Identification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis

Identification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis | 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 Identification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis Kainan Lin, Zhenyan Pan, Renke He, Hanchu Wang, Kai Zhou, Liangshan Mu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-80648/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 Purpose: Endometriosis was a common gynecological disease, however, the specific mechanism and the key molecules of endometriosis remained uncertain. This study aimed to single out key genes associated with poor prognosis, and further uncover underlying mechanisms. Methods: Data regarding mRNA expression profiles used in this study were retrieved from the Gene Expression Omnibus (GEO) database, a total of three mRNA expression profiles were included for subsequent analysis (GSE31515, GSE58178 and GSE120103). Then, we conducted Gene Ontology analysis (GO analysis), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis by the software R. Results: A total of 304 differentially expressed genes (DEGs) between endometriosis tissues and normal endometrium tissues were identified in integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis reveals that the DEGs of endometriosis were closely associated with molecular origin of bacteria. KEGG pathway enrichment analysis indicates that the DEGs were mainly involved in AGE-RAGE signaling pathway in diabetic complications. In addition, PPI of these DEGs was visualized by Cytoscape platform with utilization of Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identifies 10 potential DEGs-related protein targets, including CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, HSP90B1. Conclusion: In conclusion, the present study reveals that bacterial contamination, defect of female reproductive system development, retrograde menstruation and the AGE-RAGE signaling pathway may be involved in the development of endometriosis In addition, these identified DEGs may be of clinical significance for the diagnosis and treatment of the endometriosis. Sexual & Reproductive Medicine Cancer Biology endometriosis integrated bioinformatics differentially expressed genes signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Endometriosis was a common gynecological disease manifested by active endometrium infiltrated into peri-uterine sites, such as pelvic cavity (i.e. ovaries, external structure of uterus, uterosacral ligaments and pouch of Douglas) as well as the wall of pelvic organs [ 1 ]. Around 10–15% of reproductive-aged woman worldwide suffered from endometriosis which caused chronic pelvic pain and infertility [ 2 ]. Although endometriosis was first identified and described in the early 20th century, there was no consensus on etiological theory to this day. The most widely accepted theory was proposed by Sampson which assumed that endometrium fragments migrated to pelvic cavity via fallopian tube with the menstrual blood flow and then implanted in the ovary and other sites within the body [ 3 ]. Coelomic metaplasia theory proposed by Mayer and immunology theory had also been proved to be credible by several researches [ 4 , 5 ]. There were three main phenotypes of endometriosis with clinical description: peritoneal endometriosis, ovarian endometriosis, and deep-infiltrating endometriosis [ 6 ]. And revised classification criteria released by American Society for Reproductive Medicine was widely used to classify severity of endometriosis from minimal (I) to severe (IV) in clinical practice [ 7 ]. Diagnostic laparoscopy was the most accurate way to diagnose endometriosis patients. Besides, the location of pain, infertility, positive results from medical imaging examination and CA125 evaluation in blood sample could also predict onset of endometriosis. However, due to the invasive injury as well as inaccuracy of diagnosis, uncovering underlying mechanisms of onset and progression of endometriosis was crucial for medical therapy. Endometriosis was a complex disease which was related to multiple factors such as immunology, endocrinology, genetics, and environmental factors. Studies showed that immediate family member of endometriosis patients had significantly increased risk of developing endometriosis [ 8 ]. In this regard, identifying endometriosis-related genetic variants were critical for susceptible populations. The differentially expressed genes (DEGs) could reveal signaling pathways potentially linked to development and progression of endometriosis. In light of small samples and inconsistent study methods, sample integration of included studies showed huge heteroscedasticity. As the emergence of newly developed study methods, integrated-bioinformatic analysis had been proven as a reliable tool in molecular-biological study of breast cancer and lung cancer [ 9 , 10 ]. In this study, three microarray expression datasets were downloaded and a total of 57 samples, including 27 cases of ovarian endometriosis and 30 normal endometrium samples from healthy female populations as control group, were included in this study. After identifying the DEGs, we did Gene Ontology enrichment (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, protein-protein interaction (PPI) network and visualization was constructed. Through this series of analysis, numerous key signaling pathways and potential candidate genes involved in development and progression of endometriosis are identified. Results of this study provide potential molecular targets to help improve capacity of diagnosis and treatment for endometriosis. Materials And Methods Gene expression data Microarray data of mRNA expression profiles related to progression of ovarian endometriosis were extracted and downloaded from GEO database ( http://www.ncbi.nlm.nih . gov/geo) of National Coalition Building Institute (NCBI). "Ovarian endometriosis" were selected as keywords for data retrieval, and species types were limited to homo sapiens, 22 datasets associated with ovary endometriosis were retrieved. After preliminary screening, gene expression profiles of GSE31515, GSE58178 and GSE120103 met the inclusion criteria of this study and thus were downloaded for further analysis. The dataset GSE31515 contained sequencing data from 3 endometriosis tissue samples and 6 healthy endometrial tissue samples. The platform used for finding influence of oxidative stress on endometriotic stromal cells (GSE31515) was GPL6480 Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F (Probe Name version). The gene expression profiling of primary stromal cell cultures isolated from human endometrium and ovarian endometriosis (GSE58178) which contained data from 6 healthy human endometrial tissues and 6 Human endometriotic tissues was based on GPL6947 Illumina HumanHT-12 V3.0 expression beadchip platform. The dataset GSE120103 contained 18 endometrioma samples and 18 control endometrium specimens, platform for analyzing GSE120103 was GPL6480 Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F (Probe Name version). Both platform and series matrix files were downloaded as CSV data format in this study. The dataset