Identification of key genes associated with infertile endometriosis based on bioinformatic analysis

Identification of key genes associated with infertile endometriosis based on bioinformatic analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of key genes associated with infertile endometriosis based on bioinformatic analysis Xiaocong Chen, Hongjuan Ye, Xin Xie, Yang Wang, Jie Cheng, Hui Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5380458/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Endometriosis is a common disease among women of childbearing age. However, the molecular mechanism behind it is still unknown. Therefore, new biomarkers and therapeutic targets are needed to improve the diagnosis and treatment of infertile women. Methods: Microarray datasets GSE7305, GSE7307, and GSE51981 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between control and endometriosis. The STRING database and Cytoscape software constructed protein-protein interaction (PPI) and hub gene networks. At the same time, the three data sets were screened for co-differentially expressed genes related to mitosis. Subsequently, we identified mitosis-related hub genes (MRHGs) associated with both mitosis-related genes and hub genes. Next, enrichment analysis for target genes was performed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the mRNA-miRNA network was constructed. Finally, GSE25628 and GSE6364 were used to verify the expression of MRHGs individually, while GSE120103 was employed to ascertain the influence of mitosis-related genes on female fertility. Results: A total of 93 DEGs were identified in the endometriosis datasets. Then, we placed 11 potential mitosis-related downregulated hub genes, among which eight showed good diagnostic properties of endometriosis, and two showed good diagnostic properties of infertile endometriosis. The main enriched GO functions revealed that the cell cycle mitotic pathway may be the critical pathway in endometriosis. Meanwhile, mRNA-miRNA interaction networks were constructed by choosing co-expressed mRNAs and miRNAs. Furthermore, cordycepin showed high drug-targeting relevance in infertile endometriosis. Conclusions: We identified eight mitosis-related hub genes as potential biomarkers for diagnosing and treating endometriosis. CENPE and CCNA2 might be associated with infertile endometriosis by affecting the endometrial secretory phase transition. In addition, cordycepin may be a potential clinical treatment for people with infertility-related endometriosis. Endometriosis infertility differentially expressed genes biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights • This article uses bioinformatic methods to identify target genes for the treatment of endometriosis. • This article adopts a new search method for target genes based on the common characteristics of infertility and endometriosis. • Scan suitable therapeutic agents for target genes from the drug database. Introduction The endometriosis prevalence seems to be considerably higher in sub-fertile women, ranging from 20 to 50%. The causes of infertility in women with endometriosis mainly involve anatomical distortions, endocrine abnormalities, and immune disorders [ 1 – 3 ]. Implantation of the embryo depends on the endometrial inflammatory mechanism during pregnancy; endometriosis creates opportunities for chronic inflammation and disrupts endometrial receptivity, leading to infertility [ 4 ]. Infertility is one of the most common problems among couples worldwide [ 5 ]. The study found that implantation rate, clinical pregnancy rate, and ovarian response were lower in patients with endometriosis than in patients with tubal infertility [ 6 – 9 ]. Infertile patients with endometriosis impacted endometrial receptivity and endometrium decidualization [ 10 ]. Human endometrium has a crucial role in the implantation process [ 11 ]. Endometrial deindividuation is regulated by the synergistic action of maternal steroid hormones, estrogen, and progesterone and is a necessary prerequisite for embryo implantation in early pregnancy. Uterine stromal cell mitosis plays a vital role in this process, and stromal cells must undergo mitotic expansion before full decidualization [ 12 , 13 ]. Despite much evidence showing relationships between endometriosis and infertility, the mechanisms implicated in endometriosis-associated infertility are so far not fully understood. Therefore, screening differentially expressed genes between endometriosis and infertility may provide a new way to identify the pathogenesis of infertile endometriosis and give a new direction for treatment. Materials and methods Data collection The NCBI GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ) offers a comprehensive and publicly accessible collection of functional genomic datasets and high-throughput microarrays based on next-generation sequencing. We downloaded gene expression data from this database for our study.[ 14 ]. We acquired six datasets related to endometriosis and endometriosis-related infertility from GEO. One of these datasets, GSE120103, pertained to endometriosis-related infertility, while the other five, namely GSE7305, GSE7307, GSE51981, GSE25628, and GSE6364, were focused on endometriosis. The dataset named GSE7305 consisted of 20 samples, with 10 samples each of ectopic and normal endometrium. Another dataset, GSE7307, comprised eight normal endometrium samples and eight ectopic endometrium samples. Similarly, GSE51981 had eight normal endometrium samples and eight ectopic endometrium samples, while GSE25628 had six normal endometrium samples and six ectopic endometrium samples. In addition, GSE6364 had 11 normal endometrium samples and 10 ectopic endometrium samples. Finally, GSE120103 contained four normal endometrium samples from fertility and four ectopic endometrial samples from infertility. Following the transformation of identity documents, in cases where more than one probe matched a single gene, the gene expression value was determined by calculating the average expression value. Before analysis, raw data underwent log2-transformation and quantile-normalization. Table 1 provides detailed information on all six datasets, while the study design is presented as a flow chart in Fig. 1 . Table 1 Gene expression datasets used in this study Accession Platform Experiment type Control(n) Endometriosis(n) Infertile Endometriosis (n) Sample type GSE7305 GPL570 mRNA array 10 10 Endometrium GSE7307 GPL570 mRNA array 8 8 Endometrium GSE51981 GPL570 mRNA array 8 8 Endometrium GSE25628 GPL571 mRNA array 6 6 Endometrium GSE6364 GPL570 mRNA array 11 10 Endometrium GSE120103 GPL6480 mRNA array 4 4 Endometrium Identification of DEGs The microarray data from four datasets was downloaded and analyzed using various statistical techniques. Principal component analysis (PCA) was performed to verify the data's reproducibility. The differential expression analysis was carried out utilizing the 'limma' package of R software [ 15 ], and genes with an adjusted P-value less than 0.05 and absolute fold change more significant than one were considered differentially expressed genes (DEGs). To visualize the results, various plots such as box line plots, heat maps, and volcano maps were generated using the 'ggplot2' and 'heatmap'(version 3.3.6) packages of R software (version 4.2.1) [ 16 ]. PPI network construction and module analysis In this study, we used the Search Tool for the Retrieval of Interacting Genes (STRING) version 12.0 online database to predict the protein-protein interaction (PPI) network of differentially expressed genes (DEGs). The PPI network provides valuable insights into the generation or development of diseases. We considered interactions with a combined score greater than 0.4 statistically significant and used Cytoscape version 3.9.1, an open-source bioinformatics software platform, to visualize the network [ 17 ]. The Cytoscape software was utilized to import and analyze STRING data. The MCC algorithm of the Cytohubba plugin was employed to identify the top 20% scoring genes, which were then labeled as hub genes. Mitosis-related genes from the GeneCards database were used to match with the hub genes, resulting in the identification of overlapping genes associated with mitosis. Construction of an mRNA-miRNA regulatory network To predict the interactions between differentially expressed mRNA and miRNA in ectopic endometrium samples, we utilized the miRNet database ( https://www.mirnet.ca/ ). The mRNA-miRNA regulatory network was then visualized in Cytoscape software, providing an overview of the interactions between mRNAs and miRNAs, which can be considered potential targets. GO and KEGG pathway enrichment analyses of genes. The FRHGs were subjected to GO and KEGG pathway analysis using the ClusterProfiler package in R. The analysis considered three criteria: molecular function (MF), cellular component (CC), and biological process (BP). For statistical significance, an adjusted P value of 0.05 was used as per the Benjamini-Hochberg method, and the analysis was limited to Homo sapiens. Gene set enrichment analysis (GSEA) To investigate biological signaling pathways, we conducted a GSEA analysis. We identified the KEGG pathway showing significant enrichment based on the net enrichment score (NES), gene ratio, and P value. We considered |NES| greater than one and FDR q less than 0.25 as indicators of significant enrichment. Validation of mitosis hub genes In this study, we used 'pROC' (version 1.18.0) and 'ggplot2' packages (version 3.3.6) of R software (version 4.2.1) to perform Receiver Operating Characteristic (ROC) curve analysis. The analysis helped us to determine the sensitivity and specificity of target genes. We quantified the results by calculating the area under the ROC curve (AUC). The genes with AUC > 0.6 were considered diagnostic. Possible drugs for target genes The DGIdb (Drug-Gene Interaction Database) is a web resource that helps organize genes of the druggable genome into known drug interactions and potentially druggable targets. We utilized module DEGs (Differentially Expressed Genes) of endometriosis and significantly evaluated DEGs in DGIdb to identify potentially druggable DEGs. Statistical analysis Statistical analysis was performed using R version 4.2.1 and GraphPad Prism 8 software. The data was presented as mean ± standard deviation, and an unpaired Student's t-test was employed to compare groups. A P-value of less than 0.05 was considered statistically significant. Results Screening of mitosis-related hub genes in patients with endometriosis The expression matrix of GSE7305, GSE7307, and GSE51981 were normalized. The box plots showed that the distribution trend of the box graph is a straight line, and no library size effects were noticeable after normalization (Fig. 2 A, C, E). PCA of the three datasets was performed separately, and the PCA results that showed good repeatability of the data were visually compared (Fig. 2 B, D, F). We identified 823 upregulated and 657 downregulated DEGs in the GSE7305 dataset, 1686 upregulated and 1393 downregulated DEGs in the GSE7307 dataset, and 1937 upregulated and 1188 downregulated DEGs in the GSE51981 dataset. The volcano plots of DEGs in the above three datasets were generated in R, shown in Fig. 3 A- 3 C. Venn plots showed that 93 genes were commonly differentially expressed among the three datasets (Fig. 3 D, E), of which 32 were upregulated, and 61 were downregulated (Table S1 ). Subsequently, we generated the PPI network (Fig. 3 G) by determining the interactions among the DEGs obtained above. Use Cytoscape (version 3.8.1) to import STRING analysis data and identify the top 20% of genes with the highest scores as hub genes. Finally, 20 hub genes were determined, which were: CCNA2, BUB1B, NCAPG, TOP2A, CCNB1, KIF11, CENPE, KIF20A, NUSAP1, KIF15, NDC80, KIF4A, KIF14, SPAG5, NEK2, SMC4, DTL, FBXO5, TYMS and DEPDC1 (Fig. 3 H). In addition, we analyzed the expression of 12 mitosis-related down-regulated genes in three datasets. Venn diagram analysis of the three data sets found that the genes BUB1B, KIF11, CCNB1, NDC80, NEK2, CCNA2, KIF4A, NCAPG, CENPE, KIF14, BORA and FBXO5 were down-regulated genes associated with mitosis (Fig. 3 F). Interestingly, some of these genes overlap with the hub above genes, namely BUB1B, KIF11, CCNB1, NDC80, NEK2, CCNA2, KIF4A, NCAPG, CENPE, KIF14, and FBXO5. They are both hub and differentially expressed genes in mitosis (Fig. 3 I). GSE25628 confirmed the expression and diagnostic value of the MRHGs We used the GSE25628 dataset to detect the expression of selected target genes, and the results showed that the differential expression of eight MRHGs (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) between control and endometriosis patients was consistent with predictions (Fig. 5 A-E). Then, functional enrichment and identification of ectopic and normal endometrium by GSEA showed that most of the genes were located in the cell cycle mitotic pathway (NES = -2.443; P.adj < 0.001; FDR < 0.001) (Fig. 5 F). ROC curves using ectopic and normal endometrial data showed that these eight genes are of great value in diagnosing endometriosis. Figure G-N showed the AUC and 95% CI of eight MRHGs. The representative images were obtained from the Human Protein Atlas (HPA) and referenced to annotate the expression of these molecules in normal tissues (Fig. 5 O). GO/KEGG enrichment analyses of MRHGs and construction of the gene network. We performed GO and KEGG enrichment analyses on eight differentially expressed mitosis hub genes. The mitotic nuclear division and sister chromatid segregation were the most significant enrichment in GO categories. At the same time, the DEGs were mainly involved in the cell cycle, oocyte meiosis, and progesterone-mediated oocyte maturation in KEGG enrichment analysis (Fig. 4 A, B). Using the miRNet tool, we obtained 235 target miRNAs of 8 specifically expressed MRHGs and determined 420 mRNA-miRNA pairs. Then, according to the prediction results, we constructed a co-expressed network of mRNAs and miRNAs comprising 243 nodes and 420 edges (Fig. 4 C). BUB1B was regulated by 32 miRNAs, 115 miRNAs regulated CCNA2, 32 miRNAs held CENPE, 56 miRNAs controlled FBXO5, 61 miRNAs restrained KIF11, KIF4A was regulated by 34 miRNAs, 61 miRNAs regulated NCAPG, and NEK2 was hindered by 32 miRNAs (Table S2). We identified 28 miRNAs by searching the PubMed database for literature on endometriosis and miRNAs mentioned above (Table 2 ). Table 2 miRNAs associated with endometriosis reported in the pubmed database miRNA doi hsa-let-7i-5p 10.1007/s43032-020-00148-z . hsa-mir-124-3p, hsa-mir-126-3p 10.1016/j.yexcr.2019.05.010 10.1186/s12905-023-02250-1 10.1007/s13258-021-01184-y hsa-mir-125b-4p, hsa-mir-29a-3p 10.1093/humrep/dez116 hsa-mir-125b-5p 10.1016/j.fertnstert.2019.04.011 hsa-mir-15a-5p, hsa-mir-15b-5p, hsa-mir-16-5p, has-mir-195-5p, hsa-mir-320a, hsa-mir-320b, hsa-mir-92a-3p 0.1080/09537104.2022.2042233 hsa-mir-182-5p 10.1177/2058738420976309 hsa-mir-203a-3p 10.1080/09513590.2022.2076830 hsa-mir-214-3p, hsa-mir-22-3p 10.1093/humrep/dead216 hsa-mir-218-5p 10.1186/s12958-022-00928-z hsa-mir-24 10.1007/s00404-021-05963-6 hsa-mir-340-5p 10.1007/s00404-021-05963-6 hsa-mir-375 10.1007/s43032-022-00854-w hsa-mir-378a-3p 10.1016/j.rbmo.2018.05.007 hsa-mir-421 10.1515/hmbci-2022-0039 hsa-mir-429 10.3892/mmr.2021.12055 hsa-mir-449a, hsa-mir-873-5p 10.5603/GP.a2022.0078 hsa-mir-483-5p 10.1186/1477-7827-11-78 hsa-mir-502-5p 10.1080/07391102.2023.2291834 Differential expression analysis of genes related to both endometrial decidualization and mitosis-related hub genes in GSE6364 The transition of regularly cycling endometrium from a proliferative capacity to a differentiated 'decidual' phenotype requires widespread changes in gene expression in preparation for implantation. This process appears delayed in women with severe endometriosis. The 8 MRHGs were tested with GSE6364, which contained early secretory (ESE) and mid-secretory (MSE) phases of endometrium from two groups (Control and endometriosis). The data showed no significant differences in 8 MRHGs of the endometrium in MSE and ESE in the control group (Fig. 6 A-C). However, they were significantly down-regulated in endometriosis (Fig. 6 D-H). Differential expression analysis of genes related to both infertility and mitosis-related hub genes in GSE120103 Endometrial stromal cell decidualization was the key to embryo implantation. Impaired decidualization may lead to infertility. We further investigated the effect of endometriosis on fertility. We used the GSE120103 dataset containing ectopic endometrium of infertile endometriosis to detect the expression of screening target genes. The three datasets were normalized, and the distribution trends of box plots were straight lines (Fig. 7 A). PCA results showed good repeatability of the data (Fig. 7 B). Two MRHGs (CCNA2, CENPE) were downregulated in GSE120103, shown by Venn plots (Fig. 7 C). The volcano plots and heat maps of MRHGs were illustrated in Figs. 7 D and 7 E. The statistical plots for individual genes are shown in Fig. 7 F. We finally found that CCNA2 and CENPE were related to infertile endometriosis. Differential expression analysis of genes related to infertility and mitosis targeted drug prediction. By regulating mitosis, the DGIdb database predicted potential target drugs associated with critical genes that may treat infertility endometriosis. Finally, eight target drugs were expected, and the data were shown in Table 3 . One of the most noteworthy was cordycepin, which can inhibit the proliferation of human epithelial endometriosis cells, activate apoptosis, and promote the quality of aging of oocytes in vitro [ 18 , 19 ]. Table 3 Drug that may be effective against infertile endometriosis Drug Regulatory approval Indication Interaction score GSK-923295 Not Approved 58.9800539 Cordycepin Not Approved 8.42572199 Genistein Approved 0.32406623 Ethinyl estradiol Approved Contraceptive 0.99126141 Suramin Not Approved 0.5617148 Seliciclib Not Approved Antineoplastic agent 1.404287 TNF-alpha Not Approved 0.7021435 Tamoxifen Approved Hormonal, antineoplastic agent 0.27625318 Discussion Endometriosis is a chronic disease influenced by environmental and genetic factors, which has a high socioeconomic impact. The infertile endometriosis has apparent characteristics of reduced deindividuation and impaired uterine receptivity. Endometrial decidualization was the essential process to prepare the endometrium for pregnancy. Women with endometriosis are at increased risk of infertility by impacting the endometrial decidualization process [ 20 ]. Uterine stromal cell mitosis plays an essential role in this process and must undergo mitotic expansion before full decidualization [ 12 , 13 ]. This study takes advantage of the similarity between endometriosis and infertility in mitosis to find a target for infertility treatment. Based on the analysis of three data sets, GSE7305, GSE7307, and GSE51981, a PPI network was constructed in the GEO database, and DEGs screening were performed on the three data sets. We obtained 93 DGEs by taking the intersection of three endometriosis datasets. Then, we identified 11 potential mitosis-related downregulated hub genes, among which eight genes (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) showed good diagnostic properties of endometriosis and two genes (CCNA2, CENPE) were closely related to infertile endometriosis. The main enriched GO functions revealed that the cell cycle mitotic pathway may be the critical pathway in endometriosis. It has been reported that assessing mitosis rates provides additional diagnostic value for advanced endometriosis [ 21 ], which hints at the accuracy of the signaling pathway we predict. There is significant evidence that eight hub genes related to mitosis play a crucial role in endometriosis or fertility. It is reported that KIF4A is a member of the kinase protein superfamily and is involved in a series of cellular processes, such as chromosome aggregation and cytoplasmic division during mitosis. KIF4A may regulate the meiosis of mouse oocytes by affecting the precise