Identification of Differentially Expressed Genes and Signalling Pathways Related to Ovarian Endometriosis Based on Integrated Bioinformatic Analysis

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This study aimed to identify 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, and a total of three mRNA expression profiles were included in subsequent analyses (GSE31515, GSE58178 and GSE120103). We divided all differentially expressed genes (DEGs) into up-regulated and down-regulated groups. Then, we conducted Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis using R software. Results A total of 304 DEGs were identified between endometriosis tissues and normal endometrium tissues using integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis revealed that the up-regulated DEGs of endometriosis were closely associated with voltage-gated calcium channel activity, whereas the down-regulated DEGs were enriched in uterus development. KEGG pathway enrichment analysis indicated that the up-regulated DEGs were mainly involved in cytokine-cytokine receptor interaction, whereas down-regulated DEGs were enriched in protein processing in the endoplasmic reticulum. In addition, PPIs of these DEGs were visualized using the Cytoscape platform and the Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identified 10 potential DEG-related protein targets, including CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, and HSP90B1. Conclusion In conclusion, the present study reveals that voltage-gated calcium channel activity, uterus development, cytokine-cytokine receptor interaction and protein processing in the endoplasmic reticulum may be involved in the development of endometriosis. In addition, these identified DEGs may exhibit clinical significance for the diagnosis and treatment of endometriosis. Endocrinology & Metabolism endometriosis integrated bioinformatics differentially expressed genes signalling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Endometriosis is a common gynaecological disease caused by active endometrium that infiltrates into peri-uterine sites, such as pelvic cavity (i.e., ovaries, external structure of uterus, uterosacral ligaments and pouch of Douglas) and the wall of pelvic organs [ 1 ]. Approximately 10–15% of reproductive-aged woman worldwide suffer from endometriosis, which causes chronic pelvic pain and infertility [ 2 ]. Although endometriosis was first identified and described in the early 20th century, there has been no consensus on etiological theory to date. The most widely accepted theory was proposed by Sampson; this theory assumed that endometrium fragments migrated to pelvic cavity via the fallopian tube with the menstrual blood flow and were subsequently implanted in the ovary and other sites within the body [ 3 ]. The coelomic metaplasia theory proposed by Mayer and an immunological theory were also demonstrated to be credible by several studies [ 4 , 5 ]. Three main phenotypes of endometriosis are observed clinically: peritoneal endometriosis, ovarian endometriosis, and deep-infiltrating endometriosis [ 6 ]. Revised classification criteria released by the American Society for Reproductive Medicine are widely used to classify the severity of endometriosis from minimal (I) to severe (IV) in clinical practice [ 7 ]. Diagnostic laparoscopy is the most accurate method to diagnose endometriosis patients. In addition, the location of pain, infertility, positive results from medical imaging examination and CA125 evaluation in blood samples could also predict the onset of endometriosis. However, due to the invasive nature of these procedures as well as diagnostic inaccuracies, uncovering underlying mechanisms of the onset and progression of endometriosis is crucial for medical therapy. Endometriosis is a complex disease that is related to multiple factors, such as immunology, endocrinology, genetics, and environmental factors. Studies showed that immediate family members of endometriosis patients exhibit a significantly increased risk of developing endometriosis [ 8 ]. In this regard, identifying endometriosis-related genetic variants is critical for susceptible populations. The differentially expressed genes (DEGs) could reveal signalling pathways potentially linked to the development and progression of endometriosis. Given the limited number of samples and inconsistent study methods, sample integration of included studies revealed significant heteroscedasticity. With the emergence of newly developed study methods, integrated bioinformatic analysis has been demonstrated to be a reliable tool in molecular and biological studies 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 performed GO analysis and KEGG pathway analysis. Then, the PPI network was constructed and visualized. Through this series of analysis, numerous key signalling pathways and potential candidate genes involved in the development and progression of endometriosis are identified. Results of this study provide potential molecular targets to help improve the 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 the GEO database ( http://www.ncbi.nlm.nih . gov/geo) of National Coalition Building Institute (NCBI). "Ovarian endometriosis" was selected as keyword for data retrieval, and species types were limited to Homo sapiens , and 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 to assess the influence of oxidative stress on endometriotic stromal cells (GSE31515) was GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K 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 bead chip platform. The dataset GSE120103 contained 18 endometrioma samples and 18 control endometrium specimens, and the platform for analysing GSE120103 was GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version). Both platform and series matrix files were downloaded in the CSV data format in this study. The dataset information was displayed in Table 1. Data processing Gene sequence annotation was conducted using the platform file through Strawberry-Perl-5.30.2.1 ( https://www.perl.org/get.html ), and then the data were formatted into the gene expression matrix for subsequent operations. We then merged three gene expression matrices that were converted from the three GSE datasets mentioned above into a single gene expression matrix through Straw-perl-5.30.2.1. Genes that were not simultaneously expressed in the 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 packages in the 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 identify differentially expressed mRNAs. This study was conducted based on the thresholds of adjust P-value 1. In addition, R software was used to construct heat maps and volcanic maps of DEGs between the case group and the control group. Pathway enrichment analysis The GO analysis is divided into three