information was displayed in Table 1. Data processing Gene sequence annotation was conducted with the platform file through Strawberry-Perl-5.30.2.1 ( https://www.perl.org/get.html ), followed by data format into gene expression matrix for subsequent operations. We then merged three gene expression matrix which were converted from enrolled 3 GSE datasets mentioned above into a single gene expression matrix through Straw-perl-5.30.2.1. Genes that were not simultaneously expressed in three gene matrixes were excluded from this study. Then, we used R 3.6.3 ( https://www.r-project.org/ ) for subsequent data processing. For batch normalization of data, we used limma and sva package in Bioconductor 3.11 tool ( http://www.bioconductor.org/packages/release/bioc/html/limma .html; http://www.bioconductor.org/packages/release/bioc/html/sva.html ). In addition, Limma R software package was used to single out differentially expressed mRNAs. This study was conducted with the thresholds of adjust P value 1. In addition, the software R was used to construct heat map and volcanic map of DEGs between the case group and the control group. Pathway enrichment analysis The Gene Ontology Analysis (GO analysis) could be divided into three parts: Molecular Function (MF), Biological Process (BP) and Cellular Component (CC). Individual proteins or genes could be identified by serial number correspondence or sequence annotation, and GO number was located to corresponding term, namely functional category or cell type. In order to better understand DEGs-associated pathways as well as corresponding molecular mechanisms in pathogenesis of endometriosis, we subsequently conducted GO and KEGG pathway enrichment analysis through clusterProfiler package in the Bioconductor 3.11 tool. P < 0.05 was considered for statistical significance. The most relevant function pathway of DEGs was downloaded from metascape online database ( http://metascape.org/ ), and location of each DEG was annotated in the function pathway. PPI network construction The protein-protein interaction (PPI) among DEGs-encoded proteins was analyzed based on STRING online database ( http://string-db.org/ ) with combined score of ≥ 0.4 as cut-off value. In order to simplify diagrams, we removed all isolated or partially connected nodes and finally constructed a full-scale DEGs network. Data from STRING database were imported into CytoScape 3.8 ( https://cytoscape.org/ ) for visual processing. CytoHubba plug-ins loaded in CytoScape software were used to construct and analyze functional modules. Results Identification of DEGs in ovarian endometriosis 30 normal women were enrolled as the control group and 27 patients with ovary endometriosis as the case group in this study. After randomly merging data from different mRNA expression profiles, we used R 3.6.3 for batch normalization in order to eliminate effects of different experimental factors. |log2FC|>1 and P < 0.05 was considered as cut-off value for data inclusion. In addition, we used limma package to identify DEGs in datasets GSE31515, GSE58178 and GSE120103. The results show that 304 DEGs, which contains 185 down-regulated genes(logFC 0) in the ectopic endometrial tissue (Table 2), are simultaneously identified in three mRNA expression profiles We subsequently constructed volcano plots and cluster heatmaps of detected DEGs by R3.6.3. Data are presented in Fig. 1 and Fig. 2 , respectively. Gene ontology and KEGG pathway analysis of DEGs in ovarian endometriosis We used R3.6.3 to convert gene symbol into entrez ID for further analysis. biological processes (BP), molecular function (MF) and cell component (CC) were three major categories of GO analysis. All 304 DEGs were analyzed based on R3.6.3 software, and results of GO analysis regarding BP, MF and CC were shown in that Fig. 3 . For BP analysis DEGs are particularly enriched in pathways related to molecular origin of bacteria, reproductive structure, reproductive system development, cellular response to lipopolysaccharide, extracellular structure organization, regulation of voltage-gated calcium channel activity, uterus development (adjust P-value < 0.005); for MF analysis, identified GEGs are mainly enriched in molecular activities regarding collagen binding, heparin binding, actin binding, sulfur compound binding, glycosaminoglycan binding, platelet-derived growth factor binding, extracellular matrix structural constituent (adjust P-value < 0.005); for CC analysis, DEGs are mainly enriched in endoplasmic reticulum lumen, collagen-containing extracellular matrix, endoplasmic reticulum chaperone complex, extracellular matrix component, melanosome, pigment granule, as well as Arp2/3 protein complex (adjust P-value < 0.005). And the results of GO functional analysis which include the BP, MF and CC analysis are displayed in Fig. 3 . The results of KEGG analysis are shown in Fig. 4 and Table 3, which demonstrate that detected DEGs are mainly enriched in AGE-RAGE signaling pathway in diabetic complications, Protein processing in endoplasmic reticulum,and Pathogenic Escherichia coli infection (P < 0.005). In order to uncover the most pertinent functional pathway related to DEGs, we downloaded specific pathway diagram from Metascape online database and labeled the 8 DEGs related to AGE-RAGE signaling pathway in diabetic complications (Fig. 5 ). Protein–protein interaction network (PPI) and modular analysis We used Cytoscape to construct PPI network subsequently, which consisted of 219 nodes (proteins) and 542 edges (interactions). Then we applied cytoHubba to analyze the most significant interaction in the network and the results show that interaction between CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, HSP90B1 are the most significant interaction among selected DEGs, as shown in Fig. 6 . Discussion Accumulating evidences suggested that endometrium of endometriosis patients exhibits anomaly gene expression profiles that were distinct from healthy populations [ 11 ]. In order to explore such discrepancy, we selected three gene expression profile datasets. Through analyzing these datasets, a total of 304 DEGs were identified, including 185 unregulated and 119 downregulated genes. Furthermore, these DEGs were found to be promising evidences which supported some new hypotheses of the onset of endometriosis. DEGs were significantly enriched in response to molecule of bacterial origin and response to lipopolysaccharide (LPS) in biological process and pathogenic Escherichia coli ( E-coli ) infection pathway. Previous studies suggested that genital track and peritoneal infections such as E-coli infections could increase the risk of endometriosis [ 12 , 13 ]. Studies had discovered higher amount of E-coli in menstrual blood, as well as higher endotoxin level in menstrual fluid and peritoneal fluid of endometriosis patients compared with healthy populations. Khan et al. had proposed a theory called “bacterial contamination hypothesis”, which assumed that LPS triggered pro-inflammatory response in pelvis and promoted onset of endometriosis through Toll-like receptor 4 [ 14 ]. Genes related to reproductive system and structure were differentially expressed between healthy populations