separation of spindle tissue and chromosomes, and the loss of KIF4A may be related to aneuploidy of aging oocytes [ 22 ]. KIF4A can bind to BUB1 to regulate the expression of BUB1. BUB1B is an oocyte-expressed growth factor essential for follicular development. Endometriosis harms granular cells, and MnBP concentrations are associated with endometriosis. MnBP may affect the function of granulocyte cells by altering the expressions of BUB1B, CDC20, and cyclin B1 genes [ 23 – 25 ]. NEK2 is a protein that is crucial in regulating various cell cycle processes. Its overproduction can disrupt the natural development of male germ cells, leading to abnormalities [ 26 ]. FBXO5 has been identified as a genuine substrate of beta-Trcp1. It has been observed that beta-Trcp1 plays a vital role in regulating the proper sequence of meiotic and mitotic events. KIF11 is a type of kinesin composed of four identical subunits, and its protein expression is at its highest level during mitosis. If the recruitment of KIF11 on meiotic spindles is reduced, it could impair the proper functioning of these spindles [ 27 , 28 ]. CENPE is expressed during mitosis and plays critical roles in inaccurate chromosome alignment and as an emerging target for chemotherapy in clinical oncology [ 29 ]. The protein CCNA2 plays a crucial role in regulating the cell cycle progression, specifically in promoting cell proliferation. It is an indispensable component for the development of embryos [ 30 ]. NCAPG is a critical component of condensin I, a protein complex that helps compact DNA during cell division. Its role in chromatin condensation during mitosis is well established. Recent studies have shown that NCAPG is also closely linked to fertility traits in Japanese Black cattle [ 31 ]. Co-expressed and miRNAs were chosen to construct mRNA-miRNA interaction networks to identify the critical miRNAs for the prognosis of endometriosis. As Fig. 4 shows, BUB1B had 32 miRNAs, CCNA2 had 115 miRNAs, CENPE had 35 miRNAs, FBXO5 had 56 miRNAs, KIF11 had 61 miRNAs, KIF4A had 34 miRNAs, NCAPG had 61 miRNAs, NEK2 had 26 miRNAs. Endometriosis and fertility have been found to have links with multiple miRNAs. Numerous studies have reported on the association of miRNAs with these conditions. For example, Hsa-let-7i-5p, hsa-mir-125b-4p, and hsa-mir-29a-3p could be potential biomarkers and therapeutic targets for diagnosing and treating endometriosis [ 32 , 33 ]. Moreover, miR-124-3p significantly regulates cell proliferation and invasion of ectopic endometrium through multiple pathways [ 34 – 36 ]. What is more, Hsa-mir-22-3p is not only an essential gene in granulosa cells of patients with biochemical primary ovarian insufficiency, which is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in infertility, but also a sensitive and specific indicator in distinguishing endometriosis from non-endometriosis [ 37 , 38 ]. Although all eight screened MRHGs have been reported to mediate mitosis, it should be noted that there is a lack of evidence regarding their ability to regulate mitosis in endometriosis, especially in infertile endometriosis. Moreover, the sample size of infertile patients with endometriosis is limited, and more comprehensive investigations are necessary to analyze the underlying mechanisms in the future. Conclusion Our study has identified eight potential mitosis hub genes (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) that exhibit excellent diagnostic properties for endometriosis. We have also constructed a network of associated mRNA-miRNA pathways. In addition, we have found that two of these hub genes (MRHGs) are related to infertile endometriosis and have shown high drug-targeting relevance for cordycepin. These findings provide new insights into the unique relationship between mitosis and endometriosis. Although the limited sample size is a challenge, our study provides a solid foundation for further research in the field of cross-cutting endometriosis and infertility. By addressing this limitation through collaborative efforts, validation studies, and integrating various data types, we can improve our understanding of the pathogenesis in which endometriosis damages female fertility and pave the way for improved diagnosis and targeted treatment of this condition. Declarations Disclosure statement The authors reported no potential conflict of interest. Ethical statement The Declaration of Helsinki conducted the study (as revised in 2013). Data availability statement The datasets supporting this study's findings are available in the GEO database at http://www.ncbi.nlm.nih.gov/geo/. Funding This work was supported by the National Nature Science Foundation of China (No. 82101734 & 82171691) and the Jiangxi Province Commission of Science and Technology (No. 202312397). Author Contribution LYS contributed substantially to writing the draft and preparing the manuscript; YW&HW conceptualized and drew the hypothesis for the study; XCC&HJY&XX made tables and figures for the manuscript; SGX critically revised the work; JC&LLC contributed to study design and manuscript editing. All authors have read and approved the final manuscript. References Taylor HS, Kotlyar AM, Flores VA (2021) Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet 397(10276):839–852 Tanbo T, Fedorcsak P (2017) Endometriosis-associated infertility: aspects of pathophysiological mechanisms and treatment options. Acta Obstet Gynecol Scand 96(6):659–667 Bonavina G, Taylor HS (2022) Endometriosis-associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne) 13:1020827 Lessey BA, Kim JJ (2017) Endometrial receptivity in the eutopic endometrium of women with endometriosis is affected, and let me show you why. Fertil Steril 108(1):19–27 Shaulov T, Sierra S, Sylvestre C (2020) Recurrent implantation failure in IVF: A Canadian Fertility and Andrology Society Clinical Practice Guideline. Reprod Biomed Online 41(5):819–833 Zhong C, Gao L, Shu L et al (2021) Analysis of IVF/ICSI Outcomes in Endometriosis Patients With Recurrent Implantation Failure: Influence on Cumulative Live Birth Rate. Front Endocrinol (Lausanne) 12:640288 Published 2021 Jul 30 Coccia ME, Rizzello F, Mariani G, Bulletti C, Palagiano A, Scarselli G (2011) Impact of endometriosis on in vitro fertilization and embryo transfer cycles in young women: a stage-dependent interference. Acta Obstet Gynecol Scand 90(11):1232–1238 Al-Azemi M, Bernal AL, Steele J, Gramsbergen I, Barlow D, Kennedy S (2000) Ovarian response to repeated controlled stimulation in in-vitro fertilization cycles in patients with ovarian endometriosis. Hum Reprod 15(1):72–75 Boucret L, Bouet PE, Riou J et al (2020) Endometriosis Lowers the Cumulative Live Birth Rates in IVF by Decreasing the Number of Embryos but Not Their Quality. J Clin Med 9(8):2478 Kim TH, Yoo JY, Choi KC et al (2019) Loss of HDAC3 results in nonreceptive endometrium and female infertility. Sci Transl Med 11(474):eaaf7533 Ranisavljevic N, Raad J, Anahory T, Grynberg M, Sonigo C (2019) Embryo transfer strategy and therapeutic options in infertile patients with thin endometrium: a systematic review. J Assist Reprod Genet 36(11):2217–2231 Wang W, Li Q, Bagchi IC, Bagchi MK (2010) The CCAAT/enhancer binding protein beta is a critical regulator of steroid-induced mitotic expansion of uterine stromal cells during decidualization. Endocrinology 151(8):3929–3940 Muter J, Lucas ES, Chan YW et al (2015) The clock protein period 2 synchronizes mitotic expansion and decidual transformation of human endometrial stromal cells. FASEB J 29(4):1603–1614 Clough E, Barrett T (2016) The Gene Expression Omnibus Database. Methods Mol Biol 1418:93–110 Davis S, Meltzer PS (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23(14):1846–1847 Gu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32(18):2847–2849 Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431–432 Imesch P, Hornung R, Fink D, Fedier A (2011) Cordycepin (3'-deoxyadenosine), an inhibitor of mRNA polyadenylation, suppresses proliferation and activates apoptosis in human epithelial endometriotic cells in vitro. Gynecol Obstet Invest 72(1):43–49 Li C, Zhu L, Liu JX et al (2023) Cordycepin delays postovulatory aging of oocytes by inhibiting maternal mRNA degradation via DCP1A polyadenylation suppression. Cell Mol Life Sci 80(12):372 Giudice LC, Kao LC, Endometriosis (2004) Lancet 364(9447):1789–1799 Wetzk M, Grübling N, Forberger A et al (2023) Diagnostic Benefit of the Detection of Mitotic Figures in Endometriotic Lesions [published correction appears in Geburtshilfe Frauenheilkd. ;83(8):1043]. Geburtshilfe Frauenheilkd . 