Introduction 介绍

Endometriosis is a frequently occurring gynaecological disease characterised by chronic pelvic pain, dysmenorrhea and infertility1. Its prevalence is estimated to be 10–15% of reproductive age females2 and around to 20–48% in infertile women3. Despite a number of theories being suggested to describe the molecular mechanisms underlying the development of endometriosis such as: Sampson’s theory of retrograde menstruation4, ectopic implantation, epigenetic factors5, immune and inflammatory factors6,7, eutopic endometrial determinism8, and stem cell factors9; disease pathogenesis is still not fully understood.
子宫内膜异位症是一种常见的妇科疾病，其特征是慢性盆腔疼痛，痛经和不孕 1 。据估计，其患病率在育龄女性 2 中为10-15%，在不孕女性 3 中约为20-48%。尽管提出了许多理论来描述子宫内膜异位症发展的分子机制，例如：Sampson的逆行月经理论 4 ，异位植入，表观遗传因素 5 ，免疫和炎症因素 6,7 ，在位子宫内膜决定论 8 和干细胞因素 9 ;疾病发病机制仍然没有完全理解。

At present, there have been several studies on the gene expression profiles of endometriosis10,11,12,13, which have identified various differentially expressed genes (DEGs) involved in the development of endometriosis. However, due to heterogeneity between each independent experiment as a result of variations in tissue or specimens and/or different data processing methods, the identification of these DEGs is inconsistent. In this study, we integrated different studies using a non-biased approach, which may resolve these problems and enable the discovery of effective and reliable molecular markers.
目前，已经有几项关于子宫内膜异位症 10,11,12,13 的基因表达谱的研究，这些研究已经确定了参与子宫内膜异位症发展的各种差异表达基因（DEG）。然而，由于组织或标本的变化和/或不同的数据处理方法导致的每个独立实验之间的异质性，这些DEG的识别是不一致的。在这项研究中，我们整合了不同的研究使用无偏见的方法，这可能会解决这些问题，并使有效和可靠的分子标记的发现。

We downloaded 3 microarray datasets GSE1169111, GSE730512, GSE1276813, from Gene Expression Omnibus database (GEO), which contain gene expression data from endometriosis tissues and normal endometrial tissues. We then performed deep bioinformatic analysis, including identifying common DEGs, gene ontology (GO), Hallmark pathway enrichment and protein-protein interaction (PPI) network analysis. The findings were further validated by immunohistochemistry (IHC) staining in endometrial tissues from endometriosis or control patients. The aim of this study was to identify common DEGs and important pathways, and to explore potential candidate biomarkers for the diagnosis and therapeutic targets in endometriosis.
我们从Gene Expression Omnibus数据库（GEO）下载了3个微阵列数据集GSE 11691 11 、GSE 7305 12 、GSE 12768 13 ，其中包含来自子宫内膜异位症组织和正常子宫内膜组织的基因表达数据。然后，我们进行了深入的生物信息学分析，包括识别常见的DEG，基因本体（GO），Hallmark途径富集和蛋白质-蛋白质相互作用（PPI）网络分析。这些发现通过子宫内膜异位症患者或对照组子宫内膜组织的免疫组化染色进一步验证。本研究的目的是确定常见的DEG和重要的途径，并探索潜在的候选生物标志物，用于子宫内膜异位症的诊断和治疗靶点。

Methods 方法

Original data collection 原始数据采集

We used “endometriosis” as a keyword on the Gene Expression Omnibus (GEO) database, and 3 datasets (GSE11691, GSE7305 and GSE12768) were collected. GSE11691 was in GPL96 platform, [HG-U133A] Affymetrix Human Genome U133A Array, which included 9 endometriosis and 9 normal endometrial samples (Control samples). GSE7305 was in GPL570 platform, [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, which included 10 endometriosis and 10 normal endometrial samples (Control samples). GSE12768 was in GPL7304 platform, institute Cochin HG18 60mer expression array 47Kl, which included 2 endometriosis and 2 normal endometrial samples (Control samples). The platform and series matrix files were downloaded.
我们使用“子宫内膜异位症”作为基因表达综合数据库（GEO）的关键词，收集了3个数据集（GSE 11691，GSE 7305和GSE 12768）。GSE 11691在GPL 96平台，[HG-U133 A] Affytron Human Genome U133 A Array中，其中包括9份子宫内膜异位症和9份正常子宫内膜样本（对照样本）。GSE 7305在GPL 570平台[HG-U133_Plus_2] Affytechnic Human Genome U133 Plus 2.0 Array中，其中包括10份子宫内膜异位症和10份正常子宫内膜样本（对照样本）。GSE 12768在GPL 7304平台上，研究所Cochin HG 18 60 mer表达阵列47 K1，其包括2个子宫内膜异位症和2个正常子宫内膜样品（对照样品）。平台和系列矩阵文件已下载。

