRNAs associated with m6A, the possibility of ascertaining new biomarkers that could potentially be employed in EM diagnosis and treatment is offered. However, this research has some limitations. First, the number of experimental samples used for m6A methylation analysis was small, and more samples must be investigated. Second, the molecular mechanisms of hub genes require systematic and comprehensive exploration. Third, for the small molecule compounds discovered, research is needed on the mechanism by which they regulate EM and through further optimization to improve their bioavailability, inhibitory effect, and therapeutic effect. These screened drugs are expected to become useful tool molecules to help researchers explore the mechanisms of m6A-related biological processes and have positive implications for the clinical treatment of EM. Conclusions In summary, with the exploration of m6A regulators and m6A-related genes in EM, we used the GEO database, WGCNA, Lasso analysis, ROC, immune infiltration evaluation, and enrichment analysis. Ultimately, we discovered that METTL3, RMB15B, FTO, YTHDF1, and YTHDF2 might be vital m6A regulators, and GGT5 and CAMK1D may be essential m6A-related genes of EM. Importantly, we also searched for a few efficient small-molecule agents that provide new views for the treatment of EM. These results provide fresh insights into m6A methylation in EM and reveal potential biomarkers and precision medicine for EM. Abbreviations m6A N6- methyladenosine GEO Gene Expression Omnibus WGCNA Weighted Gene Co-Expression Network Analysis ROC receiver operating characteristic curve PPI protein‒protein interaction DEGs differentially expressed genes MAD median absolute deviation TOM topological overlap matrix GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes SOCS2 suppressor of cytokine signaling 2 Declarations Acknowledgements we would like thank all collaborators for their help and support in the study. We also thank the staff members of the Gene Expression Omnibus database and the Turku Endometriosis Database. Conflict of interest statement No conflict of interest. Authors’ contributions Data curation, Yanan He; Formal analysis, Chengcheng Ren; Funding acquisition, Guangmei Zhang; Investigation, Yanan He and Dejun Wang; Methodology, Chengcheng Ren and Guangmei Zhang; Project administration, Jixin Li; Resources, Liyuan Sun; Software, Chengcheng Ren; Supervision, Guangmei Zhang; Validation, Dejun Wang; Writing – original draft, Chengcheng Ren; Writing – review & editing, Chengcheng Ren. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the National Natural Science Foundation of China, grant number: 81971359, and the Natural Science Foundation of Heilongjiang Province, grant number: LH2019HO27. Availability of data and materials All data are included in this article and its additional files. 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Identification and Analysis of Potential Autophagy-Related Biomarkers in Endometriosis by WGCNA. Front Mol Biosci. 2021;8:743012. Chen M, et al. Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition in the development of endometriosis. Sci Rep. 2020;10(1):8442. Uimari O, et al. Genome-wide genetic analyses highlight mitogen-activated protein kinase (MAPK) signaling in the pathogenesis of endometriosis. Hum Reprod. 2017;32(4):780–93. Strauß T et al. Impact of Musashi-1 and Musashi-2 Double Knockdown on Notch Signaling and the Pathogenesis of Endometriosis. Int J Mol Sci, 2022. 23(5). Cui Z, et al. Identification and Exploration of Novel Macrophage M2-Related Biomarkers and Potential Therapeutic Agents in Endometriosis. Front Mol Biosci. 2021;8:656145. Aihaiti Y, et al. Therapeutic Effects of Naringin in Rheumatoid Arthritis: Network Pharmacology and Experimental Validation. Front Pharmacol. 2021;12:672054. Seeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des. 2010;24(5):417–22. Additional Declarations No competing interests reported. Supplementary Files TableSI.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3003927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":206134659,"identity":"3390b324-719c-48f8-83d8-a2ea2064eaa5","order_by":0,"name":"Chengcheng Ren","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chengcheng","middleName":"","lastName":"Ren","suffix":""},{"id":206134660,"identity":"258037dd-ae79-4e34-89de-cd3340b56c61","order_by":1,"name":"Yanan He","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"He","suffix":""},{"id":206134661,"identity":"8a081f32-acaa-4897-82e8-09c315c4abae","order_by":2,"name":"Dejun Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dejun","middleName":"","lastName":"Wang","suffix":""},{"id":206134662,"identity":"e19d8fb4-3733-4963-90af-4969a8ee7fe4","order_by":3,"name":"Jixin Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jixin","middleName":"","lastName":"Li","suffix":""},{"id":206134663,"identity":"f61f6656-aee8-4f29-806c-1c7d8c6381bf","order_by":4,"name":"Liyuan Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Sun","suffix":""},{"id":206134664,"identity":"a33112af-b48a-49b2-b8e7-eed00f2c770c","order_by":5,"name":"Guangmei Zhang","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Guangmei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-05-31 