{"paper_id":"bd18bb5c-cc66-4fb4-8510-0e8f0cbbb8a0","body_text":"Hakimi et al. \nMiddle East Fertility Society Journal           (2025) 30:32  \nhttps://doi.org/10.1186/s43043-025-00247-4\nRESEARCH Open Access\n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which \npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line \nto the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory \nregulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this \nlicence, visit http://creativecommons.org/licenses/by/4.0/.\nMiddle East Fertility\nSociety Journal\nBioinformatics analysis for identifying \nhub genes in endometriosis and recurrent \nimplantation failure: molecular pathways \nto enhanced IVF success\nParvin Hakimi1†, Mahshid Alborzi2†, Nahideh Afshar Zakariya1, Khadijeh Pouya3, Maryam Rezazadeh4* and \nSoudeh Ghafouri‑Fard5* \nAbstract \nBackground Recurrent embryo implantation failure (RIF) poses a considerable obstacle in the management \nof in vitro fertilization (IVF), as IVF failure has been linked to the presence of endometriosis, the growth of endometrial‑\nlike tissue outside the uterus. Therefore, this study aimed to reveal the molecular mechanisms connecting endome‑\ntriosis and RIF, offering valuable knowledge on potential therapeutic targets and biomarkers.\nMethods A comprehensive investigation was conducted on gene expression data from the GEO database, focus‑\ning on three datasets related to endometriosis and RIF, which revealed distinct gene expression patterns and facili‑\ntated functional enrichment analysis to identify significant biological processes and molecular pathways associated \nwith these differentially expressed genes. Protein–protein interaction networks were also established to identify \ncritical genes.\nResults A total of 43 differentially expressed genes (DEGs) were identified, shared between endometriosis and RIF, \nwith enrichment analysis highlighting pathways related to interleukin‑6 signaling, FOXO‑mediated transcription, \nsmooth muscle contraction, and semaphorin interactions. Gene ontology studies revealed the significance of sig‑\nnal transduction and apoptosis regulation. ESR1, SOCS3, MYH11, CYP11A1, and CLU were identified as hub genes \nwith potential as therapeutic targets and diagnostic indicators.\nConclusion This study advances our understanding of the molecular framework underlying endometriosis and RIF. \nThis presents potential possibilities for tailored treatment approaches and enhanced therapeutic results for individuals \nexperiencing repeated or severe reproductive difficulties.\nKeywords Recurrent embryo implantation failure, In vitro fertilization, Endometriosis, Bioinformatics, Functional \nenrichments, Hub gene\n†Parvin Hakimi and Mahshid Alborzi contributed equally to this work.\n*Correspondence:\nMaryam Rezazadeh\nRezazadeh.mary@gmail.com\nSoudeh Ghafouri‑Fard\ns.ghafourifard@sbmu.ac.ir\nFull list of author information is available at the end of the article\n\nPage 2 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \nIntroduction\nRecurrent embryo implantation failure (RIF) is a per -\nplexing syndrome that poses significant obstacles in the \nrealm of in  vitro fertilization (IVF), causing frustration \namong both patients and professionals [1, 2]. However, \nit is widely acknowledged that RIF can be defined as the \ninability to achieve clinical conception following three \nor more excellent-quality embryo transfers or the trans -\nfer of ≥ 10 embryos over multiple periods, with the spe -\ncific number of transfers determined by each individual \nreproductive medical center [3]. There are varying defini-\ntions of RIF among IVF centers. It is well acknowledged \nthat the inability to achieve pregnancy after two or more \ncycles of embryo transfer for people is considered RIF \n[4, 5]. These failures have the potential to impose sig -\nnificant psychological and financial burdens on infertile \ncouples [6]. Currently, there is a growing focus on strate -\ngies to enhance pregnancy outcomes in individuals with \nRIF, including the development of personalized endome -\ntrial receptivity assays (e.g., the Endometrial Receptivity \nArray) to guide optimal embryo transfer timing [7]. His -\ntorically, the primary factor attributed to RIF has been \nthe perceived quality of the embryo. Impaired uterine \nreceptivity has been identified as a significant factor con -\ntributing to treatment failure in cases where high-quality \nembryos are transplanted [8]. In general, several factors \nrelated to structural abnormalities of the uterus, such as \ncongenital disabilities and acquired conditions [9], endo -\nmetrial thickness [10, 11], chronic endometritis [12], \nendometrial perfusion [13], and uterine peristalsis [14] \nhave the potential to influence endometrial receptivity \nand subsequently affect embryo implantation.\nFurthermore, the presence of ectopic endometrial tis -\nsue, known as endometriosis, has been identified as a \nprobable factor in IVF in some instances [15, 16]. Endo-\nmetriosis represents a significant hurdle for women in \ntheir reproductive years, incorporating chronic pain and \ndiminished fertility. The presence of an estrogen-depend-\nent stroma and endometrial glands, typically located in \nthe pelvic region but not exclusively, distinguishes this \nsyndrome [17]. Because surgical visualization remains \nthe diagnostic gold standard, prevalence estimates vary \nwidely [18], underscoring the need for non-invasive \nbiomarkers such as circulating microRNAs or cytokine \npanels. Endometriosis is progressively being accepted as \na widespread medical problem associated with infertil -\nity and a primary element contributing to the failure of \nIVF procedures. However, the extent to which endome -\ntriosis adversely affects the outcomes of in vitro fertiliza -\ntion remains a point of contention [15]. An earlier study \nhas proven that the existence of endometriosis influences \nthe reaction to ovarian stimulation [16]. There is a wide -\nspread opinion that the presence of endometriotic lesions \nin the pelvic area creates an unfavorable microenviron -\nment that interferes with the steps of oocyte fertilization \nand the early development of the embryo within the fal -\nlopian tubes in vivo. With regard to IVF initiatives, ovar -\nian endometriosis may affect the ovarian reserve and \nsensitivity to ovarian stimulation. These outcomes con -\ntribute to lower incidences of fertilization and pregnancy \nin patients with endometriosis receiving IVF compared \nto other patient cohorts [16].