Analysis for Differentially Expressed Genes (DEGs)
差异表达基因（DEG）

RStudio software (version 3.6) was used to process and standardise the files. The CEL files of three datasets were downloaded from GEO. Raw data of the Affymetrix platform were normalised by Robust Multi-array Average (RMA) function in the affy package (version 1.64.0). Multiple probes relating to the same gene were deleted and summarised as the median value for further analysis. These 3 datasets were analyzed using the limma package (version 3.40.6) in the RStudio14, and genes with P value <0.05 and Log[FoldChange] (Log[FC]) > 1 were considered as DEGs. Overlapping DEGs from three databases were screened for subsequent GO, Hallmark pathway enrichment and PPI analysis, and were displayed with Venn diagrams.
使用RStudio软件（版本3.6）处理和分析文件。从GEO下载了三个数据集的CEL文件。通过affy软件包（版本1.64.0）中的稳健多阵列平均值（RMA）函数对Affytek平台的原始数据进行归一化。删除与相同基因相关的多个探针，并总结为中值以供进一步分析。使用RStudio 14 中的limma包（版本3.40.6）分析这3个数据集，并且P值<0.05和Log[FoldChange]（Log[FC]）> 1的基因被认为是DEG。对来自三个数据库的重叠DEG进行筛选，用于随后的GO、Hallmark途径富集和PPI分析，并用维恩图显示。

Analysis for GO and pathway enrichment
GO和途径富集的分析

GO Biological Processes of DEGs were analyzed through online DAVID software15 (version 6.8), P value <0.05 as the cutoff criterion was considered statistically significant. The Hallmark pathway enrichment analysis was performed in Metascape16. P value <0.05 as the cutoff criterion was considered statistically significant.
通过在线大卫软件 15 （版本6.8）分析DEG的GO生物过程，P值<0.05作为截断标准被认为是统计学显著的。在Metascape 16 中进行Hallmark途径富集分析。P值<0.05作为截止标准被认为是统计学显著的。

Protein-protein interaction (PPI) network analysis
蛋白质相互作用网络分析

The PPI of DEGs-encoded proteins was demonstrated by STRING (version 11.0)17, with search limited to “Homo sapiens” and a score> 0.700 corresponding to high confidence interaction as significant. Network construction and analyses were performed by Cytoscape (version 3.7.1). In addition, the function and pathway enrichment analysis were performed for DEGs in the modules by ClueGo (version 2.5.4), P value <0.05 was considered to be significant.
通过STRING（版本11.0） 17 证明DEG编码蛋白的PPI，其中搜索限于“智人”，并且对应于高置信度相互作用的得分> 0.700为显著的。通过Cytoscape（版本3.7.1）进行网络构建和分析。此外，使用CNOGO2.5.4版对模块中DEG进行功能和途径富集分析，P值<0.05被认为具有显著性。

Clinical sample collection
临床样本采集

From June to October 2019, laparoscopic surgeries were performed in Jiangxi Maternal and Child Health Hospital (Nanchang, China), and 6 cases were pathologically diagnosed as ovarian endometriosis. On the staging criteria of endometriosis as stipulated by American Fertility Society revised (AFS-r), all patients with endometriosis were stage IV. Eutopic endometrial tissues were collected. The average age of the patients was (32.71 ± 1.12) years. Meanwhile, 6 cases of endometrial tissue were selected from patients with benign ovarian teratoma as the control group. The average age of patients was (32.18 ± 1.22) years.
2019年6月至10月，在江西省妇幼保健院（中国南昌）行腹腔镜手术，6例患者病理诊断为卵巢子宫内膜异位症。根据美国生育学会修订版（AFS-r）子宫内膜异位症分期标准，所有患者均为IV期。收集在位子宫内膜组织。平均年龄（32.71 ± 1.12）岁。  同时选取6例卵巢良性畸胎瘤患者的子宫内膜组织作为对照组。平均年龄（32.18 ± 1.22）岁。  

All the collected endometrial tissues were diagnosed as proliferative endometrium after pathological histological diagnosis. There was no significant difference in the age of patients in each group (P value> 0.05). All menstrual cycles were normal, non-pregnant or non-lactation, and no hormonal medication was taken 6 months before the operation, and no obvious medical and surgical diseases and complications were found.
所有采集的子宫内膜组织经病理组织学诊断均为增殖期子宫内膜。各组患者年龄差异无统计学意义（P值> 0.05）。所有患者月经周期均正常，未妊娠或哺乳，术前6个月未服用激素类药物，未发现明显内外科疾病及并发症。

