{"paper_id":"bae6b720-499f-45d5-844d-b1124289f646","body_text":"Ovarian endometriosis has a high prevalence and affects millions of women worldwide. The origin of endometriosis is still puzzling. It is generally considered to be derived from endometrium [ 1 ], however, other hypotheses were also provided. For instance, a latest report suggested that ~ 60% of endometriosis may originate from fallopian tube [ 2 ]. Ovarian origin recently, researchers found and confirmed the existence of cells expressing two germ cell line specific proteins (deadboxpolypeptide4/interferoninducedtrans, membraneprotein3, ddx4/ifitm3) in ectopic ovarian EMS lesions, suggesting that ovary-originated stem cell may contribute to the occurrence and progression of ovary EMS lesions [ 3 ]. To answer this question, we resorted to paired DNA methylation assay for ovarian endometriosis(ovarian endometrioma), normal ovarian and endometrial tissues in a small pilot cohort.\nEpigenetic modifications regulate the transcriptional process, and abnormalities lead to various diseases. Among the most investigated and vital modifications, DNA methylation was linked to alterations in endometrial function/dysfunction-related genes, modulating cell proliferation, inflammation/immune response, angiogenesis, and steroid hormone response. These findings offer a deep understanding of epigenetic reprogramming and steroid hormone effects in endometrium aiding in the pathogenesis and pathophysiology of endometriosis [ 4 – 6 ].\nPrevious reports have shown that the endometriosis pathophysiology involves aberrant promoter methylation of several genes, for instance, HOXA10 [ 7 ], steroidogenic factor 1 [ 8 ] and aromatase [ 9 ]. As a new research area, genome-wide DNA methylation analysis can clarify the relationship between DNA methylation state and endometriosis. Our study utilized a microarray-based approach to determine the accurate DNA methylation level. The abnormal methylation expression between ovarian endometriosis cells, eutopic endometrium and ovarian tissues was analyzed.\n\nEndometrium was biopsied from 4 fertile women receiving surgery for histologically confirmed unilocular/multilocular ovarian endometriosis (2:2). Normal ovarian tissue and eutopic endometrium of the same patient were used as controls. Specimens were obtained from subjects without hormone-based treatments at least 1 quarter before specimen acquisition and menstruation just clean, that is, endometrial hyperplasia. Upon surgical dissection, partial tissue was cryopreserved for microarray analyses. Endometrium and cysts were rinsed by Dulbecco’s modified eagle’s medium (DMEM) comprising of glutamine, 50 mg/ml streptomycin, and 50iu/ml penicillin (all from Invitrogen, Paisley, UK), and subsequently cut to a size of 1mm 3 . Then, ESC was isolated via screening with 70 mm nylon mesh after 2 h of collagenase (Sigma, Mo, USA) procession in a shaking incubator at 37℃. The filtrate was washed three times. The human investigation committee of Tongji University endorsed the research.\nIllumina 850k methylation EPIC Bead Array data was processed through a standard pipeline using the ChAMP package in R. The ChAMP can load raw intensity data in the form of IDAT or matrix containing methylation value (beta) of each probe [ 10 ]. After filtering and normalization, the 12 samples were combined into an array including only overlapping CpG sites and finally 743,800 probes were included. Principal component analysis (PCA) was firstly run on normalized methylation data to evaluate the similarity of methylation profile in 12 samples.\nBumphunter method was implemented to identify differentially methylated regions (DMR) within ChAMP (ChAMP.DMR function). The methylation beta value of each DMR was calculated using the mean value of all the probes that included in each DMR.