\nThe field of bioinformatics has yielded significant find -\nings regarding the correlation between endometrio -\nsis and IVF failure. A study employed transcriptomic \nanalysis to investigate the underlying biological mecha -\nnisms associated with endometrial receptivity (ER) in \nvarious physiological contexts, specifically focusing on \nnatural cycles [19]. The goal of this study was to dis -\ncover the unique ER profile associated with regulated \novarian stimulation cycles, which might assist in detect -\ning genetic abnormalities in patients with RIF undergo -\ning IVF. Through the reanalysis of microarray data, this \nstudy revealed a significant downregulation of cell adhe -\nsion function, indicating potential disruptions in embryo \nimplantation [19]. These bioinformatics-driven inves -\ntigations highlight the significance of employing com -\nputational approaches to unravel the complex interplay \nbetween endometriosis and IVF failure. By elucidating \nthe molecular mechanisms involved, these studies con -\ntribute to a deeper understanding of the factors con -\ntributing to IVF outcomes and may facilitate the design \nof targeted interventions and personalized treatment \nmodalities for endometriosis.\nIn this study, we integrate multiple microarray datasets \nto uncover shared differentially expressed genes (DEGs) \nand pathways between endometriosis and RIF, with the \ngoal of identifying candidate biomarkers and druggable \ntargets for clinical application. By mapping these shared \nmolecular signatures—ranging from inflammatory \ncytokine networks to oxidative‐stress regulators—we \naim to lay the groundwork for future clinical trials testing \ninterventions to enhance endometrial receptivity. Ulti -\nmately, these insights have the potential to inform preci -\nsion medicine strategies and improve IVF success rates \nfor patients burdened by RIF and endometriosis.\nMaterial and methods\nMicroarray data source\nThe gene expression datasets used in this investiga -\ntion were obtained from the GEO database, accessible \nat https:// www. ncbi. nlm. nih. gov/ geo/. A total of 2055 \nrecords pertaining to endometriosis and 159 datasets \npertaining to RIF in Homo sapiens were acquired from \nthe designated database. Datasets were selected based \non the following criteria: (1) inclusion of both disease \n\nPage 3 of 12\nHakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n \nand control samples, (2) availability of raw/processed \ndata, (3) sample size ≥ 5 per group, (4) human endo -\nmetrial tissue focus, and (5) exclusion of low-quality or \nnon-relevant studies (e.g., non-endometrial samples, \nanimal/cell-line studies). After conducting a thorough \nreview, three specific gene expression profiles (GSE7305 \n[20], GSE11691 [21], and GSE26787 [22]) were selected. \nTwo distinct platforms were used in this study. GSE7305 \nand GSE26787 used the Affymetrix GPL570 platform, \nwhereas GSE11691 used the Affymetrix GPL96 platform. \nAll data were publicly available via the Internet, and the \ncurrent research did not include any experiments on \neither human subjects or animals performed by any of \nthe authors.\nThe identification of shared DEGs\nThe analysis was performed using the GEOexplorer [23] \nweb platform (https:// geoex  plorer. rosal ind. kcl. ac. uk) \nto ensure interactive and reproducible microarray data \nassessment. Raw CEL files from GSE11691 (18 samples: \nnine normal endometrium vs. nine endometrium from \nindividuals with endometriosis), GSE7305 (20 samples: \nten endometriosis vs. ten normal endometrium), and \nGSE26787 (10 samples: five RIF vs. five viable endome -\ntrium) were retrieved.\nAll datasets underwent identical preprocessing—\nbackground correction, quantile normalization, and \nlog₂-transformation—using the affy R package. Normali -\nzation success was confirmed by inspecting density plots \n(Fig. 1). Due to the use of different Affymetrix platforms \n(GPL96 for GSE11691 vs. GPL570 for GSE7305), the two \nendometriosis datasets were analyzed independently to \navoid cross-platform artifacts.\nDifferential expression analysis was performed with \nmoderated t-tests in limma, applying thresholds of \n|log₂ fold-change|≥ 1.5 and Benjamini–Hochberg \nadjusted p-value < 0.01 to define significant DEGs. The \n|log₂FC|≥ 1.5 cutoff was selected based on precedent \nin endometriosis transcriptome studies to prioritize the \nmost robust expression changes. All DEGs without valid \ngene-symbol annotations were excluded.\nFinally, shared DEGs were identified by intersecting \nthe independent DEG lists from GSE7305 and GSE11691 \n(endometriosis) with those from GSE26787 (RIF). The \noverlap of genes was visualized in a Venn diagram (Fig. 2) \nand used for downstream functional analyses.\nGene ontology (GO) and Reactome pathway enrichment \nanalyses of DEGs\nWe implemented the EnrichR library for functional \nannotation and pathway enrichment analysis to explore \nthe potential biological mechanisms associated with \nDEGs. Our investigation involved the utilization of GO \nand Reactome pathway analysis techniques [24]. GO, a \nvaluable bioinformatics tool, allows the annotation of \ngenes and examination of the biological processes associ -\nated with these genes [25, 26]. Reactome, an extensively \ncurated online library and knowledge resource, provides \ncomprehensive information on biological pathways and \ntheir corresponding interactions [27]. A statistical signifi-\ncance level of p < 0.05 was considered appropriate.\nProtein–protein interaction (PPI) network of DEGs and hub \ngenes\nThe Search Tool for the Retrieval of Interacting Genes \nDatabase (STRING) is commonly used to assess PPI data \n[28]. Accessible at https:// string- db. org/, this web-based \nresource proves valuable in the examination of the func -\ntional associations between proteins, offering potential \ninsights into the etiology and progression of diseases. In \nthis study, the STRING database was used to probe the \nidentified DEGs from the two datasets to uncover poten -\ntial interconnections between these genes. Interactions \nwith a cumulative score exceeding 0.4 were deemed sta -\ntistically significant and included in the construction of \nthe PPI network. To fulfill this objective, Cytoscape, a \nwidely accessible bioinformatics software system devel -\noped for the visualization of biological interaction net -\nworks, was used [29]. In addition, we identified hub genes \nthat may play a significant role in the development of \nendometriosis and RIF by evaluating the Maximal Clique \nCentrality (MCC) of each protein node in Cytoscape \nusing the CytoHubba plugin [30]. In conclusion, the iden-\ntification of shared hub genes revealed the top five genes \nthat exhibited significant MCC values in the context of \nRIF and endometriosis.