2022;82(1):85–92 Tang F, Pan MH, Lu Y, Wan X, Zhang Y, Sun SC (2018) Involvement of Kif4a in Spindle Formation and Chromosome Segregation in Mouse Oocytes. Aging Dis 9(4):623–633 Jin W, Ye L (2021) KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression. Mol Med Rep 24(1):516 Chou YC, Chen YC, Chen MJ, Chang CW, Lai GL, Tzeng CR (2020) Exposure to Mono-n-Butyl Phthalate in Women with Endometriosis and Its Association with the Biological Effects on Human Granulosa Cells. Int J Mol Sci 21(5):1794 Racki WJ, Richter JD (2006) CPEB controls oocyte growth and follicle development in the mouse. Development 133(22):4527–4537 Rhee K, Wolgemuth DJ (2002) Tcp10 promoter-directed expression of the Nek2 gene in mouse meiotic spermatocytes. Mol Cells 13(1):85–90 Guardavaccaro D, Kudo Y, Boulaire J et al (2003) Control of meiotic and mitotic progression by the F box protein beta-Trcp1 in vivo. Dev Cell 4(6):799–812 Dong J, Jin L, Bao S et al (2023) Ectopic expression of human TUBB8 leads to increased aneuploidy in mouse oocytes. Cell Discov 9(1):105 Published 2023 Oct 24 El-Arabey AA, Salama SA, Abd-Allah AR (2018) CENP-E as a target for cancer therapy: Where are we now? Life Sci 208:192–200 Chotiner JY, Wolgemuth DJ, Wang PJ (2019) Functions of cyclins and CDKs in mammalian gametogenesis†. Biol Reprod 101(3):591–601 Kawaguchi F, Tsuchimura M, Oyama K et al (2020) Effect of DNA markers on the fertility traits of Japanese Black cattle for improving beef quantity and quality. Arch Anim Breed 63(1):9–17 Gu CL, Zhang Z, Fan WS et al (2020) Identification of MicroRNAs as Potential Biomarkers in Ovarian Endometriosis. Reprod Sci 27(9):1715–1723 Vanhie A, O D, Peterse D et al (2019) Plasma miRNAs as biomarkers for endometriosis. Hum Reprod 34(9):1650–1660 Liu S, Qiu J, Tang X, Cui H, Zhang Q, Yang Q (2019) LncRNA-H19 regulates cell proliferation and invasion of ectopic endometrium by targeting ITGB3 via modulating miR-124-3p. Exp Cell Res 381(2):215–222 Liu Y, Xie C, Li T et al (2023) PCGEM1 promotes cell proliferation and migration in endometriosis by targeting miR-124-3p-mediated ANTXR2 expression. BMC Womens Health 23(1):104 Yuan D, Zhu D, Yin B et al (2022) Expression of lncRNA NEAT1 in endometriosis and its biological functions in ectopic endometrial cells as mediated via miR-124-3p. Genes Genomics 44(5):527–537 Liu B, Liu L, Sulaiman Z et al (2024) Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network and key genes in granulosa cells of patients with biochemical primary ovarian insufficiency. J Assist Reprod Genet 41(1):15–29 Nazri HM, Greaves E, Quenby S, Dragovic R, Tapmeier TT, Becker CM (2023) The role of small extracellular vesicle-miRNAs in endometriosis. Hum Reprod 38(12):2296–2311 Additional Declarations No competing interests reported. Supplementary Files 121TableS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5380458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377215186,"identity":"a331cde6-e100-4a4f-8f52-3c5916cd5a79","order_by":0,"name":"Xiaocong Chen","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaocong","middleName":"","lastName":"Chen","suffix":""},{"id":377215193,"identity":"f68c9a25-0e0f-4391-89ff-8de87e700cd0","order_by":1,"name":"Hongjuan Ye","email":"","orcid":"","institution":"The Shanghai Towako Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Ye","suffix":""},{"id":377215194,"identity":"be9381da-0ac8-4814-8c26-de8549eafb4c","order_by":2,"name":"Xin Xie","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Xie","suffix":""},{"id":377215195,"identity":"ebb30def-010f-412e-adb0-1e8e5560d903","order_by":3,"name":"Yang Wang","email":"","orcid":"","institution":"the First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":377215196,"identity":"6a9274d6-8871-45c3-b2fb-aabe42fb8c6c","order_by":4,"name":"Jie Cheng","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Cheng","suffix":""},{"id":377215197,"identity":"c3410dad-6bd6-4f71-92a6-b633a5a1780d","order_by":5,"name":"Hui Yang","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""},{"id":377215198,"identity":"7c2ec2b3-cb9d-459b-9687-f8ec5c985001","order_by":6,"name":"Linlin Chang","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Chang","suffix":""},{"id":377215199,"identity":"cd30e72b-3d96-47aa-8199-979f372f044d","order_by":7,"name":"Songguo Xue","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Songguo","middleName":"","lastName":"Xue","suffix":""},{"id":377215201,"identity":"aa3c51a1-a24b-4578-9331-03493de8d4d3","order_by":8,"name":"Liya Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACPjBZwCDHxt5+gDgtbGDSgMGYj+dMAmlaEudJOBgQqYW9+QHjD4PD6W0SDAkMPyq2EaGF55gBM4/B4dw26cYDjD1nbhOhRSLBgJkBpEXmQAIzYxsxWuSffwA7DKyXOC0SPAYMQIclkKCFJ6cA6Jd0wzZgIB8kyi/87Mc3MP6osJaXb28/+OBHBRFagID9BwNDM5h1gCj1UFBHiuJRMApGwSgYaQAAmugz3wMUSasAAAAASUVORK5CYII=","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Liya","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-11-03 04:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5380458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5380458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69253055,"identity":"2804bd40-14f6-4d1a-9a4f-5c51e40a55b9","added_by":"auto","created_at":"2024-11-18 11:58:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78702,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall protocol of this study. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; RIF, repeated implantation failure.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/554b2d62fbcd05c8fac355df.png"},{"id":69253058,"identity":"7d9cbdda-096c-4797-8533-bdcfcce2840c","added_by":"auto","created_at":"2024-11-18 11:58:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208424,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized expression matrices (A, C, E) and PCA diagrams (B, D, F) of the GSE7305, GSE7307, and GSE51981 datasets. PCA, principal component analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/df13984ef7148b137cbe9452.png"},{"id":69253057,"identity":"837d788e-b47d-4db2-83ef-4da44cd2a85b","added_by":"auto","created_at":"2024-11-18 11:58:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":344700,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed genes and mitosis-related hub genes of the GSE7305, GSE7307, and GSE51981 datasets. (A-C) The volcano plots of GSE7305, GSE7307, and GSE51981; (D) Differential genes in the three datasets; (E) STRING; (F) Hub genes; (G) Mitosis-related genes; (H) Mitosis-related hub genes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/d67ca09e36d15d53b0cbaf38.png"},{"id":69253286,"identity":"0e58b168-3879-4e20-a8e4-70f4e0e77db7","added_by":"auto","created_at":"2024-11-18 12:06:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":438425,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the expression and diagnostic ROC curves of mitosis-related hub genes. (A-D) Normalized expression matrices, PCA diagrams, volcano plots, and heat map of GSE25628; (E) Comparison of the expression of mitosis-related hub genes in Control and Endometriosis samples; (F) Signaling pathways where the mitosis-related hub genes are predominant in Control and Endometriosis samples; (G-N) Diagnostic ROC curves of mitosis-related hub genes in Control and Endometriosis samples; (O) The protein endometrium localization of mitosis-related hub genes. **, P\u0026lt;0.01. ROC is the receiver operating characteristic; TPR is the true positive rate; FPR is the false positive rate.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/a5970e5b28c380b0a52cf050.png"},{"id":69253056,"identity":"9d32bbfe-41e0-4b73-8ee2-0909bcaab70e","added_by":"auto","created_at":"2024-11-18 11:58:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":558239,"visible":true,"origin":"","legend":"\u003cp\u003eGO/KEGG and mRNA-miRNA regulatory network of mitosis-related hub genes. (A, B) GO categories and KEGG pathways; (C) mRNA-miRNA regulatory network. BP, biological process; MF, molecular function; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/4f50cea4fa782a48e3373a19.png"},{"id":69253284,"identity":"27fcd620-29be-4e8e-aa45-aa58232972c8","added_by":"auto","created_at":"2024-11-18 12:06:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":194084,"visible":true,"origin":"","legend":"\u003cp\u003e(A-F) Normalized expression matrices, principal component analysis, and volcano plots of 2 GSE6364 datasets (Control group ESE and MSE/ Endometriosis group ESE and MSE); (G) The heat map of GSE6364 Endometriosis group; (H) The downregulation percentage of mitosis-related hub genes in Control and Endometriosis samples from ESE to MSE phase. ESE, Early Secretory; MSE, Mid-secretory. ***, P\u0026lt;0.001, *, P\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/538e5b50e08fa7db12af6ab4.png"},{"id":69253285,"identity":"831e920b-1cf1-4593-b413-ac9d1b6cb713","added_by":"auto","created_at":"2024-11-18 12:06:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119973,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression analysis of genes related to infertility and mitosis-related hub genes in GSE120103. (A, B) Normalized expression matrices, principal component analysis; (C) Differential genes between mitosis-related hub genes and infertile endometriosis; (D, E) Volcano plots and heat map of mitosis-related hub genes in GSE120103; (F) Differences of mitosis-related hub genes in GSE120103; ***, P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/a1aa292d4eea06569d91c666.png"},{"id":73361911,"identity":"a90ec656-7a9f-4e5c-93e5-edc603706569","added_by":"auto","created_at":"2025-01-09 08:54:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2833605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/b6c3d1bb-fd51-4e9c-a3c0-6fea8be5be0b.pdf"},{"id":69253062,"identity":"dbf5f468-9fbe-418b-b757-6868a44abe4a","added_by":"auto","created_at":"2024-11-18 11:58:22","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":25947,"visible":true,"origin":"","legend":"","description":"","filename":"121TableS.docx","url":"https://assets-eu.researchsquare.com/files/rs-5380458/v1/cd1c3072e58fbfd71c763d81.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of key genes associated with infertile endometriosis based on bioinformatic analysis","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; This article uses bioinformatic methods to identify target genes for the treatment of endometriosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; This article adopts a new search method for target genes based on the common characteristics of infertility and endometriosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026bull; Scan suitable therapeutic agents for target genes from the drug database.