parts: Molecular Function (MF), Biological Process (BP) and Cellular Component (CC). Individual proteins or genes were identified by serial number correspondence or sequence annotation, and the GO number was used to locate the corresponding term, namely functional category or cell type. To better understand the pathways associated with DEGs in the pathogenesis of endometriosis and their corresponding molecular mechanisms, we first divided DEGs into up-regulated and down-regulated groups. The enrichment analysis of the GO and KEGG pathways was then performed using the Bioconductor 3.11 tool via clusterProfiler package for the up-regulated and down-regulated groups, separately. P < 0.05 indicated statistical significance. The most relevant functional pathways of DEGs were determined using R package “pathview”, and the location of each DEG was annotated in the functional pathway. PPI network construction The PPI among DEGs-encoded proteins was analysed based on Search Tool for the Retrieval of Interacting Genes (STRING) online database ( http://string-db.org/ ) using a combined score of ≥ 0.4 as the cut-off value. To simplify diagrams, we removed all isolated or partially connected nodes and finally constructed a full-scale DEG network. Data from the 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 analyse functional modules. Results Identification of DEGs in ovarian endometriosis In total, 30 healthy women were enrolled as the control group, and 27 patients with ovary endometriosis served 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 to eliminate effects of different experimental factors. Here, |log2FC| > 1 and P < 0.05 served as cut-off values for data inclusion. In addition, we used the 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 using 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 R 3.6.3 to convert gene symbols into entrez IDs for further analysis. BP, MF and CC represent the three major categories of GO analysis. Based on R 3.6.3 software, 185 up-regulated genes and 119 down-regulated genes were analysed separately, and results of GO analysis regarding BP, MF and CC are presented in Fig. 3 . Regarding BP analysis, up-regulated DEGs are particularly enriched in the regulation of voltage-gated calcium channel activity, positive regulation of cation channel activity, positive regulation of voltage-gated calcium channel activity and response to molecule of bacterial origin. In addition, down-regulated DEGs are particularly enriched in uterus development, the planar cell polarity pathway involved in neural tube closure, appendage development and limb development (adjusted P-value < 0.005). Regarding MF analysis, identified up-regulated DEGs are mainly enriched in sarcolemma, sarcoplasmic reticulum, Z disc and sarcoplasm, whereas down-regulated DEGs are mainly enriched in collagen-containing extracellular matrix, endoplasmic reticulum lumen, cell-substrate junction and melanosome (adjusted P-value < 0.005). Regarding CC analysis, up-regulated DEGs are mainly enriched in receptor ligand activity, signalling receptor activator activity, chemokine activity and cytokine activity, whereas down-regulated DEGs are mainly enriched in collagen binding, platelet-derived growth factor binding and peptide disulphide oxidoreductase activity (adjust P-value < 0.005). GO functional analysis results, which include the BP, MF and CC analysis, are displayed in Fig. 3 . KEGG analysis results are shown in Fig. 4 , Tables 3 and 4. These results demonstrate that detected up-regulated DEGs are mainly enriched in cytokine-cytokine receptor interaction, malaria and the IL-17 signalling pathway, whereas down-regulated DEGs are mainly enriched in protein processing in endoplasmic reticulum, tight junctions and Salmonella infection (P < 0.005). To uncover the most pertinent functional pathway related to DEGs, we constructed a specific pathway diagram using R package “pathview” and labelled the 9 down-regulated DEGs related to protein processing in the endoplasmic reticulum and 10 up-regulated DEGs related to cytokine-cytokine receptor interaction (Figs. 5 and 6 ). Protein–protein interaction network (PPI) and modular analysis We used Cytoscape to construct the PPI network, which consisted of 219 nodes (proteins) and 542 edges (interactions). Then we applied cytoHubba to analyse the most significant interaction in the network, and the results show that interactions among CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1 , and HSP90B1 are the most significant interaction among selected DEGs, as shown in Fig. 7 . Discussion Accumulating evidence suggests that endometrium of endometriosis patients exhibits anomalous gene expression profiles that are distinct from healthy populations [ 11 ]. To explore such a discrepancy, we selected three gene expression profile datasets. By analysing these datasets, a total of 304 DEGs were identified, including 185 unregulated and 119 downregulated genes. Furthermore, these DEGs provided promising evidence that supported some new hypotheses of the onset of endometriosis. The most significant symptom of endometriosis is chronic pelvic pain, which is related with the activation of voltage-gated calcium channels. Calcium channels play a critical role in neuropathic pain development through the regulation of the release of excitatory neurotransmitters as part of the second messenger system [ 12 ]. In particular, although the endometriosis implants are ectopic, they are highly innervated [ 13 ]. A pilot trial showed that the use of gabapentin as a calcium channel blocker could manage pain effectively [ 14 ]. Genes related to uterus development were differentially expressed between healthy populations and endometriosis patients. Crispi et al. proposed the hypothesis that endometriosis may be connected with defects during the development of the female reproductive system. These abnormal genes might be associated with the erroneous localization of the cells that generate the ectopic endometrium during the migration phase [ 15 ]. BMP6 belongs to the transforming growth factor-beta super-family that includes proteins involved in cell growth and differentiation [ 16 ]. SERPINE1 is a member of the serine protease inhibitor family that contains inhibitors of tissue plasminogen activator and are involved in tissue remodelling. SERPINE1 exhibited significantly higher expression in ovarian cancer patients, indicating a potential role in tumour invasion and metastasis [ 17 ]. Up-regulated SERPINE1 expression could also explain some malignant characteristics of ectopic endometrium; however, samples included in this study were obtained from patients with deep infiltrating endometriosis. KEGG analysis revealed that up-regulated DEGs were related to cytokine-cytokine receptor interaction. In patients with