This study was approved by the Ethics Committee of Jiangxi Maternal and Child Health Hospital, China (No. EC-KT-201904). All patients had signed the informed consent for the study protocol. The experimental scheme was approved by the academic committee of Jiangxi Maternal and Child Health Hospital, and the experimental methods were carried out in accordance with the guidelines of the academic committee.
本研究已获得中国江西省妇幼保健院伦理委员会批准（编号EC-KT-201904）。所有患者均签署了研究方案的知情同意书。实验方案经江西省妇幼保健院学术委员会批准，实验方法按照学术委员会指导原则进行。

Immunohistochemistry (IHC) and image analysis
免疫组织化学（IHC）和图像分析

Fresh tissue specimens were taken during the operation, rinsed with physiological saline to remove blood and other impurities, fixed with 10% formaldehyde, dehydrated with conventional gradient ethanol and embedded in paraffin, continuously sliced with a paraffin microtome, and baked at 65 °C for 1 h to dewax, and removed the glass. Tablets, soak in xylene for 40 min, and soak in absolute ethanol for 20 min. Rinse once in PBS, add the configured sodium citrate solution (pure water: sodium citrate = 1000:1), and heat to boiling. Discard the sodium citrate solution after cooling, wash with PBS, and anti-CXCL12 antibody (1:200; Proteintech, Wuhan, China, 17402-1-AP) or anti-E-cadherin (CDH1) antibody (1:200; Proteintech, Wuhan, China, 20874-1-AP) was incubated, followed by incubation with goat anti-mouse/rabbit IgG polymer antibody. After rinsing with PBS three times, staining was visualised using the peroxide substrate solution diaminobenzidine. Counterstained by haematoxylin, the slides were dehydrated in graded alcohol and mounted.
术中取新鲜组织标本，用生理盐水冲洗去除血液等杂质，10%甲醛固定，常规梯度乙醇脱水石蜡包埋，石蜡切片机连续切片，65 ℃烘烤1 h脱蜡，取玻璃。片剂，二甲苯浸泡40 min，无水乙醇浸泡20 min，PBS冲洗1次，加入配置的枸橼酸钠溶液（纯水：枸橼酸钠= 1000：1），加热至沸腾。冷却后弃去柠檬酸钠溶液，用PBS洗涤，孵育抗CXCL 12抗体（1：200; Proteintech，Wuhan，China，17402-1-AP）或抗E-钙粘蛋白（CDH 1）抗体（1：200; Proteintech，Wuhan，China，20874-1-AP），然后与山羊抗小鼠/兔IgG聚合物抗体孵育。用PBS冲洗三次后，使用过氧化物底物溶液二氨基联苯胺使染色可视化。 用苏木精复染，将载玻片在分级酒精中脱水并封片。

Image-pro Plus software was used to convert the image format and the grayscale units into optical density (IOD) units. Then area, density and IOD were selected for measure according to the manufactor’s protocol.
使用Image-pro Plus软件将图像格式和灰度单位转换为光密度（IOD）单位。然后根据制造商的方案选择面积、密度和IOD进行测量。

Statistical analysis 统计分析

Student’s t-test was used for statistical analysis between two different groups when variables were normally distributed, which was confirmed by Q-Q plots and the Shapiro-Wilk test (SPSS 18.0, Armonk, NY, USA). P value <0.05 was considered statistically significant.
当变量呈正态分布时，使用学生t检验进行两个不同组之间的统计分析，这通过Q-Q图和Shapiro-Wilk检验（SPSS 18.0，阿尔蒙克，纽约，美国）证实。P值<0.05为有统计学意义。

Ethics approval and consent to participate
伦理批准和参与同意

This study was approved by the Ethics Committee of Jiangxi Provincial Maternal and Child Health Hospital, China (No. EC-KT-201904). All patients have signed the informed consent for the study protocol and reserve the right to withdraw at any time.
本研究已获得中国江西省妇幼保健院伦理委员会批准（编号EC-KT-201904）。所有患者均已签署研究方案的知情同意书，并保留随时退出的权利。

Results 结果

Identification of Differentially Expressed Genes (DEGs) using integrated bioinformatics
利用整合生物信息学鉴定差异表达基因（DEG）