\nThe heatmap was drawn using all the DMRs generated from ChAMP.DMR function. The |log2(FoldChange)| > 0.5 and Family-wise error rate (FWER) adjusted p-value < 0.05 was considered a statistical significance threshold. Therefore, the DMR regions with a |log2(FoldChange)| > 0.5 and FWER < 0.05 was considered as significantly differentially methylated regions. The DMRs region were annotated using ChIPseeker package in R bioconductor. The genomic regions for significantly differentially methylated regions were annotated with the definition of TSS3000 (3000 upstream and downstream of transcription initiation locus respectively) as the promoter region. Gene ontology (GO) functional enrichment analysis was done for the genes associated with differential methylation regions. And adjusted p-value < 0.05 was considered a statistical significance threshold.\nTotal RNA was extracted from ectopic and eutopic samples. cDNA was obtained after reverse transcription reversal by Evo M-MLV RT Kit (Accurate Biology Co. Ltd, AG11601). The qRT-PCR was performed by Evo M-MLV One Step RT-PCR Kit (Accurate Biology Co. Ltd, AG11607). Each target gene was compared to β-actin. The expression of target mRNA was calculated based on 2-ΔΔCt method.\nAll the statistical analyses were performed using R software (version 4.0.1). P-values less than 0.05 were considered statistically significant. The Kolmogorov-Smirnov test was applied to evaluate the normality of the distribution of the variables. For qRT-PCR, statistical analyses were conducted using a student t-test (data with normal distribution) or Mann-Whitney test (data with skewed distribution) as appropriate by GraphPad Prism software 9.0 (GraphPad Software Inc).\n\nTotal of 12 samples from 4 patients, each with endometriosis, ovarian and endometrial tissues were profiled with illumina 850k methylation array. Principal component analysis clearly showed that endometriosis samples were tightly clustered with samples of ovarian tissue (Fig.  1 ). One of endometrial sample was located further away from other samples as an outlier, and this sample also showed a slightly different normalized beta distribution compared with other samples (Fig.  2 ). Then we plotted the heatmap of DNA methylation beta value matrix after normalization, and clustered the samples by hierarchical clustering. Again, 1B_E exhibited a hybrid DNA methylation profile.(Fig.  3 ) Therefore, we removed this sample for the downstream analyses. To confirm this result, we also performed PCA on our samples as well as publically available DNA methylation data on healthy endometrial biopsies [ 11 ], and as expected, healthy endometrial samples from public data were closer to our endometrial samples (Fig.  4 ).\nFig. 1 PCA plot of DNA methylation data from endometriosis, normal ovarian and endometrial tissues\nPCA plot of DNA methylation data from endometriosis, normal ovarian and endometrial tissues\nFig. 2 One of endometrial sample was located further away from other samples as an outlier, and this sample also showed a slightly different normalized beta distribution compared with other samples\nOne of endometrial sample was located further away from other samples as an outlier, and this sample also showed a slightly different normalized beta distribution compared with other samples\nFig. 3 Hierarchical clustering analysis of 12 samples based on methylation levels. Top 1000 CpGs with the highest variance among 12 samples were included. Color mapping from blue to red indicates methylation level from low to high. Group C: ovarian endometrial cysts; N: normal_ovarian; E: eutopic endometriosis\nHierarchical clustering analysis of 12 samples based on methylation levels. Top 1000 CpGs with the highest variance among 12 samples were included. Color mapping from blue to red indicates methylation