08:44:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3003927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3003927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38044950,"identity":"d0615533-cf6a-4123-897c-133de7c42587","added_by":"auto","created_at":"2023-06-05 17:38:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235575,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3003927/v1/f81853f37a0ee8ba43606b50.png"},{"id":38044946,"identity":"81eac7ce-1ee7-4962-b540-d9b97a58d491","added_by":"auto","created_at":"2023-06-05 17:38:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":301276,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with 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version\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3003927/v1/52d1acee40a6d85f124673e2.png"},{"id":38045925,"identity":"6a66a59f-b88f-4478-b777-3e6113d7beb4","added_by":"auto","created_at":"2023-06-05 17:54:49","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":2310607,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-3003927/v1/ffc7027737afc4adc297e126.png"},{"id":38340100,"identity":"57cecf87-bf05-4a7e-903c-c95cd8a6a5d3","added_by":"auto","created_at":"2023-06-10 20:59:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3039025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3003927/v1/f1179882-2992-48a8-9c52-c171338d0951.pdf"},{"id":38045818,"identity":"ac6d80ec-428f-4e13-977d-d3c6c954ce62","added_by":"auto","created_at":"2023-06-05 17:46:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142329,"visible":true,"origin":"","legend":"","description":"","filename":"TableSI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3003927/v1/ab769c824b018151636ac4ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discovery of N6-methyladenosine modification regulators and their related mRNAs in endometriosis","fulltext":[{"header":"Backgroud","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEndometriosis (EM) refers to the appearance of endometrial tissue (glands and stroma) outside the body of the uterus[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although EM is a benign disease, it has many malignant characteristics, such as infiltration, metastasis, and recurrence[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, EM is also known as a neoplastic disease. Since its pathogenesis has not been elucidated, the diagnosis and treatment of EM are complex issues[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. There are some theories about the etiology of EM, including immunity and inflammation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], menstrual blood reflux[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and epigenetic factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], but none of them can completely elucidate the pathogenesis of EM. Therefore, an in-depth investigation of the pathogenesis of EM and the identification of effective prevention and treatment targets are urgent issues to be addressed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eN6-methyladenosine (m6A) methylation is a dynamically reversible posttranscriptional modification[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As early as the 1970s, m6A modification of mRNA has attracted the attention of scientists[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, due to the limited amount of technological tools at that time, researchers did not achieve many breakthroughs. With advances in molecular biology and sequencing technology, m6A methylation research has become a hot spot in basic research and clinical research[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. M6A methylation is mainly regulated by three enzymes: m6A methyltransferases, m6A demethylases, and m6A reader proteins[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. M6A methylation governs the stability of mRNA at the posttranscriptional level and plays a vital role in various tumors[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous studies have revealed that the methyltransferase METTL3 promotes the migration and invasion of endometrial mesenchymal cells[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The m6A methylated reader protein YTHDF2 can induce target cells to secrete inflammatory factors by regulating the NF-κB signaling pathway and aggravating the inflammatory response[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the functions of m6A methylation in EM have rarely been reported.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this article, data from the GSE25628 and GSE7305 datasets were analyzed and integrated to screen out abnormal m6A regulators and m6A-related biomarkers through the GEO2R online software, Weighted Gene Co-Expression Network Analysis (WGCNA), Lasso analysis, Receiver Operating Characteristic curve (ROC), immune infiltration evaluation, and enrichment analysis. A protein‒protein interaction (PPI) network was established to identify vital nodes. In addition, the GSE23339 database and Turku Endometriosis Database were used to validate the key genes. Finally, we evaluated drugs for EM and provided new ideas for diagnostic and therapeutic targets.