All datasets (GSE7305, GSE11691 and GSE12768) were first normalised by Robust Multi-array Average (RMA) (Supplementary Figs. 1–3). Differential expression analysis was performed on these datasets in limma, and those genes with P value <0.05 and Log[FoldChange] (Log[FC]) > 1 were considered as DEGs. In GSE7305, 1,313 DEGs were identified, of which 728 genes were up-regulated and 585 down-regulated. In GSE11691, 877 DEGs were identified, with 573 up-regulated and 304 down-regulated. In GSE12768, 3,212 DEGs were identified, with 1,627 up-regulated and 1,585 down-regulated. The expression of the top 50 DEGs for all three datasets were visualised on heat maps (Fig. 1a–c). All DEGs were highlighted in Volcano plots (Fig. 2a–c). By comparing DEGs, which appeared in all 3 datasets, 186 DEGs were identified (Table 1), including 118 up-regulated (Fig. 2d) and 68 down-regulated (Fig. 2e).
所有数据集（GSE 7305、GSE 11691和GSE 12768）首先通过稳健多阵列平均（RMA）进行归一化（补充图10）。第1- 3段）。在limma中对这些数据集进行差异表达分析，并且那些P值<0.05且Log[FoldChange]（Log[FC]）> 1的基因被认为是DEG。在GSE 7305中，鉴定了1，313个DEG，其中728个基因上调，585个基因下调。在GSE 11691中，鉴定了877个DEG，其中573个上调，304个下调。在GSE 12768中，确定了3，212个DEG，其中1，627个上调，1，585个下调。所有三个数据集的前50个DEG的表达在热图上可视化（图1a-c）。在火山图中突出显示了所有DEG（图2a-c）。通过比较出现在所有3个数据集中的DEG，鉴定了186个DEG（表1），包括118个上调（图2d）和68个下调（图2 e）。

Figure 1 图1
figure 1

Heat maps and hierarchical clustering of the top 50 DEGs in endometriosis microarray datasets. Heat maps and hierarchal clustering analysis of top 50 DEGs in microarray datasets GSE7305 (a), GSE12768 (b), and GSE11691 (c). DEGs are those genes with P value <0.05 and Log[FC] > 1. Red indicates up-regulation and blue down-regulation.
子宫内膜异位症微阵列数据集中前50个DEG的热图和层次聚类。微阵列数据集GSE 7305（a）、GSE 12768（B）和GSE 11691（c）中前50个DEG的热图和层次聚类分析。DEG是P值<0.05且Log[FC] > 1的那些基因。红色表示上调，蓝色表示下调。

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Figure 2 图2
figure 2

Volcano plots and Venn diagrams of DEGs in endometriosis microarray datasets. Volcano plots showing DEGs in GSE7305 (a), GSE12768 (b) and GSE11691 (c). DEGs are those genes with P value <0.05 and [logFC]> 1. Red indicates relative up-regulated genes and blue indicates down-regulated genes. Venn diagrams of up-regulated (d) or down-regulated (e) DEGs from these three datasets, as indicated.
子宫内膜异位症微阵列数据集中DEG的火山图和维恩图。显示GSE 7305（a）、GSE 12768（B）和GSE 11691（c）中DEG的火山图。DEG是P值<0.05且[logFC]> 1的那些基因。红色表示相对上调基因，蓝色表示下调基因。如图所示，来自这三个数据集的上调（d）或下调（e）DEG的维恩图。

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Table 1 DEGs in endometriosis are identified by integrated bioinformatics.
表1子宫内膜异位症中DEG的生物信息学分析。
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Gene Ontology (GO) functional enrichments in DEGs
基因本体（GO）功能丰富DEG

We then performed gene ontology (GO) enrichment analysis of DEGs in endometriosis using DAVID. The results were grouped into three categories: including molecular functions (MF), cellular component (CC) and biological process (BP) (Tables 2–4). The molecular functions of DEGs were mainly involved in calcium ion binding, heparin binding and structural molecule activity (Fig. 3a; Table 2). In the cellular component, DEGs were mainly involved in extracellular exosome, extracellular space and extracellular region (Fig. 3a; Table 3). In the biological process, DEGs were mainly involved in cell adhesion, epithelial cell differentiation, inflammatory response and extracellular exosome (Fig. 3a; Table 4).
然后，我们使用大卫对子宫内膜异位症中的DEG进行基因本体（GO）富集分析。结果分为三类：包括分子功能（MF），细胞组分（CC）和生物过程（BP）（表2- 4）。DEG的分子功能主要涉及钙离子结合、肝素结合和结构分子活性（图3a;表2）。在细胞组分中，DEG主要涉及细胞外外泌体、细胞外间隙和细胞外区域（图3a;表3）。在生物学过程中，DEG主要参与细胞粘附、上皮细胞分化、炎症反应和细胞外外来体（图3a;表4）。

Table 2 Molecular Function (MF) analysis of DEGs in endometriosis.
表2子宫内膜异位症中DEGs的分子功能分析
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Table 3 Cellular component analysis of DEGs in endometriosis.
表3子宫内膜异位症DEG细胞成分分析。
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Table 4 Biological process analysis of DEGs in endometriosis.
表4子宫内膜异位症DEG的生物学过程分析。
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Figure 3 图3
figure 3