endometriosis, cytokine levels in the retained fluid were increased compared with that in normal subjects, and ectopic lesions typically occurred at the site of retained fluid. This finding suggested that cytokines might play an important role in endometrial cell peritoneal implantation, infiltration, vascular regeneration and local hyperplasia. TNF (tumour necrosis factor) is a cytokine derived from macrophages and monocytes that plays an important role in immune regulation, cell growth and differentiation. Enzyme linked immunosorbent assay results revealed significantly increased TNF-α levels in peritoneal fluid of patients with endometriosis and infertility compared with the control group (P < 0.05). Increased TNF in the circulating blood attracts activated monocytes and inflammatory cells to the peritoneal cavity, and the number of monocytes was also increased in the local immune system, which stimulates the growth of ectopic endothelium [ 18 ]. In patients with endometriosis, a high concentration of VEGF (vascular endothelial growth factor) was noted in the peritoneal fluid. Endometrial cells were locally attached and infiltrated the endothelium, and a large number of blood vessels are required to ensure their growth. VEGF is a proliferating factor that plays a major role in neovascularization. Bioinformatic analysis and molecular biology characterization indicated that pro-inflammatory cytokines induced VEGF-C overexpression to enhance lymphangiogenesis in lymphoendothelial cells by inhibiting COUP-TFII levels [ 19 ]. IL-8 is an angiogenic factor that promotes the proliferation of new blood vessels, allowing the ectopic endometrium to grow and proliferate, resulting in extensive pelvic adhesions. IL-8 is involved in the pathological process of endometriosis. Ectopic endometrial stromal cells exhibited significantly increased IL-6 and IL-8 mRNA gene expression and secretion compared with orthotopic and control endometrial stromal cells prior to treatment [ 20 ]. On the other hand, KEGG analysis suggested that down-regulated DEGs were related to protein processing in the endoplasmic reticulum. The endoplasmic reticulum played an important role in the interaction between multiple intracellular organelles along with collateral bio-activities, especially including processes associated with the proteasome and attached ribosome, protein synthesis and other processes. Accumulation of unfolded or misfolded proteins during endoplasmic reticulum stress activated a homeostatic coping mechanism called the unfolded protein response. Female reproductive tissues were highly active at cellular, molecular and genetic levels, and this activity requires the endoplasmic reticulum. In certain severe conditions, the unfolded protein response was not sufficient to restore normal endoplasmic reticulum function, which further contributed to the pathogenesis of various diseases, including endometriosis [ 21 ]. GATA6 is a fertility-related gene that is expressed in vertebrate ovaries [ 19 ]. GATA6 overexpression, which is induced by aberrant methylation in endometriotic cells, regulated the expression of steroid metabolism and steroid hormone receptors. For example, the transcripts of oestrogen receptor α and progesterone receptor are reduced, whereas oestrogen receptor β transcript levels were increased. MMP11 is significantly reduced by the overexpression of GATA6 as shown in Table 2. This process transformed healthy endometrium away from spontaneous decidualization and toward the disease phenotype by restricting the ability of endometrial stromal cells to decidualize. PRL and IGFBP1 expression expected to be inhibited, but these genes were up-regulated [ 22 ]. A few bioinformatic analyses related to endometriosis have been performed. Tissues in the control groups from datasets were included. Zhang and Wang et al., Yao et al., and Cheng et al. obtained samples from not only endometriosis patients but also healthy women [ 23 – 25 ]. In addition, samples defined as ectopic endometrium were obtained from various regions, which weakened the strength of the conclusions. In this study, we include samples from endometriomas from endometriosis patients and ectopic endometrium from healthy 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. Conclusion In conclusion, this study reveals the possibility of new pathological hypotheses for endometriosis, including bacterial contamination, defects in female reproductive system development and retrograde menstruation. This study also identified new drug targets in addition to the oestrogen receptor. Abbreviations GEO: Gene Expression Omnibus DEGs: Differentially expressed genes KEGG: Kyoto Encyclopedia of Genes and Genomes PPI: Protein-protein interaction STRING: The Search Tool for the Retrieval of Interacting Genes MF: Molecular Function BP: Biological Process CC: Cellular Component TNF: Tumour necrosis factor Declarations Funding This work was supported by the National Natural Science Foundation of China (No. 82001503), the China Postdoctoral Science Foundation (2020M671760) and Wenzhou Science & Technology Bureau (2020Y1326). 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. Acknowledgements Not applicable. References Méar L, Herr M, Fauconnier A, Pineau C, Vialard F. Polymorphisms and endometriosis: a systematic review and meta-analyses. Hum Reprod Update. 2020;26(1):73-102. doi: 10.1093/humupd/dmz034. Dunselman GA, Vermeulen N, Becker C, Calhaz-Jorge C, D'Hooghe T, De Bie B, et al. ESHRE guideline: management of women with endometriosis. Hum Reprod. 2014;29(3):400-12. doi: 10.1093/humrep/det457. Sampson JA. Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation. 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Tables Due to technical limitations, tables are only available as a download in the Supplemental Files section. Supplementary Files Table1.pdf Description of GEO ovary endometriosis data Table2.pdf Identifying DEGs in ovary endometriosis by integrated microarray Table3.pdf KEGG pathway analysis of down-regulated DEGs in ovary endometriosis Table4.pdf KEGG pathway analysis of up-regulated DEGs in ovary endometriosis genematrix.xlsx Gene Matrix Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-161050","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":11189501,"identity":"ab505f5f-3acb-43e6-b6c7-9dbabc55af7a","order_by":0,"name":"Kainan Lin","email":"","orcid":"","institution":"Wenzhou Medical University First Affiliated Hospital: The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kainan","middleName":"","lastName":"Lin","suffix":""},{"id":11189502,"identity":"ceb253c3-b0e3-484c-b74c-19de66cd746f","order_by":1,"name":"Zhenyan Pan","email":"","orcid":"","institution":"Wenzhou Medical University First Affiliated Hospital: The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenyan","middleName":"","lastName":"Pan","suffix":""},{"id":11189503,"identity":"5f914127-991d-47ba-963f-f3b5a4ef584e","order_by":2,"name":"Renke He","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Renke","middleName":"","lastName":"He","suffix":""},{"id":11189504,"identity":"a5bbda07-d835-49ad-80bc-e7f4e09413f4","order_by":3,"name":"Hanchu Wang","email":"","orcid":"","institution":"Wenzhou Medical University First Affiliated Hospital: The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hanchu","middleName":"","lastName":"Wang","suffix":""},{"id":11189505,"identity":"01fa1393-9fd2-40cd-abbc-3ea8de1af180","order_by":4,"name":"Kai Zhou","email":"","orcid":"","institution":"Wenzhou Medical University First Affiliated Hospital: The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhou","suffix":""},{"id":11189506,"identity":"0f6dbadf-cdbb-40be-b358-4c99937af05d","order_by":5,"name":"Liangshan Mu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPmYwdQBEMD5g4AHRCfi1sCFpYTYgTgsDQgubBIRDSAs787OHX/7ckTcXO2NW+UPmMAM/e44Bw88d+BzGZm4s2/bMcOfsHLMbEjyHGSR73hgw9p7B6xczacmGw4wbbgO1GAC1GNzIMWBmbMOnhf2btMSfw/YgLQUJQC32hLXwmEl+YDucCNLCcABkiwRhLWXSjG2HkzfcTiuWbOBJ55E486zgYC8eLfz8x7dJ/vhz2HbD7eSNH3/2WMvxtydvfPATjxYQYOaBsRh7IJF5AL8GoMIfcOYPPMpGwSgYBaNgxAIAIXFMuJpiHcIAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Liangshan","middleName":"","lastName":"Mu","suffix":""}],"badges":[],"createdAt":"2021-01-27 18:18:26","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-161050/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-161050/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5834311,"identity":"097a83ba-635d-4f28-843d-3db604bb775d","added_by":"auto","created_at":"2021-02-10 18:24:59","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-161050/v2/83b02288ed1c15c459201923.png"},{"id":5834310,"identity":"969f02af-604c-4e44-99c2-80714074fdf1","added_by":"auto","created_at":"2021-02-10 18:24:59","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-161050/v2/b3f6863d091f048cf943c787.png"},{"id":5834574,"identity":"a0c37847-0e77-4c4d-a393-90d5cf76bad1","added_by":"auto","created_at":"2021-02-10 18:27:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1852726,"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.jpg","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/4924e68838bcca6aa0b4aa1e.jpg"},{"id":5834033,"identity":"a099d815-150e-4b37-a2fe-067cef828383","added_by":"auto","created_at":"2021-02-10 18:21:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1041300,"visible":true,"origin":"","legend":"KEGG pathway analysis of DEGs in ovary endometriosis","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/8f0abb49ae8c394c7576dcb9.jpg"},{"id":5834315,"identity":"846d936c-571d-45a9-9b5b-ba2aa2bd5ebe","added_by":"auto","created_at":"2021-02-10 18:24:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94965,"visible":true,"origin":"","legend":"Analysis of the most relevant functional pathway. Nine genes (PDIA6, STT3A, SSR2, PDIA3, LMAN2, RRBP1, HSP90B1, HSPA2 and PDIA4) are significantly enriched in the Protein processing in endoplasmic reticulum. ","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/cf2d6269ab397218c5f1fe34.png"},{"id":5834318,"identity":"d7415bac-bc68-46b4-9a7e-188b1d5ae193","added_by":"auto","created_at":"2021-02-10 18:24:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153457,"visible":true,"origin":"","legend":"Analysis of the most relevant functional pathway. Ten genes (CXCL17, BMP6, CCL3L3, CCL2, CCL8, INHA, GHR, CXCL2 and PRL) are significantly enriched in the Cytokine-cytokine receptor interaction. ","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/3793acae6d18a5a0256896b0.png"},{"id":5834319,"identity":"662b24a9-8798-468d-ba7f-a2931beb46e9","added_by":"auto","created_at":"2021-02-10 18:24:59","extension":"png","order_by":7,"title":"Figure 7","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":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/dd74b314b37527486567f5ec.png"},{"id":13658065,"identity":"dff9e455-fe83-492c-96b5-2679d66720eb","added_by":"auto","created_at":"2021-09-17 10:13:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2091253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/65c75049-edd9-48e2-9ac7-554b89f0cda9.pdf"},{"id":5834628,"identity":"f17096bf-908f-4d06-9142-950aa80630e8","added_by":"auto","created_at":"2021-02-10 18:30:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28409,"visible":true,"origin":"","legend":"Description of GEO ovary endometriosis data","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/58a4522dcaa000746077282d.pdf"},{"id":5834028,"identity":"f30881bd-b68d-4874-8579-091baa2af7cf","added_by":"auto","created_at":"2021-02-10 18:21:59","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21754,"visible":true,"origin":"","legend":"Identifying DEGs in ovary endometriosis by integrated microarray","description":"","filename":"Table2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/e021840b9f24ddfe307b69c0.pdf"},{"id":5834314,"identity":"c72372e9-2d24-42c2-b25b-a753629ddab7","added_by":"auto","created_at":"2021-02-10 18:24:59","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":39627,"visible":true,"origin":"","legend":"KEGG pathway analysis of down-regulated DEGs in ovary endometriosis","description":"","filename":"Table3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/5ae71817a731f7ad8f9245d1.pdf"},{"id":5834627,"identity":"0118ea5f-a98f-46c5-af86-3b104406ad7f","added_by":"auto","created_at":"2021-02-10 18:30:59","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":40581,"visible":true,"origin":"","legend":"KEGG pathway analysis of up-regulated DEGs in ovary endometriosis","description":"","filename":"Table4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/37289395c6c57cf1b15b320c.pdf"},{"id":5834572,"identity":"fa46007b-18c3-4c99-a0bf-bd071fbf23a9","added_by":"auto","created_at":"2021-02-10 18:27:59","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12424,"visible":true,"origin":"","legend":"Gene Matrix","description":"","filename":"genematrix.