and endometriosis patients. Crispi et al. proposed the hypothesis that endometriosis may be connected with defect during development of female reproductive system. BMP6 belonged to the transforming growth factor-beta super-family that included proteins involved in cell growth and differentiation [ 15 ]. SERPINE1 was a member of serine protease inhibitor family that contained inhibitors of tissue plasminogen activator and were involved in tissue remodeling. It exhibited significant higher expression in ovarian cancer patients, which indicated a potential role in tumor invasion and metastasis [ 16 ]. Up-regulated SERPINE1 expression could also be account for some malignant characteristics of ectopic endometrium, although samples included in this study were taken from patients with deep infiltrating endometriosis. Endometriosis was characterized by chronic inflammation, hypoxia and cellular transformation which was related to AGE-RAGE pathway. Activation of receptor for advanced glycation end products (RAGE) dramatically upregulated multiple intracellular signal pathways including protein kinase C and MAPK/NF-κB which promoted expression of pro-inflammatory cytokines [ 17 ]. In addition, PI3K-Akt-dependent pathways were involved in degradation of extracellular matrix and cell apoptosis which facilitated the implantation and invasiveness of endometriosis[ 18 ]. The endoplasmic reticulum (ER) played an important role in interaction between multiple intracellular organelles along with collateral bio-activities especially proteasome and attached ribosome, protein synthesis as well as other processes. Accumulation of unfolded or misfolded proteins during ER stress activated a homeostatic coping mechanism called the unfolded protein response (UPR). Female reproductive tissues were highly active at cellular, molecular and genetic levels which requires participation of ER. In certain severe conditions, UPR were not sufficient to restore normal ER function, which further contributed to the pathogenesis of various diseases including endometriosis [ 19 ]. GATA6 was a fertility-related gene which expressed in vertebrate ovary [ 20 ]. The overexpression of GATA6 which was induced by aberrant methylation in endometriotic cells regulated the expression of steroid metabolism and steroid hormone receptors. For example, the transcript of estrogen receptor α and progesterone receptor was reduced meanwhile estrogen receptor β was increased. MMP11 is proved to significant reduced by the overexpression of GATA6 in Table 2. It transformed healthy endometrium away from spontaneous decidualization and toward the disease phenotype by restricting the ability of endometrial stromal cells to decidualize. Theoretically, PRL and IGFBP1 were expected to be blocked, but we find them up-regulated [ 21 ]. There are few analyses related with endometriosis through bioinformatic. Tissues in control group from datasets were included in Zhang and Wang et al., Yao et al., and Cheng et al. sampled from not only endometriosis patients but also healthy women [ 22 , 23 , 18 ]. Besides, samples defined as ectopic endometrium were from various regions which weakened the strength of conclusion. In this study, we include samples from endometriomas from endometriosis patients and eutopic endometrium from health women. However, as this study is only based on analysis, further studies with larger samples and clinical trials are required to confirm the association of identified genes in endometriosis. In conclusion, this study reveals the possibility of new pathological hypothesis for endometriosis, including bacterial contamination, defect of female reproductive system development and retrograde menstruation. It is also identified new drug targets other than estrogen receptor. Declarations Funding This work was supported by the China Postdoctoral Science Foundation (2020M671760). Conflicts of interest The authors declare that they have no competing interests. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Written informed consent for publication was obtained from all participants. Availability of data and material All data generated or analyzed during this study are included in the published article. Code availability All software and codes used during this study are included in the published article. Authors' contributions KN Lin: Data analysis and manuscript writing. ZY Pan: Data analysis and manuscript writing. RK He: Data extraction and manuscript review. HC Wang: Data extraction and manuscript review. K Zhou: Project conception and manuscript review. LS Mu: Project conception and manuscript review. References Méar L, Herr M, Fauconnier A, Pineau C, Vialard F (2020) Polymorphisms and endometriosis: a systematic review and meta-analyses. Hum Reprod Update 26 (1):73-102. doi:10.1093/humupd/dmz034 Dunselman GA, Vermeulen N, Becker C, Calhaz-Jorge C, D'Hooghe T, De Bie B, Heikinheimo O, Horne AW, Kiesel L, Nap A, Prentice A, Saridogan E, Soriano D, Nelen W (2014) ESHRE guideline: management of women with endometriosis. Hum Reprod 29 (3):400-412. doi:10.1093/humrep/det457 Sampson JA (1927) Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation. Am J Pathol 3 (2):93-110.143 Vinatier D, Orazi G, Cosson M, Dufour P (2001) Theories of endometriosis. Eur J Obstet Gynecol Reprod Biol 96 (1):21-34. doi:10.1016/s0301-2115(00)00405-x Badawy SZ, Cuenca V, Stitzel A, Tice D (1987) Immune rosettes of T and B lymphocytes in infertile women with endometriosis. J Reprod Med 32 (3):194-197 Nisolle M, Donnez J (1997) Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities. Fertil Steril 68 (4):585-596. doi:10.1016/s0015-0282(97)00191-x Revised American Society for Reproductive Medicine classification of endometriosis: 1996 (1997). Fertil Steril 67 (5):817-821. doi:10.1016/s0015-0282(97)81391-x Matalliotakis IM, Arici A, Cakmak H, Goumenou AG, Koumantakis G, Mahutte NG (2008) Familial aggregation of endometriosis in the Yale Series. Arch Gynecol Obstet 278 (6):507-511. doi:10.1007/s00404-008-0644-1 Zhang Y, Li Y, Wang Q, Zhang X, Wang D, Tang HC, Meng X, Ding X (2017) Identification of an lncRNA‑miRNA‑mRNA interaction mechanism in breast cancer based on bioinformatic analysis. Mol Med Rep 16 (4):5113-5120. doi:10.3892/mmr.2017.7304 Chang H, Sasson A, Srinivasan S, Golhar R, Greenawalt DM, Geese WJ, Green G, Zerba K, Kirov S, Szustakowski J (2019) Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer. Mol Diagn Ther 23 (4):507-520. doi:10.1007/s40291-019-00408-y Aghajanova L, Velarde MC, Giudice LC (2010) Altered gene expression profiling in endometrium: evidence for progesterone resistance. Semin Reprod Med 28 (1):51-58. doi:10.1055/s-0029-1242994 Koninckx PR, Ussia A, Tahlak M, Adamyan L, Wattiez A, Martin DC, Gomel V (2019) Infection as a potential cofactor in the genetic-epigenetic pathophysiology of endometriosis: a systematic review. Facts Views Vis Obgyn 11 (3):209-216 Lin WC, Chang CY, Hsu YA, Chiang JH, Wan L (2016) Increased Risk of Endometriosis in Patients With Lower Genital Tract Infection: A Nationwide Cohort Study. Medicine (Baltimore) 95 (10):e2773. doi:10.1097/md.0000000000002773 Khan KN, Fujishita A, Hiraki K, Kitajima M, Nakashima M, Fushiki S, Kitawaki