Integrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis

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Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach. Methods : Six microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein-protein interaction (PPI) network of differentially expressed genes, miRNA, and lncRNA was constructed using STRING and Cytoscape, then the top 15 hub genes in the PPI network were identified using the CytoHubba. Results : A total of 551 common DEGs were identified among four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA were identified from all studies, along with 12 lncRNA that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG. Conclusions : Our in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The identified hub genes, miRNA, and lncRNA may also serve as potential biomarkers to predict the occurrence and prognosis of endometriosis. Obstetrics & Gynecology Bioinformatic Differentially expressed genes endometriosis lncRNA miRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Endometriosis is a systemic and multifactorial disease with most gynecological afflictions, affecting the quality of life of women in reproductive age [ 1 , 2 ]. Histologically, it is defined by the presence of endometrial-like tissue outside the uterus, which involves a complex interaction between pain, hormonal dependence, ovarian function disruption, and compromised endometrial receptivity [ 1 , 3 ]. The estimated prevalence of endometriosis varied from 0.2–71.4%, as the heterogeneity of diagnostic and selection bias overwhelmingly accounts for its variability [ 4 ]. Despite advancements in contemporary medicine, there is no common ground in understanding the pathogenesis of endometriosis [ 2 ]. Its pathogenomic architecture comprises complex genetic and epigenetic features, including gene polymorphisms, interactions of gene nets and functional protein modules, and different metabolic pathways that are altered by biological process imbalances [ 5 ]. These variations are responsible for the aberrant modulation of steroidogenesis, inflammation, apoptosis, adhesion, angiogenesis, proliferation, and hormone signaling. Therefore, determining the molecular basis of endometriosis may encourage early detection, better disease management, and a new approach to treatment strategies [ 6 , 7 ]. Genetic studies provide a critical way to better understand the process behind the development of endometriosis as a debilitating illness [ 8 ]. In the last decade, advances in high-throughput genotyping technology and DNA sequencing have made significant progress in discovering the gene signatures of endometriosis. In the recent meta-analysis, these cutting-edge methods reported a 2% and 5% variation among all cases and severe cases of endometriosis, respectively [ 9 ]. However, defining potential genes for endometriosis is difficult due to a limited understanding of etiopathogenetic mechanisms, which may involve multiple genes and pathways. Numerous variables have also raised consequential concerns about candidate gene research, including study power, effect size, negative results bias, statistical and technological challenges, and experimental design flaws [ 8 , 10 ]. The present study therein aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using a non-biased bioinformatic approach and a large sample from six combined studies. Methods Data extraction Gene expression datasets of endometriosis were collected from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/ ). Studies on endometriosis-associated gene expression alterations were searched using the terms “Endometriosis” and “Homo Sapiens”. The inclusion criteria were as follows: (1) studies evaluating ectopic or eutopic tissue of endometriosis; (2) studies containing at least four cases and four normal controls (non-endometriosis); and (3) studies providing publicly available annotation data in the GEO database. Six studies were subjected to data collection and analysis, including GSE23339 [ 11 ], GSE7305 [ 12 ], GSE25628 [ 13 ], GSE51981 [ 14 ], GSE120103 [ 15 ], GSE87809 [ 16 ] (Table 1 ). All datasets were analyzed with the GEO2R tool, available at the GEO repository. The samples were divided into endometriosis vs control groups, and the following settings were applied: P-value adjustment was performed using the Benjamini & Hochberg with a significance cutoff of 0.05, and no forces normalization or log transformation was applied. The study’s flow chart is presented in Fig. 1 . Table 1 Characteristics of studies selected for bioinformatic analysis Study Dataset Samples (n) Tissue Type of endometriosis Protocol design Total Group Hawkins et al. [11] GSE23339 19 10 EN, 9 N-EN Ovary and endometrial Ovarian endometrioma Illumina’s Human WG-6 2.0 arrays Hever et al. [12] GSE7305 20 10 EN, 10 N-EN Ovary Ovarian endometrioma Affymetrix HU133 Plus 2.0 arrays Crispi et al. [13] GSE25628 13 7 EN, 6 N-EN Ectopic endometrial Deep filtrating endometriosis Affymetrix HGU133A 2.0 arrays Tamaresis et al. [14] GSE51981 65 43 EN, 22 N-EN Endometrial - Affymetrix HU133 Plus 2.0 arrays Bhat et al. [15] GSE120103 15 9 EN, 9 N-EN Endometrial Ovarian endometrioma Illumina HiSeq2500 RNA-seq Yotova et al. [16] GSE87809 9 4 EN, 5-N-EN Ovary Ovarian endometrioma Illumina HiSeq2000 RNA-seq EN individuals with endometriosis, N-EN individuals without endometriosis Identification of DEGs Differentially expressed genes (DEGs) were analyzed between the endometriosis and normal endometrium tissues in each dataset with a determined |log2FC| > 1 and the P-value < 0.05. After removing the inconsistent DEGs, the overlapped genes of all datasets were analyzed using InteractiVenn ( http://www.interactivenn.net/ ) to find the common DEGs. In addition, the SR Plot tool ( https://bioinformatics.com.cn/plot_basic_cluster_heatmap_plot_024_en ) was employed to generate the fold-change heatmap of the shared DEGs under the following settings: Euclidean distance method, complete cluster method, and bidirectional cluster orientation. Identification of mRNA, miRNA, and lncRNA The ShinyGO ( http://bioinformatics.sdstate.edu/go/ ) online tool was used to identify the RNA components (mRNA, lncRNA, miRNA) of each dataset. After removing the inconsistent data, the shared miRNA and lncRNA of all datasets were integrated using InteractiVenn ( http://www.interactivenn.net/ ). To generate the miRNA network, miRNA and their respective targets were identified using the MIENTURNET online tools ( http://userver.bio.uniroma1.it/apps/mienturnet/ ). Furthermore, analysis of lncRNA and their targets was performed using the LncRRIsearch ( http://rtools.cbrc.jp/LncRRIsearch/ ) with a determined energy threshold of -20 kcal/mol. The targets with a P-value < 0.05 was considered significant and subjected to further analysis. Functional and pathway enrichment analysis Gene ontology (GO) and Kyoto Encyclopaedia Genes and Genomes (KEGG) pathway enrichment analysis were performed using The ShinyGO ( http://bioinformatics.sdstate.edu/go/ ). The GO terms, including Biological process (BP), Cellular components (CC), and Molecular function (MF) of common DEGs, were ranked according to the fold enrichment and the P value in each term. The results were then presented as bubble charts generated by the SR Plot ( https://bioinformatics.com.cn/en?keywords=bubble ). Network construction and hub genes analysis The STRING database ( http://string-db.org/ ) was used to predict the protein-protein interaction (PPI) between the common DEGs, with the confidence interaction score ≥ 0.9. After removing the unconnected nodes, the network obtained from the STRING was downloaded and analyzed. All the interaction networks between common DEGs, miRNA-targets, and lncRNA-targets were visualized using the Cytoscape software version 3.10 ( https://cytoscape.org/ ). Additionally, the CytoHubba plugin of the Cytoscape was employed to screen the top 15 hub genes of the PPI network using the degree topology method. Results Included studies A total of 6 studies, including GSE23339 [ 11 ], GSE7305 [ 12 ], GSE25628 [ 13 ], GSE51981 [ 14 ], GSE120103 [ 15 ], and GSE87809 [ 16 ], were selected for