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-161050/v2/757fa6b4f0448862432dd9ca.xlsx"}],"financialInterests":"","formattedTitle":"Identification of differentially expressed genes and signalling pathways related to ovarian endometriosis based on integrated bioinformatic analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a common gynaecological disease caused by active endometrium that infiltrates into peri-uterine sites, such as pelvic cavity (i.e., ovaries, external structure of uterus, uterosacral ligaments and pouch of Douglas) and the wall of pelvic organs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Approximately 10\u0026ndash;15% of reproductive-aged woman worldwide suffer from endometriosis, which causes 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 has been no consensus on etiological theory to date. The most widely accepted theory was proposed by Sampson; this theory assumed that endometrium fragments migrated to pelvic cavity via the fallopian tube with the menstrual blood flow and were subsequently implanted in the ovary and other sites within the body [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The coelomic metaplasia theory proposed by Mayer and an immunological theory were also demonstrated to be credible by several studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThree main phenotypes of endometriosis are observed clinically: peritoneal endometriosis, ovarian endometriosis, and deep-infiltrating endometriosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Revised classification criteria released by the American Society for Reproductive Medicine are widely used to classify the severity of endometriosis from minimal (I) to severe (IV) in clinical practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Diagnostic laparoscopy is the most accurate method to diagnose endometriosis patients. In addition, the location of pain, infertility, positive results from medical imaging examination and CA125 evaluation in blood samples could also predict the onset of endometriosis. However, due to the invasive nature of these procedures as well as diagnostic inaccuracies, uncovering underlying mechanisms of the onset and progression of endometriosis is crucial for medical therapy.\u003c/p\u003e\u003cp\u003eEndometriosis is a complex disease that is related to multiple factors, such as immunology, endocrinology, genetics, and environmental factors. Studies showed that immediate family members of endometriosis patients exhibit a significantly increased risk of developing endometriosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this regard, identifying endometriosis-related genetic variants is critical for susceptible populations. The differentially expressed genes (DEGs) could reveal signalling pathways potentially linked to the development and progression of endometriosis. Given the limited number of samples and inconsistent study methods, sample integration of included studies revealed significant heteroscedasticity. With the emergence of newly developed study methods, integrated bioinformatic analysis has been demonstrated to be a reliable tool in molecular and biological studies 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 performed GO analysis and KEGG pathway analysis. Then, the PPI network was constructed and visualized. Through this series of analysis, numerous key signalling pathways and potential candidate genes involved in the development and progression of endometriosis are identified. Results of this study provide potential molecular targets to help improve the diagnosis and treatment for endometriosis.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eGene expression data\u003c/h2\u003e\u003cp\u003eMicroarray data of mRNA expression profiles related to progression of ovarian endometriosis were extracted and downloaded from the 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\" was selected as keyword for data retrieval, and species types were limited to \u003cem\u003eHomo sapiens\u003c/em\u003e, and 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 to assess the influence of oxidative stress on endometriotic stromal cells (GSE31515) was GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K 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 bead chip platform. The dataset GSE120103 contained 18 endometrioma samples and 18 control endometrium specimens, and the platform for analysing GSE120103 was GPL6480 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version). Both platform and series matrix files were downloaded in the CSV data format in this study. The dataset information was displayed in Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eData processing\u003c/h2\u003e\u003cp\u003eGene sequence annotation was conducted using 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), and then the data were formatted into the gene expression matrix for subsequent operations. We then merged three gene expression matrices that were converted from the three GSE datasets mentioned above into a single gene expression matrix through Straw-perl-5.30.2.1. Genes that were not simultaneously expressed in the 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 packages in the Bioconductor 3.11 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/limma.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 identify differentially expressed mRNAs. This study was conducted based on the thresholds of adjust P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2fold change (FC)| \u0026gt; 1. In addition, R software was used to construct heat maps and volcanic maps of DEGs between the case group and the control group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003ePathway enrichment analysis\u003c/h2\u003e\u003cp\u003eThe GO analysis is divided into three parts: Molecular Function (MF), Biological Process (BP) and Cellular Component (CC). Individual proteins or genes were identified by serial number correspondence or sequence annotation, and the GO number was used to locate the corresponding term, namely functional category or cell type. To better understand the pathways associated with DEGs in the pathogenesis of endometriosis and their corresponding molecular mechanisms, we first divided DEGs into up-regulated and down-regulated groups. The enrichment analysis of the GO and KEGG pathways was then performed using the Bioconductor 3.11 tool via clusterProfiler package for the up-regulated and down-regulated groups, separately. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. The most relevant functional pathways of DEGs were determined using R package \u0026ldquo;pathview\u0026rdquo;, and the location of each DEG was annotated in the functional pathway.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003ePPI network construction\u003c/h2\u003e\u003cp\u003eThe PPI among DEGs-encoded proteins was analysed based on Search Tool for the Retrieval of Interacting Genes (STRING) online database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003c/span\u003e) using a combined score of \u0026ge;\u0026thinsp;0.4 as the cut-off value. To simplify diagrams, we removed all isolated or partially connected nodes and finally constructed a full-scale DEG network. Data from the 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 analyse functional modules.