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe endometriosis prevalence seems to be considerably higher in sub-fertile women, ranging from 20 to 50%. The causes of infertility in women with endometriosis mainly involve anatomical distortions, endocrine abnormalities, and immune disorders [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Implantation of the embryo depends on the endometrial inflammatory mechanism during pregnancy; endometriosis creates opportunities for chronic inflammation and disrupts endometrial receptivity, leading to infertility [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInfertility is one of the most common problems among couples worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The study found that implantation rate, clinical pregnancy rate, and ovarian response were lower in patients with endometriosis than in patients with tubal infertility [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Infertile patients with endometriosis impacted endometrial receptivity and endometrium decidualization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Human endometrium has a crucial role in the implantation process [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Endometrial deindividuation is regulated by the synergistic action of maternal steroid hormones, estrogen, and progesterone and is a necessary prerequisite for embryo implantation in early pregnancy. Uterine stromal cell mitosis plays a vital role in this process, and stromal cells must undergo mitotic expansion before full decidualization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite much evidence showing relationships between endometriosis and infertility, the mechanisms implicated in endometriosis-associated infertility are so far not fully understood. Therefore, screening differentially expressed genes between endometriosis and infertility may provide a new way to identify the pathogenesis of infertile endometriosis and give a new direction for treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe NCBI GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) offers a comprehensive and publicly accessible collection of functional genomic datasets and high-throughput microarrays based on next-generation sequencing. We downloaded gene expression data from this database for our study.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We acquired six datasets related to endometriosis and endometriosis-related infertility from GEO. One of these datasets, GSE120103, pertained to endometriosis-related infertility, while the other five, namely GSE7305, GSE7307, GSE51981, GSE25628, and GSE6364, were focused on endometriosis. The dataset named GSE7305 consisted of 20 samples, with 10 samples each of ectopic and normal endometrium. Another dataset, GSE7307, comprised eight normal endometrium samples and eight ectopic endometrium samples. Similarly, GSE51981 had eight normal endometrium samples and eight ectopic endometrium samples, while GSE25628 had six normal endometrium samples and six ectopic endometrium samples. In addition, GSE6364 had 11 normal endometrium samples and 10 ectopic endometrium samples. Finally, GSE120103 contained four normal endometrium samples from fertility and four ectopic endometrial samples from infertility. Following the transformation of identity documents, in cases where more than one probe matched a single gene, the gene expression value was determined by calculating the average expression value. Before analysis, raw data underwent log2-transformation and quantile-normalization. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed information on all six datasets, while the study design is presented as a flow chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" 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\u003eGene expression datasets used in this study\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperiment type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEndometriosis(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInfertile Endometriosis (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSample type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE7305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE7307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE6364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL6480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRNA array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEndometrium\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\n\u003ch3\u003eIdentification of DEGs\u003c/h3\u003e\n\u003cp\u003eThe microarray data from four datasets was downloaded and analyzed using various statistical techniques. Principal component analysis (PCA) was performed to verify the data's reproducibility. The differential expression analysis was carried out utilizing the 'limma' package of R software [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and genes with an adjusted P-value less than 0.05 and absolute fold change more significant than one were considered differentially expressed genes (DEGs). To visualize the results, various plots such as box line plots, heat maps, and volcano maps were generated using the 'ggplot2' and 'heatmap'(version 3.3.6) packages of R software (version 4.2.1) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePPI network construction and module analysis\u003c/h3\u003e\n\u003cp\u003eIn this study, we used the Search Tool for the Retrieval of Interacting Genes (STRING) version 12.0 online database to predict the protein-protein interaction (PPI) network of differentially expressed genes (DEGs). The PPI network provides valuable insights into the generation or development of diseases. We considered interactions with a combined score greater than 0.4 statistically significant and used Cytoscape version 3.9.1, an open-source bioinformatics software platform, to visualize the network [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Cytoscape software was utilized to import and analyze STRING data. The MCC algorithm of the Cytohubba plugin was employed to identify the top 20% scoring genes, which were then labeled as hub genes. Mitosis-related genes from the GeneCards database were used to match with the hub genes, resulting in the identification of overlapping genes associated with mitosis.\u003c/p\u003e\n\u003ch3\u003eConstruction of an mRNA-miRNA regulatory network\u003c/h3\u003e\n\u003cp\u003eTo predict the interactions between differentially expressed mRNA and miRNA in ectopic endometrium samples, we utilized the miRNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mRNA-miRNA regulatory network was then visualized in Cytoscape software, providing an overview of the interactions between mRNAs and miRNAs, which can be considered potential targets.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGO and KEGG pathway enrichment analyses of genes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe FRHGs were subjected to GO and KEGG pathway analysis using the ClusterProfiler package in R. The analysis considered three criteria: molecular function (MF), cellular component (CC), and biological process (BP). For statistical significance, an adjusted P value of 0.05 was used as per the Benjamini-Hochberg method, and the analysis was limited to Homo sapiens.\u003c/p\u003e\n\u003ch3\u003eGene set enrichment analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eTo investigate biological signaling pathways, we conducted a GSEA analysis. We identified the KEGG pathway showing significant enrichment based on the net enrichment score (NES), gene ratio, and P value. We considered |NES| greater than one and FDR q less than 0.25 as indicators of significant enrichment.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of mitosis hub genes\u003c/h2\u003e \u003cp\u003eIn this study, we used 'pROC' (version 1.18.0) and 'ggplot2' packages (version 3.3.6) of R software (version 4.2.1) to perform Receiver Operating Characteristic (ROC) curve analysis. The analysis helped us to determine the sensitivity and specificity of target genes. We quantified the results by calculating the area under the ROC curve (AUC). The genes with AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.6 were considered diagnostic.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePossible drugs for target genes\u003c/h3\u003e\n\u003cp\u003eThe DGIdb (Drug-Gene Interaction Database) is a web resource that helps organize genes of the druggable genome into known drug interactions and potentially druggable targets. We utilized module DEGs (Differentially Expressed Genes) of endometriosis and significantly evaluated DEGs in DGIdb to identify potentially druggable DEGs.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using R version 4.2.1 and GraphPad Prism 8 software. The data was presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and an unpaired Student's t-test was employed to compare groups. A P-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eScreening of mitosis-related hub genes in patients with endometriosis\u003c/h2\u003e \u003cp\u003eThe expression matrix of GSE7305, GSE7307, and GSE51981 were normalized. The box plots showed that the distribution trend of the box graph is a straight line, and no library size effects were noticeable after normalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C, E). PCA of the three datasets was performed separately, and the PCA results that showed good repeatability of the data were visually compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, D, F).\u003c/p\u003e \u003cp\u003eWe identified 823 upregulated and 657 downregulated DEGs in the GSE7305 dataset, 1686 upregulated and 1393 downregulated DEGs in the GSE7307 dataset, and 1937 upregulated and 1188 downregulated DEGs in the GSE51981 dataset. The volcano plots of DEGs in the above three datasets were generated in R, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. Venn plots showed that 93 genes were commonly differentially expressed among the three datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E), of which 32 were upregulated, and 61 were downregulated (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, we generated the PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eG) by determining the interactions among the DEGs obtained above. Use Cytoscape (version 3.8.1) to import STRING analysis data and identify the top 20% of genes with the highest scores as hub genes. Finally, 20 hub genes were determined, which were: CCNA2, BUB1B, NCAPG, TOP2A, CCNB1, KIF11, CENPE, KIF20A, NUSAP1, KIF15, NDC80, KIF4A, KIF14, SPAG5, NEK2, SMC4, DTL, FBXO5, TYMS and DEPDC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eIn addition, we analyzed the expression of 12 mitosis-related down-regulated genes in three datasets. Venn diagram analysis of the three data sets found that the genes BUB1B, KIF11, CCNB1, NDC80, NEK2, CCNA2, KIF4A, NCAPG, CENPE, KIF14, BORA and FBXO5 were down-regulated genes associated with mitosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Interestingly, some of these genes overlap with the hub above genes, namely BUB1B, KIF11, CCNB1, NDC80, NEK2, CCNA2, KIF4A, NCAPG, CENPE, KIF14, and FBXO5. They are both hub and differentially expressed genes in mitosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGSE25628 confirmed the expression and diagnostic value of the MRHGs\u003c/h2\u003e \u003cp\u003eWe used the GSE25628 dataset to detect the expression of selected target genes, and the results showed that the differential expression of eight MRHGs (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) between control and endometriosis patients was consistent with predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-E). Then, functional enrichment and identification of ectopic and normal endometrium by GSEA showed that most of the genes were located in the cell cycle mitotic pathway (NES = -2.443; P.