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData processing\u003c/h2\u003e \u003cp\u003eThe GSE25628 and GSE7305 microarray data were obtained from the Gene Expression Omnibus (GEO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/geo/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The GSE25628 dataset, which includes seven ectopic endometrium samples, six normal endometrium samples, and nine eutopic endometrium samples, was derived on the GPL571 platform. The GSE7305 dataset, which consists of ten ectopic endometrium samples and ten normal endometrium samples, was extracted on the GPL 570 platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially expressed genes\u003c/h2\u003e \u003cp\u003eGEO2R online software was applied to analyze the GSE25628 and GSE7305 microarrays and identify differentially expressed genes (DEGs). |logFC|\u0026gt;1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were set as the cutoff criteria for DEGs. The Venn diagram tool was used to display intersecting genes.\u003c/p\u003e \u003cp\u003eThe expression of m6A regulators in EM\u003c/p\u003e \u003cp\u003eThe m6A regulators were downloaded from a previous study[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Using the \u0026lsquo;Kruskal test \u0026rsquo; in R Package, 17 m6A regulators were investigated in ectopic endometrium samples, normal endometrium samples, and eutopic endometrium samples, including five writers, eleven readers, and one eraser. The heatmap of these 17 m6A regulators was unraveled by \u0026lsquo;pheatmap\u0026rsquo; in the R package, and the correlations between them were evaluated with Spearman\u0026rsquo;s correlation analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eKey module and gene selection from WGCNA\u003c/h2\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA), a systematic biology technique, was employed to elaborate gene association between samples[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. First, the median absolute deviation (MAD) of each gene was calculated, and 50% of genes accompanied by the minimum MAD were eliminated. Secord, outlier genes, and samples were removed by the good-Samples Genes method, and a scale-free co-expression network was established. Third, an adjacency matrix was constructed by soft thresholding power. The adjacency was then transformed into a topological overlap matrix (TOM), and the gene ratio and dissimilarity were computed. Fourth, average linkage hierarchical clustering was applied to classify the genes that expressed identical profiles into gene modules according to the Tom-based different measure. Fifth, the differences in module characteristic genes were calculated, and an ideal module dendrogram was selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExploration of candidate m6A regulators and ROC analysis\u003c/h2\u003e \u003cp\u003eLasso is a linear regression analysis using L1 regularization for achieving a sparsification and feature selection model[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u0026lsquo;glmnet\u0026rsquo; in the R package was adopted to integrate survival time, survival status, and gene expression. The lasso-cox technique was employed to construct a regression model. In the end, 3-fold cross validation was set up to harvest five optimal genes. The ROC was exhibited to appraise the diagnostic value of selected genes by \u0026lsquo;pROC\u0026rsquo; in the R package. An AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 was conceived as having excellent diagnostic value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration evaluation\u003c/h2\u003e \u003cp\u003eESTIMATE was applied to compute the immune score, stromal score, and ESTIMATE score for ectopic endometrium samples, normal endometrium samples, and eutopic endometrium samples. CIBERSORT was utilized to identify the relative diversity of 22 kinds of infiltrating immune cells in each sample. Through barplot, the proportion of infiltrating immune cells was visualized. With the vioplot, the relative proportion of different types of immune cells between the above groups was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) analysis is an enrichment for gene functions and gene products, including biological process (BP), cellular component (CC) and molecular function (MF)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a large-scale database for the exploration of gene functions[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Via the R package cluster Profilter, gene enrichment analysis was conducted. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR of \u0026lt;\u0026thinsp;0.1 were set as statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eProtein‒protein interaction network analysis and hub gene selection\u003c/h2\u003e \u003cp\u003eM6A regulators and 339 candidate genes were uploaded to the STRING database to establish their protein‒protein interaction (PPI) networks[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The minimum required interaction score was set at \u0026gt;\u0026thinsp;0.9, and the disconnected genes were hidden in the network. Cytoscape software was used to present the PPI network and select hub genes. A Venn diagram was applied to display the hub genes in betweenness, degree and closeness different algorithm analyses through Cytoscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of novel hub genes in other databases\u003c/h2\u003e \u003cp\u003eFor other database identification, the authors sought the gene expression profile dataset GSE23339 derived from the platform GPL6102 and the Turku Endometriosis Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://endometdb.utu.fi\u003c/span\u003e\u003cspan address=\"https://endometdb.utu.