J (2018) Bacterial contamination hypothesis: a new concept in endometriosis. Reprod Med Biol 17 (2):125-133. doi:10.1002/rmb2.12083 Hinck AP (2012) Structural studies of the TGF-βs and their receptors - insights into evolution of the TGF-β superfamily. FEBS Lett 586 (14):1860-1870. doi:10.1016/j.febslet.2012.05.028 Komiyama S, Aoki D, Saitoh E, Komiyama M, Udagawa Y (2011) Biological significance of plasminogen activator inhibitor-1 expression in ovarian clear cell adenocarcinoma. Eur J Gynaecol Oncol 32 (6):611-614 Wilson RB (2018) Hypoxia, cytokines and stromal recruitment: parallels between pathophysiology of encapsulating peritoneal sclerosis, endometriosis and peritoneal metastasis. Pleura Peritoneum 3 (1):20180103. doi:10.1515/pp-2018-0103 Dai FF, Bao AY, Luo B, Zeng ZH, Pu XL, Wang YQ, Zhang L, Xian S, Yuan MQ, Yang DY, Liu SY, Cheng YX (2020) Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis. Exp Ther Med 19 (1):264-272. doi:10.3892/etm.2019.8214 Guzel E, Arlier S, Guzeloglu-Kayisli O, Tabak MS, Ekiz T, Semerci N, Larsen K, Schatz F, Lockwood CJ, Kayisli UA (2017) Endoplasmic Reticulum Stress and Homeostasis in Reproductive Physiology and Pathology. Int J Mol Sci 18 (4). doi:10.3390/ijms18040792 J B, SC B, C S - GATA4 and GATA6 silencing in ovarian granulosa cells affects levels of mRNAs. D - 0375040 (- 1945-7170 (Electronic)):T - ppublish MT D, D R, D M, CM E, ME P, DC B, T K, M O, N J, Y D, SE B - Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor. D - 101239074 (- 1553-7404 (Electronic)):T - epublish Z Z, L R, M L, X Y - Analysis of key candidate genes and pathways of endometriosis pathophysiology by a. D - 8807913 (- 1473-0766 (Electronic)):T - ppublish M C, Y Z, H X, C H, RM E, D H, X Z, Y W - Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition. D - 101563288 (- 2045-2322 (Electronic)):T - epublish Tables Due to technical limitations, table 1-3 PDFs are only available as a download in the Supplemental Files section. Supplementary Files Table1.pdf Table2.pdf Table3.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-80648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":2795202,"identity":"b3dff5b1-b28f-4b4e-a34b-f7196c5dcc60","order_by":0,"name":"Kainan Lin","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kainan","middleName":"","lastName":"Lin","suffix":""},{"id":2795203,"identity":"7c8d252b-b9c1-47d6-8686-c61d5d509711","order_by":1,"name":"Zhenyan Pan","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenyan","middleName":"","lastName":"Pan","suffix":""},{"id":2795204,"identity":"e19b9528-119d-4bb5-96eb-1dc28ddcbe88","order_by":2,"name":"Renke He","email":"","orcid":"","institution":"School of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Renke","middleName":"","lastName":"He","suffix":""},{"id":2795205,"identity":"e895cc9e-4002-403e-b07f-42aca911a7d9","order_by":3,"name":"Hanchu Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanchu","middleName":"","lastName":"Wang","suffix":""},{"id":2795206,"identity":"9da8e563-8335-428f-8887-aca12f118c15","order_by":4,"name":"Kai Zhou","email":"","orcid":"https://orcid.org/0000-0002-1341-2042","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhou","suffix":""},{"id":2795207,"identity":"4cf2ae70-82d0-4ac1-b7bd-cb276df5b83a","order_by":5,"name":"Liangshan Mu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPmYwdQBEMD4Gs5mZG/BqYUPSwmzMwGAApBgJaGFAaGGTBmthIKSFnfnZwy9/7sibi50xqy6o+BPN3w7U8qNiGx6HsZkby7Y9M9w5O8fs9owzBrkzDjM2MPacuY3PL2bSkg2HGTfcBmrhbTPIBbIbmBnb8Glh/yYt8eewPUhLMUjLfMJaeMwkP7AdTgRpYQZp2UCEljJpxrbDyRtupxVL85wxzt0I1HIQn1/4+Y9vk/zx57DthtvJGz/zVMjlzjt/+OCDHxW4tYAAMw+6yAG86oGA8QchFaNgFIyCUTCyAQCl3VWiiLKbdwAAAABJRU5ErkJggg==","orcid":"","institution":"School of Medicine, Zhejiang University, Hangzhou, 310058, People’s Republic of China","correspondingAuthor":true,"prefix":"","firstName":"Liangshan","middleName":"","lastName":"Mu","suffix":""}],"badges":[],"createdAt":"2020-09-19 10:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-80648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-80648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2708909,"identity":"3bc69fae-324e-45f4-87bf-25cc0bc40ab5","added_by":"auto","created_at":"2020-09-30 20:08:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":480896,"visible":true,"origin":"","legend":"Volcanic maps of DGEs in the integrated datasets. The red points represent genes with significantly up-regulated expression that are screened under the thresholds of log2(fold change)\u003e1.0 and a P-value of \u003c0.05; The green points represent genes with significantly down-regulated expression that are detected under the thresholds of log2(fold change)\u003c-1.0 and a P-value of \u003c0.05","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure1.png"},{"id":2708910,"identity":"697541bb-ecf9-4427-abe5-4a82c6d63e10","added_by":"auto","created_at":"2020-09-30 20:08:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":471199,"visible":true,"origin":"","legend":"Clustering heatmap of the DEGs identified on the criteria of |log2(fold change)|\u003e1.0 and a P-value of \u003c0.05. Heatmap is based on the integrated datasets. Red shading manifests that the expression of genes is relatively upregulated, while green shading indicates that the expression of genes is correspondingly downregulated","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure2.png"},{"id":2708912,"identity":"e80b6cd0-ec2d-4521-9016-694e4c582cd3","added_by":"auto","created_at":"2020-09-30 20:08:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3712911,"visible":true,"origin":"","legend":"Gene ontology analysis of DEGs in ovary endometriosis. A for biological processes (BP); B for molecular function (MF); C for cell component (CC)","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure3.png"},{"id":2708914,"identity":"7f17396e-c893-42c8-b9d0-af26925041d6","added_by":"auto","created_at":"2020-09-30 20:08:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":872631,"visible":true,"origin":"","legend":"KEGG pathway analysis of DEGs in ovary endometriosis","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure4.png"},{"id":2708916,"identity":"16d22260-6c07-4083-8675-bf939f220388","added_by":"auto","created_at":"2020-09-30 20:08:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40515,"visible":true,"origin":"","legend":"Analysis of the most relevant functional pathway. Eight genes (COL4A6, CCND1, VCAM1, IL6, SERPINE1, CCL2, COL3A1 and COL1A2) are significantly enriched in the AGE-RAGE signaling pathway in diabetic complications. COL means COL4A6, COL1A2 and COL3A1. CycD1 means CCND1. PAI-1 means SERPINE1. MCP-1 means CCL2","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure5.png"},{"id":2708917,"identity":"27a9f959-99c1-48f9-8a37-2046bf746ad8","added_by":"auto","created_at":"2020-09-30 20:08:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4708846,"visible":true,"origin":"","legend":"Common DEGs PPI network constructed by STRING online database and Core protein analysis. There is a total of 219 DEGs in the DEGs PPI network complex. The nodes meant proteins; the edges meant the interaction of proteins. Core protein analysis via cytoHubba in Cytoscape software (rank by the degree)","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Figure6.png"},{"id":13596948,"identity":"58f16070-40ab-4b99-b045-de813d86dbcc","added_by":"auto","created_at":"2021-09-17 05:30:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1921389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/d9696221-7c7e-44aa-af0f-85ac5d7bdead.pdf"},{"id":2708911,"identity":"71c186ae-fefa-4f72-8c48-1446d37a4f5b","added_by":"auto","created_at":"2020-09-30 20:08:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28409,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Table1.pdf"},{"id":2708913,"identity":"afbfc50f-9c5c-440d-a872-3438825d5983","added_by":"auto","created_at":"2020-09-30 20:08:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21754,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Table2.pdf"},{"id":2708915,"identity":"396c10c9-49e7-43fe-85a9-28ad929fbed3","added_by":"auto","created_at":"2020-09-30 20:08:26","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20872,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-80648/v1/Table3.