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.001; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). ROC curves using ectopic and normal endometrial data showed that these eight genes are of great value in diagnosing endometriosis. Figure G-N showed the AUC and 95% CI of eight MRHGs. The representative images were obtained from the Human Protein Atlas (HPA) and referenced to annotate the expression of these molecules in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eO).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGO/KEGG enrichment analyses of MRHGs and construction of the gene network.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe performed GO and KEGG enrichment analyses on eight differentially expressed mitosis hub genes. The mitotic nuclear division and sister chromatid segregation were the most significant enrichment in GO categories. At the same time, the DEGs were mainly involved in the cell cycle, oocyte meiosis, and progesterone-mediated oocyte maturation in KEGG enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003eUsing the miRNet tool, we obtained 235 target miRNAs of 8 specifically expressed MRHGs and determined 420 mRNA-miRNA pairs. Then, according to the prediction results, we constructed a co-expressed network of mRNAs and miRNAs comprising 243 nodes and 420 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). BUB1B was regulated by 32 miRNAs, 115 miRNAs regulated CCNA2, 32 miRNAs held CENPE, 56 miRNAs controlled FBXO5, 61 miRNAs restrained KIF11, KIF4A was regulated by 34 miRNAs, 61 miRNAs regulated NCAPG, and NEK2 was hindered by 32 miRNAs (Table S2). We identified 28 miRNAs by searching the PubMed database for literature on endometriosis and miRNAs mentioned above (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\u003emiRNAs associated with endometriosis reported in the pubmed database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edoi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-let-7i-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s43032-020-00148-z\u003c/span\u003e\u003cspan address=\"10.1007/s43032-020-00148-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ehsa-mir-124-3p, hsa-mir-126-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.yexcr.2019.05.010\u003c/span\u003e\u003cspan address=\"10.1016/j.yexcr.2019.05.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12905-023-02250-1\u003c/span\u003e\u003cspan address=\"10.1186/s12905-023-02250-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13258-021-01184-y\u003c/span\u003e\u003cspan address=\"10.1007/s13258-021-01184-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-125b-4p, hsa-mir-29a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/humrep/dez116\u003c/span\u003e\u003cspan address=\"10.1093/humrep/dez116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-125b-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.fertnstert.2019.04.011\u003c/span\u003e\u003cspan address=\"10.1016/j.fertnstert.2019.04.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-15a-5p, hsa-mir-15b-5p, hsa-mir-16-5p, has-mir-195-5p, hsa-mir-320a, hsa-mir-320b, hsa-mir-92a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1080/09537104.2022.2042233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-182-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/2058738420976309\u003c/span\u003e\u003cspan address=\"10.1177/2058738420976309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-203a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/09513590.2022.2076830\u003c/span\u003e\u003cspan address=\"10.1080/09513590.2022.2076830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-214-3p, hsa-mir-22-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/humrep/dead216\u003c/span\u003e\u003cspan address=\"10.1093/humrep/dead216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-218-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12958-022-00928-z\u003c/span\u003e\u003cspan address=\"10.1186/s12958-022-00928-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00404-021-05963-6\u003c/span\u003e\u003cspan address=\"10.1007/s00404-021-05963-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-340-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00404-021-05963-6\u003c/span\u003e\u003cspan address=\"10.1007/s00404-021-05963-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s43032-022-00854-w\u003c/span\u003e\u003cspan address=\"10.1007/s43032-022-00854-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-378a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rbmo.2018.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.rbmo.2018.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/hmbci-2022-0039\u003c/span\u003e\u003cspan address=\"10.1515/hmbci-2022-0039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/mmr.2021.12055\u003c/span\u003e\u003cspan address=\"10.3892/mmr.2021.12055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-449a, hsa-mir-873-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5603/GP.a2022.0078\u003c/span\u003e\u003cspan address=\"10.5603/GP.a2022.0078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-483-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1477-7827-11-78\u003c/span\u003e\u003cspan address=\"10.1186/1477-7827-11-78\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsa-mir-502-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/07391102.2023.2291834\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2023.2291834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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 \u003cb\u003eDifferential expression analysis of genes related to both endometrial decidualization and mitosis-related hub genes in GSE6364\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe transition of regularly cycling endometrium from a proliferative capacity to a differentiated 'decidual' phenotype requires widespread changes in gene expression in preparation for implantation. This process appears delayed in women with severe endometriosis. The 8 MRHGs were tested with GSE6364, which contained early secretory (ESE) and mid-secretory (MSE) phases of endometrium from two groups (Control and endometriosis). The data showed no significant differences in 8 MRHGs of the endometrium in MSE and ESE in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). However, they were significantly down-regulated in endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-H).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression analysis of genes related to both infertility and mitosis-related hub genes in GSE120103\u003c/h2\u003e \u003cp\u003eEndometrial stromal cell decidualization was the key to embryo implantation. Impaired decidualization may lead to infertility. We further investigated the effect of endometriosis on fertility. We used the GSE120103 dataset containing ectopic endometrium of infertile endometriosis to detect the expression of screening target genes. The three datasets were normalized, and the distribution trends of box plots were straight lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). PCA results showed good repeatability of the data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Two MRHGs (CCNA2, CENPE) were downregulated in GSE120103, shown by Venn plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The volcano plots and heat maps of MRHGs were illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eE. The statistical plots for individual genes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003eF. We finally found that CCNA2 and CENPE were related to infertile endometriosis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential expression analysis of genes related to infertility and mitosis targeted drug prediction.