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which was used to confirm the hub genes in EM samples[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation of small molecular therapeutic drugs\u003c/h2\u003e \u003cp\u003eThe differentially expressed m6A-related mRNAs were introduced into the CMap database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a publicly accessible validation of small molecular drug website[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nml.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nml.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for the 3D structure of the established potential small molecules[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The STITCH database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://stitch.embl.de\u003c/span\u003e\u003cspan address=\"http://stitch.embl.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to search for the connection between hub genes and small molecules[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking analysis\u003c/h2\u003e \u003cp\u003eFirst, the authors downloaded the 3D structures of small molecules from the PubChem database, imported the frames into ChemBio3D Ultra 14.0, and set the minimum RMS gradient to 0.001. They then performed hydrogenation, charge calculation, charge distribution, and the rotatable key set in AutoDockTools-1.5.6. Second, they downloaded protein structures from the PDB database, used PyMOL2.3.0 to remove protein crystal water and primitive ligands, and then carried the protein structures to AutoDockTools-1.5.6. Third, PyMOL2.3.0 was applied to estimate the interaction mode of the docking results[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eA comparison of all laboratory results between the two sets was carried out through Student\u0026rsquo;s t test. GraphPad Prism 7.0 software was used for statistical data analysis. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant (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).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of DEGs in EM\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure 1 illustrates the experimental protocol. A total of 3208 DEGs were screened from GSE25628 and GSE7305 using the online method GEO2R. As shown in Fig.\u0026nbsp;2A, B, the expression of the DEGs was displayed via a volcano plot. There were 6257 and 6842 DEGs from the comparison of ectopic endometrium samples and normal endometrium samples in GSE25628 and GSE7305, respectively. Based on the Venn diagram, 3208 intersection genes were visualized (Fig.\u0026nbsp;2C). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays information about the database, including the type, sample groups, numbers, and platform.\u003c/p\u003e \u003c/div\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\u003eInformation of GEO database applied in the article\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE series\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePlatform\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE25628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGPL571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE7305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGEO, gene expression omnibus; EC, Ectopic endometrium; NE, Normal endometrium; EU, Eutopic endometrium.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe expression of m6A regulators in EM\u003c/h2\u003e \u003cp\u003eAltogether, 17 m6A regulators were selected among the ectopic, normal and eutopic groups, including eleven readers, five writers, and one eraser (Fig.\u0026nbsp;3A). When compared to the NE and EU groups, ten m6A regulators were remarkably downregulated in the EC group, namely, METTL3, WTAP, RBM15, RBM15B, YTHDC1, YTHDF2, YTHDF3, FMR1, LRPPRC, and HNRNPA2B. In contrast, YTHDF1 and FTO were obviously upregulated in the EC group compared with the NE group (Fig.\u0026nbsp;3B). The interaction network of m6A regulators is shown in Fig.\u0026nbsp;3C. Of note, LRPPRC was hidden because it was not connected to the others. The correlation coefficient among these m6A regulators was investigated in EC samples (Fig.\u0026nbsp;3D). The three pairs with the highest positive values were METTL3 and RBM15, LRPPRC and RBM15, and YTHDC1 and HNRNPA2B1 (r\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eKey module and gene selection from WGCNA\u003c/h2\u003e \u003cp\u003eWGCNA was utilized to substantiate the construction of a gene-correlated module in EM. The scale independence (ẞ=8, scale-free R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9) and the mean connectivity network were revealed (Fig.\u0026nbsp;4A, B). Figure\u0026nbsp;4C presents the clustering dendrogram in the EC, NE, and EU groups. Based on this power, 21 gene modules were produced, which are depicted in Fig.\u0026nbsp;4D, E. The correlation between EM and the 21 gene modules is displayed in Fig.\u0026nbsp;3F, and the dark olive-green module (451 genes) indicated the highest connectivity with the EC group. As shown in Fig.