level from low to high. Group C: ovarian endometrial cysts; N: normal_ovarian; E: eutopic endometriosis\nFig. 4 Endometriosis samples closely resembled normal ovarian tissues, but not endometrial tissues\nEndometriosis samples closely resembled normal ovarian tissues, but not endometrial tissues\nAs for DMRs analysis, we used the software ChAMP to explore the DMRs and the methylation beta value of each DMR was calculated using the mean value of all the probes that included in each DMR. The heatmap was generated using the beta value of DMRs. And from the heatmap we can see the ovarian endometrial cysts (group C) and normal_ovarian (group N) are clusted together, suggesting that the methylation profile of the cyst is more similar to ovarian tissues compared with endometrial tissue (Fig.  5  ).\nFig. 5 Hierarchical clustering analysis of the three groups based on DMRs. Color mapping from blue to red indicates methylation level from low to high. Group C: ovarian endometrial cysts; N: normal_ovarian; E: eutopic endometriosis\nHierarchical clustering analysis of the three groups based on DMRs. Color mapping from blue to red indicates methylation level from low to high. Group C: ovarian endometrial cysts; N: normal_ovarian; E: eutopic endometriosis\nWe finally got 431 DMRs (with p-value < 0.05) using the champ.DMR function. But the counts of DMRs with |log2(FoldChange)| > 0.5 and adjusted p-value < 0.05 was 55 (Fig.  6 ). Figure  7  shows the GO analysis of Genes with lower methylation levels in eutopic endometriosis (group E) compared with in ovarian endometrial cysts (group C). And Fig.  8  shows the GO analysis of Genes with higher methylation levels in eutopic endometriosis (group E) compared with ovarian endometrial cysts (group C).\nFig. 6 Volcano plot of log2(fold change) against ‑log10(p.adj ) of DMRs. The red blue spots stand for DMRs with adjusted p-value < 0.05 and log2FC > 0.5, and blue spots stand for DMRs with adjusted p-value < 0.05 and log2FC < -0.5, grey spots stand for non‑significant DMRs. The horizontal dash line denotes adjusted p-value < 0.05; the vertical dash lines denote |log2FC|>0.5. Group C: ovarian endometrial cysts; E: eutopic endometriosis\nVolcano plot of log2(fold change) against ‑log10(p.adj ) of DMRs. The red blue spots stand for DMRs with adjusted p-value < 0.05 and log2FC > 0.5, and blue spots stand for DMRs with adjusted p-value < 0.05 and log2FC < -0.5, grey spots stand for non‑significant DMRs. The horizontal dash line denotes adjusted p-value < 0.05; the vertical dash lines denote |log2FC|>0.5. Group C: ovarian endometrial cysts; E: eutopic endometriosis\nFig. 7 Significantly enriched term by the genes with higher methylation levels in ovarian endometrial cysts (group C). Genes with higher methylated levels in eutopic endometriosis (group E) compared with ovarian endometrial cysts (group C) GO analysis\nSignificantly enriched term by the genes with higher methylation levels in ovarian endometrial cysts (group C). Genes with higher methylated levels in eutopic endometriosis (group E) compared with ovarian endometrial cysts (group C) GO analysis\nFig. 8 Significantly enriched term by the genes with lower methylation levels in ovarian endometrial cysts (group C)\nSignificantly enriched term by the genes with lower methylation levels in ovarian endometrial cysts (group C)\nMost of previous studies compared the endometriosis with endometrial tissues; therefore we analyzed differential DNA methylation between endometriosis and endometrial tissues and identified the differentially methylated regions (Table  S1 ). In comparison with eutopic endometriosis, many genes in ovarian endometrial cysts (choESC) had different degrees of methylation, high and low (Tables  1  and  2 ),With acquired methylation profiles data, we identified the detailed features of aberrantly methylated genes in choESC using gene cards (the humor gene database) analyses. Depending on pathway analysis of screened genes, there are abnormalities of relevant signal transduction pathways involving endometriosis onset and progression, developmental processes, human early embryonic development, regulation of caspase, tyrosine, and mRNA metabolic processes et al. It indicated an abnormal expression pattern of choESC in peritoneal environment.