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBy regulating mitosis, the DGIdb database predicted potential target drugs associated with critical genes that may treat infertility endometriosis. Finally, eight target drugs were expected, and the data were shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. One of the most noteworthy was cordycepin, which can inhibit the proliferation of human epithelial endometriosis cells, activate apoptosis, and promote the quality of aging of oocytes in vitro [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\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\u003eDrug that may be effective against infertile endometriosis\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\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory approval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteraction score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSK-923295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.9800539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCordycepin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.42572199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenistein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32406623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthinyl estradiol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContraceptive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99126141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuramin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5617148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeliciclib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAntineoplastic agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.404287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNF-alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Approved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7021435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamoxifen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHormonal, antineoplastic agent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27625318\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"},{"header":"Discussion","content":"\u003cp\u003eEndometriosis is a chronic disease influenced by environmental and genetic factors, which has a high socioeconomic impact. The infertile endometriosis has apparent characteristics of reduced deindividuation and impaired uterine receptivity. Endometrial decidualization was the essential process to prepare the endometrium for pregnancy. Women with endometriosis are at increased risk of infertility by impacting the endometrial decidualization process [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Uterine stromal cell mitosis plays an essential role in this process and must undergo mitotic expansion before full decidualization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This study takes advantage of the similarity between endometriosis and infertility in mitosis to find a target for infertility treatment.\u003c/p\u003e \u003cp\u003eBased on the analysis of three data sets, GSE7305, GSE7307, and GSE51981, a PPI network was constructed in the GEO database, and DEGs screening were performed on the three data sets. We obtained 93 DGEs by taking the intersection of three endometriosis datasets. Then, we identified 11 potential mitosis-related downregulated hub genes, among which eight genes (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) showed good diagnostic properties of endometriosis and two genes (CCNA2, CENPE) were closely related to infertile endometriosis. The main enriched GO functions revealed that the cell cycle mitotic pathway may be the critical pathway in endometriosis. It has been reported that assessing mitosis rates provides additional diagnostic value for advanced endometriosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which hints at the accuracy of the signaling pathway we predict.\u003c/p\u003e \u003cp\u003eThere is significant evidence that eight hub genes related to mitosis play a crucial role in endometriosis or fertility. It is reported that KIF4A is a member of the kinase protein superfamily and is involved in a series of cellular processes, such as chromosome aggregation and cytoplasmic division during mitosis. KIF4A may regulate the meiosis of mouse oocytes by affecting the precise separation of spindle tissue and chromosomes, and the loss of KIF4A may be related to aneuploidy of aging oocytes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. KIF4A can bind to BUB1 to regulate the expression of BUB1. BUB1B is an oocyte-expressed growth factor essential for follicular development. Endometriosis harms granular cells, and MnBP concentrations are associated with endometriosis. MnBP may affect the function of granulocyte cells by altering the expressions of BUB1B, CDC20, and cyclin B1 genes [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. NEK2 is a protein that is crucial in regulating various cell cycle processes. Its overproduction can disrupt the natural development of male germ cells, leading to abnormalities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. FBXO5 has been identified as a genuine substrate of beta-Trcp1. It has been observed that beta-Trcp1 plays a vital role in regulating the proper sequence of meiotic and mitotic events. KIF11 is a type of kinesin composed of four identical subunits, and its protein expression is at its highest level during mitosis. If the recruitment of KIF11 on meiotic spindles is reduced, it could impair the proper functioning of these spindles [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CENPE is expressed during mitosis and plays critical roles in inaccurate chromosome alignment and as an emerging target for chemotherapy in clinical oncology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The protein CCNA2 plays a crucial role in regulating the cell cycle progression, specifically in promoting cell proliferation. It is an indispensable component for the development of embryos [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. NCAPG is a critical component of condensin I, a protein complex that helps compact DNA during cell division. Its role in chromatin condensation during mitosis is well established. Recent studies have shown that NCAPG is also closely linked to fertility traits in Japanese Black cattle [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCo-expressed and miRNAs were chosen to construct mRNA-miRNA interaction networks to identify the critical miRNAs for the prognosis of endometriosis. As Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows, BUB1B had 32 miRNAs, CCNA2 had 115 miRNAs, CENPE had 35 miRNAs, FBXO5 had 56 miRNAs, KIF11 had 61 miRNAs, KIF4A had 34 miRNAs, NCAPG had 61 miRNAs, NEK2 had 26 miRNAs. Endometriosis and fertility have been found to have links with multiple miRNAs. Numerous studies have reported on the association of miRNAs with these conditions. For example, Hsa-let-7i-5p, hsa-mir-125b-4p, and hsa-mir-29a-3p could be potential biomarkers and therapeutic targets for diagnosing and treating endometriosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, miR-124-3p significantly regulates cell proliferation and invasion of ectopic endometrium through multiple pathways [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. What is more, Hsa-mir-22-3p is not only an essential gene in granulosa cells of patients with biochemical primary ovarian insufficiency, which is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in infertility, but also a sensitive and specific indicator in distinguishing endometriosis from non-endometriosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough all eight screened MRHGs have been reported to mediate mitosis, it should be noted that there is a lack of evidence regarding their ability to regulate mitosis in endometriosis, especially in infertile endometriosis. Moreover, the sample size of infertile patients with endometriosis is limited, and more comprehensive investigations are necessary to analyze the underlying mechanisms in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study has identified eight potential mitosis hub genes (KIF4A, BUB1B, NEK2, FBXO5, KIF11, CENPE, CCNA2, and NCAPG) that exhibit excellent diagnostic properties for endometriosis. We have also constructed a network of associated mRNA-miRNA pathways. In addition, we have found that two of these hub genes (MRHGs) are related to infertile endometriosis and have shown high drug-targeting relevance for cordycepin. These findings provide new insights into the unique relationship between mitosis and endometriosis.\u003c/p\u003e\n\u003cp\u003eAlthough the limited sample size is a challenge, our study provides a solid foundation for further research in the field of cross-cutting endometriosis and infertility. By addressing this limitation through collaborative efforts, validation studies, and integrating various data types, we can improve our understanding of the pathogenesis in which endometriosis damages female fertility and pave the way for improved diagnosis and targeted treatment of this condition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors reported no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Declaration of Helsinki conducted the study (as revised in 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting this study's findings are available in the GEO database at http://www.ncbi.nlm.nih.gov/geo/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Nature Science Foundation of China (No. 82101734 \u0026amp; 82171691) and the Jiangxi Province Commission of Science and Technology (No. 202312397).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLYS contributed substantially to writing the draft and preparing the manuscript; YW\u0026amp;HW conceptualized and drew the hypothesis for the study; XCC\u0026amp;HJY\u0026amp;XX made tables and figures for the manuscript; SGX critically revised the work; JC\u0026amp;LLC contributed to study design and manuscript editing. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTaylor HS, Kotlyar AM, Flores VA (2021) Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet 397(10276):839\u0026ndash;852\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanbo T, Fedorcsak P (2017) Endometriosis-associated infertility: aspects of pathophysiological mechanisms and treatment options. Acta Obstet Gynecol Scand 96(6):659\u0026ndash;667\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonavina G, Taylor HS (2022) Endometriosis-associated infertility: From pathophysiology to tailored treatment. Front Endocrinol (Lausanne) 13:1020827\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLessey BA, Kim JJ (2017) Endometrial receptivity in the eutopic endometrium of women with endometriosis is affected, and let me show you why. Fertil Steril 108(1):19\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaulov T, Sierra S, Sylvestre C (2020) Recurrent implantation failure in IVF: A Canadian Fertility and Andrology Society Clinical Practice Guideline. Reprod Biomed Online 41(5):819\u0026ndash;833\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong C, Gao L, Shu L et al (2021) Analysis of IVF/ICSI Outcomes in Endometriosis Patients With Recurrent Implantation Failure: Influence on Cumulative Live Birth Rate. Front Endocrinol (Lausanne) 12:640288 Published 2021 Jul 30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoccia ME, Rizzello F, Mariani G, Bulletti C, Palagiano A, Scarselli G (2011) Impact of endometriosis on in vitro fertilization and embryo transfer cycles in young women: a stage-dependent interference. Acta Obstet Gynecol Scand 90(11):1232\u0026ndash;1238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Azemi M, Bernal AL, Steele J, Gramsbergen I, Barlow D, Kennedy S (2000) Ovarian response to repeated controlled stimulation in in-vitro fertilization cycles in patients with ovarian endometriosis. Hum Reprod 15(1):72\u0026ndash;75\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoucret L, Bouet PE, Riou J et al (2020) Endometriosis Lowers the Cumulative Live Birth Rates in IVF by Decreasing the Number of Embryos but Not Their Quality. J Clin Med 9(8):2478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim TH, Yoo JY, Choi KC et al (2019) Loss of HDAC3 results in nonreceptive endometrium and female infertility. Sci Transl Med 11(474):eaaf7533\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanisavljevic N, Raad J, Anahory T, Grynberg M, Sonigo C (2019) Embryo transfer strategy and therapeutic options in infertile patients with thin endometrium: a systematic review. J Assist Reprod Genet 36(11):2217\u0026ndash;2231\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Li Q, Bagchi IC, Bagchi MK (2010) The CCAAT/enhancer binding protein beta is a critical regulator of steroid-induced mitotic expansion of uterine stromal cells during decidualization. Endocrinology 151(8):3929\u0026ndash;3940\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuter J, Lucas ES, Chan YW et al (2015) The clock protein period 2 synchronizes mitotic expansion and decidual transformation of human endometrial stromal cells. FASEB J 29(4):1603\u0026ndash;1614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClough E, Barrett T (2016) The Gene Expression Omnibus Database. Methods Mol Biol 1418:93\u0026ndash;110\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis S, Meltzer PS (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23(14):1846\u0026ndash;1847\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu Z, Eils R, Schlesner M (2016) Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32(18):2847\u0026ndash;2849\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431\u0026ndash;432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImesch P, Hornung R, Fink D, Fedier A (2011) Cordycepin (3'-deoxyadenosine), an inhibitor of mRNA polyadenylation, suppresses proliferation and activates apoptosis in human epithelial endometriotic cells in vitro. Gynecol Obstet Invest 72(1):43\u0026ndash;49\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Zhu L, Liu JX et al (2023) Cordycepin delays postovulatory aging of oocytes by inhibiting maternal mRNA degradation via DCP1A polyadenylation suppression. Cell Mol Life Sci 80(12):372\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiudice LC, Kao LC, Endometriosis (2004) Lancet 364(9447):1789\u0026ndash;1799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWetzk M, Gr\u0026uuml;bling N, Forberger A et al (2023) Diagnostic Benefit of the Detection of Mitotic Figures in Endometriotic Lesions [published correction appears in Geburtshilfe Frauenheilkd. ;83(8):1043]. \u003cem\u003eGeburtshilfe Frauenheilkd\u003c/em\u003e. 2022;82(1):85\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang F, Pan MH, Lu Y, Wan X, Zhang Y, Sun SC (2018) Involvement of Kif4a in Spindle Formation and Chromosome Segregation in Mouse Oocytes. Aging Dis 9(4):623\u0026ndash;633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin W, Ye L (2021) KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression. Mol Med Rep 24(1):516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChou YC, Chen YC, Chen MJ, Chang CW, Lai GL, Tzeng CR (2020) Exposure to Mono-n-Butyl Phthalate in Women with Endometriosis and Its Association with the Biological Effects on Human Granulosa Cells. Int J Mol Sci 21(5):1794\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRacki WJ, Richter JD (2006) CPEB controls oocyte growth and follicle development in the mouse. Development 133(22):4527\u0026ndash;4537\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhee K, Wolgemuth DJ (2002) Tcp10 promoter-directed expression of the Nek2 gene in mouse meiotic spermatocytes. Mol Cells 13(1):85\u0026ndash;90\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuardavaccaro D, Kudo Y, Boulaire J et al (2003) Control of meiotic and mitotic progression by the F box protein beta-Trcp1 in vivo. Dev Cell 4(6):799\u0026ndash;812\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong J, Jin L, Bao S et al (2023) Ectopic expression of human TUBB8 leads to increased aneuploidy in mouse oocytes. Cell Discov 9(1):105 Published 2023 Oct 24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Arabey AA, Salama SA, Abd-Allah AR (2018) CENP-E as a target for cancer therapy: Where are we now? Life Sci 208:192\u0026ndash;200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChotiner JY, Wolgemuth DJ, Wang PJ (2019) Functions of cyclins and CDKs in mammalian gametogenesis\u0026dagger;. Biol Reprod 101(3):591\u0026ndash;601\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawaguchi F, Tsuchimura M, Oyama K et al (2020) Effect of DNA markers on the fertility traits of Japanese Black cattle for improving beef quantity and quality. Arch Anim Breed 63(1):9\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu CL, Zhang Z, Fan WS et al (2020) Identification of MicroRNAs as Potential Biomarkers in Ovarian Endometriosis. Reprod Sci 27(9):1715\u0026ndash;1723\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanhie A, O D, Peterse D et al (2019) Plasma miRNAs as biomarkers for endometriosis. Hum Reprod 34(9):1650\u0026ndash;1660\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Qiu J, Tang X, Cui H, Zhang Q, Yang Q (2019) LncRNA-H19 regulates cell proliferation and invasion of ectopic endometrium by targeting ITGB3 via modulating miR-124-3p. Exp Cell Res 381(2):215\u0026ndash;222\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Xie C, Li T et al (2023) PCGEM1 promotes cell proliferation and migration in endometriosis by targeting miR-124-3p-mediated ANTXR2 expression. BMC Womens Health 23(1):104\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan D, Zhu D, Yin B et al (2022) Expression of lncRNA NEAT1 in endometriosis and its biological functions in ectopic endometrial cells as mediated via miR-124-3p. Genes Genomics 44(5):527\u0026ndash;537\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu B, Liu L, Sulaiman Z et al (2024) Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network and key genes in granulosa cells of patients with biochemical primary ovarian insufficiency. J Assist Reprod Genet 41(1):15\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazri HM, Greaves E, Quenby S, Dragovic R, Tapmeier TT, Becker CM (2023) The role of small extracellular vesicle-miRNAs in endometriosis. Hum Reprod 38(12):2296\u0026ndash;2311\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometriosis, infertility, differentially expressed genes, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-5380458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5380458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Endometriosis is a common disease among women of childbearing age. However, the molecular mechanism behind it is still unknown. Therefore, new biomarkers and therapeutic targets are needed to improve the diagnosis and treatment of infertile women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Microarray datasets GSE7305, GSE7307, and GSE51981 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between control and endometriosis. The STRING database and Cytoscape software constructed protein-protein interaction (PPI) and hub gene networks. At the same time, the three data sets were screened for co-differentially expressed genes related to mitosis. Subsequently, we identified mitosis-related hub genes (MRHGs) associated with both mitosis-related genes and hub genes. Next, enrichment analysis for target genes was performed by Gene Ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the mRNA-miRNA network was constructed. Finally, GSE25628 and GSE6364 were used to verify the expression of MRHGs individually, while GSE120103 was employed to ascertain the influence of mitosis-related genes on female fertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 93 DEGs were identified in the endometriosis datasets. Then, we placed 11 potential mitosis-related downregulated hub genes, among which eight showed good diagnostic properties of endometriosis, and two showed good diagnostic properties of infertile endometriosis. The main enriched GO functions revealed that the cell cycle mitotic pathway may be the critical pathway in endometriosis. Meanwhile, mRNA-miRNA interaction networks were constructed by choosing co-expressed mRNAs and miRNAs. Furthermore, cordycepin showed high drug-targeting relevance in infertile endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e We identified eight mitosis-related hub genes as potential biomarkers for diagnosing and treating endometriosis. CENPE and CCNA2 might be associated with infertile endometriosis by affecting the endometrial secretory phase transition. In addition, cordycepin may be a potential clinical treatment for people with infertility-related endometriosis.\u003c/p\u003e","manuscriptTitle":"Identification of key genes associated with infertile endometriosis based on bioinformatic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-18 11:58:17","doi":"10.21203/rs.3.rs-5380458/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":"b959df25-e901-4946-b9cf-cbaad4d19088","owner":[],"postedDate":"November 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-20T09:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-18 11:58:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5380458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5380458","identity":"rs-5380458","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}