data extraction based on the mentioned inclusion criteria. The collective number of endometriosis and control subjects from all datasets was 83 and 61, respectively. The studies were conducted either by microarray (N = 4) or RNA-sequencing (N = 2) and predominantly evaluated stage IV of ovarian endometriosis. Based on the study design, the whole dataset was analyzed only in three studies, as the subjects in the remaining studies were grouped based on several criteria, including severity, fertility status, or tissue source. Notably, moderate/severe endometriosis, fertile patients, and ectopic tissue were selected from those remaining studies. Detailed information on all studies is shown in Table 1 . Differentially expressed genes in endometriosis We independently analyzed the endometriosis-associated DEGs from each study, which varied from 1213 to 4680 genes. The RNA components were primarily identified as protein-coding genes (mRNA), marking ~ 96.3% of the DEGs in all studies. Moreover, about 0.1% and 2.8% altered genes corresponded as miRNA and lncRNA, indicating the particular role of this regulatory RNA in endometriosis pathogenesis. DEGs component in each study is presented in Table 2 . Table 2 RNA compositions of DEGs present in each study Dataset mRNA lncRNA miRNA Total N % N % N % GSE23339 1213 100% 0 0% 0 0 1213 GSE7305 1551 97.7% 33 2.07% 1 0.06% 1585 GSE25628 2796 99.6% 10 0.35% 1 0.035% 2807 GSE51981 4317 98.7% 52 1.18% 1 0.02% 4370 GSE120103 4447 94.9% 232 4.95% 1 0.02% 4680 GSE87809 2201 90.7% 212 8.74% 12 0.49% 2425 mRNA messenger RNA, lncRNA long noncoding RNA, miRNA microRNA We further analyzed the overlapping DEGs from all datasets, resulting in 551 common DEGs among four or more studies (Fig. 2 A). The combined log fold change from these common DEGs yielded 292 upregulated and 259 downregulated genes associated with endometriosis. The expression pattern of the shared DEGs between studies is presented by a heatmap graph in Fig. 3 . miRNA and lncRNA expression altered in endometriosis In total, we found 16 differentially expressed miRNAs in five studies, from which the GSE87809 [ 16 ] dataset marked the highest number of alterations (Table 2 , Fig. 2 B). The targets of these miRNAs were identified using MIENTURNET online tools. Furthermore, the top 5 enriched miRNAs with at least three interactions revealed ~ 60 target genes, which were then used to construct the miRNAs-target network (Fig. 7 ). Those five enriched miRNAs are hsa-miR-25, hsa-miR-503, hsa-miR-302b, hsa-miR-424, and hsa-miR-10a. Alteration of gene expressions was also observed in 539 lncRNAs, including 12 lncRNA that were common to at least in 3 studies (Table 2 , Fig. 2 C). The targets of these miRNAs were identified using lncRRIsearch database, resulting in ~ 202 target genes for the nine enriched lncRNAs, denoted as MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3. GO functional and KEGG pathway enrichment GO functional annotation and KEGG pathway analyses were performed using the ShinyGo online tool. The biological processes (BP) of the common DEGs were found to be associated with morphogenesis and development, cellular and chemical responses, and regulation of cell signaling and communication. For the cellular components (CC), the extracellular parts were found to be predominantly altered in endometriosis. Moreover, the common DEGs' molecular functions (MF) represent several molecular binding roles, including cell adhesion, cytoskeletal protein, kinase, and signaling receptor (Fig. 4 ). We conducted a KEGG pathway enrichment analysis of the shared DEGs and enriched miRNAs. As shown in Fig. 5 A and Table 3 , the interaction of extracellular matrix receptor, cell cycle, and focal adhesion, along with several signaling pathways (p53, P13K-Akt, and MAPK), were the enriched pathways of the shared DEGs. Moreover, the selected miRNA showed the highest number of hits on nucleocytoplasmic transport, carcinogenesis receptor activation, and several signaling pathways (Hippo, Hedgehog, prolactin, p53, and FoxO) (Fig. 5 B). Table 3 KEGG pathway of common DEGs Pathway n Genes Genes ECM-receptor interaction 16 COL4A3 COL6A2 COMP ITGA11 LAMA1 HMMR TNC ITGA6 ITGB8 LAMA4 LAMC2 RELN THBS2 FRAS1 ITGA8 CD44 p53 signaling pathway 12 CHEK1 SFN IGF1 FAS GADD45B SERPINE1 PMAIP1 RRM2 CCNB1 CCND2 CCNB2 CCNE2 Focal adhesion 23 MYL9 COL4A3 COL6A2 COMP DOCK1 ITGA11 FYN LAMA1 TNC IGF1 ITGA6 ITGB8 LAMA4 LAMC2 MET MYLK PPP1R12B MAPK8 RELN THBS2 PIP5K1B ITGA8 CCND2 Cell cycle 16 CHEK1 ORC6 SFN MAD2L1 GADD45B BUB1 BUB1B TTK CCNA2 CCNA1 CCNB1 CCND2 CCNB2 CCNE2 CDK1 CDC6 Pathways in cancer 45 COL4A3 CSF1R DAPK1 AGTR1 EPAS1 MECOM FGFR1 FGFR3 PLCB1 HEY1 HEY2 FOS LPAR3 LAMA1 GSTM3 IGF1 FAS IL2RA IL6 IL6ST CXCL8 ITGA6 JAK3 LAMA4 LAMC2 MET MMP1 GADD45B PMAIP1 MAPK8 PTCH1 PTGER3 PTGER4 RAD51 CXCL12 SP1 WNT5A WNT2B ZBTB16 PAX8 FZD4 CCNA2 CCNA1 CCND2 CCNE2 PI3K-Akt signaling pathway 30 COL4A3 COL6A2 COMP CSF1R ERBB3 FGFR1 FGFR3 ITGA11 LPAR3 LAMA1 NR4A1 TNC IGF1 IL2RA IL6 ITGA6 ITGB8 JAK3 LAMA4 LAMC2 MET NGF NTRK2 PPP2R2C RELN BRCA1 THBS2 ITGA8 CCND2 CCNE2 Cellular senescence 14 CHEK1 IL6 CXCL8 ITPR1 GADD45B SERPINE1 MAP2K6 CCNA2 CCNA1 CCNB1 CCND2 CCNB2 CCNE2 CDK1 Cell adhesion molecules 14 CDH3 VCAN CADM1 ICAM1 ITGA6 ITGB8 NRCAM JAM2 CLDN5 VCAM1 VTCN1 ITGA8 CLDN1 CD4 PPI network and hub genes identification The protein-protein interaction (PPI) network of DEGs was analyzed using the STRING database with a confidence interaction score ≥ 0.9. After removing the unconnected nodes, the network construction by Cytoscape exhibited 178 nodes and 683 edges, comprising upregulated and downregulated genes (Fig. 6 A). To further identify endometriosis-associated hub genes, CytoHubba was used to rank the top 15 hub genes in the PPI network based on the degree score. Those 15 hub genes denoted as CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, and NCAPG (Table 4 , Fig. 6 B). In addition, the enriched miRNA and lncRNA network with their respective targets were presented in Fig. 7 and Fig. 8 , respectively. Table 4 Top 15 hub genes with the highest score, analyzed with degree method Rank Symbol Gene name Score 1 CDK1 Cyclin-dependent kinase 1 47 2 CCNB1 Cyclin B1 39 3 KIF11 Kinesin family member 11 38 4 CCNA2 Cyclin A2 38 5 BUB1B BUB1B mitotic checkpoint serine/threonine kinase B 36 6 DLGAP5 DLG-associated protein 5 34 7 BUB1 BUB1 mitotic checkpoint serine/threonine kinase 34 8 TOP2A Topoisomerase (DNA) II alpha 33 9 ASPM Abnormal spindle microtubule assembly 33 10 CEP55 Centrosomal protein 55 31 11 CENPF Centromere protein F 31 12 TPX2 TPX2, microtubule nucleation factor 31 13 CCNB2 Cyclin B2 30 14 KIF2C Kinesin family member C1 29 15 NCAPG Non-SMC condensin I complex subunit G 29 Discussions Endometriosis pathophysiology is still enigmatic and far from being elucidated, as no single theory could explain all reported observations [ 17 ]. However, the constituent of genetic and epigenetic alterations in endometriosis is also a rapidly emerging research [ 18 ]. As proposed by Konickx et al., the genetic/epigenetic theory enhances the previously known theories of endometriosis by adding current knowledge of the cumulative genetic and epigenetic modifications transmitted at birth and acquired throughout life [ 17 ]. Accordingly, transcriptomic studies have shed light on the molecular basis of endometriosis through Genome-Wide Association Studies (GWAS). However, a shortcoming of recent GWAS is the relatively narrow approach, disregarding the potential impacts of long-range regulatory elements. Thus, linking GWAS with transcriptional analysis should help enlighten the essential genes associated with endometriosis [ 18 ]. Despite the massive data provided by different studies, constraints and challenges continue to affect the results, from the sample size to technical issues [ 19 ]. Combining larger samples from several studies is a promising approach to better comprehend the endometriosis-linked mechanism. In the present study, we identified several endometriosis-associated features, including differentially expressed genes (DEGs), miRNA, and lncRNA. Expectedly, protein-coding genes accounted for the predominantly altered DEGs (~ 96,3%), of which 551 genes were common in at least four or more studies. As presented in Fig. 6 A, the constructed network of DEGs (confidence score > 0.9) clearly differentiates the clusters of up and downregulated genes with comparable proportions. These results signify how gene alterations influence one another, resulting in intricate relationships to arouse and cease the essential process of endometriosis progression. These genes were linked to cellular and chemical responses and play a particular role in cell signaling and communication. Moreover, we pointed out that the alteration of extracellular components was primarily affected, and various molecular binding roles, such as cell adhesion, cytoskeletal protein, and kinase, marked the molecular function of gene ontology. Intriguingly, we also found that Extracellular matrix (ECM) receptor interaction is the most enriched pathway, as reported by previous studies [ 20 ]. ECM components play a critical function in cellular networks. Klemmt et al. reported that the stromal cells of women with endometriosis display increased DNA synthesis, attachment, and proliferative capacity in response to soluble ECM components [ 21 ]. Accordingly, our findings also pinpoint the focal adhesion and cell cycle as the enriched pathways, which may explain the aftereffects of ECM alterations. Moreover, several integrin family genes were aberrated, including ITGA11, ITGA6, ITGB8, and ITGA8 (Table 3 ). It is known that integrins mediate the adhesion of cells to ECM components, such as collagen types I and IV, fibronectin, and laminin [ 22 ]. This environment was supported by our findings, of which COL4A3, COL6A2, LAMA4, LAMC2, and LAMA1 were altered in endometriosis patients (Table 3 ). Given the growing evidence of interrelation between endometriosis and cancer, our results may confirm their shared connection. About 45 genes were associated with the pathway in cancer (Table 3 ), marking the highest number of hits of the KEGG pathway of studied DEGs. Similarly, Ni et al. reported that 571 DEGs overlapped between endometriosis and ovarian cancer [ 23 ], and several cancer mutations were harbored in 79% of endometriosis patients, as reported by Anglesio et al. [ 24 ]. However, our obtained hub genes may contradict the profound mechanism, as we found all downregulation of the following genes: CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG (Fig. 6 , Table 4 ). These genes control cell cycle machinery, and their unscheduled up-regulation or down-regulation can hinder the cell death sentence, leading to uncontrolled cell proliferation and malignant transformation [ 25 ]. Therefore, we suggest that certain inhibitory mechanisms and other intricate processes were involved in endometriosis progression. Regarding the epigenetic roles and mechanisms, we further analyzed the shared miRNAs and lncRNAs from all datasets. MicroRNAs are highly stable non-coding RNAs that post-transcriptionally regulate gene expression, affecting cell death and proliferation, inflammation, and other pathological mechanism [ 26 ]. Among 16 identified miRNAs, the network of five miRNA-target with highest interaction were analyzed, namely miR-25, miR-503, miR-302b, miR-424 and miR-10a (Fig. 7 ). Previous study demonstrated a significant decrease in miR-25 in ectopic and eutopic endometrium, and miR-503 suppression were reported to regulate CD97 expression and related JAK2/STAT3 pathway [ 27 , 28 ]. Moreover, a study by Lin et al. showed that miR-302 induces demethylation of overall genomic DNA, activating several transcription factors such as Oct4, Sox2, Nanog, and Lin28. miR-302 has also been known for its inhibitory roles in various cancers through cyclin D1 inhibition [ 29 ]. We identified 9 enriched lncRNA and their respective target, namely MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3 (Fig. 8 ). lncRNAs, as well as miRNA, exhibit pivotal role in endometriosis etiology. GATA2-AS1 is a 2358 bp lncRNA that performed as a tumor suppressor for inhibit proliferation. This lncRNA is controlling HOXB4 and ALDH1A2 expression, which associated with greater invasiveness of ectopic implants in ovarian endometriosis [ 30 ]. On the other hand, study showed that HAND2-AS1 expression leads to an increase in DNA methylation of HAND2, and lncRNA MALAT1 were reported to inhibit apoptosis of endometrial stromal cells through miR-126-5p-CREB1 axis by activating PI3K-AKT pathway [ 31 ]. Given the drawbacks of the present study, the significant findings should be interpreted deliberately. First, the heterogeneity of individual donors in terms of their specific pathophysiological condition must generate the disparities between the expression pattern of the endometriosis and control group, necessitating experimental validation of our findings. 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Szubert M, Nowak-Glück A, Domańska-Senderowska D, Szymańska B, Sowa P, Rycerz A, et al. miRNA Expression Profiles in Ovarian Endometriosis and Two Types of Ovarian Cancer—Endometriosis-Associated Ovarian Cancer and High-Grade Ovarian Cancer. Int J Mol Sci 2023;24. https://doi.org/10.3390/ijms242417470. Yanokura M, Banno K, Iida M, Irie H, Umene K, Masuda K, et al. Micrornas in endometrial cancer: Recent advances and potential clinical applications. EXCLI J 2015;14:190–8. https://doi.org/10.17179/excli2014-590. Liu H, Liang J, Dai X, Peng Y, Xiong W, Zhang L, et al. Transcriptome-wide N6-methyladenosine (m6A) methylation profiling of long non-coding RNAs in ovarian endometriosis. Genomics 2024;116. https://doi.org/10.1016/j.ygeno.2024.110803. Feng Y, Tan B-Z. LncRNA MALAT1 inhibits apoptosis of endometrial stromal cells through miR-126-5p-CREB1 axis by activating PI3K-AKT pathway. Mol Cell Biochem 2020;475:185–94. https://doi.org/10.1007/s11010-020-03871-y. 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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-4923357","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":340996372,"identity":"a646d6f8-97c8-458a-88b9-273063a7b5e0","order_by":0,"name":"Clara Riski Amanda","email":"","orcid":"https://orcid.org/0009-0005-2657-9342","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Riski","lastName":"Amanda","suffix":""},{"id":340996373,"identity":"0dc62f80-f6ff-41a4-bad8-baadac372b80","order_by":1,"name":"Fadilah","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"Fadilah","suffix":""},{"id":340996374,"identity":"1b2a6651-28ba-4019-8640-3fc4e47f2477","order_by":2,"name":"Andon Hestiantoro","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Andon","middleName":"","lastName":"Hestiantoro","suffix":""},{"id":340996375,"identity":"a7373573-fa5a-4dfa-9157-36153bee9d4c","order_by":3,"name":"Dwi Anita Suryandari","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Dwi","middleName":"Anita","lastName":"Suryandari","suffix":""},{"id":340996376,"identity":"5330f9e1-5e7a-4d1b-98e5-7953bc80341f","order_by":4,"name":"Raden Muharam","email":"","orcid":"","institution":"University of Indonesia","correspondingAuthor":false,"prefix":"","firstName":"Raden","middleName":"","lastName":"Muharam","suffix":""},{"id":340996377,"identity":"9965ebc5-b247-42bb-920e-ba30af1593e3","order_by":5,"name":"Togas Tulandi","email":"","orcid":"","institution":"McGill University","correspondingAuthor":false,"prefix":"","firstName":"Togas","middleName":"","lastName":"Tulandi","suffix":""},{"id":340996378,"identity":"b043456e-9163-4250-888b-6929e42b3aac","order_by":6,"name":"Asmarinah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYNACAzBiOPABSLCxk6Ll4AyQFmYSLGJg5gGxCGnh7z+d+Lmi4I68OQP7w8M2v7bJ8zEzMH74mINbi8SN3M2SZwyeGe5s4DE4nNt327CNmYFZcuY2PNbc4N0g2WBwmHHDAR6Gw7k9txmBWtiYefFokT9/dvNPoBb7DQfYHxy27LltT1CLwYHcbSBbEjccYDA4zPDjdiJBLYY3crdZArUkbzjMY3Cwt+F2chszYzNev8gBHXaz4c9h2w3H2x9/+PHntu389uaDHz7i8z4cgKKDsQ3EYmwgRj0M/CFF8SgYBaNgFIwUAABZVVZtwnIJewAAAABJRU5ErkJggg==","orcid":"","institution":"University of Indonesia","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Asmarinah","suffix":""}],"badges":[],"createdAt":"2024-08-16 07:51:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4923357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4923357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62749084,"identity":"6e7d5a96-a4e1-4dfd-b73a-ae5f59f3045e","added_by":"auto","created_at":"2024-08-19 04:58:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38610,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the regulatory network construction in endometriosis by integrated bioinformatic analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/28ee2adf834cf7a241e2bfec.png"},{"id":62749092,"identity":"0b2c5b1d-7d07-456a-9cec-e6e3d48456a1","added_by":"auto","created_at":"2024-08-19 04:58:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204907,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram intersection of the selected studies. \u003cstrong\u003eA\u003c/strong\u003e Differentially expressed genes (DEGs). The genes presented are common in at least four studies. \u003cstrong\u003eB\u003c/strong\u003eDifferentially expressed microRNA (miRNA). \u003cstrong\u003eC\u003c/strong\u003e Differentially expressed long noncoding RNA (lncRNA) that is common in at least three studies.