\nTable 1 Hypomethylation in endometrial tissues compared the endometriosis Gene Symbol Gene name DMR value (Endometrial vs. Cyst) Delta_beta Log2 Fold Change Ratio P  value Function HOXB1 homeotic genes B1 3.749 0.386 0.813 0 Important growth and development regulatory genes regulator of retinoic acid  response elements (RAREs) MCC membrane compartment of Can1 3.709 0.288 2.587 0 one cytoplasm membrane microdomain regulating metabolism, cellular morphogenesis, signaling cascades, and mRNA degradation. TMEM101 transmembrane protein 101 3.695 0.297 2.629 0 upregulation and increase the expression of NF-κB in ovary cancer. CASS4 Homo sapiens Cas scaffolding protein family  member 4 3.624 0.515 1.530 0.004 Possible docking protein which may play a role for tyrosine-kinase-based signaling related to cell adhesion. Regulates FAK activity, focal adhesion integrity, and cell  spreading. BST2 bone marrow stromal cell antigen 2 3.612 0.450 1.713 0 Regulating Type-I interferon and interleukin-6 generation FABP3 Fatty Acid Binding Protein 3 3.150 0.298 2.098 0.004 During the early stages of pregnancy, upregulating cell growth and proliferation in uterine endometrium FAIM2 Fas Apoptotic Inhibitory Molecule 3.052 0.317 2.376 0.012 specifically protecting cells from Fas-induced apoptosis SHANK2 SH3 and multiple ankyrin  repeat domains protein 3.051 0.353 2.140 0.004 A protein-coding gene relating to autism TCF21 Transcription Factor 21 3.030 0.433 1.563 0.008 augmenting steroidogenic factor (SF) 1 and  estrogen receptor β (ERβ) PRDM1 positive regulatory domain 1 2.983 0.431 1.269 0.012 increased IL-10 + Th17 cells are significantly MIR365A MicroRNA 365a 2.974 0.378 0.842 0.016 involving posttranscriptional modulation of genes via regulating mRNA stabilization and translation FOXP1 Forkhead Box P1 2.849 0.443 1.492 0.02 activating Wnt/β-cateninsignaling pathway  in endometriosis LOC339803 LOC339803 2.841 0.199 2.358 0.028 Enhancing HCC invasion and migration via miR-30a-5p/ SNAIL1. In human atherosclerotic lesions, serve as potential biomarkers for lesion hypoxia. ZNF311 Zinc Finger Protein311 2.833 0.330 1.838 0 upregulated in grade-depended glioma patients of adverse outcome RNASE1 Ribonuclease A Family Member 1 2.773 0.364 1.027 0.008 a key player in regulation of vascular homeostasis LYPLAL1 Long non coding RNA lysophospholipase -like l 2.771 0.206 1.338 0.008 Long non coding RNA lysophospholipase-like2 PAX2 Paired Box Homeotic Gene 2 2.670 0.291 1.741 0.036 Its mutations contribute to optical nerve glioma and kidney dysplasia NUP210L Nucleoporin 210 2.663 0.328 0.868 0.036 Encoding a membrane-spanning glycoprotein which participates in formation of nuclear pore complex SLC1A2 Solute Carrier Family 1 Member 2 2.657 0.329 1.856 0.04 Encoding a solute transporter protein MX2 MX Dynamin Like GTPase 2 2.646 0.252 1.770 0.04 Encoding a protein of dynamin and GTPases families, with nuclear and cytoplasmic forms. SVIL Supervillin 2.632 0.369 0.863 0.04 Encoded product contributes to myosin II assembly and disintegration of focal adhesions. TERC Telomerase RNA Component 2.614 0.227 1.500 0.024 Encoding telomerase. PROCA1 Protein Interacting With Cyclin A1 2.588 0.259 1.982 0.032 Enables cyclin binding activity. Predicted to be  involved in arachidonic acid secretion and  phospholipid metabolic process ARHGAP25 Rho GTPase Activating Protein 