\u0026nbsp;4G, a dramatic positive correlation was acquired between gene significance and module membership in the dark olive-green module for the EC group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExploration of candidate m6A regulators and ROC analysis\u003c/h2\u003e \u003cp\u003eLasso analysis was employed to screen candidate m6A regulators. With this method, five m6A regulators were found, namely, YTHDF1, RBM15B, FTO, METTL3, and YTHDF2, by lambda\u0026thinsp;=\u0026thinsp;0.09781592 (Fig.\u0026nbsp;5A) and 3-fold cross-validation for shrinking parameters (Fig.\u0026nbsp;5B). Established by the Lasso analysis, the ROC curve was used to evaluate the sensitivity and specificity of the five m6A regulators, and their AUCs were 0.86, 1.00, 1.00, 0.86, and 0.93, respectively (Fig.\u0026nbsp;5C). The results demonstrated that the five m6A genes possess obvious diagnostic value for EM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration evaluation\u003c/h2\u003e \u003cp\u003ePrevious studies have certified that immune factors are involved in the development of EM, and whether m6A regulators participate in the immune process needs to be explored. Immune cell infiltration analysis demonstrated that m6A regulators were associated with the immune system. In the barplot visualization, the relative percent of 22 types of immune cells in each tissue is shown (Fig.\u0026nbsp;6A). Via the vioplot, EC patients had a higher degree of CD8 T cells and monocytes and a lower degree of follicular helper T cells and activated dendritic cells (Fig.\u0026nbsp;6B). The correlation of 22 kinds of immune cells indicated that gamma data T cells were most relevant to memory resting CD4 T cells (r\u0026thinsp;=\u0026thinsp;0.83) (Fig.\u0026nbsp;6C). Furthermore, as shown in Fig.\u0026nbsp;6D, the stromal score and ESTIMATE score were highest in the EC group, indicating that the immune microenvironment had a stronger association with EM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo investigate genes more closely associated with EM, 339 candidate genes were confirmed between the DEGs from GEO2R and the essential genes from WGCNA by a Venn diagram (Fig.\u0026nbsp;7A). The top results of KEGG analysis showed that candidate genes were mainly associated with \u0026ldquo;cell adhesion molecules\u0026rdquo; and \u0026ldquo;vascular smooth muscle contraction\u0026rdquo; (Fig.\u0026nbsp;7B). The top results of GO analysis revealed that candidate genes were primarily enriched in \u0026ldquo;biological adhesion,\u0026rdquo; \u0026ldquo;circulatory system development,\u0026rdquo; and \u0026ldquo;tube development\u0026rdquo; in BP terminology, \u0026ldquo;external encapsulating structure,\u0026rdquo; \u0026ldquo;anchoring junction,\u0026rdquo; and \u0026ldquo;supramolecular polymer\u0026rdquo; in CC terminology, and \u0026ldquo;cytoskeletal protein binding\u0026rdquo; and \u0026ldquo;protein-containing complex binding\u0026rdquo; in MF terminology (Fig.\u0026nbsp;7C). Details of the GO and KEGG analyses are listed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein‒protein interaction network analysis and hub gene selection.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFive m6A regulators and 339 candidate genes were uploaded to the STRING database to explore their connections. Of the 339 candidate genes, only ten interacted with m6A regulators: CAMK1D, RBM47, GGT5, COIL, RC3H2, G3BP2, DDX3X, ADAM19, DNAJC15, and LTC4S. (Fig.\u0026nbsp;8A). The PPI network of m6A-related genes consisted of 15 nodes and 21 edges (Fig.\u0026nbsp;8B). The cytoHubba plug-in was applied to select intersecting genes by three algorithms \u0026ndash; degree, betweenness, and closeness. The hub genes were RC3H2, GGT5, G3BP2, and CAMK1D, overlapping three other features among m6A-related mRNA-expressed genes (Fig.\u0026nbsp;8C), which might have pivotal roles in m6A methylation in EM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of novel hub genes in other databases\u003c/h2\u003e \u003cp\u003eTo verify the findings, the authors searched the Turku Endometriosis Database to identify the expression of the hub genes (Fig.\u0026nbsp;9A). The four gene expression trends between the two groups were consistent with the analysis results, although it was not known whether the differences were statistically significant. Moreover, the GSE23339 database was applied to confirm the expression level of these hub genes (Fig.\u0026nbsp;9B). Among the four genes, GGT5 and CAMK1D were selected for follow-up analysis because of their high statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eValidation of small molecular therapeutic drugs\u003c/h2\u003e \u003cp\u003eBy retrieving the CMAP database, abundant and remarkable small-molecule agents targeting EM were screened. The top five small-module agents with high correlations with EM were fananserin, scopolamine, buphenine, rucaparib, and ruxolitinib. The 3D structures of the above five chemicals that may be selected for potential treatment options were acquired from PubChem and are shown in Fig.