GO analysis and Hallmark pathway enrichment of DEGs in endometriosis. (a) GO analysis of DEGs in endometritis visualised on a bar chart clustered by molecular functions, cellular component and biological process. (b) Hallmark pathway enrichment of DEGs in endometriosis visualised on a bar chart, showing number of shared genes (count) and -Log10 (P value).
子宫内膜异位症中DEGs的GO分析和Hallmark途径富集。(a)在柱状图上可视化的结肠炎中DEG的GO分析，通过分子功能、细胞组分和生物过程聚类。(b)子宫内膜异位症中DEG的标志性途径富集在条形图上可视化，显示共享基因的数量（计数）和-Log 10 （P值）。

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Signaling pathway enrichment in DEGs
DEG中的信号通路富集

Signaling pathway enrichment of DEGs in endometriosis was performed using Metascape. The most significantly enriched pathways were submitted to Hallmark genes hit analysis. Hallmark pathway enrichment analysis identified epithelial mesenchymal transition (EMT), estrogen response late and estrogen response early as top pathways (Fig. 3b; Table 5).
使用Metascape进行子宫内膜异位症中DEGs的信号通路富集。将最显著富集的途径提交给Hallmark基因命中分析。Hallmark途径富集分析鉴定了上皮间质转化（EMT）、雌激素反应晚期和雌激素反应早期为顶级途径（图3b;表5）。

Table 5 Hallmark pathway enrichment analysis of DEGs in endometriosis.
表5子宫内膜异位症中DEG的Hallmark途径富集分析。
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Protein-protein interaction (PPI) network analysis in DEGs
DEG中的蛋白质相互作用（PPI）网络分析

PPI analysis was performed using the online STRING database and Cytoscape software. After removing the isolated nodes and the partially connected nodes, a grid network was constructed using the Cytoscape software (Fig. 4). Pathway enrichment analysis revealed that the genes were mainly involved in vascular smooth muscle contraction, cell adhesion molecules, NF-κB pathway, complement and coagulation cascade.
使用在线STRING数据库和Cytoscape软件进行PPI分析。在去除孤立节点和部分连接节点后，使用Cytoscape软件构建网格网络（图4）。途径富集分析显示，这些基因主要参与血管平滑肌收缩、细胞粘附分子、NF-κB途径、补体和凝血级联反应。

Figure 4 图4
figure 4

PPI network analysis of DEGs in endometriosis. Protein-Protein Interaction Network of DEGs from all datasets generated in String.db (v. 11) and visualised in Cytoscape (v. 3.7.1). (a) PPI network analysis of DEGs. (b–d) Representative local association graphs in PPI network analysis. Nodes indicate proteins/genes and lines indicate protein-protein interaction. Pink indicates up-regulation and green indicates down-regulation.
子宫内膜异位症DEG的PPI网络分析来自String.db（v. 11）中生成的所有数据集并在Cytoscape（v. 3.7.1）中可视化的DEG的蛋白质-蛋白质相互作用网络。(a)DEG的PPI网络分析。（b-d）PPI网络分析中的代表性局部关联图。节点表示蛋白质/基因，线表示蛋白质-蛋白质相互作用。粉色表示上调，绿色表示下调。

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Candidate gene expression analysis and validations
候选基因表达分析和验证

Hallmark pathway enrichment analysis of DEGs in endometriosis identified 15 EMT-associated genes (CXCL12, TAGLN, ACTA2, MYL9, VCAM1, DPYSL3, FMOD, GAS1, PTX3, ENO2, BGN, COL8A2, COL11A1, THBS2, NID) (Table 5). In PPI network analysis, CXCL12 was found to be connected to a hub gene C3, while ACTG2, ACTA2, MYL9 and MYH11 formed a connected component sub-network. In addition, a change in the expression of E-cadherin (CDH1) is the prototypical epithelial cell marker of EMT. As a result, although CDH1 is not listed in Gene Set Hallmark_EMT, it was included in further analysis. Expression levels of these 6 genes (CXCL2, ACTA2, MYL9, ACTG2, MYH11 and CDH1) were analysed in these three databases (Fig. 5). Significant increases were observed in CXCL2, ACTA2, MYL9, ACTG2 and MYH11 across all three databases. A significant decrease in CDH1 was observed in all three databases. We further investigated the expression of E-cadherin (CDH1) and CXCL12 in endometriosis or control tissues by IHC. As shown in Fig. 6, E-cadherin was significantly down-regulated in endometriosis (Fig. 6a; P value = 0.028), while CXCL12 was significantly increased in endometriosis (Fig. 6b; P value = 0.015).
子宫内膜异位症中DEG的Hallmark途径富集分析鉴定了15种EMT相关基因（CXCL 12、TAGLN、ACTA 2、MYL 9、VCAM 1、DPYSL 3、FMOD、GAS 1、PTX 3、ENO 2、BGN、COL 8A 2、COL 11 A1、THBS 2、NID）（表5）。在PPI网络分析中，发现CXCL 12连接到枢纽基因C3，而ACTG 2，ACTA 2，MYL 9和MYH 11形成连接的组件子网络。此外，E-cadherin（CDH 1）表达的变化是EMT的原型上皮细胞标志物。因此，尽管CDH 1未在Gene Set Hallmark_EMT中列出，但将其纳入进一步分析中。在这三个数据库中分析这6个基因（CXCL 2、ACTA 2、MYL 9、ACTG 2、MYH 11和CDH 1）的表达水平（图5）。在所有三个数据库中观察到CXCL 2、ACTA 2、MYL 9、ACTG 2和MYH 11显著增加。在所有三个数据库中均观察到CDH 1显著降低。我们进一步研究了E-cadherin（CDH 1）和CXCL 12在子宫内膜异位症或对照组织中的表达。 如图6所示，E-钙粘蛋白在子宫内膜异位症中显著下调（图6a; P值= 0.028），而CXCL 12在子宫内膜异位症中显著增加（图6 b; P值= 0.015）。