25 2.577 0.300 1.637 0.004 Encoding negative regulators of Rho GTPases. DAW1 Dynein Assembly Factor With WD Repeats 1 2.569 0.226 0.679 0.02 Maybe the upstream of some processes, including cerebrospinal fluid circulation; determination of left/right symmetry; and outer dynein arm assembly. MAGI2-AS3 MAGI2 Antisense RNA 3 2.563 0.264 1.783 0.048 An RNA Gene, affiliated with the lncRNA class. Diseases associated with MAGI2-AS3 include Nephrotic Syndrome, Type 15 and Breast Cancer. TNFSF13B TNF Superfamily Member 13b 2.488 0.179 2.000 0.036 modulating B cells proliferation and  differentiation. HOXA2 Homeobox A2 2.3473 0.330 0.864 0.008 Encoding a DNA-binding  transcription factor governing morphogenesis,  and differentiation. SGIP1 SH3GL Interacting Endocytic Adaptor 1 2.103 0.196 1.691 0.016 May involve in clathrin-mediated endocytosis and energy homeostasis. ASCL2 Achaete-Scute Family BHLH Transcription Factor 2 2.028 0.257 0.838 0 related pathways are Embryonic and Induced  Pluripotent Stem Cells and Lineage-specific  Markers and Human Early Embryo Development. EPM2AIP1 EPM2A Interacting Protein 1 2.013 0.133 1.007 0.028 MLH1 and EPM2AIP1 genes share a common  promoter whose methylation has been shown to affect both genes. UBD Ubiquitin D 1.818 0.140 1.309 0.004 Encoded protein participates in aggresomes formation, mitotic regulation, and dendritic cell maturation. RNF39 RING finger protein 39 1.6484 0.201 0.535 0.012 Its variants were linked to viral diseases and autoimmune diseases. TBX3 T-Box Transcription Factor 3 1.567 0.272 0.756 0.004 Regulating developmental processes. PON1 Paraoxonase 1 1.390 0.180 0.537 0.016 Displaying lactonase and ester hydrolase activity. ZIC1 Zic Family Member 1 1.157 0.138 0.750 0.032 Encoding a C2H2-type zinc finger proteins.\nHypomethylation in endometrial tissues compared the endometriosis\nImportant growth and development regulatory genes regulator of retinoic acid\nresponse elements (RAREs)\nHomo sapiens Cas scaffolding protein family\nmember 4\nPossible docking protein which may play a role for tyrosine-kinase-based signaling related to cell adhesion. Regulates FAK\nactivity, focal adhesion integrity, and cell\nspreading.\nSH3 and multiple ankyrin\nrepeat domains protein\naugmenting steroidogenic factor (SF) 1 and\nestrogen receptor β (ERβ)\nactivating Wnt/β-cateninsignaling pathway\nin endometriosis\nEnhancing HCC invasion and migration via miR-30a-5p/ SNAIL1.\nIn human atherosclerotic lesions, serve as potential biomarkers for lesion hypoxia.\nLong non coding RNA lysophospholipase\n-like l\nEnables cyclin binding activity. Predicted to be\ninvolved in arachidonic acid secretion and\nphospholipid metabolic process\nDynein Assembly Factor With WD\nRepeats 1\nAn RNA Gene, affiliated with the\nlncRNA class. Diseases associated with MAGI2-AS3 include Nephrotic Syndrome,\nType 15 and Breast Cancer.\nmodulating B cells proliferation and\ndifferentiation.\nEncoding a DNA-binding\ntranscription factor governing morphogenesis,\nand differentiation.\nAchaete-Scute Family BHLH\nTranscription\nFactor 2\nrelated pathways are Embryonic and Induced\nPluripotent Stem Cells and Lineage-specific\nMarkers and Human Early Embryo Development.\nMLH1 and EPM2AIP1 genes share a common\npromoter whose methylation has been shown to affect both genes.