\u003c/p\u003e\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\u003eIn total, 30 healthy women were enrolled as the control group, and 27 patients with ovary endometriosis served 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 to eliminate effects of different experimental factors. Here, |log2FC| \u0026gt; 1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 served as cut-off values for data inclusion. In addition, we used the 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 using 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.\u0026nbsp;\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 R 3.6.3 to convert gene symbols into entrez IDs for further analysis. BP, MF and CC represent the three major categories of GO analysis. Based on R 3.6.3 software, 185 up-regulated genes and 119 down-regulated genes were analysed separately, and results of GO analysis regarding BP, MF and CC are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Regarding BP analysis, up-regulated DEGs are particularly enriched in the regulation of voltage-gated calcium channel activity, positive regulation of cation channel activity, positive regulation of voltage-gated calcium channel activity and response to molecule of bacterial origin. In addition, down-regulated DEGs are particularly enriched in uterus development, the planar cell polarity pathway involved in neural tube closure, appendage development and limb development (adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Regarding MF analysis, identified up-regulated DEGs are mainly enriched in sarcolemma, sarcoplasmic reticulum, Z disc and sarcoplasm, whereas down-regulated DEGs are mainly enriched in collagen-containing extracellular matrix, endoplasmic reticulum lumen, cell-substrate junction and melanosome (adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Regarding CC analysis, up-regulated DEGs are mainly enriched in receptor ligand activity, signalling receptor activator activity, chemokine activity and cytokine activity, whereas down-regulated DEGs are mainly enriched in collagen binding, platelet-derived growth factor binding and peptide disulphide oxidoreductase activity (adjust P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005). GO functional analysis results, which include the BP, MF and CC analysis, are displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. KEGG analysis results are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Tables\u0026nbsp;3 and 4. These results demonstrate that detected up-regulated DEGs are mainly enriched in cytokine-cytokine receptor interaction, malaria and the IL-17 signalling pathway, whereas down-regulated DEGs are mainly enriched in protein processing in endoplasmic reticulum, tight junctions and \u003cem\u003eSalmonella\u003c/em\u003e infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005). To uncover the most pertinent functional pathway related to DEGs, we constructed a specific pathway diagram using R package \u0026ldquo;pathview\u0026rdquo; and labelled the 9 down-regulated DEGs related to protein processing in the endoplasmic reticulum and 10 up-regulated DEGs related to cytokine-cytokine receptor interaction (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u0026nbsp;\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 the PPI network, which consisted of 219 nodes (proteins) and 542 edges (interactions). Then we applied cytoHubba to analyse the most significant interaction in the network, and the results show that interactions among \u003cem\u003eCCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1\u003c/em\u003e, and \u003cem\u003eHSP90B1\u003c/em\u003e are the most significant interaction among selected DEGs, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccumulating evidence suggests that endometrium of endometriosis patients exhibits anomalous gene expression profiles that are distinct from healthy populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To explore such a discrepancy, we selected three gene expression profile datasets. By analysing these datasets, a total of 304 DEGs were identified, including 185 unregulated and 119 downregulated genes. Furthermore, these DEGs provided promising evidence that supported some new hypotheses of the onset of endometriosis.\u003c/p\u003e\u003cp\u003eThe most significant symptom of endometriosis is chronic pelvic pain, which is related with the activation of voltage-gated calcium channels. Calcium channels play a critical role in neuropathic pain development through the regulation of the release of excitatory neurotransmitters as part of the second messenger system [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In particular, although the endometriosis implants are ectopic, they are highly innervated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A pilot trial showed that the use of gabapentin as a calcium channel blocker could manage pain effectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenes related to uterus development were differentially expressed between healthy populations and endometriosis patients. Crispi et al. proposed the hypothesis that endometriosis may be connected with defects during the development of the female reproductive system. These abnormal genes might be associated with the erroneous localization of the cells that generate the ectopic endometrium during the migration phase [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. BMP6 belongs to the transforming growth factor-beta super-family that includes proteins involved in cell growth and differentiation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. SERPINE1 is a member of the serine protease inhibitor family that contains inhibitors of tissue plasminogen activator and are involved in tissue remodelling. SERPINE1 exhibited significantly higher expression in ovarian cancer patients, indicating a potential role in tumour invasion and metastasis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Up-regulated SERPINE1 expression could also explain some malignant characteristics of ectopic endometrium; however, samples included in this study were obtained from patients with deep infiltrating endometriosis.