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/bdd47090d5c5dfde0d13d853.png"},{"id":62749086,"identity":"7398e311-ea3e-400f-8ef7-d2d736c122ef","added_by":"auto","created_at":"2024-08-19 04:58:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48623,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the DEGs common in at least four studies comprised 551 DEGs. Groups A and B correspond to microarrays and RNA-sequencing studies, respectively. Red and blue colors represent the relative gene expression among all studies.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/d23e774168e544c70cd7a31a.png"},{"id":62749085,"identity":"3add74ce-0596-4056-8a9e-2f62455d31a3","added_by":"auto","created_at":"2024-08-19 04:58:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125721,"visible":true,"origin":"","legend":"\u003cp\u003eGene ontology (GO) of 551 common DEGs. The GO terms (Biological process, cellular components, and molecular function) were ranked according to fold enrichment and the P value for each term.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/18e67a1f93957d15b1c0db44.png"},{"id":62749090,"identity":"5f390c69-6571-4fce-93cc-b0a72ce8acc4","added_by":"auto","created_at":"2024-08-19 04:58:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90342,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enriched pathway of common DEGs and miRNA. \u003cstrong\u003eA\u003c/strong\u003e. KEGG pathway of 551 common DEGs, ranked by the fold enrichment. \u003cstrong\u003eB\u003c/strong\u003e. KEGG pathway of top 5 miRNA, provided by gene ratio and P adjustment.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/dca8dfbe80438bfacc9defc7.png"},{"id":62749091,"identity":"6775f9b6-40e1-4881-b6cd-f0042b7fe14a","added_by":"auto","created_at":"2024-08-19 04:58:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":328151,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory network of common DEGs. \u003cstrong\u003eA\u003c/strong\u003e. Protein-protein interaction (PPI) network of common DEGs with the confidence score 0.9, resulting in 178 nodes and 683 edges. Red and blue colors represent the upregulated and downregulated genes, respectively. The continuity of the colors represents the relative gene expression. \u003cstrong\u003eB. \u003c/strong\u003eThe top 15 hub genes of the PPI network are ranked by degree; color represents the score intensity.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/5b0a79bef80cd4a576217ec6.png"},{"id":62749364,"identity":"87ec12c4-9f66-4333-a853-cec990ff291c","added_by":"auto","created_at":"2024-08-19 05:06:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95906,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network of common differentially expressed micro RNA (miRNA). The RNAs correspond to the nodes and their respective target on the edges.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/ac6047772882859b541696af.png"},{"id":62749088,"identity":"9917e707-b78a-4339-871b-e4a5b88e7637","added_by":"auto","created_at":"2024-08-19 04:58:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":239514,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction network of common differentially expressed long noncoding RNA (lncRNA). The RNAs correspond to the nodes and their respective target on the edges.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/e8c6267849a823cbc54f045d.png"},{"id":62750254,"identity":"0d4896d2-b472-41cf-b027-a7cd1d1fd611","added_by":"auto","created_at":"2024-08-19 05:22:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1627963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4923357/v1/a5c54eed-e3f7-4bac-9080-7417ce779f84.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntegrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a systemic and multifactorial disease with most gynecological afflictions, affecting the quality of life of women in reproductive age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Histologically, it is defined by the presence of endometrial-like tissue outside the uterus, which involves a complex interaction between pain, hormonal dependence, ovarian function disruption, and compromised endometrial receptivity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The estimated prevalence of endometriosis varied from 0.2\u0026ndash;71.4%, as the heterogeneity of diagnostic and selection bias overwhelmingly accounts for its variability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite advancements in contemporary medicine, there is no common ground in understanding the pathogenesis of endometriosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its pathogenomic architecture comprises complex genetic and epigenetic features, including gene polymorphisms, interactions of gene nets and functional protein modules, and different metabolic pathways that are altered by biological process imbalances [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These variations are responsible for the aberrant modulation of steroidogenesis, inflammation, apoptosis, adhesion, angiogenesis, proliferation, and hormone signaling. Therefore, determining the molecular basis of endometriosis may encourage early detection, better disease management, and a new approach to treatment strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenetic studies provide a critical way to better understand the process behind the development of endometriosis as a debilitating illness [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the last decade, advances in high-throughput genotyping technology and DNA sequencing have made significant progress in discovering the gene signatures of endometriosis. In the recent meta-analysis, these cutting-edge methods reported a 2% and 5% variation among all cases and severe cases of endometriosis, respectively [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, defining potential genes for endometriosis is difficult due to a limited understanding of etiopathogenetic mechanisms, which may involve multiple genes and pathways. Numerous variables have also raised consequential concerns about candidate gene research, including study power, effect size, negative results bias, statistical and technological challenges, and experimental design flaws [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The present study therein aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using a non-biased bioinformatic approach and a large sample from six combined studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eGene expression datasets of endometriosis were collected from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Studies on endometriosis-associated gene expression alterations were searched using the terms \u0026ldquo;Endometriosis\u0026rdquo; and \u0026ldquo;Homo Sapiens\u0026rdquo;. The inclusion criteria were as follows: (1) studies evaluating ectopic or eutopic tissue of endometriosis; (2) studies containing at least four cases and four normal controls (non-endometriosis); and (3) studies providing publicly available annotation data in the GEO database. Six studies were subjected to data collection