\nTable 2 Hypermethylation in endometrial tissues compared the endometriosis Gene Symbol Gene name DMR value (Endometrial vs. Cyst) Delta_beta Log2 Fold Change Ratio P  value Function SHF Src Homology 2 Domain Containing F -2.107 -0,172 -1.249 0.016 May enable phosphotyrosine residue  binding activation, and participate in apoptosis. ESR1 Estrogen Receptor 1 -2.177 -0.296 -1.520 0.016 Encoding estrogen receptor, vital for hormone binding, and transcriptional activation. ESR2 Estrogen Receptor 2 -2.280 0.036 Encoding the estrogen receptor 2. RBM24 RNA Binding Motif Protein 24 -2.414 -0.249 -1.816 0.036 Involved in several processes, including negative regulation of cytoplasmic translation, and regulation of mRNA metabolic process. LMO7DN LIM Domain 7 downstream neighbour -2.487 -0.334 -0.728 0.02 Involved in lung cancer prognosis. ZC3H12D Zinc Finger CCCH-Type Containing 12D -2.541 -0.327 -1.454 0.02 Predicted to enable endoribonuclease activity and mRNA binding activity, and negatively modulate G1/S transition and cell growth CACNB2 Calcium Voltage-Gated Channel Auxiliary Subunit Beta 2 -2.550 -0.234 -1.729 0.02 Encoding a subunit of voltage-dependent calcium channel proteins. C17orf107(CHRNE) Cholinergic Receptor Nicotinic Epsilon Subunit -2.563 -0.197 -2.101 0.048 After binding acetylcholine, AChR undergoes an alteration in conformation opening an ionotransduction channels across the plasma membrane HAND2-AS1 HAND2 Antisense RNA 1 -2.605 -0.383 -1.315 0.044 Predicted to be involved in positive regulation of gene expression, and possess a positive effect on cardiac right ventricle morphogenesis. PEMT Phosphatidylethanolamine N- Methyltransferase -2.800 -0.346 -0.731 0.028 Converting phosphatidylethanolamine to phosphatidylcholine by sequential  methylation. ZNF22 Zinc Finger Protein22 -2.837 -0.259 -1.282 0.012 Modulating cell migration, adhesion, and cycle. STRA6 Signaling Receptor And Transporter Of Retinol STRA6 -3.033 -0.430 -1.369 0 Encoding a membrane protein responsible for retinol metabolism. LINC00460 Long Intergenic Non-Protein Coding RNA 460 -3.090 -0.351 -1.068 0.004 play vital roles in the pathogenesis, tumorigenesis,  and angiogenesis of cancers. LRMDA Leucine Rich Melanocyte Differentiation Associated -3.270 -0.442 -1.282 0.004 This gene encodes a leucine-rich repeat protein. EMX2OS EMX2OS -3.272 -0.445 -1.943 0 Modulating ovarian cancer cells through miR-654-3p/AKT3/PD-L1. RNF19A RING finger protein 19 A -3.461 -0.448 -1.526 0 Interacting with alpha synuclein in neurons. HOXC4 Homeobox C4 -3.463 -0.435 -2.510 0 Related to immunodeficiency with Hyper-Igm, and lymphoma. TFAMP1 Transcription Factor A, Mitochondrial Pseudogene 1 -3.641 -0.391 -0.901 0.004 Encoding a mitochondrial transcription factor. FMN1 Formin 1 -4.035 -0.407 -2.890 0 Modulating development of adhesion junction  and linear actin polymerization.\nHypermethylation in endometrial tissues compared the endometriosis\nMay enable phosphotyrosine residue\nbinding activation, and participate in apoptosis.\nZinc Finger CCCH-Type Containing\n12D\nCalcium Voltage-Gated Channel\nAuxiliary Subunit Beta 2\nCholinergic Receptor Nicotinic Epsilon\nSubunit\nPhosphatidylethanolamine N-\nMethyltransferase\nConverting phosphatidylethanolamine to phosphatidylcholine by sequential\nmethylation.\nSignaling Receptor And Transporter Of\nRetinol\nSTRA6\nLong Intergenic Non-Protein Coding\nRNA 460\nplay vital roles in the pathogenesis, tumorigenesis,\nand angiogenesis of cancers.\nLeucine Rich Melanocyte Differentiation\nAssociated\nTranscription Factor A, Mitochondrial\nPseudogene 1\nModulating development of adhesion junction\nand linear actin polymerization.\nGO term enrichment analysis showed that the hypomethylation genes in ovarian endometrial cysts were primarily engaged in embryonic organ development and embryonic organ development, stem cell population maintenance, et al. And the hypermethylation genes in ovarian endometrial cysts were primarily engaged in mRNA destabilization, female uterus and genitalia development, et al. The function of genes related to steroid binding, transcription factor activity and receptor activity.(Figures  7  and  8  ).