\nResults\nIdentification of DEGs in endometriosis and RIF\nWe performed differential expression analysis separately \nfor each dataset using the GEOexplorer web tool. Raw \nCEL files were first background–corrected and quantile-\nnormalized, and probe-level data were collapsed to gene-\nlevel using the highest-variance probe per gene. We then \napplied the limma package’s linear modeling pipeline \nwith empirical Bayes moderation. To control for mul -\ntiple tests, we used the Benjamini–Hochberg method \nto adjust the p-values. The inclusion criteria for DEGs \nwere |log₂ fold‐change|≥ 1.5 and adjusted p-value < 0.01. \nBefore analysis, dataset normalization was verified by \ninspecting the density plots of the log₂‐intensity distribu -\ntions (Fig. 1). Genes lacking annotations in the final gene \nlist were excluded. Under these criteria, GSE7305 yielded \n773 DEGs, GSE11691 yielded 37 DEGs, and GSE26787 \nyielded 506 DEGs, respectively. Intersection analy -\nsis identified 43 genes differentially expressed in both \n\nPage 4 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \nendometriosis (combined GSE7305 and GSE11691) and \nRIF (GSE26787) (Fig. 2). Table 1 lists these shared DEGs, \nincluding gene symbols, log₂ fold-change, and adjusted \np-values in each dataset.\nFunctional enrichment analysis\nReactome pathway analysis revealed that “Interleu -\nkin-6 Family Signaling, ” “FOXO-mediated Transcrip -\ntion of Oxidative Stress, Metabolic, and Neuronal \nGenes, ” “Smooth Muscle Contraction, ” “Semaphorin \nInteractions, ” and “Muscle Contraction” were the top \nfive enriched pathways associated with the DEGs. The \ntop five enriched BPs were “Negative Regulation of \nSequestering of Calcium Ion, ” “Release of Sequestered \nCalcium Ion into Cytosol, ” “Regulation of Apoptotic \nProcess, ” “Response to Organic Cyclic Compound, ” and \n“Calcium Ion Transmembrane Import into Cytosol. ” A \ndetailed depiction of the functional enrichment net -\nwork is shown in Fig.  3, and more information is pro -\nvided in Table 2 .\nFig. 1 Density plot of gene expression microarray datasets. This density plot illustrates the distribution of gene expression values across three \ndistinct datasets: GSE11691, GSE26787, and GSE7305. The x‑axis represents the density of gene expression values, while the y‑axis represents \nthe intensity. This plot serves as an initial examination of data normalization, providing insights into the distribution and intensity of gene \nexpression values within the analyzed datasets\n\nPage 5 of 12\nHakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n \nIdentification of hub genes and network analysis\nThe PPI network associated with the identified DEGs that \nare often seen in both endometriosis and RIF was ana -\nlyzed using the Cytoscape STRING plugin. The top five \nhub genes were identified based on the MCC using the \nCytoscape cytohubba plugin. The genes that were cat -\negorized included ESR1, SOCS3, MYH11, CYP11A1, and \nCLU, as shown in Fig. 4. Table 3 provides a detailed over-\nview of the hub genes shared between endometriosis and \nRIF.\nDiscussion\nThe relationship between endometriosis and IVF out -\ncomes has been the subject of ongoing research and \ndebate. Recent research has provided insights into the \npossible impact of endometriomas, a common manifes -\ntation of endometriosis, on the success rates of IVF. In \nthis context, Hamdan et  al. highlighted the restricted \nexploration of the unfavorable consequences linked \nwith endometrioma during IVF/ICSI procedures [31]. \nThis study underlined the significance of consider -\ning the risks of surgical intervention and its potential \nimpact on ovarian reserve compared to the complica -\ntions that arise from the persistence of endometriomas \nduring IVF/ICSI [31]. Moreover, Coccia et  al. investi -\ngated the function of peritoneal fluid in women with \nmoderate endometriosis and its impact on the efficacy \nof reproductive interventions. The study’s findings indi -\ncated that the absence of peritoneal fluid from infer -\ntile women with moderate endometriosis in the media \nresulted in an increase in the fertilization capacity of \noocytes and enhanced embryo development potential \n[32]. As research further explores the intricate connec -\ntion between endometriosis and IVF failure, it becomes \nimperative to uncover strategies that can enhance the \nprobability of successful conception in women affected \nby this condition. Such strategies could include the \ntargeted modulation of peritoneal cytokine profiles or \nFig. 2 Venn diagram of shared DEGs in endometriosis and RIF. The Venn diagram visually represents the overlap of DEGs identified in three \ndistinct datasets: GSE7305, GSE11691, and GSE26787, each associated with either endometriosis or RIF. Notably, 43 DEGs were found to be shared \nbetween the endometriosis datasets and the RIF dataset. This intersection highlights specific genes with potential relevance to both endometriosis \nand RIF\nTable 1 Shared genes in endometriosis and RIF\nGene symbols of DEGs shared in endometriosis and RIF\nGALNT15, DAPK1, MYH11, LMOD1, RNASE4, PAPSS2, CLU, DPP6, CYP11A1, PTGER3, ARHGEF28, IGFBP5, CPXM2, PODN, TWIST2, PIGR, TDGF1P3///\nTDGF1, ITPR1, CCDC80, SYNPO, EHBP1L1, FBXO32, LAMA4, RPP25, MAN1C1, SOCS3, MIR3671///MIR101‑1, FRZB, MCOLN3, PLCH1, PLXNA4, NSG1, \nSORBS2, LIFR, MFAP3L, IGKC, HOXB‑AS3, BIRC5, TMEM100, ESR1, ANGPT1, SPDEF, TRH\n\nPage 6 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \nrefined surgical approaches to balance ovarian reserve \npreservation with lesion removal.\nTo identify the shared molecular components and \npathways implicated in both endometriosis and RIF, a \ncomprehensive bioinformatics analysis was performed \nto evaluate the association between genes showing dif -\nferential expression and the respective conditions. Our \nstudy used bioinformatics technologies to identify shared \nDEGs linked to endometriosis and RIF. The primary goal \nof this study was to discover previously unknown func -\ntional genes and pathways that play a role in the devel -\nopment of both disorders. Following the identification of \nthe top five Reactome pathway enrichments for shared \nDEGs between endometriosis and RIF, our comprehen -\nsive bioinformatics analysis aimed to uncover the poten -\ntial molecular connections and underlying pathways \nimplicated in both conditions. The Reactome pathway \nenrichment results highlighted significant biological \nFig. 3 Functional enrichment network. This comprehensive network diagram illustrates functional enrichment analysis results, encompassing \nGO biological processes and Reactome pathway enrichment. In the network, nodes represent DEGs, which are key molecular players associated \nwith endometriosis and RIF. These DEGs are interconnected with various functional