and analysis, including GSE23339 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], GSE7305 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], GSE25628 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], GSE51981 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], GSE120103 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], GSE87809 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All datasets were analyzed with the GEO2R tool, available at the GEO repository. The samples were divided into endometriosis vs control groups, and the following settings were applied: P-value adjustment was performed using the Benjamini \u0026amp; Hochberg with a significance cutoff of 0.05, and no forces normalization or log transformation was applied. The study\u0026rsquo;s flow chart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of studies selected for bioinformatic analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSamples (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of endometriosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProtocol design\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHawkins et al. [11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE23339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 EN, 9 N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOvary and endometrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOvarian endometrioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIllumina\u0026rsquo;s Human WG-6 2.0 arrays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHever et al. [12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE7305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 EN, 10 N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOvarian endometrioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAffymetrix HU133 Plus 2.0 arrays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrispi et al. [13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE25628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 EN, 6 N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEctopic endometrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeep filtrating endometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAffymetrix HGU133A 2.0 arrays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamaresis et al. [14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE51981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 EN, 22 N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEndometrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAffymetrix HU133 Plus 2.0 arrays\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBhat et al. [15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE120103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 EN, 9 N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEndometrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOvarian endometrioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIllumina HiSeq2500 RNA-seq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYotova et al. [16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE87809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 EN, 5-N-EN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOvary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOvarian endometrioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIllumina HiSeq2000 RNA-seq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eEN individuals with endometriosis, N-EN individuals without endometriosis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were analyzed between the endometriosis and normal endometrium tissues in each dataset with a determined |log2FC| \u0026gt; 1 and the P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. After removing the inconsistent DEGs, the overlapped genes of all datasets were analyzed using InteractiVenn (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.interactivenn.net/\u003c/span\u003e\u003cspan address=\"http://www.interactivenn.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to find the common DEGs. In addition, the SR Plot tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.com.cn/plot_basic_cluster_heatmap_plot_024_en\u003c/span\u003e\u003cspan address=\"https://bioinformatics.com.cn/plot_basic_cluster_heatmap_plot_024_en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to generate the fold-change heatmap of the shared DEGs under the following settings: Euclidean distance method, complete cluster method, and bidirectional cluster orientation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of mRNA, miRNA, and lncRNA\u003c/h2\u003e \u003cp\u003eThe ShinyGO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) online tool was used to identify the RNA components (mRNA, lncRNA, miRNA) of each dataset. After removing the inconsistent data, the shared miRNA and lncRNA of all datasets were integrated using InteractiVenn (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.interactivenn.net/\u003c/span\u003e\u003cspan address=\"http://www.interactivenn.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To generate the miRNA network, miRNA and their respective targets were identified using the MIENTURNET online tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://userver.bio.uniroma1.it/apps/mienturnet/\u003c/span\u003e\u003cspan address=\"http://userver.bio.uniroma1.it/apps/mienturnet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Furthermore, analysis of lncRNA and their targets was performed using the LncRRIsearch (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rtools.cbrc.jp/LncRRIsearch/\u003c/span\u003e\u003cspan address=\"http://rtools.cbrc.jp/LncRRIsearch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a determined energy threshold of -20 kcal/mol. The targets with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant and subjected to further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eGene ontology (GO) and Kyoto Encyclopaedia Genes and Genomes (KEGG) pathway enrichment analysis were performed using The ShinyGO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GO terms, including Biological process (BP), Cellular components (CC), and Molecular function (MF) of common DEGs, were ranked according to the fold enrichment and the P value in each term. The results were then presented as bubble charts generated by the SR Plot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.com.cn/en?keywords=bubble\u003c/span\u003e\u003cspan address=\"https://bioinformatics.com.cn/en?keywords=bubble\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNetwork construction and hub genes analysis\u003c/h2\u003e \u003cp\u003eThe STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the protein-protein interaction (PPI) between the common DEGs, with the confidence interaction score\u0026thinsp;\u0026ge;\u0026thinsp;0.9. After removing the unconnected nodes, the network obtained from the STRING was downloaded and analyzed. All the interaction networks between common DEGs, miRNA-targets, and lncRNA-targets were visualized using the Cytoscape software version 3.10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, the CytoHubba plugin of the Cytoscape was employed to screen the top 15 hub genes of the PPI network using the degree topology method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncluded studies\u003c/h2\u003e \u003cp\u003eA total of 6 studies, including GSE23339 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], GSE7305 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], GSE25628 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], GSE51981 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], GSE120103 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and GSE87809 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], were selected for data extraction based on the mentioned inclusion criteria. The collective number of endometriosis and control subjects from all datasets was 83 and 61, respectively. The studies were conducted either by microarray (N\u0026thinsp;=\u0026thinsp;4) or RNA-sequencing (N\u0026thinsp;=\u0026thinsp;2) and predominantly evaluated stage