\nWe compared our differentially methylated sites and only found consistent results on hyper-methylation on ESR1 [ 1 ,  12 ].Consistently, downregulation of ESR1 mRNA level in endometriosis was also reported in few studies [ 12 ,  13 ]. Then we searched for the literature for the differentially methylated genes we identified and examined whether the associated expression patterns were observed. Indeed, we observed consistent results on 5 genes: TNFSF13B, FOXP1, TCF21, BST2 and STRA6 [ 14 – 20 ].Taken together, despite little overlap with previously characterized genes, the characterized methylated genes in endometriosis were consistent with the reported expression changes in endometriosis(Table  3 ).RT-PCR results showed that ER1,STAR6 and PEMT were significantly downregulated in ectopic tissues, and BST2,TCF21 and FOXP1 were significantly upregulated in ectopic tissues (Fig.  9  ). In summary, we validated the top candidate genes in endometriosis which might be regulated by DNA methylation.\nTable 3 The differentially methylated genes were consistent with the reported expression changes in endometriosis Gene Symbol Gene name DMR value (Endometrial vs. Cyst) Delta_beta Log2 Fold Change Ratio P  value Function Hypomethylation BST2 bone marrow stromal cell antigen 2 3.612 0.450 1.713 0 Regulating type-I interferon and interleukin-6 generation Modulating embryo-maternal immune. TCF21 Transcription Factor 21 3.030 0.433 1.563 0.008 Interacting with upstream stimulatory factor 2, transactivating SF-1 and Erβ promoters, and modulating estrogen pathway and fibrosis in endometriosis. FOXP1 Forkhead Box P1 2.849 0.443 1.492 0.02 FOXP1 activating Wnt/β-cateninsignaling pathway in endometriosis.Its knockdown reverted endometrium cell phenotypes. TNFSF13B TNF Superfamily Member 13b 2.488 0.179 2.000 0.036 Modulating B cells proliferation and  differentiation. Increased in serum of endometriosis subjects. Hypermethylation ESR1 Estrogen Receptor 1 -2.177 -0.296 -1.520 0.016 Encoding estrogen receptor, vital for hormone binding, and transcriptional activation. STRA6 Signaling Receptor And Transporter Of Retinol STRA6 -3.033 -0.430 -1.369 0 Vital for retinol binding protein, the retinol uptake into cells. Downregulating STRA6 enhanced endogenous estradiol synthesis. PEMT Phosphatidylethanolamine N- Methyltransferase -2.800 -0.259 -1.282 0.028 Converting phosphatidylethanolamine to phosphatidylcholine by sequential methylation. Its polymorphism may induce infertility of endometriosis women.\nThe differentially methylated genes were consistent with the reported expression changes in endometriosis\nRegulating type-I interferon and interleukin-6 generation\nModulating embryo-maternal immune.\nModulating B cells proliferation and\ndifferentiation. Increased in serum of endometriosis subjects.\nSignaling Receptor And Transporter Of\nRetinol\nSTRA6\nPhosphatidylethanolamine N-\nMethyltransferase\nFig. 9 qRT-PCR validation of the gene expression of the candidate genes. ER1,STAR6 and PEMTwere significantly downregulated in ectopic tissues, and BST2,TCF21 and FOXP1 were significantly upregulated in ectopic tissues. *** P  < 0.001, ** P  < 0.01, * P  < 0.05, ns, not significant\nqRT-PCR validation of the gene expression of the candidate genes. ER1,STAR6 and PEMTwere significantly downregulated in ectopic tissues, and BST2,TCF21 and FOXP1 were significantly upregulated in ectopic tissues. *** P  < 0.001, ** P  < 0.01, * P  < 0.05, ns, not significant\n\nIn this work, we presented a very small cohort of paired DNA methylation analysis of normal ovarian, endometrial and endometriosis samples. Despite the very small sample size, results were very clear that endometriosis samples closely resembled normal ovarian tissues, but not endometrial tissues. Such results suggested that, instead of migrating from endometrial tissues, endometriosis might be originated from ovarian tissues. Such results did not rule out the possibility that endometriosis was indeed seeded by endometrial cells, but drastic DNA methylation reprogramming resulted in highly similar DNA methylation profiles to the ovarian tissues.