enrichments, including biological processes and pathways. \nThe connections between DEGs and functional enrichments signify the significant involvement of specific genes in particular biological processes \nand pathways. This network serves as a valuable resource for understanding the functional relationships between DEGs and functional enrichments, \nfacilitating the identification of potential therapeutic targets and diagnostic markers for endometriosis and RIF\nTable 2 Functional enrichments of the EC metastasis DEGs\nTerm Library p-value q-value z-score Combined score\nRegulation of signal transduction (GO: 0009966) GO_Biological_Process 0.00007175 0.03379 13.13 125.3\nNegative regulation of cellular amide metabolic process (GO: 0034249) GO_Biological_Process 0.0008951 0.1193 17.52 123\nPositive regulation of supramolecular fiber organization (GO: 1,902,905) GO_Biological_Process 0.001054 0.1193 16.52 113.2\nRegulation of apoptotic process (GO: 0042981) GO_Biological_Process 0.001088 0.1193 4.947 33.76\nInterleukin‑6 Family Signaling R‑HSA‑6783589 Reactome 0.001266 0.1874 43.15 287.9\nCardiac muscle cell development (GO: 0055013) GO_Biological_Process 0.001266 0.1193 43.15 287.9\nFOXO‑mediated transcription of oxidative stress, metabolic and neu‑\nronal genes R‑HSA‑9615017\nReactome 0.001849 0.1874 35.15 221.2\nSmooth muscle contraction R‑HSA‑445355 Reactome 0.004034 0.1874 23.13 127.5\nSemaphorin interactions R‑HSA‑373755 Reactome 0.008746 0.1874 15.28 72.41\nFOXO‑mediated transcription R‑HSA‑9614085 Reactome 0.009011 0.1874 15.04 70.81\n\nPage 7 of 12\nHakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n \nprocesses and interactions that may contribute to the \npathogenesis of endometriosis and RIF. First, the “Inter -\nleukin-6 Family Signaling” pathway is a critical regulatory \npathway involved in the immune response and inflam -\nmation [33]. This pathway includes various interleukin-6 \nfamily cytokines that play pivotal roles in modulating \nimmune and inflammatory reactions in both RIF and \nendometriosis. The available data indicate that the func -\ntional instability of the endometrial immune system is \na significant pathophysiological mechanism associated \nwith RIF. During implantation, many types of interleu -\nkins (ILs) are released by epithelial and stromal endome -\ntrial cells. These interleukins include IL-6, IL-10, IL-12, \nIL-15, IL-18, and leukemia inhibitory factor [34]. These \nILs establish a network that coordinates the growth and \ndevelopment of uterine natural killer cells. Addition -\nally, they regulate the activity of regulatory T and B cells, \nwhich suppress the production of antibodies that may \nharm the fetus. Furthermore, ILs facilitate trophoblast \ninvasion and decidua formation [34]. Elevated serum IL-6 \nconcentrations are associated with infertility in patients \nwith endometriosis [35]. The presence of disorders linked \nto IL-6 and IL-6R has been shown to be associated with \nthe development of endometriosis [36]. Endometriosis \ngrowth is facilitated by an elevation in soluble interleu -\nkin-6 receptor levels within the peritoneal fluid, which \nsubsequently enhances the bioactivity of IL-6 [36]. These \nfindings suggest that targeting IL‐6 signalingusing mono -\nclonal antibodies [37] and immunotherapy [38] could be \nexplored in clinical trials to improve implantation success \nin women with endometriosis and RIF.\nFurthermore, the pathway known as “FOXO-mediated \ntranscription of oxidative stress, metabolic and neuronal \ngenes” plays a crucial role in directing the transcrip -\ntional regulation of genes associated with oxidative stress \nand metabolic activities. The regulation of this pathway \nis governed by the FOXO family of transcription fac -\ntors, which are involved in the modulation of cellular \nresponses to oxidative stress and metabolic dysfunction \n[39]. Although the exact mechanisms linking endome -\ntriosis and RIF to abnormalities in the FOXO-mediated \ntranscription pathway are not yet fully understood, based \non the shared DEGs enrichment results, aberrant activa -\ntion or suppression of this pathway may influence cellular \nresponses to oxidative stress and metabolic dysfunction, \nwhich have been implicated in both endometriosis and \nFig. 4 The construction of PPI networks. A The PPI network was established by utilizing data from the STRING database. B The PPI network \nwas constructed by applying a combined score > 0.4 threshold and subsequently identifying the hub genes. Rectangular shapes are utilized \nto symbolize genes, while lines are employed to depict the interactions between proteins encoded by those genes. The hub genes are \ndistinguished in the network with a gradient of colors ranging from red to yellow, which includes ESR1, SOCS3, MYH11, CYP11A1, and CLU\nTable 3 Expression details of shared hub genes in endometriosis \nand RIF\nHub genes RIF Endometriosis\nadj. p. val logFC adj. p. val logFC\nESR1 0.0001 2.5336 3.06E‑10 − 2.9213\nSOCS3 0.0005 − 1.5912 0.0007 1.9471\nMYH11 0.0021 2.9752 7.55E‑08 2.2143\nCYP11A1 0.0016 − 2.0553 0.0025 1.8904\nCLU 0.0003 − 2.4571 2.96E‑08 2.3214\n\nPage 8 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \nRIF pathogenesis. Emerging compounds that modulate \nFOXO function [40], some of which are under investiga -\ntion in oncology settings, may offer a starting point for \nfuture studies aimed at protecting endometrial cells from \noxidative injury and potentially improving implantation \noutcomes.\nThe “Smooth Muscle Contraction” pathway emerged \nas a significant player in both conditions, indicating the \npotential involvement of smooth muscle cells in patho -\nphysiology. Research revealed that Smooth muscle cells \nare often present in endometriotic lesions and have \nbeen identified in several types of endometriotic lesions, \nincluding peritoneal, ovarian, deep-infiltrating, and \nadenomyotic lesions [41]. The “Semaphorin Interactions” \npathway offers insights into cellular communication and \nguidance cues during development and tissue homeosta -\nsis. Semaphorins are a group of proteins that are secreted \nand connected to the cell membrane. These proteins play \ncrucial roles in regulating several developmental pro -\ncesses, such as building neural circuits, bone develop -\nment, and angiogenesis [42]. Furthermore, it has been \nshown that they have a significant influence on several \nstages of immunological responses [43]. The available \nevidence indicates that the presence of a chronic inflam -\nmatory state in endometriosis may potentially lead to an \nelevation in semaphorin levels, potentially impacting the \ninnervation in peritoneal endometriosis [44]. Meanwhile, \nthe involvement of semaphorin Interactions in RIF and \ninfertility requires further research. In general, Reactome \npathway enrichment analysis provides valuable insights \ninto the common molecular constituents and pathways \nunderlying endometriosis and RIF. Through the identifi -\ncation of these potential functional pathways, our under -\nstanding of the intricate mechanisms that contribute to \nthe emergence and advancement of these conditions is \nimproved.