Figure 5 图5
figure 5

Expression levels of 6 genes in endometriosis microarray datasets. Graphs showing expression levels of CXCL12 (a), ACTA2 (b), ACTG2 (c), CDH1 (d), MYL9 (e) and MYH11 (f) in endometrial tissues from control (blue) or endometriosis (purple) patients in three endometriosis microarray datasets, as indicated. Data are mean ± s.d. *P value <0.05.** P value <0.01. *** P value <0.001.
子宫内膜异位症微阵列数据集中6个基因的表达水平。显示在三个子宫内膜异位症微阵列数据集中，来自对照（蓝色）或子宫内膜异位症（紫色）患者的子宫内膜组织中CXCL 12（a）、ACTA 2（B）、ACTG 2（c）、CDH 1（d）、MYL 9（e）和MYH 11（f）的表达水平的图，如所示。数据为平均值± s. d。*P值<0.05。** P值<0.01。* P值<0.001。

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Figure 6 图6
figure 6

Expression levels of E-cadherin (CDH1) and CXCL12 in endometriosis. Representative E-cadherin (a) or CXCL12 (b) expression in endometrial tissues from control or endometriosis patients. Scale bars: 50 μm. Graphs showing comparisons of E-cadherin (a, P = 0.028) or CXCL12 (b, P = 0.015) expression in endometrial tissues from 6 control or endometriosis patients. Data are mean ± s.d.
E-cadherin（CDH 1）和CXCL 12在子宫内膜异位症中的表达对照或子宫内膜异位症患者子宫内膜组织中代表性的E-钙粘蛋白（a）或CXCL 12（B）表达。比例尺：50 μm。显示来自6名对照或子宫内膜异位症患者的子宫内膜组织中E-钙粘蛋白（a，P = 0.028）或CXCL 12（B，P = 0.015）表达的比较的图。数据为平均值± s. d。

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Discussion 讨论

Endometriosis occurs in about 10–15% of reproductive age females and the etiology is unknown1,2. At present there is no cure and the treatment options available are limited. The disease has a high recurrence rate, which adds to its large socio-economic impact18. Endometriosis is the growth of cells derived from the endometrium outside the uterus, such as the ovaries, peritoneum, intestines and vagina19. In a small number of cases (0.5–1%) endometriosis can lead to tumor formation20. The underlying mechanisms of the disease are similar to malignant tumors such as cell proliferation, differentiation, apoptosis, migration, cell adhesion, invasion, and neurovascularisation21.
子宫内膜异位症发生在大约10-15%的育龄女性中，病因不明 1,2 。目前没有治愈方法，可用的治疗选择有限。这种疾病的复发率很高，这增加了其巨大的社会经济影响 18 。子宫内膜异位症是指子宫内膜细胞在子宫外生长，如卵巢、腹膜、肠和阴道 19 。在少数情况下（0.5-1%）子宫内膜异位症可导致肿瘤形成 20 。该疾病的潜在机制类似于恶性肿瘤，例如细胞增殖、分化、凋亡、迁移、细胞粘附、侵袭和神经血管化 21 。

Utilising data from 3 microarray datasets (GSE1169111, GSE730512, GSE1276813), we identified DEGs between endometriosis tissues and normal endometrial samples, including 118 up-regulated and 68 down-regulated genes. GO functional analysis based on these DEGs shows that DEGs are mainly enriched in cell adhesion, inflammatory response, and extracellular exosome. These findings are similar to those previously published22.
利用来自3个微阵列数据集（GSE 11691 11 、GSE 7305 12 、GSE 12768 13 ）的数据，我们鉴定了子宫内膜异位症组织和正常子宫内膜样品之间的DEG，包括118个上调和68个下调基因。基于这些DEG的GO功能分析表明，DEG主要富集在细胞粘附、炎症反应和细胞外外泌体中。这些发现与之前发表的 22 相似。