IV of ovarian endometriosis. Based on the study design, the whole dataset was analyzed only in three studies, as the subjects in the remaining studies were grouped based on several criteria, including severity, fertility status, or tissue source. Notably, moderate/severe endometriosis, fertile patients, and ectopic tissue were selected from those remaining studies. Detailed information on all studies is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferentially expressed genes in endometriosis\u003c/h3\u003e\n\u003cp\u003eWe independently analyzed the endometriosis-associated DEGs from each study, which varied from 1213 to 4680 genes. The RNA components were primarily identified as protein-coding genes (mRNA), marking\u0026thinsp;~\u0026thinsp;96.3% of the DEGs in all studies. Moreover, about 0.1% and 2.8% altered genes corresponded as miRNA and lncRNA, indicating the particular role of this regulatory RNA in endometriosis pathogenesis. DEGs component in each study is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRNA compositions of DEGs present in each study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE23339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE7305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE25628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.035%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE51981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE120103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE87809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003emRNA messenger RNA, lncRNA long noncoding RNA, miRNA microRNA\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed the overlapping DEGs from all datasets, resulting in 551 common DEGs among four or more studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The combined log fold change from these common DEGs yielded 292 upregulated and 259 downregulated genes associated with endometriosis. The expression pattern of the shared DEGs between studies is presented by a heatmap graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003emiRNA and lncRNA expression altered in endometriosis\u003c/h2\u003e \u003cp\u003eIn total, we found 16 differentially expressed miRNAs in five studies, from which the GSE87809 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] dataset marked the highest number of alterations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The targets of these miRNAs were identified using MIENTURNET online tools. Furthermore, the top 5 enriched miRNAs with at least three interactions revealed\u0026thinsp;~\u0026thinsp;60 target genes, which were then used to construct the miRNAs-target network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Those five enriched miRNAs are hsa-miR-25, hsa-miR-503, hsa-miR-302b, hsa-miR-424, and hsa-miR-10a.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlteration of gene expressions was also observed in 539 lncRNAs, including 12 lncRNA that were common to at least in 3 studies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The targets of these miRNAs were identified using lncRRIsearch database, resulting in ~\u0026thinsp;202 target genes for the nine enriched lncRNAs, denoted as MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGO functional and KEGG pathway enrichment\u003c/h2\u003e \u003cp\u003eGO functional annotation and KEGG pathway analyses were performed using the ShinyGo online tool. The biological processes (BP) of the common DEGs were found to be associated with morphogenesis and development, cellular and chemical responses, and regulation of cell signaling and communication. For the cellular components (CC), the extracellular parts were found to be predominantly altered in endometriosis. Moreover, the common DEGs' molecular functions (MF) represent several molecular binding roles, including cell adhesion, cytoskeletal protein, kinase, and signaling receptor (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted a KEGG pathway enrichment analysis of the shared DEGs and enriched miRNAs. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the interaction of extracellular matrix receptor, cell cycle, and focal adhesion, along with several signaling pathways (p53, P13K-Akt, and MAPK), were the enriched pathways of the shared DEGs. Moreover, the selected miRNA showed the highest number of hits on nucleocytoplasmic transport, carcinogenesis receptor activation, and several signaling pathways (Hippo, Hedgehog, prolactin, p53, and FoxO) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG pathway of common DEGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en Genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM-receptor interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOL4A3\u0026nbsp; COL6A2\u0026nbsp; COMP\u0026nbsp; ITGA11\u0026nbsp; LAMA1\u0026nbsp; HMMR\u0026nbsp; TNC\u0026nbsp; ITGA6\u0026nbsp; ITGB8\u0026nbsp; LAMA4\u003c/p\u003e \u003cp\u003eLAMC2\u0026nbsp; RELN\u0026nbsp; THBS2\u0026nbsp; FRAS1\u0026nbsp; ITGA8\u0026nbsp; CD44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep53 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHEK1\u0026nbsp; SFN\u0026nbsp; IGF1\u0026nbsp; FAS\u0026nbsp; GADD45B\u0026nbsp; SERPINE1\u0026nbsp; PMAIP1\u0026nbsp; RRM2\u0026nbsp; CCNB1\u0026nbsp; CCND2\u003c/p\u003e \u003cp\u003eCCNB2\u0026nbsp; CCNE2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFocal adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYL9\u0026nbsp; COL4A3\u0026nbsp; COL6A2\u0026nbsp; COMP\u0026nbsp; DOCK1\u0026nbsp; ITGA11\u0026nbsp; FYN\u0026nbsp; LAMA1\u0026nbsp; TNC\u0026nbsp; IGF1\u0026nbsp; ITGA6\u0026nbsp; ITGB8\u0026nbsp; LAMA4\u0026nbsp; LAMC2\u0026nbsp; MET\u0026nbsp; MYLK\u0026nbsp; PPP1R12B\u0026nbsp; MAPK8\u0026nbsp; RELN\u0026nbsp; THBS2\u0026nbsp; PIP5K1B\u003c/p\u003e \u003cp\u003eITGA8\u0026nbsp; CCND2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHEK1\u0026nbsp; ORC6\u0026nbsp; SFN\u0026nbsp; MAD2L1\u0026nbsp; GADD45B\u0026nbsp; BUB1\u0026nbsp; BUB1B\u0026nbsp; TTK\u0026nbsp; CCNA2\u0026nbsp; CCNA1\u003c/p\u003e \u003cp\u003eCCNB1\u0026nbsp; CCND2\u0026nbsp; CCNB2\u0026nbsp; CCNE2\u0026nbsp; CDK1\u0026nbsp; CDC6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathways in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOL4A3\u0026nbsp; CSF1R\u0026nbsp; DAPK1\u0026nbsp; AGTR1\u0026nbsp; EPAS1\u0026nbsp; MECOM\u0026nbsp; FGFR1\u0026nbsp; FGFR3\u0026nbsp; PLCB1\u0026nbsp; HEY1\u003c/p\u003e \u003cp\u003eHEY2\u0026nbsp; FOS\u0026nbsp; LPAR3\u0026nbsp; LAMA1\u0026nbsp; GSTM3\u0026nbsp; IGF1\u0026nbsp; FAS\u0026nbsp; IL2RA\u0026nbsp; IL6\u0026nbsp; IL6ST\u0026nbsp; CXCL8\u0026nbsp; ITGA6\u003c/p\u003e \u003cp\u003eJAK3\u0026nbsp; LAMA4\u0026nbsp; LAMC2\u0026nbsp; MET\u0026nbsp; MMP1\u0026nbsp; GADD45B\u0026nbsp; PMAIP1\u0026nbsp; MAPK8\u0026nbsp; PTCH1\u0026nbsp; PTGER3\u0026nbsp; PTGER4\u0026nbsp; RAD51\u0026nbsp; CXCL12\u0026nbsp; SP1\u0026nbsp; WNT5A\u0026nbsp; WNT2B\u0026nbsp; ZBTB16\u0026nbsp; PAX8\u0026nbsp; FZD4\u0026nbsp; CCNA2\u0026nbsp; CCNA1\u0026nbsp; CCND2\u0026nbsp; CCNE2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOL4A3\u0026nbsp; COL6A2\u0026nbsp; COMP\u0026nbsp; CSF1R\u0026nbsp; ERBB3\u0026nbsp; FGFR1\u0026nbsp; FGFR3\u0026nbsp; ITGA11\u0026nbsp; LPAR3\u0026nbsp; LAMA1\u0026nbsp; NR4A1\u0026nbsp; TNC\u0026nbsp; IGF1\u0026nbsp; IL2RA\u0026nbsp; IL6\u0026nbsp; ITGA6\u0026nbsp; ITGB8\u0026nbsp; JAK3\u0026nbsp; LAMA4\u0026nbsp; LAMC2\u0026nbsp; MET\u0026nbsp; NGF\u003c/p\u003e \u003cp\u003eNTRK2\u0026nbsp; PPP2R2C\u0026nbsp; RELN\u0026nbsp; BRCA1\u0026nbsp; THBS2\u0026nbsp; ITGA8\u0026nbsp; CCND2\u0026nbsp; CCNE2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellular senescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHEK1\u0026nbsp; IL6\u0026nbsp; CXCL8\u0026nbsp; ITPR1\u0026nbsp; GADD45B\u0026nbsp; SERPINE1\u0026nbsp; MAP2K6\u0026nbsp; CCNA2\u0026nbsp; CCNA1\u003c/p\u003e \u003cp\u003eCCNB1\u0026nbsp; CCND2\u0026nbsp; CCNB2\u0026nbsp; CCNE2\u0026nbsp; CDK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell adhesion molecules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCDH3\u0026nbsp; VCAN\u0026nbsp; CADM1\u0026nbsp; ICAM1\u0026nbsp; ITGA6\u0026nbsp; ITGB8\u0026nbsp; NRCAM\u0026nbsp; JAM2\u0026nbsp; CLDN5\u0026nbsp; VCAM1\u003c/p\u003e \u003cp\u003eVTCN1\u0026nbsp; ITGA8\u0026nbsp; CLDN1\u0026nbsp; CD4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePPI network and hub genes identification\u003c/h2\u003e \u003cp\u003eThe protein-protein interaction (PPI) network of DEGs was analyzed using the STRING database with a confidence interaction score\u0026thinsp;\u0026ge;\u0026thinsp;0.9. After removing the unconnected nodes, the network construction by Cytoscape exhibited 178 nodes and 683 edges, comprising upregulated and downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To further identify endometriosis-associated hub genes, CytoHubba was used to rank the top 15 hub genes in the PPI network based on the degree score. Those 15 hub genes denoted as CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, and NCAPG (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In addition, the enriched miRNA and lncRNA network with their respective targets were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 15 hub genes with the highest score, analyzed with degree method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyclin-dependent kinase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCNB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyclin B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIF11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKinesin family member 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCNA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyclin A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUB1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBUB1B mitotic checkpoint serine/threonine kinase B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDLGAP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDLG-associated protein 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBUB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBUB1 mitotic checkpoint serine/threonine kinase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOP2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTopoisomerase (DNA) II alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbnormal spindle microtubule assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEP55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentrosomal protein 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCENPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentromere protein F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTPX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTPX2, microtubule nucleation factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCNB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyclin B2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIF2C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKinesin family member C1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCAPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-SMC condensin I complex subunit G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eEndometriosis pathophysiology is still enigmatic and far from being elucidated, as no single theory could explain all reported observations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the constituent of genetic and epigenetic alterations in endometriosis is also a rapidly emerging research [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As proposed by Konickx et al., the genetic/epigenetic theory enhances the previously known theories of endometriosis by adding current knowledge of the cumulative genetic and epigenetic modifications transmitted at birth and acquired throughout life [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, transcriptomic studies have shed light on the molecular basis of endometriosis through Genome-Wide Association Studies (GWAS). However, a shortcoming of recent GWAS is the relatively narrow approach, disregarding the potential impacts of long-range regulatory elements. Thus, linking GWAS with transcriptional analysis should help enlighten the essential genes associated with endometriosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite the massive data provided by different studies, constraints and challenges continue to affect the results, from the sample size to technical issues [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Combining larger samples from several studies is a promising approach to better comprehend the endometriosis-linked mechanism.\u003c/p\u003e \u003cp\u003eIn the present study, we identified several endometriosis-associated features, including differentially expressed genes (DEGs), miRNA, and lncRNA. Expectedly, protein-coding genes accounted for the predominantly altered DEGs (~\u0026thinsp;96,3%), of which 551 genes were common in at least four or more studies. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, the constructed network of DEGs (confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.9) clearly differentiates the clusters of up and downregulated genes with comparable proportions. These results signify how gene alterations influence one another, resulting in intricate relationships to arouse and cease the essential process of endometriosis progression. These genes were linked to cellular and chemical responses and play a particular role in cell signaling and communication. Moreover, we pointed out that the alteration of extracellular components was primarily affected, and various molecular binding roles, such as cell adhesion, cytoskeletal protein, and kinase, marked the molecular function of gene ontology.\u003c/p\u003e \u003cp\u003eIntriguingly, we also found that Extracellular matrix (ECM) receptor interaction is the most enriched pathway, as reported by previous studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. ECM components play a critical function in cellular networks. Klemmt et al. reported that the stromal cells of women with endometriosis display increased DNA synthesis, attachment, and proliferative capacity in response to soluble ECM components [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Accordingly, our findings also pinpoint the focal adhesion and cell cycle as the enriched pathways, which may explain the aftereffects of ECM alterations. Moreover, several integrin family genes were aberrated, including ITGA11, ITGA6, ITGB8, and ITGA8 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is known that integrins mediate the adhesion of cells to ECM components, such as collagen types I and IV, fibronectin, and laminin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This environment was supported by our findings, of which COL4A3, COL6A2, LAMA4, LAMC2, and LAMA1 were altered in endometriosis patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the growing evidence of interrelation between endometriosis and cancer, our results may confirm their shared connection. About 45 genes were associated with the pathway in cancer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), marking the highest number of hits of the KEGG pathway of studied DEGs. Similarly, Ni et al. reported that 571 DEGs overlapped between endometriosis and ovarian cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and several cancer mutations were harbored in 79% of endometriosis patients, as reported by Anglesio et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, our obtained hub genes may contradict the profound mechanism, as we found all downregulation of the following genes: CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These genes control cell cycle machinery, and their unscheduled up-regulation or down-regulation can hinder the cell death sentence, leading to uncontrolled cell proliferation and malignant transformation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, we suggest that certain inhibitory mechanisms and other intricate processes were involved in endometriosis progression.