\nEnriched GO pathways offer valuable insights into the \nshared molecular mechanisms underlying endometrio -\nsis and RIF, with the top five pathways having meaning -\nful implications for both conditions. However, several \nGO enrichments were novel in endometriosis and RIF, \nwhich can be a basis for further studies, including the \nregulation of the cellular amide metabolic process, reg -\nulation of supramolecular fiber organization, and car -\ndiac muscle cell development. Among these factors, \naberrant signal transduction pathways have emerged as \ncritical contributors to endometriosis development. For \ninstance, a study conducted by Kim et al. found compel -\nling evidence linking aberrant signal transduction to this \ncomplex gynecological condition. Researchers observed a \nsignificant increase in the levels of phospho-signal trans -\nducer and activator of transcription-3 (pSTAT3) and its \ndownstream signaling protein, HIF1A, in the eutopic \nendometrium of women diagnosed with endometrio -\nsis compared to those who did not have the condition \n[45]. The aberrant induction of pSTAT3 and HIF1A is \nhypothesized to have a significant impact on endome -\ntriosis development [45]. These findings suggest that the \nregulation of signal transduction pathways may be the \nkey to understanding and potentially addressing RIF in \nindividuals with endometriosis. In contrast, the control \nof the apoptotic process is implicated in the pathogenesis \nof endometriosis. Apoptosis, also known as programmed \ncell death, is an intrinsic biological mechanism that \ncontributes to the preservation of tissue homeostasis \nby eliminating impaired or surplus cells [46, 47]. In the \ncontext of endometriosis, the equilibrium between cel -\nlular proliferation and programmed cell death, known as \napoptosis, is disturbed, resulting in the development of \nendometrial tissue in locations outside the uterus [48]. \nResearch findings indicate that there is a modification \nin the expression of genes associated with apoptosis in \nendometriotic tissue, indicating that the disruption of \napoptosis may play a role in the initiation and progres -\nsion of the disease [49]. Current therapeutic interven -\ntions include a range of hormone-based therapies that \ntarget the reduction of circulating estrogen levels to those \nobserved in postmenopausal individuals. GnRH agonist \nincubation enhances the apoptotic rate in eutopic and \nectopic endometrial cells from endometriosis-affected \nwomen [50, 51]. The increase in the apoptotic rate may \nbe attributed to changes in the expression of genes asso -\nciated with apoptosis subsequent to the administra -\ntion of a GnRH agonist. Treatment with GnRH agonists \ninfluences the expression of various genes, including \nthose encoding apoptotic factors [52]. The combination \nof GnRH agonist downregulation and hormone replace -\nment therapy (HRT) has been shown to enhance repro -\nductive outcomes in frozen-thawed embryo transfer \ncycles among older patients (aged 36–43 years) diag -\nnosed with idiopathic RIF [53]. Prior research on retro -\nspective self-control has shown that the use of a protocol \ncombining gonadotropin-releasing hormone agonist and \nhormone replacement therapy (GnRH agonist–HRT) has \nthe potential to enhance the incidence of successful preg-\nnancies in frozen embryo transfer cycles among patients \nwho have encountered RIF subsequent to IVF treatment \n[54]. These enriched GO pathways notably shed light on \nthe shared molecular mechanisms of endometriosis and \nRIF. The regulation of signal transduction pathways and \nthe role of apoptosis are significant factors. Further inves-\ntigation of these pathways could provide critical insights \nfor developing targeted therapies for both conditions.\nIn the context of unraveling the intricate molecular \nlandscape underlying endometriosis and RIF, the con -\ncept of hub genes emerged as a pivotal aspect of our \n\nPage 9 of 12\nHakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n \nanalysis. Hub genes are more inclined to function as \nmaster signaling and transcription regulators because of \ntheir propensity to participate in several connections and \nmaintain the overall integrity of the network [55]. Conse-\nquently, hubs have the potential to serve as valuable tar -\ngets for therapeutic interventions and as biomarkers. Our \nstudy identified the top five hub genes with a high degree \nof connectivity: ESR1, SOCS3, MYH11, CYP11A1, and \nCLU.\nEndometriosis is a gynecological disease characterized \nby inflammation that relies on estrogen [56]. The identi -\nfication of estrogen receptor 1 gene (ESR1) as an onco -\ngene for endometrial cancer is a recent development [57]. \nMultiple signals in the ESR1 area related to endometrio -\nsis and other reproductive characteristics and diseases \nhave been identified using genome-wide association \nstudies (GWAS) [58]. Previous investigations have dis -\nproven the indications within the ESR1 gene region that \nare linked to the possibility of developing endometrio -\nsis. This discovery emphasizes the potential importance \nof ESR1 in reproductive health [58]. This study aimed to \nexamine the hormonal and genetic control of genes in \nthe ESR1 area of the human endometrium. The results \nrevealed noteworthy fluctuations in hormone levels and \nreceptor expression throughout the menstrual cycle. In \naddition, a significant correlation between ESR1 and PGR \nexpression was observed, suggesting the possibility of co-\nregulation [58]. Genetic variations in the ESR1 gene, spe -\ncifically the single nucleotide polymorphism (SNP) ESR1 \nrs9340799, have been associated with infertility related \nto endometriosis and failure of IVF procedures. Women \nwho possess the GG genotype have been found to have \na four-fold higher likelihood of developing endometrio -\nsis and a three-fold higher likelihood of experiencing IVF \nfailure, even after accounting for age-related factors [59]. \nGiven its central role in estrogen signaling, ESR1 expres -\nsion levels could be quantified in endometrial biopsies via \nimmunohistochemistry or qPCR as part of a “receptivity \npanel” to stratify patients before IVF.