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eEndometriosis was a common gynecological disease manifested by active endometrium infiltrated into peri-uterine sites, such as pelvic cavity (i.e. ovaries, external structure of uterus, uterosacral ligaments and pouch of Douglas) as well as the wall of pelvic organs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Around 10\u0026ndash;15% of reproductive-aged woman worldwide suffered from endometriosis which caused chronic pelvic pain and infertility [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although endometriosis was first identified and described in the early 20th century, there was no consensus on etiological theory to this day. The most widely accepted theory was proposed by Sampson which assumed that endometrium fragments migrated to pelvic cavity via fallopian tube with the menstrual blood flow and then implanted in the ovary and other sites within the body [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Coelomic metaplasia theory proposed by Mayer and immunology theory had also been proved to be credible by several researches [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere were three main phenotypes of endometriosis with clinical description: peritoneal endometriosis, ovarian endometriosis, and deep-infiltrating endometriosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. And revised classification criteria released by American Society for Reproductive Medicine was widely used to classify severity of endometriosis from minimal (I) to severe (IV) in clinical practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Diagnostic laparoscopy was the most accurate way to diagnose endometriosis patients. Besides, the location of pain, infertility, positive results from medical imaging examination and CA125 evaluation in blood sample could also predict onset of endometriosis. However, due to the invasive injury as well as inaccuracy of diagnosis, uncovering underlying mechanisms of onset and progression of endometriosis was crucial for medical therapy.\u003c/p\u003e \u003cp\u003eEndometriosis was a complex disease which was related to multiple factors such as immunology, endocrinology, genetics, and environmental factors. Studies showed that immediate family member of endometriosis patients had significantly increased risk of developing endometriosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this regard, identifying endometriosis-related genetic variants were critical for susceptible populations. The differentially expressed genes (DEGs) could reveal signaling pathways potentially linked to development and progression of endometriosis. In light of small samples and inconsistent study methods, sample integration of included studies showed huge heteroscedasticity. As the emergence of newly developed study methods, integrated-bioinformatic analysis had been proven as a reliable tool in molecular-biological study of breast cancer and lung cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, three microarray expression datasets were downloaded and a total of 57 samples, including 27 cases of ovarian endometriosis and 30 normal endometrium samples from healthy female populations as control group, were included in this study. After identifying the DEGs, we did Gene Ontology enrichment (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, protein-protein interaction (PPI) network and visualization was constructed. Through this series of analysis, numerous key signaling pathways and potential candidate genes involved in development and progression of endometriosis are identified. Results of this study provide potential molecular targets to help improve capacity of diagnosis and treatment for endometriosis.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eGene expression data\u003c/h2\u003e\n\u003cp\u003eMicroarray data of mRNA expression profiles related to progression of ovarian endometriosis were extracted and downloaded from GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih\u003c/span\u003e\u003c/span\u003e. gov/geo) of National Coalition Building Institute (NCBI). \"Ovarian endometriosis\" were selected as keywords for data retrieval, and species types were limited to homo sapiens, 22 datasets associated with ovary endometriosis were retrieved. After preliminary screening, gene expression profiles of GSE31515, GSE58178 and GSE120103 met the inclusion criteria of this study and thus were downloaded for further analysis. The dataset GSE31515 contained sequencing data from 3 endometriosis tissue samples and 6 healthy endometrial tissue samples. The platform used for finding influence of oxidative stress on endometriotic stromal cells (GSE31515) was GPL6480 Agilent-014850 Whole Human Genome Microarray 4\u0026thinsp;\u0026times;\u0026thinsp;44K G4112F (Probe Name version). The gene expression profiling of primary stromal cell cultures isolated from human endometrium and ovarian endometriosis (GSE58178) which contained data from 6 healthy human endometrial tissues and 6 Human endometriotic tissues was based on GPL6947 Illumina HumanHT-12 V3.0 expression beadchip platform. The dataset GSE120103 contained 18 endometrioma samples and 18 control endometrium specimens, platform for analyzing GSE120103 was GPL6480 Agilent-014850 Whole Human Genome Microarray 4\u0026thinsp;\u0026times;\u0026thinsp;44K G4112F (Probe Name version). Both platform and series matrix files were downloaded as CSV data format in this study. The dataset information was displayed in Table\u0026nbsp;1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eData processing\u003c/h2\u003e\n\u003cp\u003eGene sequence annotation was conducted with the platform file through Strawberry-Perl-5.30.2.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.perl.org/get.html\u003c/span\u003e\u003c/span\u003e), followed by data format into gene expression matrix for subsequent operations. We then merged three gene expression matrix which were converted from enrolled 3 GSE datasets mentioned above into a single gene expression matrix through Straw-perl-5.30.2.1. Genes that were not simultaneously expressed in three gene matrixes were excluded from this study. Then, we used R 3.6.