\nA recent report confirmed genome-wide DNA methylation profiles of endometriosis tissue subtypes, focusing on promoters [ 21 ]. They found a large number of methylation differences compared with the eutopic endometrium and verified with clinical data. At the same time, Yoshiaki et al. revealed a more accurate genome-wide DNA methylation map, which was derived from the homogenous ESCs of eutopic endometrium and ovarian endometrium cysts with endometriosis. Some differentially methylated or expressed genes (Nr5a1, star, STRA6 and HSD17B2) were involved in steroidogenesis, also verified in numerous clinical specimens by independent methods [ 20 ]. Our strategy was to investigate whether there were methylation differences between multilocular ovarian cysts and unilocular ovarian cysts and normal ovarian tissues.\nMany genes in choESC acquired from endometrium cysts had different degrees of methylation. Basing on acquired methylation profiles, specific features of aberrantly methylated genes in choESC were evaluated using gene cards (the humor gene database) analysis. There are abnormalities of relevant signal transduction pathways in endometriosis pathogenesis and progression. Abnormal methylation status exists in genes related to proliferation and apoptosis, and immune responses, indicating an abnormal expression of choESC in peritoneal environment. Meanwhile, choesc showed abnormal differentiation, such as neurogenesis and embryogenesis. This means that choesc may have differentiated into other types of cells, suggesting the existence of abnormal developmental processes. The above findings add the possible involvement of DNA epigenetic modifications in endometriosis pathogenesis.\nWe compared our differentially methylated sites and only found consistent results on hyper-methylation on ESR1. Consistently, downregulation of ESR1 mRNA level in endometriosis was also reported in few studies. Then we searched for the literature for the differentially methylated genes we identified and examined whether the associated expression patterns were observed. Indeed, we observed consistent results on 5 genes: TNFSF13B, FOXP1, TCF21, BST2 PEMT and STRA6. Increased TNFSF13B B lymphocyte stimulator protein was identified in serum of endometriosis subjects [ 14 ]. Suppressing FOXP1 reverted the endometrium cell phenotype, involving decreased collagen gel contraction, cell growth and migratory movement [ 15 ].TCF21 could transactivate SF-1 and Erβ promoters in ESCs [ 16 ], modulating estrogen pathway and fibrosis of endometriosis [ 17 ]. rs4244593 of PEMT-related polymorphism modulated the choline or phospholipids generation, inducing infertility of endometriosis women [ 19 ].STRA6 is vital for retinol binding protein, and retinol uptake into cells.\nThe correlation analysis between DNA methylation and transcriptomes identified an anticipated positive relation, as well as a negative relation. It is impossible to characterize the close relationship between DNA methylation and mRNA expression due to the lesser gene coverage of beadchip specifications and the analysis of only two CPGs per gene. In genome-wide observation, a complex network may exist between DNA methylation and transcription.\nAltogether, this study provides basic DNA methylation data on ovarian endometriosis compared with ovarian tissue and eutopic endometrium using untreated cultured ESCs. We believe that this kind of new information will contribute to the future research on treatment strategies and preventive drugs for endometriosis.\n\nBelow is the link to the electronic supplementary material.\nSupplementary Material 1\nSupplementary Material 1","source_license":"CC-BY-4.0","license_restricted":false}