\u003c/p\u003e\u003cp\u003eKEGG analysis revealed that up-regulated DEGs were related to cytokine-cytokine receptor interaction. In patients with endometriosis, cytokine levels in the retained fluid were increased compared with that in normal subjects, and ectopic lesions typically occurred at the site of retained fluid. This finding suggested that cytokines might play an important role in endometrial cell peritoneal implantation, infiltration, vascular regeneration and local hyperplasia. TNF (tumour necrosis factor) is a cytokine derived from macrophages and monocytes that plays an important role in immune regulation, cell growth and differentiation. Enzyme linked immunosorbent assay results revealed significantly increased TNF-α levels in peritoneal fluid of patients with endometriosis and infertility compared with the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Increased TNF in the circulating blood attracts activated monocytes and inflammatory cells to the peritoneal cavity, and the number of monocytes was also increased in the local immune system, which stimulates the growth of ectopic endothelium [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In patients with endometriosis, a high concentration of VEGF (vascular endothelial growth factor) was noted in the peritoneal fluid. Endometrial cells were locally attached and infiltrated the endothelium, and a large number of blood vessels are required to ensure their growth. VEGF is a proliferating factor that plays a major role in neovascularization. Bioinformatic analysis and molecular biology characterization indicated that pro-inflammatory cytokines induced VEGF-C overexpression to enhance lymphangiogenesis in lymphoendothelial cells by inhibiting COUP-TFII levels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. IL-8 is an angiogenic factor that promotes the proliferation of new blood vessels, allowing the ectopic endometrium to grow and proliferate, resulting in extensive pelvic adhesions. IL-8 is involved in the pathological process of endometriosis. Ectopic endometrial stromal cells exhibited significantly increased IL-6 and IL-8 mRNA gene expression and secretion compared with orthotopic and control endometrial stromal cells prior to treatment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOn the other hand, KEGG analysis suggested that down-regulated DEGs were related to protein processing in the endoplasmic reticulum. The endoplasmic reticulum played an important role in the interaction between multiple intracellular organelles along with collateral bio-activities, especially including processes associated with the proteasome and attached ribosome, protein synthesis and other processes. Accumulation of unfolded or misfolded proteins during endoplasmic reticulum stress activated a homeostatic coping mechanism called the unfolded protein response. Female reproductive tissues were highly active at cellular, molecular and genetic levels, and this activity requires the endoplasmic reticulum. In certain severe conditions, the unfolded protein response was not sufficient to restore normal endoplasmic reticulum function, which further contributed to the pathogenesis of various diseases, including endometriosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGATA6 is a fertility-related gene that is expressed in vertebrate ovaries [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. GATA6 overexpression, which is induced by aberrant methylation in endometriotic cells, regulated the expression of steroid metabolism and steroid hormone receptors. For example, the transcripts of oestrogen receptor α and progesterone receptor are reduced, whereas oestrogen receptor β transcript levels were increased. MMP11 is significantly reduced by the overexpression of GATA6 as shown in Table\u0026nbsp;2. This process transformed healthy endometrium away from spontaneous decidualization and toward the disease phenotype by restricting the ability of endometrial stromal cells to decidualize. PRL and IGFBP1 expression expected to be inhibited, but these genes were up-regulated [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA few bioinformatic analyses related to endometriosis have been performed. Tissues in the control groups from datasets were included. Zhang and Wang et al., Yao et al., and Cheng et al. obtained samples from not only endometriosis patients but also healthy women [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, samples defined as ectopic endometrium were obtained from various regions, which weakened the strength of the conclusions. In this study, we include samples from endometriomas from endometriosis patients and ectopic endometrium from healthy 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"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study reveals the possibility of new pathological hypotheses for endometriosis, including bacterial contamination, defects in female reproductive system development and retrograde menstruation. This study also identified new drug targets in addition to the oestrogen receptor.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGEO: Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eDEGs: Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003ePPI: Protein-protein interaction\u003c/p\u003e\n\u003cp\u003eSTRING: The Search Tool for the Retrieval of Interacting Genes\u003c/p\u003e\n\u003cp\u003eMF: Molecular Function\u003c/p\u003e\n\u003cp\u003eBP: Biological Process\u003c/p\u003e\n\u003cp\u003eCC: Cellular Component\u003c/p\u003e\n\u003cp\u003eTNF: Tumour necrosis factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82001503), the China Postdoctoral Science Foundation (2020M671760) and Wenzhou Science \u0026amp; Technology Bureau (2020Y1326).\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.\u003cbr /\u003eZY Pan: Data analysis and manuscript writing.\u003cbr /\u003eRK He: Data extraction and manuscript review.\u003cbr /\u003eHC Wang: Data extraction and manuscript review.\u003cbr /\u003eK Zhou: Project conception and manuscript review. \u003cbr /\u003eLS Mu: Project conception and manuscript review. \u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM\u0026eacute;ar L, Herr M, Fauconnier A, Pineau C, Vialard F. Polymorphisms and endometriosis: a systematic review and meta-analyses. Hum Reprod Update. 2020;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, et al. ESHRE guideline: management of women with endometriosis. Hum Reprod. 2014;29(3):400-12. doi: 10.1093/humrep/det457.\u003c/li\u003e\n\u003cli\u003eSampson JA. Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation. Am J Pathol. 1927;3(2):93-110.43.\u003c/li\u003e\n\u003cli\u003eVinatier D, Orazi G, Cosson M, Dufour P. Theories of endometriosis. Eur J Obstet Gynecol Reprod Biol. 2001;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. Immune rosettes of T and B lymphocytes in infertile women with endometriosis. J Reprod Med. 1987;32(3):194-7.\u003c/li\u003e\n\u003cli\u003eNisolle M, Donnez J. Peritoneal endometriosis, ovarian endometriosis, and adenomyotic nodules of the rectovaginal septum are three different entities. Fertil Steril. 1997;68(4):585-96. doi: 10.1016/s0015-0282(97)00191-x.\u003c/li\u003e\n\u003cli\u003eRevised American Society for Reproductive Medicine classification of endometriosis: 1996. Fertil Steril. 1997;67(5):817-21. 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. Familial aggregation of endometriosis in the Yale Series. Arch Gynecol Obstet. 2008;278(6):507-11. doi: 10.1007/s00404-008-0644-1.\u003c/li\u003e\n\u003cli\u003eZhang Y, Li Y, Wang Q, Zhang X, Wang D, Tang HC, et al. Identification of an lncRNA‑miRNA‑mRNA interaction mechanism in breast cancer based on bioinformatic analysis. Mol Med Rep. 2017;16(4):5113-20. doi: 10.3892/mmr.2017.7304.