Importantly, Hallmark pathway enrichment analysis identified EMT as the most significant pathway. A number of studies have implicated EMT in the development of endometriosis23,24,25. EMT is a biological process where immotile epithelial cells acquire phenotypes of motile mesenchymal cells, this is accompanied by changes in cell morphology and gene expression26. It creates favourable conditions for the implantation and growth of endometriotic lesions27. During EMT the expression of a number of epithelial surface markers are lost including E-cadherin (CDH1), keratin, Desmoplakin, Mucin-1 and claudin; whilst a number of mesenchymal makers are up-regulated such as N-cadherin, vimentin, and fibronectin28,29. Numerous signaling pathways are suggested to participate in EMT induction, including transforming growth factor β (TGF-β)30, Wnt/β-catenin signaling pathway31, estrogen receptor β (ER-β)32, epidermal growth factor (EGF)33, mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK)34, NF-κB35, estrogen receptor (ER)-α36 and hypoxia-inducible factor (HIF)-1α37. The activities of these pathways appear to be interconnected to one another, and depend on the particular epithelial or endothelial cell type affected, different signaling molecules mediate their interconnection or crosstalk. Previous studies have also found that EMT can be induced by pro-inflammatory cytokines in endometriosis, such as TGF-β38, tumor necrosis factor (TNF)-α39 and interleukin (IL)-640. The mechanisms that present or activate TGF-β in the tissue microenvironment are of importance for the EMT response41. TGF-β induced EMT mediated by inflammatory cells in the tumor microenvironment is promoted by leukotriene B4 receptor 2, which, in response to leukotriene B4, activates reactive oxygen species (ROS) and NF-κB transcriptional activity that facilitates the establishment of EMT by TGF-β42.
重要的是，霍尔马克途径富集分析确定EMT为最重要的途径。许多研究表明EMT与子宫内膜异位症的发生有关。EMT是一种生物学过程，其中不动上皮细胞获得运动间充质细胞的表型，这伴随着细胞形态和基因表达的变化 26 。它为增生性病变 27 的植入和生长创造了有利条件。在EMT期间，许多上皮表面标志物的表达丢失，包括E-钙粘蛋白（CDH 1）、角蛋白、桥粒斑蛋白、粘蛋白-1和claudin;而许多间充质标志物上调，如N-钙粘蛋白、波形蛋白和纤连蛋白 28,29 。 许多信号通路被认为参与EMT诱导，包括转化生长因子β（TGF-β） 30 、Wnt/β-连环蛋白信号通路 31 、雌激素受体β（ER-β） 32 、表皮生长因子（EGF） 33 、丝裂原活化蛋白激酶（MAPK）/细胞外信号调节激酶（ERK） 34 、NF-κB 35 、雌激素受体（ER）-α 36 和缺氧诱导因子（HIF）-1α 37 。这些途径的活动似乎是相互关联的，并取决于受影响的特定上皮或内皮细胞类型，不同的信号分子介导它们的互连或串扰。既往研究也发现子宫内膜异位症中促炎细胞因子如TGF-β 38 、肿瘤坏死因子（TNF）-α 39 和白细胞介素（IL）-6 40 可诱导EMT。在组织微环境中存在或激活TGF-β的机制对于EMT反应 41 是重要的。 由肿瘤微环境中的炎性细胞介导的TGF-β诱导的EMT由白三烯B4受体2促进，其响应于白三烯B4而激活活性氧（ROS）和NF-κB转录活性，从而促进TGF-β 42 建立EMT。