\nMoreover, the protein known as suppressor of cytokine \nsignaling 3 (SOCS3) plays a crucial role in the regulation \nof cytokine signaling [60]. Previous studies have shown \nthe involvement of this factor in the process of human \nendometrial stromal cell differentiation into decidual \ncells, a crucial step for successful embryo implantation \n[61]. The transduction of interleukin 11 (IL-11) signal -\ning occurs via the signal transducers and activators of \ntranscription (STAT) proteins. In response to cytokine-\ninduced phosphorylation of STAT, the stimulation of \nsuppressor of cytokine signaling (SOCS) proteins takes \nplace, which serves as a negative feedback mechanism \nto impede the activation of cytokine receptors [61]. It is \nworth mentioning that the presence of immunoreactive \nIL-11 in  vivo is not detectable in the endometrium of \nsome women experiencing primary infertility and endo -\nmetriosis during the fertilization window. Additionally, \nthe synthesis of IL-11 is diminished in the endometrium \nof women with recurrent miscarriages when compared \nto women who are fertile without any complications [62, \n63]. The study demonstrated that the overexpression of \nSOCS3 in human endometrial stromal cells resulted in \na decrease in IL-11-induced pSTAT3 and hindered the \nprocess of decidualization. These findings suggest that \nSOCS3 plays a crucial role in regulating cellular differen -\ntiation [61]. Additional research is required to elucidate \nthe precise role of SOCS3 in the pathogenesis of endo -\nmetriosis and RIF, and its possibility to serve as a non-\ninvasive biomarker for implantation competence.\nThe MYH11 gene, which is classified as a protein-cod -\ning gene of the myosin heavy chain family, is responsible \nfor encoding smooth muscle myosin [64]. In a research \ninvestigation, MYH11 was found to be a distinctive \nmarker associated with a specific subgroup of corneal \nendothelial cells [65]. Recent research using bioinfor -\nmatics techniques has identified MYH11 as one of the \nhub genes related to endometriosis [66]. CYP11A1, also \nreferred to as cytochrome P450 side chain cleavage, is an \nenzyme located in the mitochondria. Its primary func -\ntion is the catalysis of the cleavage of cholesterol’s side \nchain, resulting in the production of pregnenolone. Preg -\nnenolone serves as the shared precursor for many ster -\noid hormones [67, 68]. The upregulation of CYP11A1 in \nectopic endometrial tissue may lead to elevated amounts \nof estrogen in the local environment, hence exerting an \nimpact on the physiological activities of embryonic stem \ncells [69]. Within the realm of endometriosis, a schol -\narly investigation has successfully identified CYP11A1 \nas among the genes exhibiting hypomethylation and \ndisplaying heightened expression levels in individu -\nals affected by ovarian endometriosis [70]. CLU, often \ncalled clusterin, is a glycoprotein that exhibits a virtu -\nally universal tissue distribution and plays a role in sev -\neral biological processes, including neurodegeneration \nin Alzheimer’s disease and the genesis and development \nof cancer [71, 72]. In the context of endometriosis, CLU \nlevels were found to have a modest increase in cases with \nthe condition, but interestingly, these levels significantly \ndecreased in patients with endometriosis who were \nreceiving contraception [73].\nFurthermore, our analysis revealed a notable distinc -\ntion in the expression patterns of the identified hub genes \nbetween endometriosis and RIF. Specifically, MYH11 \nwas the only gene among the five that demonstrated \nconsistent upregulation in both conditions, as evidenced \nby positive logFC values in both RIF (logFC = 2.9752) \nand endometriosis (logFC = 2.2143). This consistency \n\nPage 10 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \nsuggests that MYH11 may be involved in shared molecu -\nlar mechanisms underlying both endometriosis and RIF, \npotentially making it a key player in the pathophysiol -\nogy of these reproductive disorders. In contrast, ESR1, \nSOCS3, CYP11A1, and CLU exhibited opposite direc -\ntions of expression change between endometriosis and \nRIF, with upregulation in one condition and downregu -\nlation in the other (e.g., ESR1: logFC = 2.5336 in RIF vs. \n−2.9213 in endometriosis). This divergence may reflect \nthe unique molecular landscapes of these conditions and \nhighlights the complexity of their pathogenesis. The dis -\ntinct behavior of MYH11 warrants further investigation \nto elucidate its specific role in endometriosis and RIF, \nwhich could provide insights into novel therapeutic strat-\negies and diagnostic markers.\nCollectively, our findings provide a roadmap for trans -\nlating molecular insights into clinical practice: the shared \nDEGs and enriched pathways may form the basis of novel \ndiagnostic panels (e.g., IL-6 or FOXO pathway signa -\ntures) and therapeutic approaches (e.g., cytokine inhibi -\ntors, FOXO modulators). Prospective clinical studies \nshould focus on validating these biomarkers in patient \ncohorts and exploring targeted interventions to improve \nIVF success rates in women with endometriosis and RIF.\nLimitations\nOne of the hurdles of this investigation is its dependence \non bioinformatics analysis, which provides a valuable \ncomprehension of molecular pathways but lacks direct \nbiological verification. Further empirical inquiries, clinical \ntrials, and prospective research are imperative to establish \nthe clinical reliability and utility of the identified markers.\nConclusion\nIn conclusion, this work utilizes bioinformatics analysis to \nelucidate shared biological pathways and central genes linked \nto endometriosis and RIF. While these results give insight \ninto possible therapy targets and biomarkers for both ill -\nnesses, it is critical to recognize the study’s limitations. Vali-\ndating the clinical utility of the identified genes and pathways \nand exploring their precise functions in reproductive health \nrequires additional research. The discovery of hub genes, \nincluding ESR1, SOCS3, MYH11, CYP11A1, and CLU, estab-\nlishes a fundamental basis for further inquiries into the com-\nplex molecular terrain of endometriosis and RIF. It is worth \nmentioning that several hub genes, including ESR1 and \nSOCS3, have shown correlations with infertility due to endo-\nmetriosis and the failure of IVF procedures. Nevertheless, the \nextent of their precise participation in RIF has yet to be com-\nprehensively clarified. This study emphasizes the need for \nongoing research in the realm of reproductive health in order \nto enhance our comprehension of these diseases and estab-\nlish more efficacious approaches for diagnosis and treatment.