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003c/span\u003e) for subsequent data processing. For batch normalization of data, we used limma and sva package in Bioconductor 3.11 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.bioconductor.org/packages/release/bioc/html/limma\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/limma\u003c/a\u003e.html; http://www.bioconductor.org/packages/release/bioc/html/sva.html\u003c/span\u003e\u003c/span\u003e). In addition, Limma R software package was used to single out differentially expressed mRNAs. This study was conducted with the thresholds of adjust P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2fold change (FC)| \u0026gt; 1. In addition, the software R was used to construct heat map and volcanic map of DEGs between the case group and the control group.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003ePathway enrichment analysis\u003c/h2\u003e\n\u003cp\u003eThe Gene Ontology Analysis (GO analysis) could be divided into three parts: Molecular Function (MF), Biological Process (BP) and Cellular Component (CC). Individual proteins or genes could be identified by serial number correspondence or sequence annotation, and GO number was located to corresponding term, namely functional category or cell type. In order to better understand DEGs-associated pathways as well as corresponding molecular mechanisms in pathogenesis of endometriosis, we subsequently conducted GO and KEGG pathway enrichment analysis through clusterProfiler package in the Bioconductor 3.11 tool. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered for statistical significance. The most relevant function pathway of DEGs was downloaded from metascape online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org/\u003c/span\u003e\u003c/span\u003e), and location of each DEG was annotated in the function pathway.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003ePPI network construction\u003c/h2\u003e\n\u003cp\u003eThe protein-protein interaction (PPI) among DEGs-encoded proteins was analyzed based on STRING online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003c/span\u003e) with combined score of \u0026ge;\u0026thinsp;0.4 as cut-off value. In order to simplify diagrams, we removed all isolated or partially connected nodes and finally constructed a full-scale DEGs network. Data from STRING database were imported into CytoScape 3.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003c/span\u003e) for visual processing. CytoHubba plug-ins loaded in CytoScape software were used to construct and analyze functional modules.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eIdentification of DEGs in ovarian endometriosis\u003c/h2\u003e\n\u003cp\u003e30 normal women were enrolled as the control group and 27 patients with ovary endometriosis as the case group in this study. After randomly merging data from different mRNA expression profiles, we used R 3.6.3 for batch normalization in order to eliminate effects of different experimental factors. |log2FC|\u0026gt;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as cut-off value for data inclusion. In addition, we used limma package to identify DEGs in datasets GSE31515, GSE58178 and GSE120103. The results show that 304 DEGs, which contains 185 down-regulated genes(logFC\u0026thinsp;\u0026lt;\u0026thinsp;0) and 119 up-regulated genes (logFC\u0026thinsp;\u0026gt;\u0026thinsp;0) in the ectopic endometrial tissue (Table\u0026nbsp;2), are simultaneously identified in three mRNA expression profiles We subsequently constructed volcano plots and cluster heatmaps of detected DEGs by R3.6.3. Data are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eGene ontology and KEGG pathway analysis of DEGs in ovarian endometriosis\u003c/h2\u003e\n\u003cp\u003eWe used R3.6.3 to convert gene symbol into entrez ID for further analysis. biological processes (BP), molecular function (MF) and cell component (CC) were three major categories of GO analysis. All 304 DEGs were analyzed based on R3.6.3 software, and results of GO analysis regarding BP, MF and CC were shown in that Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. For BP analysis DEGs are particularly enriched in pathways related to molecular origin of bacteria, reproductive structure, reproductive system development, cellular response to lipopolysaccharide, extracellular structure organization, regulation of voltage-gated calcium channel activity, uterus development (adjust P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005); for MF analysis, identified GEGs are mainly enriched in molecular activities regarding collagen binding, heparin binding, actin binding, sulfur compound binding, glycosaminoglycan binding, platelet-derived growth factor binding, extracellular matrix structural constituent (adjust P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005); for CC analysis, DEGs are mainly enriched in endoplasmic reticulum lumen, collagen-containing extracellular matrix, endoplasmic reticulum chaperone complex, extracellular matrix component, melanosome, pigment granule, as well as Arp2/3 protein complex (adjust P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005). And the results of GO functional analysis which include the BP, MF and CC analysis are displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results of KEGG analysis are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;3, which demonstrate that detected DEGs are mainly enriched in AGE-RAGE signaling pathway in diabetic complications, Protein processing in endoplasmic reticulum,and Pathogenic Escherichia coli infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005). In order to uncover the most pertinent functional pathway related to DEGs, we downloaded specific pathway diagram from Metascape online database and labeled the 8 DEGs related to AGE-RAGE signaling pathway in diabetic complications (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eProtein\u0026ndash;protein interaction network (PPI) and modular analysis\u003c/h2\u003e\n\u003cp\u003eWe used Cytoscape to construct PPI network subsequently, which consisted of 219 nodes (proteins) and 542 edges (interactions). Then we applied cytoHubba to analyze the most significant interaction in the network and the results show that interaction between \u003cem\u003eCCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, HSP90B1\u003c/em\u003e are the most significant interaction among selected DEGs, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccumulating evidences suggested that endometrium of endometriosis patients exhibits anomaly gene expression profiles that were distinct from healthy populations [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. In order to explore such discrepancy, we selected three gene expression profile datasets. Through analyzing these datasets, a total of 304 DEGs were identified, including 185 unregulated and 119 downregulated genes. Furthermore, these DEGs were found to be promising evidences which supported some new hypotheses of the onset of endometriosis.