\u003c/p\u003e \u003cp\u003eRegarding the epigenetic roles and mechanisms, we further analyzed the shared miRNAs and lncRNAs from all datasets. MicroRNAs are highly stable non-coding RNAs that post-transcriptionally regulate gene expression, affecting cell death and proliferation, inflammation, and other pathological mechanism [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Among 16 identified miRNAs, the network of five miRNA-target with highest interaction were analyzed, namely miR-25, miR-503, miR-302b, miR-424 and miR-10a (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Previous study demonstrated a significant decrease in miR-25 in ectopic and eutopic endometrium, and miR-503 suppression were reported to regulate CD97 expression and related JAK2/STAT3 pathway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, a study by Lin et al. showed that miR-302 induces demethylation of overall genomic DNA, activating several transcription factors such as Oct4, Sox2, Nanog, and Lin28. miR-302 has also been known for its inhibitory roles in various cancers through cyclin D1 inhibition [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe identified 9 enriched lncRNA and their respective target, namely MALAT1, DLEU2, MSC-AS1, HAND-AS1, MIR99AHG, GATA2-AS1, LINC01140, KLF3-AS1, and MAGI2-AS3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). lncRNAs, as well as miRNA, exhibit pivotal role in endometriosis etiology. GATA2-AS1 is a 2358 bp lncRNA that performed as a tumor suppressor for inhibit proliferation. This lncRNA is controlling HOXB4 and ALDH1A2 expression, which associated with greater invasiveness of ectopic implants in ovarian endometriosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the other hand, study showed that HAND2-AS1 expression leads to an increase in DNA methylation of HAND2, and lncRNA MALAT1 were reported to inhibit apoptosis of endometrial stromal cells through miR-126-5p-CREB1 axis by activating PI3K-AKT pathway [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the drawbacks of the present study, the significant findings should be interpreted deliberately. First, the heterogeneity of individual donors in terms of their specific pathophysiological condition must generate the disparities between the expression pattern of the endometriosis and control group, necessitating experimental validation of our findings. In addition, limitations associated with the variability between studies, namely the experimental protocols (microarray or RNA-sequencing), tissue and endometriosis types, and timely sample collection (in regards to the menstrual cycle), can bias the obtained results. Therefore, a comparative experimental study with homogeneous clinical characteristics is required to validate the results. Notwithstanding, our in-depth bioinformatic analysis favorably integrates the transcriptomic data from publicly available studies, emphasizing molecular targets associated with endometriosis progression.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBonavina G, Taylor HS. Endometriosis-associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne) 2022;13. https://doi.org/10.3389/fendo.2022.1020827.\u003c/li\u003e\n \u003cli\u003eAnastasiu CV, Moga MA, Neculau AE, Bălan A, Sc\u0026acirc;rneciu I, Dragomir RM, et al. Biomarkers for the noninvasive diagnosis of endometriosis: State of the art and future perspectives. Int J Mol Sci 2020;21. https://doi.org/10.3390/ijms21051750.\u003c/li\u003e\n \u003cli\u003eKoninckx PR, Ussia A, Adamyan L, Tahlak M, Keckstein J, wattiez A, et al. The epidemiology of endometriosis is poorly known as the pathophysiology and diagnosis are unclear. Best Pract Res Clin Obstet Gynaecol 2021;71:14\u0026ndash;26. https://doi.org/10.1016/j.bpobgyn.2020.08.005.\u003c/li\u003e\n \u003cli\u003eGhiasi M, Kulkarni MT, Missmer SA. Is Endometriosis More Common and More Severe Than It Was 30 Years Ago? J Minim Invasive Gynecol 2020;27:452\u0026ndash;61. https://doi.org/10.1016/j.jmig.2019.11.018.\u003c/li\u003e\n \u003cli\u003eBaranov V, Malysheva O, Yarmolinskaya M. Pathogenomics of endometriosis development. Int J Mol Sci 2018;19. https://doi.org/10.3390/ijms19071852.\u003c/li\u003e\n \u003cli\u003eKapoor R, Stratopoulou CA, Dolmans M-M. Pathogenesis of endometriosis: New insights into prospective therapies. Int J Mol Sci 2021;22. https://doi.org/10.3390/ijms222111700.\u003c/li\u003e\n \u003cli\u003eZubrzycka A, Zubrzycki M, Perdas E, Zubrzycka M. Genetic, epigenetic, and steroidogenic modulation mechanisms in endometriosis. J Clin Med 2020;9. https://doi.org/10.3390/jcm9051309.\u003c/li\u003e\n \u003cli\u003eLagan\u0026agrave; AS, Garzon S, G\u0026ouml;tte M, Vigan\u0026ograve; P, Franchi M, Ghezzi F, et al. The pathogenesis of endometriosis: Molecular and cell biology insights. Int J Mol Sci 2019;20. https://doi.org/10.3390/ijms20225615.\u003c/li\u003e\n \u003cli\u003eFung JN, Montgomery GW. Genetics of endometriosis: State of the art on genetic risk factors for endometriosis. Best Pract Res Clin Obstet Gynaecol 2018;50:61\u0026ndash;71. https://doi.org/10.1016/j.bpobgyn.2018.01.012.\u003c/li\u003e\n \u003cli\u003eRiski Amanda C, Asmarinah, Hestiantoro A, Tulandi T, Febriyeni. Gene expression of aromatase, SF-1, and HSD17B2 in menstrual blood as noninvasive diagnostic biomarkers for endometriosis. European Journal of Obstetrics \u0026amp; Gynecology and Reproductive Biology 2024;301:95\u0026ndash;101. https://doi.org/10.1016/J.EJOGRB.2024.07.061.\u003c/li\u003e\n \u003cli\u003eHawkins SM, Creighton CJ, Han DY, Zariff A, Anderson ML, Gunaratne PH, et al. Functional MicroRNA Involved in Endometriosis. Molecular Endocrinology 2011;25:821\u0026ndash;32. https://doi.org/10.1210/me.2010-0371.\u003c/li\u003e\n \u003cli\u003eHever A, Roth RB, Hevezi P, Marin ME, Acosta JA, Acosta H, et al. Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator. Proceedings of the National Academy of Sciences 2007;104:12451\u0026ndash;6. https://doi.org/10.1073/pnas.0703451104.\u003c/li\u003e\n \u003cli\u003eCrispi S, Piccolo MT, D\u0026rsquo;avino A, Donizetti A, Viceconte R, Spyrou M, et al. 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Genomics 2024;116. https://doi.org/10.1016/j.ygeno.2024.110803.\u003c/li\u003e\n \u003cli\u003eFeng Y, Tan B-Z. LncRNA MALAT1 inhibits apoptosis of endometrial stromal cells through miR-126-5p-CREB1 axis by activating PI3K-AKT pathway. Mol Cell Biochem 2020;475:185\u0026ndash;94. https://doi.org/10.1007/s11010-020-03871-y.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Indonesia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatic, Differentially expressed genes, endometriosis, lncRNA, miRNA","lastPublishedDoi":"10.21203/rs.3.rs-4923357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4923357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: Endometriosis is a common gynecological disease with a significant economic burden. Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Six microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein-protein interaction (PPI) network of differentially expressed genes, miRNA, and lncRNA was constructed using STRING and Cytoscape, then the top 15 hub genes in the PPI network were identified using the CytoHubba.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 551 common DEGs were identified among four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA were identified from all studies, along with 12 lncRNA that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The identified hub genes, miRNA, and lncRNA may also serve as potential biomarkers to predict the occurrence and prognosis of endometriosis.\u003c/p\u003e","manuscriptTitle":"Integrated Bioinformatic Analysis Reveals the Gene Signatures, Epigenetic Roles, and Regulatory Networks in Endometriosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-19 04:58:27","doi":"10.21203/rs.3.rs-4923357/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":"a3ac29bb-20c2-4f79-8165-acb871388189","owner":[],"postedDate":"August 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36129358,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2024-08-19T04:58:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-19 04:58:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4923357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4923357","identity":"rs-4923357","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}