\u0026nbsp;10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eHunting the connection between hub genes and drugs\u003c/h2\u003e \u003cp\u003eThrough the analysis of two hub genes in the STITCH database, the authors confirmed that the 2 genes are most likely to play a vital role in EM. In the next step, they aimed to find chemical compounds acting on GGT5 and CAMK1D to observe new treatments for EM. In the compound-protein interaction network, the compounds leukotriene C4, leukotriene D4, and glutamic acid interacted with GGT5 and GSK3 inhibitor. XIII interacting with CAMK1D were identified for subsequent analysis (Fig.\u0026nbsp;11A).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMolecular docking analysis\u003c/h2\u003e \u003cp\u003eMolecular docking analysis validated the GSK3 inhibitor. XIII to CAMK1D binding fractions of -9.2 kcal/mol, which indicated that there was a compelling binding effect between them. GSK3 inhibitors interact with CAMK1D mainly through hydrogen bonding and hydrophobic forces. Guanosine forms hydrogen bonds with GLN-99 and VAL-101, and the lengths of the hydrogen bonds were 2.1 \u0026Aring;, 2.0 \u0026Aring; and 2.1 \u0026Aring;, respectively. GSK inhibitor. XIII had hydrophobic effects with VAL-82, LEU-151, GLY-30, ALA-309, VAL-37, LEU-29, and SER-164 (Fig.\u0026nbsp;11B). The same method validated glutamic acid, leukotriene C4, and leukotriene D4 to GGT5 binding fractions of -5.3 kcal/mol, -6.6 kcal/mol, and \u0026minus;\u0026thinsp;7.0 kcal/mol, respectively (Fig.\u0026nbsp;11C, D, E).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEM is a benign disease in which endometrial cells colonize and grow outside the uterine cavity, but it has the biological behavior of a malignant disease. Common symptoms of EM include dysmenorrhea, pelvic mass, and infertility, which seriously affect the quality of life of patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathogenesis of EM is still unclear. The classic theory comes from the menstrual flow reflux theory proposed by Sampson in 1927[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Works in recent years have verified that EM is considered to be a disease related to immune factors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, none of the theories can thoroughly explain the diversity of clinical manifestations and the complexity of the treatment of EM, and the core molecular mechanism has yet to be described.\u003c/p\u003e \u003cp\u003eM6A modifications are generally found in eukaryotes, and occur on transcripts, such as mRNAs, tRNAs, and rRNAs[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this experiment, five m6A regulators associated with EM were screened, including the methyltransferases METTL3 and RMB15B, the demethyltransferase FTO, and the reader proteins YTHDF1 and YTHDF2. The expression of METTL3 is upregulated in maternal hepatic tumors, which can activate the Wnt/β-catenin signaling pathway and promote the proliferation, migration, and invasion of liver cancer cells[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In the presence of p53, the tumor suppressor gene RDM1 can inhibit the phosphorylation of Raf and ERK. At the same time, METTL3 can significantly increase the number of cells in the G2/M phase by inhibiting the expression of RDM1 in hepatoma cells, thereby promoting the proliferation and colony formation of liver cancer cells[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, METTL3 can also curb the expression of suppressor of cytokine signaling 2 (SOCS2) in hepatocellular carcinoma by combining with YTHDF2; knocking out METTL3 can dramatically inhibit the tumorigenicity of hepatocellular carcinoma and lung metastases in mice[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some investigators have shown that METTL3 cooperates with YTHDF1 to promote the adhesion, migration, and invasion of bladder cancer cells by upregulating CDCP1[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; METTL3 also works with YTHDF2 to promote the development and progression of bladder cancer by reducing the mRNA levels of SETD7 and KLF4[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Studies have found that FTO can induce the development of acute myeloid leukemia, non-small cell lung cancer, breast cancer, and bladder cancer[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we wondered whether the five m6A regulators screened in this paper also play a crucial role in EM. These conjectures require a large number of subsequent experimental verifications.\u003c/p\u003e \u003cp\u003eIn this investigation, CIBERSORT was applied to analyze and evaluate the infiltration of immune cells in EM, and it was found that CD8 T cells and monocytes in the EC group had higher levels than those in the NE group. However, follicular helper T cells and activated dendritic cells had lower levels. This finding indicates that EM is influenced by immune factors. CD8 T cells are immune cells and an essential part of the tumor microenvironment. Previous research has shown that CD8 T cells, as effector cells, have an excellent predictive prognosis in various tumors, such as breast cancer, colon cancer, and gastric cancer[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Monocytes are derived from common myeloid progenitors (CMPs), and after being recruited into tissues, they can differentiate into macrophages or partial dendritic cells, maintain tissue homeostasis, or play multiple immunological functions in the case of infection, inflammation, tumorigenesis, etc. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Follicular helper T cells belong to the CD4\u0026thinsp;+\u0026thinsp;T-cell subpopulation, which can promote the maturation and differentiation of B cells and play an essential role in the pathogenesis of diverse autoimmune diseases such as systemic lupus erythematosus, rheumatoid arthritis, and primary Sjogren's syndrome[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Dendritic cells are antigen-presenting cells that are diffusely distributed in different body tissues. It effectively presents antigens to T lymphocytes and activates T lymphocyte cells, inducing an initial immune response. It plays a crucial role in the induction and regulation of the immune response[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The results of this analysis not only enrich our understanding of the immune microenvironment of EM but can also be used for the clinical application of related research to develop drugs that regulate the function of CD8 T cells, monocytes, follicular helper T cells, and dendritic cells. However, we must also realize that there are still many issues waiting to be investigated. For example, the regulation of the differentiation of monocytes in different directions in the immune microenvironment is not completely clear, and the diversity of related dendritic cells still needs to be further explored. In conclusion, immune infiltration analysis provides specific guidelines for EM immunotherapy.\u003c/p\u003e \u003cp\u003eIn the bioinformatics series, it was finally determined that GGT5 and CAMK1D were the most closely related genes to the m6A regulators. GGT5, a member of the γ-glutamyl transpeptidase gene family, is highly expressed in lung adenocarcinoma-associated fibroblasts and can increase intracellular glutathione and decrease intracellular reactive oxygen species to enhance cancer cell resistance. Downregulation of GGT5 can reduce tumor cell proliferation[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, some studies have found that GGT5 is a poor prognostic factor for gastric cancer[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. CAMK1D is a member of the Ca2+/CaM-dependent kinase CaMKI family and is widely expressed in human tissues[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CAMK1D has been confirmed to be involved in developmental differentiation and disease processes in various cells and tissues. For example, in the hematopoietic system, CAMK1D is upregulated during the differentiation of CD34\u0026thinsp;+\u0026thinsp;artificial blood stem cells into neutrophils, regulating calcium-mediated functions such as granulocyte phagocytosis, migration, and adhesion[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, CAMK1D was found to be involved in the development and progression of a variety of tumors, such as breast cancer, lung cancer, and multiple myeloma[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, GGT5 and CAMK1D were expressed at higher levels in the EC group, demonstrating that these two genes may be involved in the pathogenesis of EM.\u003c/p\u003e \u003cp\u003eThis study indicates that m6A methylation may be the primary mechanism of abnormal gene expression in patients with EM. By exploring mRNAs associated with m6A, the possibility of ascertaining new biomarkers that could potentially be employed in EM diagnosis and treatment is offered. However, this research has some limitations. First, the number of experimental samples used for m6A methylation analysis was small, and more samples must be investigated. Second, the molecular mechanisms of hub genes require systematic and comprehensive exploration. Third, for the small molecule compounds discovered, research is needed on the mechanism by which they regulate EM and through further optimization to improve their bioavailability, inhibitory effect, and therapeutic effect. These screened drugs are expected to become useful tool molecules to help researchers explore the mechanisms of m6A-related biological processes and have positive implications for the clinical treatment of EM.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, with the exploration of m6A regulators and m6A-related genes in EM, we used the GEO database, WGCNA, Lasso analysis, ROC, immune infiltration evaluation, and enrichment analysis. Ultimately, we discovered that METTL3, RMB15B, FTO, YTHDF1, and YTHDF2 might be vital m6A regulators, and GGT5 and CAMK1D may be essential m6A-related genes of EM. Importantly, we also searched for a few efficient small-molecule agents that provide new views for the treatment of EM. These results provide fresh insights into m6A methylation in EM and reveal potential biomarkers and precision medicine for EM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003em6A\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN6- methyladenosine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted Gene Co-Expression Network Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein‒protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emedian absolute deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etopological overlap matrix\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOCS2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esuppressor of cytokine signaling 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewe would like thank all collaborators for their help and support in the study. We also thank the staff members of the Gene Expression Omnibus database and the Turku Endometriosis Database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData curation, Yanan He; Formal analysis, Chengcheng Ren; Funding acquisition, Guangmei Zhang; Investigation, Yanan He and Dejun Wang; Methodology, Chengcheng Ren and Guangmei Zhang; Project administration, Jixin Li; Resources, Liyuan Sun; Software, Chengcheng Ren; Supervision, Guangmei Zhang; Validation, Dejun Wang; Writing \u0026ndash; original draft, Chengcheng Ren; Writing \u0026ndash; review \u0026amp; editing, Chengcheng Ren. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China, grant number: 81971359, and the Natural Science Foundation of Heilongjiang Province, grant number: LH2019HO27.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are included in this article and its additional files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\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\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaunders PTK, Horne AW. 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Cell. 2022;185(7):1189\u0026ndash;1207e25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou Q, et al. miR-448 targets IDO1 and regulates CD8(+) T cell response in human colon cancer. J Immunother Cancer. 2019;7(1):210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe W, et al. CD155T/TIGIT Signaling Regulates CD8(+) T-cell Metabolism and Promotes Tumor Progression in Human Gastric Cancer. Cancer Res. 2017;77(22):6375\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuilliams M, Mildner A, Yona S. Developmental and Functional Heterogeneity of Monocytes. Immunity. 2018;49(4):595\u0026ndash;613.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOveracre-Delgoffe AE, et al. Microbiota-specific T follicular helper cells drive tertiary lymphoid structures and anti-tumor immunity against colorectal cancer. Immunity. 2021;54(12):2812\u0026ndash;2824e4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollin M, Ginhoux F. Human dendritic cells. Semin Cell Dev Biol. 2019;86:1\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei JR, Dong J, Li L. Cancer-associated fibroblasts-derived gamma-glutamyltransferase 5 promotes tumor growth and drug resistance in lung adenocarcinoma. Aging. 2020;12(13):13220\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, et al. Identification of GGT5 as a Novel Prognostic Biomarker for Gastric Cancer and its Correlation With Immune Cell Infiltration. Front Genet. 2022;13:810292.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkizuki K, et al. Autoactivation of C-terminally truncated Ca(2+)/calmodulin-dependent protein kinase (CaMK) Iδ via CaMK kinase-independent autophosphorylation. 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Front Pharmacol. 2021;12:672054.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeeliger D, de Groot BL. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J Comput Aided Mol Des. 2010;24(5):417\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometriosis, m6A regulators, differentially expressed genes, immune infiltration, therapeutic drugs","lastPublishedDoi":"10.21203/rs.3.rs-3003927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3003927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eN6-methyladenosine(m6A) modification regulates the processes of RNA splicing, subcellular localization, translation and stability by changing the RNA structure and the interaction between RNA and RNA-binding proteins to ensure the timely and accurate expression of genes. In this study, we investigated m6A regulators and m6A-related genes and for the first time explored effective prevention and treatment targets in endometriosis (EM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBy incorporating the Gene Expression Omnibus (GEO) database, biological information analysis technologies, and validation of other databases, aberrant m6A-methylated genes and m6A-related genes were uncovered, as well as efficient therapeutic drugs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMETTL3, RMB15B, FTO, YTHDF1, and YTHDF2 might be vital m6A regulators, and GGT5 and CAMK1D may be essential m6A-related genes of EM. A few crucial small-molecule agents supply new views for the treatment of EM.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese results demonstrated novel insights into m6A methylation of EM and revealed potential biomarkers and precision medicine strategies for EM.\u003c/p\u003e","manuscriptTitle":"Discovery of N6-methyladenosine modification regulators and their related mRNAs in endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-05 17:38:44","doi":"10.21203/rs.3.rs-3003927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"688cf183-c3a3-48ff-a79a-3c29e1a314e8","owner":[],"postedDate":"June 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-21T16:44:07+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-05 17:38:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3003927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3003927","identity":"rs-3003927","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}