\u003c/p\u003e\n\u003cp\u003eDEGs were significantly enriched in response to molecule of bacterial origin and response to lipopolysaccharide (LPS) in biological process and pathogenic \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE-coli\u003c/em\u003e) infection pathway. Previous studies suggested that genital track and peritoneal infections such as \u003cem\u003eE-coli\u003c/em\u003e infections could increase the risk of endometriosis [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies had discovered higher amount of \u003cem\u003eE-coli\u003c/em\u003e in menstrual blood, as well as higher endotoxin level in menstrual fluid and peritoneal fluid of endometriosis patients compared with healthy populations. Khan et al. had proposed a theory called \u0026ldquo;bacterial contamination hypothesis\u0026rdquo;, which assumed that LPS triggered pro-inflammatory response in pelvis and promoted onset of endometriosis through Toll-like receptor 4 [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGenes related to reproductive system and structure were differentially expressed between healthy populations and endometriosis patients. Crispi et al. proposed the hypothesis that endometriosis may be connected with defect during development of female reproductive system. BMP6 belonged to the transforming growth factor-beta super-family that included proteins involved in cell growth and differentiation [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. SERPINE1 was a member of serine protease inhibitor family that contained inhibitors of tissue plasminogen activator and were involved in tissue remodeling. It exhibited significant higher expression in ovarian cancer patients, which indicated a potential role in tumor invasion and metastasis [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Up-regulated SERPINE1 expression could also be account for some malignant characteristics of ectopic endometrium, although samples included in this study were taken from patients with deep infiltrating endometriosis.\u003c/p\u003e\n\u003cp\u003eEndometriosis was characterized by chronic inflammation, hypoxia and cellular transformation which was related to AGE-RAGE pathway. Activation of receptor for advanced glycation end products (RAGE) dramatically upregulated multiple intracellular signal pathways including protein kinase C and MAPK/NF-\u0026kappa;B which promoted expression of pro-inflammatory cytokines [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, PI3K-Akt-dependent pathways were involved in degradation of extracellular matrix and cell apoptosis which facilitated the implantation and invasiveness of endometriosis[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe endoplasmic reticulum (ER) played an important role in interaction between multiple intracellular organelles along with collateral bio-activities especially proteasome and attached ribosome, protein synthesis as well as other processes. Accumulation of unfolded or misfolded proteins during ER stress activated a homeostatic coping mechanism called the unfolded protein response (UPR). Female reproductive tissues were highly active at cellular, molecular and genetic levels which requires participation of ER. In certain severe conditions, UPR were not sufficient to restore normal ER function, which further contributed to the pathogenesis of various diseases including endometriosis [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eGATA6 was a fertility-related gene which expressed in vertebrate ovary [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The overexpression of GATA6 which was induced by aberrant methylation in endometriotic cells regulated the expression of steroid metabolism and steroid hormone receptors. For example, the transcript of estrogen receptor \u0026alpha; and progesterone receptor was reduced meanwhile estrogen receptor \u0026beta; was increased. MMP11 is proved to significant reduced by the overexpression of GATA6 in Table\u0026nbsp;2. It transformed healthy endometrium away from spontaneous decidualization and toward the disease phenotype by restricting the ability of endometrial stromal cells to decidualize. Theoretically, PRL and IGFBP1 were expected to be blocked, but we find them up-regulated [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThere are few analyses related with endometriosis through bioinformatic. Tissues in control group from datasets were included in Zhang and Wang et al., Yao et al., and Cheng et al. sampled from not only endometriosis patients but also healthy women [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Besides, samples defined as ectopic endometrium were from various regions which weakened the strength of conclusion. In this study, we include samples from endometriomas from endometriosis patients and eutopic endometrium from health women. However, as this study is only based on analysis, further studies with larger samples and clinical trials are required to confirm the association of identified genes in endometriosis.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study reveals the possibility of new pathological hypothesis for endometriosis, including bacterial contamination, defect of female reproductive system development and retrograde menstruation. It is also identified new drug targets other than estrogen receptor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the China Postdoctoral Science Foundation (2020M671760).\u003c/p\u003e\n\u003ch2\u003eConflicts of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and material\u003c/h2\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in the published article.\u003c/p\u003e\n\u003ch2\u003eCode availability\u003c/h2\u003e\n\u003cp\u003eAll software and codes used during this study are included in the published article.\u003c/p\u003e\n\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\n\u003cp\u003eKN Lin: Data analysis and manuscript writing.\u003c/p\u003e\n\u003cp\u003eZY Pan: Data analysis and manuscript writing.\u003c/p\u003e\n\u003cp\u003eRK He: Data extraction and manuscript review.\u003c/p\u003e\n\u003cp\u003eHC Wang: Data extraction and manuscript review.\u003c/p\u003e\n\u003cp\u003eK Zhou: Project conception and manuscript review.\u003c/p\u003e\n\u003cp\u003eLS Mu: Project conception and manuscript review. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM\u0026eacute;ar L, Herr M, Fauconnier A, Pineau C, Vialard F (2020) Polymorphisms and endometriosis: a systematic review and meta-analyses. Hum Reprod Update 26 (1):73-102. doi:10.1093/humupd/dmz034\u003c/li\u003e\n\u003cli\u003eDunselman GA, Vermeulen N, Becker C, Calhaz-Jorge C, D'Hooghe T, De Bie B, Heikinheimo O, Horne AW, Kiesel L, Nap A, Prentice A, Saridogan E, Soriano D, Nelen W (2014) ESHRE guideline: management of women with endometriosis. Hum Reprod 29 (3):400-412. doi:10.1093/humrep/det457\u003c/li\u003e\n\u003cli\u003eSampson JA (1927) Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation. Am J Pathol 3 (2):93-110.143\u003c/li\u003e\n\u003cli\u003eVinatier D, Orazi G, Cosson M, Dufour P (2001) Theories of endometriosis. Eur J Obstet Gynecol Reprod Biol 96 (1):21-34. doi:10.1016/s0301-2115(00)00405-x\u003c/li\u003e\n\u003cli\u003eBadawy SZ, Cuenca V, Stitzel A, Tice D (1987) Immune rosettes of T and B lymphocytes in infertile women with endometriosis. J Reprod Med 32 (3):194-197\u003c/li\u003e\n\u003cli\u003eNisolle M, Donnez J (1997) Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities. Fertil Steril 68 (4):585-596. doi:10.1016/s0015-0282(97)00191-x\u003c/li\u003e\n\u003cli\u003eRevised American Society for Reproductive Medicine classification of endometriosis: 1996 (1997). Fertil Steril 67 (5):817-821. doi:10.1016/s0015-0282(97)81391-x\u003c/li\u003e\n\u003cli\u003eMatalliotakis IM, Arici A, Cakmak H, Goumenou AG, Koumantakis G, Mahutte NG (2008) Familial aggregation of endometriosis in the Yale Series. Arch Gynecol Obstet 278 (6):507-511. doi:10.1007/s00404-008-0644-1\u003c/li\u003e\n\u003cli\u003eZhang Y, Li Y, Wang Q, Zhang X, Wang D, Tang HC, Meng X, Ding X (2017) Identification of an lncRNA‑miRNA‑mRNA interaction mechanism in breast cancer based on bioinformatic analysis. Mol Med Rep 16 (4):5113-5120. doi:10.3892/mmr.2017.7304\u003c/li\u003e\n\u003cli\u003eChang H, Sasson A, Srinivasan S, Golhar R, Greenawalt DM, Geese WJ, Green G, Zerba K, Kirov S, Szustakowski J (2019) Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer. Mol Diagn Ther 23 (4):507-520. doi:10.1007/s40291-019-00408-y\u003c/li\u003e\n\u003cli\u003eAghajanova L, Velarde MC, Giudice LC (2010) Altered gene expression profiling in endometrium: evidence for progesterone resistance. Semin Reprod Med 28 (1):51-58. doi:10.1055/s-0029-1242994\u003c/li\u003e\n\u003cli\u003eKoninckx PR, Ussia A, Tahlak M, Adamyan L, Wattiez A, Martin DC, Gomel V (2019) Infection as a potential cofactor in the genetic-epigenetic pathophysiology of endometriosis: a systematic review. Facts Views Vis Obgyn 11 (3):209-216\u003c/li\u003e\n\u003cli\u003eLin WC, Chang CY, Hsu YA, Chiang JH, Wan L (2016) Increased Risk of Endometriosis in Patients With Lower Genital Tract Infection: A Nationwide Cohort Study. Medicine (Baltimore) 95 (10):e2773. doi:10.1097/md.0000000000002773\u003c/li\u003e\n\u003cli\u003eKhan KN, Fujishita A, Hiraki K, Kitajima M, Nakashima M, Fushiki S, Kitawaki J (2018) Bacterial contamination hypothesis: a new concept in endometriosis. Reprod Med Biol 17 (2):125-133. doi:10.1002/rmb2.12083\u003c/li\u003e\n\u003cli\u003eHinck AP (2012) Structural studies of the TGF-\u0026beta;s and their receptors - insights into evolution of the TGF-\u0026beta; superfamily. FEBS Lett 586 (14):1860-1870. doi:10.1016/j.febslet.2012.05.028\u003c/li\u003e\n\u003cli\u003eKomiyama S, Aoki D, Saitoh E, Komiyama M, Udagawa Y (2011) Biological significance of plasminogen activator inhibitor-1 expression in ovarian clear cell adenocarcinoma. Eur J Gynaecol Oncol 32 (6):611-614\u003c/li\u003e\n\u003cli\u003eWilson RB (2018) Hypoxia, cytokines and stromal recruitment: parallels between pathophysiology of encapsulating peritoneal sclerosis, endometriosis and peritoneal metastasis. Pleura Peritoneum 3 (1):20180103. doi:10.1515/pp-2018-0103\u003c/li\u003e\n\u003cli\u003eDai FF, Bao AY, Luo B, Zeng ZH, Pu XL, Wang YQ, Zhang L, Xian S, Yuan MQ, Yang DY, Liu SY, Cheng YX (2020) Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis. Exp Ther Med 19 (1):264-272. doi:10.3892/etm.2019.8214\u003c/li\u003e\n\u003cli\u003eGuzel E, Arlier S, Guzeloglu-Kayisli O, Tabak MS, Ekiz T, Semerci N, Larsen K, Schatz F, Lockwood CJ, Kayisli UA (2017) Endoplasmic Reticulum Stress and Homeostasis in Reproductive Physiology and Pathology. Int J Mol Sci 18 (4). doi:10.3390/ijms18040792\u003c/li\u003e\n\u003cli\u003eJ B, SC B, C S - GATA4 and GATA6 silencing in ovarian granulosa cells affects levels of mRNAs. D - 0375040 (- 1945-7170 (Electronic)):T - ppublish\u003c/li\u003e\n\u003cli\u003eMT D, D R, D M, CM E, ME P, DC B, T K, M O, N J, Y D, SE B - Genome-wide DNA methylation analysis predicts an epigenetic switch for GATA factor. D - 101239074 (- 1553-7404 (Electronic)):T - epublish\u003c/li\u003e\n\u003cli\u003eZ Z, L R, M L, X Y - Analysis of key candidate genes and pathways of endometriosis pathophysiology by a. D - 8807913 (- 1473-0766 (Electronic)):T - ppublish\u003c/li\u003e\n\u003cli\u003eM C, Y Z, H X, C H, RM E, D H, X Z, Y W - Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition. D - 101563288 (- 2045-2322 (Electronic)):T - epublish\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, table 1-3 PDFs are only available as a download in the Supplemental Files section.\u003c/p\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, integrated bioinformatics, differentially expressed genes, signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-80648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-80648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eEndometriosis was a common gynecological disease, however, the specific mechanism and the key molecules of endometriosis remained uncertain. This study aimed to single out key genes associated with poor prognosis, and further uncover underlying mechanisms.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData regarding mRNA expression profiles used in this study were retrieved from the Gene Expression Omnibus (GEO) database, a total of three mRNA expression profiles were included for subsequent analysis (GSE31515, GSE58178 and GSE120103). Then, we conducted Gene Ontology analysis (GO analysis), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis by the software R.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 304 differentially expressed genes (DEGs) between endometriosis tissues and normal endometrium tissues were identified in integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis reveals that the DEGs of endometriosis were closely associated with molecular origin of bacteria. KEGG pathway enrichment analysis indicates that the DEGs were mainly involved in AGE-RAGE signaling pathway in diabetic complications. In addition, PPI of these DEGs was visualized by Cytoscape platform with utilization of Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identifies 10 potential DEGs-related protein targets, including\u003cem\u003e CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, HSP90B1.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn conclusion, the present study reveals that bacterial contamination, defect of female reproductive system development, retrograde menstruation and the AGE-RAGE signaling pathway may be involved in the development of endometriosis In addition, these identified DEGs may be of clinical significance for the diagnosis and treatment of the endometriosis.\u003c/p\u003e","manuscriptTitle":"Identification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-30 20:08:23","doi":"10.21203/rs.3.rs-80648/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":"166bc5af-fcbd-45a9-97d4-cd6e3684df8c","owner":[],"postedDate":"September 30th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":650871,"name":"Sexual \u0026 Reproductive Medicine"},{"id":650872,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-01-18T11:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-30 20:08:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-80648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-80648","identity":"rs-80648","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}