\u003c/li\u003e\n\u003cli\u003eChang H, Sasson A, Srinivasan S, Golhar R, Greenawalt DM, Geese WJ, et al. Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer. Mol Diagn Ther. 2019;23(4):507-20. doi: 10.1007/s40291-019-00408-y.\u003c/li\u003e\n\u003cli\u003eAghajanova L, Velarde MC, Giudice LC. Altered gene expression profiling in endometrium: evidence for progesterone resistance. Semin Reprod Med. 2010;28(1):51-8. doi: 10.1055/s-0029-1242994.\u003c/li\u003e\n\u003cli\u003ePark J, Luo ZD. Calcium channel functions in pain processing. Channels (Austin, Tex). 2010;4(6):510-7. doi: 10.4161/chan.4.6.12869.\u003c/li\u003e\n\u003cli\u003eBellessort B, Bachelot A, Grouthier V, De Lombares C, Narboux-Neme N, Garagnani P, et al. Comparative analysis of molecular signatures suggests the use of gabapentin for the management of endometriosis-associated pain. Journal of pain research. 2018;11:715-25. doi: 10.2147/jpr.S163611.\u003c/li\u003e\n\u003cli\u003eLewis SC, Bhattacharya S, Wu O, Vincent K, Jack SA, Critchley HO, et al. Gabapentin for the Management of Chronic Pelvic Pain in Women (GaPP1): A Pilot Randomised Controlled Trial. PloS one. 2016;11(4):e0153037. doi: 10.1371/journal.pone.0153037.\u003c/li\u003e\n\u003cli\u003eCrispi S, Piccolo MT, D'Avino A, Donizetti A, Viceconte R, Spyrou M, et al. Transcriptional profiling of endometriosis tissues identifies genes related to organogenesis defects. Journal of cellular physiology. 2013;228(9):1927-34. doi: 10.1002/jcp.24358.\u003c/li\u003e\n\u003cli\u003eHinck AP. Structural studies of the TGF-\u0026beta;s and their receptors - insights into evolution of the TGF-\u0026beta; superfamily. FEBS Lett. 2012;586(14):1860-70. doi: 10.1016/j.febslet.2012.05.028.\u003c/li\u003e\n\u003cli\u003eKomiyama S, Aoki D, Saitoh E, Komiyama M, Udagawa Y. Biological significance of plasminogen activator inhibitor-1 expression in ovarian clear cell adenocarcinoma. Eur J Gynaecol Oncol. 2011;32(6):611-4.\u003c/li\u003e\n\u003cli\u003eWang XM, Ma ZY, Song N. Inflammatory cytokines IL-6, IL-10, IL-13, TNF-\u0026alpha; and peritoneal fluid flora were associated with infertility in patients with endometriosis. European review for medical and pharmacological sciences. 2018;22(9):2513-8. doi: 10.26355/eurrev_201805_14899.\u003c/li\u003e\n\u003cli\u003eLi WN, Hsiao KY, Wang CA, Chang N, Hsu PL, Sun CH, et al. Extracellular vesicle-associated VEGF-C promotes lymphangiogenesis and immune cells infiltration in endometriosis. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(41):25859-68. doi: 10.1073/pnas.1920037117.\u003c/li\u003e\n\u003cli\u003eKaramian A, Paktinat S, Esfandyari S, Nazarian H, Ali Ziai S, Zarnani AH, et al. Pyrvinium pamoate induces in-vitro suppression of IL-6 and IL-8 produced by human endometriotic stromal cells. Human \u0026amp; experimental toxicology. 2020:960327120964543. doi: 10.1177/0960327120964543.\u003c/li\u003e\n\u003cli\u003eGuzel E, Arlier S, Guzeloglu-Kayisli O, Tabak MS, Ekiz T, Semerci N, et al. Endoplasmic Reticulum Stress and Homeostasis in Reproductive Physiology and Pathology. Int J Mol Sci. 2017;18(4). doi: 10.3390/ijms18040792.\u003c/li\u003e\n\u003cli\u003eMT D, D R, D M, CM E, ME P, DC B, et al. - 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, et al. - Bioinformatic analysis reveals the importance of epithelial-mesenchymal transition. D - 101563288. (- 2045-2322 (Electronic)):T - epublish.\u003c/li\u003e\n\u003cli\u003eDai FF, Bao AY, Luo B, Zeng ZH, Pu XL, Wang YQ, et al. Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis. Exp Ther Med. 2020;19(1):264-72. doi: 10.3892/etm.2019.8214.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, tables 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":true,"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, signalling pathway","lastPublishedDoi":"10.21203/rs.3.rs-161050/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-161050/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eEndometriosis is a common gynaecological disease; however, the specific mechanism and the key molecules involved in endometriosis have not been elucidated. This study aimed to identify 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, and a total of three mRNA expression profiles were included in subsequent analyses (GSE31515, GSE58178 and GSE120103). We divided all differentially expressed genes (DEGs) into up-regulated and down-regulated groups. Then, we conducted Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis using R software.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 304 DEGs were identified between endometriosis tissues and normal endometrium tissues using integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis revealed that the up-regulated DEGs of endometriosis were closely associated with voltage-gated calcium channel activity, whereas the down-regulated DEGs were enriched in uterus development. KEGG pathway enrichment analysis indicated that the up-regulated DEGs were mainly involved in cytokine-cytokine receptor interaction, whereas down-regulated DEGs were enriched in protein processing in the endoplasmic reticulum. In addition, PPIs of these DEGs were visualized using the Cytoscape platform and the Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identified 10 potential DEG-related protein targets, including CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, and HSP90B1. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn conclusion, the present study reveals that voltage-gated calcium channel activity, uterus development, cytokine-cytokine receptor interaction and protein processing in the endoplasmic reticulum may be involved in the development of endometriosis. In addition, these identified DEGs may exhibit clinical significance for the diagnosis and treatment of endometriosis.\u003c/p\u003e","manuscriptTitle":"Identification of differentially expressed genes and signalling pathways related to ovarian endometriosis based on integrated bioinformatic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-02-10 18:21:57","doi":"10.21203/rs.3.rs-161050/v2","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}},{"code":1,"date":"2021-02-03 18:33:42","doi":"10.21203/rs.3.rs-161050/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":"61d9c756-aa42-4ee9-a380-a4f02af27696","owner":[],"postedDate":"February 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2231058,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2021-03-03T02:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-10 18:21:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-161050","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-161050","identity":"rs-161050","version":["v2"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}