In this unbiased study, we found EMT in endometriosis could be potentially induced by inflammatory cytokines such as C-X-C motif chemokine ligand 12 (CXCL12), also known as stromal cell-derived factor 1 (SDF1). CXCL12 is highly expressed in endometriosis in our analysis, which is consistent with a previous report43. CXCL12 interacts with its specific receptor, C-X-C motif chemokine receptor 4 (CXCR4), which is not consistently over-expressed in these three datasets though. The CXCL12-CXCR4 axis promotes proliferation, migration, and invasion of endometriotic cells44,45. In human papillary thyroid carcinoma, the CXCL12-CXCR4 axis promotes EMT processes by activating the NF-κB signaling pathway46. In a murine model of endometriosis both C-X-C motif chemokine receptor 7 (CXCR7) and CXCL12 expression increased with grafting time47. Expression of CXCR7 is enhanced during pathological inflammation and tumor development, and CXCR7 mediates TGFβ1-induced EMT48. However, there were no probes for CXCR7 in the microarrays analysed in our studies. In endometriosis, it is still unclear whether CXCL12 promotes EMT through the CXCL12-CXCR4 axis or the CXCL12-CXCR7 axis. PPI analysis showed that CXCL12 interacts directly with complement C3 and C-C motif chemokine ligand 21 (CCL21), and a previous study showede CCL21 is up-regulated in endometriosis, which acts through inflammatory responses49. In TGF-β-induced EMT, the expression of C-C motif chemokine receptor 7 (CCR7), the CCL21 receptor, is increased and this facilitates breast cancer cell migration50. Through IHC, we confirmed that CXCL12 is significantly increased in endometriosis, accompanied by a decrease in the expression E-cadherin (CDH1), which is consistent with bioinformatics analysis. These findings, together, suggest that CXCL12 may lead to endometriosis through EMT, although further research is required.
在这项无偏倚的研究中，我们发现子宫内膜异位症中的EMT可能由炎性细胞因子如C-X-C基序趋化因子配体12（CXCL 12），也称为基质细胞衍生因子1（SDF 1）诱导。在我们的分析中，CXCL 12在子宫内膜异位症中高度表达，这与先前的报告 43 一致。CXCL 12与其特异性受体C-X-C基序趋化因子受体4（CXCR 4）相互作用，但CXCR 4在这三个数据集中并不一致地过度表达。CXCL 12-CXCR 4轴促进增生细胞 44,45 的增殖、迁移和侵袭。在人乳头状甲状腺癌中，CXCL 12-CXCR 4轴通过激活NF-κB信号通路 46 促进EMT过程。在子宫内膜异位症的鼠模型中，C-X-C基序趋化因子受体7（CXCR 7）和CXCL 12表达均随移植时间增加 47 。CXCR 7的表达在病理性炎症和肿瘤发展过程中增强，并且CXCR 7介导TGFβ1诱导的EMT 48 。 然而，在我们的研究中分析的微阵列中没有CXCR 7的探针。在子宫内膜异位症中，CXCL 12是否通过CXCL 12-CXCR 4轴或CXCL 12-CXCR 7轴促进EMT仍不清楚。PPI分析显示，CXCL 12直接与补体C3和C-C基序趋化因子配体21（CCL 21）相互作用，先前的研究表明CCL 21在子宫内膜异位症中上调，其通过炎症反应发挥作用 49 。在TGF-β诱导的EMT中，C-C基序趋化因子受体7（CCR 7）（CCL 21受体）的表达增加，这有助于乳腺癌细胞迁移 50 。通过免疫组化证实，CXCL 12在子宫内膜异位症中显著增加，同时伴随着E-cadherin（CDH 1）表达的减少，这与生物信息学分析一致。这些发现共同表明，CXCL 12可能通过EMT导致子宫内膜异位症，尽管需要进一步的研究。

EMT in endometriosis has been suggested to be associated with smooth muscle metaplasia and fibrogenesis51,52. We found various markers for smooth muscle cells in our analysis, including ACTA2 and MYL9, which interact with ACTG2 and MYH11 in the PPI network analysis. ACTA2 (α-SMA), is considered to be a marker of fibrosis and is up-regulated in endometriosis53, which is consistent with our findings. Previous studies54,55 have shown that platelet-derived TGF-β1 can activate the TGF-β1/Smad3 signaling pathway, subsequently promoting EMT and fibroblast-to-myofibroblast trans-differentiation (FMT) in endometriotic lesions in turn, promoting smooth muscle metaplasia and ultimately leading to fibrosis.
子宫内膜异位症中的EMT已被认为与平滑肌化生和纤维化有关 51,52 。我们在分析中发现了平滑肌细胞的各种标记物，包括ACTA 2和MYL 9，它们在PPI网络分析中与ACTG 2和MYH 11相互作用。ACTA 2（α-SMA）被认为是纤维化的标志物，在子宫内膜异位症 53 中上调，这与我们的发现一致。先前的研究 54,55 表明，血小板源性TGF-β1可激活TGF-β1/Smad 3信号通路，随后依次促进增生性病变中的EMT和成纤维细胞至肌成纤维细胞转分化（FMT），促进平滑肌化生并最终导致纤维化。

Conclusion 结论

By comparing 3 microarray datasets, we have identified 186 DEGs (118 up-regulated, 68 down-regulated) which may be involved in the progression of endometriosis. GO functional analysis determined DEGs were mainly enriched in cell adhesion, inflammatory response, and extracellular exosome. EMT was the highest ranked Hallmark pathway enrichment and we proposed that it could be induced by inflammatory cytokines and associated with smooth muscle metaplasia and fibrogenesis. Further elucidating the underlying mechanisms of endometriosis is key for the development of new treatments and bio-markers.
通过比较3个微阵列数据集，我们已经确定了186个DEG（118个上调，68个下调）可能参与子宫内膜异位症的进展。GO功能分析表明DEG主要富集在细胞粘附、炎症反应和细胞外外泌体中。EMT是排名最高的Hallmark途径富集，我们认为它可能是由炎性细胞因子诱导的，并与平滑肌化生和纤维化有关。进一步阐明子宫内膜异位症的潜在机制是开发新的治疗方法和生物标志物的关键。