\nAbbreviations\nRIF  Recurrent embryo implantation failure\nIVF  In vitro fertilization\nDEGs  Differentially expressed genes\nGO  Gene ontology\nBP  Biological processes\nMF  Molecular functions\nCC  Cellular components\nPPI  Protein‑protein interaction\nSTRING  Search tool for the retrieval of interacting genes\nMCC  Maximal clique centrality\nGEO  Gene expression omnibus\nHRT  Hormone replacement therapy\nIL  Interleukin\npSTAT3  Phospho‑signal transducer and activator of transcription‑3\nFOXO  Forkhead Box O\nHIF1A  Hypoxia‑inducible factor 1 alpha\nGnRH  Gonadotropin‑releasing hormone\nESR1  Estrogen receptor 1\nSOCS3  Suppressor of cytokine signaling 3\nMYH11  Myosin heavy chain 11\nCYP11A1  Cytochrome P450 side chain cleavage\nCLU  Clusterin\nAuthors’ contributions\nThe contributions of the authors are outlined as follows: P .H. and N.A.Z. \nwere responsible for the conceptualization of the study and conducted the \ninvestigations. K.P . was tasked with software development, formal analysis, \nand validation. M.A. contributed to the creation of the original draft, while M.R. \nand S.G.F played a pivotal role in providing a critical review and conducting \ncomprehensive editing of the manuscript. Additionally, all authors actively \nparticipated in supervision and meticulously reviewed the final manuscript.\nFunding\nThis research did not receive any specific grant from funding agencies in the \npublic, commercial, or not‑for‑profit sectors.\nData availability\nNo datasets were generated or analysed during the current study.\nDeclarations\nEthics approval and consent to participate\nNot applicable.\nConsent for publication\nNot applicable.\nCompeting interests\nThe authors declare no competing interests.\nAuthor details\n1 Women’s Reproductive Health Research Center, Tabriz University of Medi‑\ncal Science, Tabriz, Iran. 2 Department of Gynecology & Obstetrics, Jahrom \nUniversity of Medical Sciences, Jahrom, Iran. 3 Department of Gynecology, \nFaculty of Medicine, Tabriz Medical Sciences, Islamic Azad University, Tabriz, \nIran. 4 Department of Medical Genetics, Faculty of Medicine, Tabriz University \nof Medical Sciences, Tabriz, Iran. 5 Department of Medical Genetics, Faculty \nof Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. \nReceived: 26 March 2025   Accepted: 12 August 2025\nReferences\n 1. Somigliana E, Vigano P , Busnelli A, Paffoni A, Vegetti W, Vercellini P (2018) \nRepeated implantation failure at the crossroad between statistics, clinics \nand over‑diagnosis. Reprod Biomed Online 36(1):32–38\n 2. Cakiroglu Y, Tiras B (2020) Determining diagnostic criteria and cause of \nrecurrent implantation failure. Curr Opin Obstet Gynecol 32(3):198–204\n\nPage 11 of 12\nHakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n \n 3. Thornhill AR, deDie‑Smulders CE, Geraedts JP , Harper JC, Harton GL, \nLavery SA et al (2005) ESHRE PGD Consortium ‘Best practice guidelines \nfor clinical preimplantation genetic diagnosis (PGD) and preimplantation \ngenetic screening (PGS).’ Hum Reprod 20(1):35–48\n 4. Tan BK, Vandekerckhove P , Kennedy R, Keay SD (2005) Investigation and \ncurrent management of recurrent IVF treatment failure in the UK. BJOG \n112(6):773–780\n 5. Polanski LT, Baumgarten MN, Quenby S, Brosens J, Campbell BK, Raine‑\nFenning NJ (2014) What exactly do we mean by ‘recurrent implanta‑\ntion failure’? A systematic review and opinion. Reprod Biomed Online \n28(4):409–423\n 6. Stanhiser J, Steiner AZ (2018) Psychosocial aspects of fertility and assisted \nreproductive technology. Obstet Gynecol Clin North Am 45(3):563–574\n 7. Rubin SC, Abdulkadir M, Lewis J, Harutyunyan A, Hirani R, Grimes CL \n(2023) Review of endometrial receptivity array: a personalized approach \nto embryo transfer and its clinical applications. J Pers Med. https:// doi. \norg/ 10. 3390/ jpm13 050749\n 8. Miller PB, Parnell BA, Bushnell G, Tallman N, Forstein DA, Higdon HL 3rd \net al (2012) Endometrial receptivity defects during IVF cycles with and \nwithout letrozole. Hum Reprod 27(3):881–888\n 9. Taylor E, Gomel V (2008) The uterus and fertility. Fertil Steril 89(1):1–16\n 10. Richter KS, Bugge KR, Bromer JG, Levy MJ (2007) Relationship between \nendometrial thickness and embryo implantation, based on 1,294 cycles \nof in vitro fertilization with transfer of two blastocyst‑stage embryos. Fertil \nSteril 87(1):53–59\n 11. Alfer J, Happel L, Dittrich R, Beckmann MW, Hartmann A, Gaumann A et al \n(2017) Insufficient angiogenesis: cause of abnormally thin endometrium \nin subfertile patients? Geburtshilfe Frauenheilkd 77(7):756–764\n 12. Johnston‑MacAnanny EB, Hartnett J, Engmann LL, Nulsen JC, Sanders \nMM, Benadiva CA (2010) Chronic endometritis is a frequent finding in \nwomen with recurrent implantation failure after in vitro fertilization. Fertil \nSteril 93(2):437–441\n 13. Silva Martins R, Helio Oliani A, Vaz Oliani D, Martinez de Oliveira J. Sub‑\nendometrial resistence and pulsatility index assessment of endometrial \nreceptivity in assisted reproductive technology cycles. Reprod Biol \nEndocrinol. 2019;17(1):62.\n 14. Zhang Y, Qian J, Zaltzhendler O, Bshara M, Jaffa AJ, Grisaru D et al (2019) \nAnalysis of in vivo uterine peristalsis in the non‑pregnant female mouse. \nInterface Focus 9(4):20180082\n 15. Radzinsky VY, Orazov MR, Ivanov II, Khamoshina MB, Kostin IN, Kavteladze \nEV et al (2019) Implantation failures in women with infertility associated \nendometriosis. Gynecol Endocrinol 35(sup1):27–30\n 16. Khalifa E, Mohammad H, Abdullah A, Abdel‑Rasheed M, Khairy M, Hosni \nM (2021) Role of suppression of endometriosis with progestins before \nIVF‑ET: a non‑inferiority randomized controlled trial. BMC Pregnancy \nChildb 21(1):264\n 17. Hickey M, Ballard K, Farquhar C (2014) Endometriosis. BMJ 348:g1752\n 18. Ávalos Marfil A, Barranco Castillo E, Martos García R, Mendoza Ladrón de \nGuevara N, Mazheika M (2021) Epidemiology of endometriosis in Spain \nand its autonomous communities: a large, nationwide study. Int J Environ \nRes Public Health 18(15):7861\n 19. Pathare ADS, Hinduja I (2022) Endometrial expression of cell adhesion \ngenes in recurrent implantation failure patients in ongoing IVF cycle. \nReprod Sci 29(2):513–523\n 20. Hever A, Roth RB, Hevezi P , Marin ME, Acosta JA, Acosta H et al \n(2007) Human endometriosis is associated with plasma cells and \noverexpression of B lymphocyte stimulator. Proc Natl Acad Sci U S A \n104(30):12451–12456\n 21. Hull ML, Escareno CR, Godsland JM, Doig JR, Johnson CM, Phillips SC et al \n(2008) Endometrial‑peritoneal interactions during endometriotic lesion \nestablishment. Am J Pathol 173(3):700–715\n 22. Lédée N, Munaut C, Aubert J, Sérazin V, Rahmati M, Chaouat G et al \n(2011) Specific and extensive endometrial deregulation is present before \nconception in IVF/ICSI repeated implantation failures (IF) or recurrent \nmiscarriages. J Pathol 225(4):554–564\n 23. Hunt GP , Grassi L, Henkin R, Smeraldi F, Spargo TP , Kabiljo R et al (2022) \nGEOexplorer: a webserver for gene expression analysis and visualisation. \nNucleic Acids Res 50(W1):W367–W374\n 24. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z et al \n(2016) Enrichr: a comprehensive gene set enrichment analysis web server \n2016 update. Nucleic Acids Res 44(W1):W90–W97\n 25. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, The Gene \nOntology Consortium et al (2000) Gene ontology: tool for the unification \nof biology. Nat Genet 25(1):25–29\n 26. Gene Ontology Consortium (2006) The Gene Ontology (GO) project in \n2006. Nucleic Acids Res 34(Database issue):D322–6\n 27. Jassal B, Matthews L, Viteri G, Gong C, Lorente P , Fabregat A et al \n(2020) The reactome pathway knowledgebase. Nucleic Acids Res \n48(D1):D498‑d503\n 28. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R et al \n(2023) The STRING database in 2023: protein‑protein association net‑\nworks and functional enrichment analyses for any sequenced genome of \ninterest. Nucleic Acids Res 51(D1):D638–D646\n 29. Shannon P , Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) \nCytoscape: a software environment for integrated models of biomolecu‑\nlar interaction networks. Genome Res 13(11):2498–2504\n 30. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: \nidentifying hub objects and sub‑networks from complex interactome. \nBMC Syst Biol 8 Suppl 4(Suppl 4):S11\n 31. Hamdan M, Dunselman G, Li TC, Cheong Y (2015) The impact of endo‑\nmetrioma on IVF/ICSI outcomes: a systematic review and meta‑analysis. \nHum Reprod Update 21(6):809–825\n 32. Coccia ME, Nardone L, Rizzello F (2022) Endometriosis and infertility: a \nlong‑life approach to preserve reproductive integrity. Int J Environ Res \nPublic Health 19(10):6162\n 33. Hirano T (2021) IL‑6 in inflammation, autoimmunity and cancer. Int \nImmunol 33(3):127–148\n 34. Pantos K, Grigoriadis S, Maziotis E, Pistola K, Xystra P , Pantou A et al (2022) \nThe role of interleukins in recurrent implantation failure: a comprehensive \nreview of the literature. Int J Mol Sci 23(4):2198\n 35. Incognito GG, Di Guardo F, Gulino FA, Genovese F, Benvenuto D, Lello C \net al (2023) Interleukin‑6 as a useful predictor of endometriosis‑associ‑\nated infertility: a systematic review. Int J Fertil Steril 17(4):226–230\n 36. Li S, Fu X, Wu T, Yang L, Hu C, Wu R (2017) Role of interleukin‑6 and its \nreceptor in endometriosis. Med Sci Monit 23:3801–3807\n 37. El‑Zayadi AA, Mohamed SA, Arafa M, Mohammed SM, Zayed A, Abdel‑\nhafez MS et al (2020) Anti‑IL‑6 receptor monoclonal antibody as a new \ntreatment of endometriosis. Immunol Res 68(6):389–397\n 38. Trunova O, Gulmamedova I, Maylyan E (2024) Immunotherapy in patients \nwith recurrent implantation failure. Med Vestn Iuga Rossii 15(4):79–89\n 39. Bernardo VS, Torres FF, da Silva DGH (2023) Foxo3 and oxidative stress: a \nmultifaceted role in cellular adaptation. J Mol Med (Berl) 101(1–2):83–99\n 40. Coomans de Brachène A, Demoulin JB (2016) FOXO transcription factors \nin cancer development and therapy. Cell Mol Life Sci 73(6):1159–72\n 41. de Barcena Arellano ML, Gericke J, Reichelt U, Okuducu AF, Ebert AD, \nChiantera V et al (2011) Immunohistochemical characterization of \nendometriosis‑associated smooth muscle cells in human peritoneal \nendometriotic lesions. Hum Reprod 26(10):2721–2730\n 42. Jongbloets BC, Pasterkamp RJ (2014) Semaphorin signalling during \ndevelopment. Development 141(17):3292–3297\n 43. Papic N, Zidovec Lepej S, Gorenec L, Grgic I, Gasparov S, Filipec Kanizaj \nT et al (2018) The association of semaphorins 3C, 5A and 6D with liver \nfibrosis stage in chronic hepatitis C. PLoS ONE 13(12):e0209481\n 44. Scheerer C, Frangini S, Chiantera V, Mechsner S (2017) Reduced sympa‑\nthetic innervation in endometriosis is associated to semaphorin 3C and \n3F expression. Mol Neurobiol 54(7):5131–5141\n 45. Kim BG, Yoo JY, Kim TH, Shin JH, Langenheim JF, Ferguson SD et al (2015) \nAberrant activation of signal transducer and activator of transcription‑3 \n(STAT3) signaling in endometriosis. Hum Reprod 30(5):1069–1078\n 46. Elmore S (2007) Apoptosis: a review of programmed cell death. Toxicol \nPathol 35(4):495–516\n 47. Singh N (2007) Apoptosis in health and disease and modulation of apop‑\ntosis for therapy: an overview. Indian J Clin Biochem 22(2):6–16\n 48. Monnin N, Fattet AJ, Koscinski I (2023) Endometriosis: update of patho‑\nphysiology, (Epi)genetic and environmental involvement. Biomedicines \n11(3):978\n 49. Pan D, Yang J, Zhang N, Wang L, Li N, Shi J et al (2022) Gonadotropin‑\nreleasing hormone agonist downregulation combined with hormone \nreplacement therapy improves the reproductive outcome in frozen‑\nthawed embryo transfer cycles for patients of advanced reproductive \nage with idiopathic recurrent implantation failure. Reprod Biol Endocrinol \n20(1):26\n\nPage 12 of 12Hakimi et al. Middle East Fertility Society Journal           (2025) 30:32 \n 50. Imai A, Takagi A, Tamaya T (2000) Gonadotropin‑releasing hormone \nanalog repairs reduced endometrial cell apoptosis in endometriosis \nin vitro. Am J Obstet Gynecol 182(5):1142–1146\n 51. Meresman GF, Bilotas MA, Lombardi E, Tesone M, Sueldo C, Barañao RI \n(2003) Effect of GnRH analogues on apoptosis and release of interleukin‑\n1beta and vascular endothelial growth factor in endometrial cell cultures \nfrom patients with endometriosis. Hum Reprod 18(9):1767–1771\n 52. Kakar SS, Winters SJ, Zacharias W, Miller DM, Flynn S (2003) Identification \nof distinct gene expression profiles associated with treatment of LbetaT2 \ncells with gonadotropin‑releasing hormone agonist using microarray \nanalysis. Gene 308:67–77\n 53. Sakamoto Y, Harada T, Horie S, Iba Y, Taniguchi F, Yoshida S et al (2003) \nTumor necrosis factor‑alpha‑induced interleukin‑8 (IL‑8) expression in \nendometriotic stromal cells, probably through nuclear factor‑kappa B \nactivation: gonadotropin‑releasing hormone agonist treatment reduced \nIL‑8 expression. J Clin Endocrinol Metab 88(2):730–735\n 54. Yang X, Huang R, Wang YF, Liang XY (2016) Pituitary suppression before \nfrozen embryo transfer is beneficial for patients suffering from idiopathic \nrepeated implantation failure. J Huazhong Univ Sci Technolog Med Sci \n36(1):127–131\n 55. Jeong H, Mason SP , Barabási AL, Oltvai ZN (2001) Lethality and centrality \nin protein networks. Nature 411(6833):41–42\n 56. Chen H, Malentacchi F, Fambrini M, Harrath AH, Huang H, Petraglia F \n(2020) Epigenetics of estrogen and progesterone receptors in endome‑\ntriosis. Reprod Sci 27(11):1967–1974\n 57. Marla S, Mortlock S, Houshdaran S, Fung J, McKinnon B, Holdsworth‑\nCarson SJ et al (2021) Genetic risk factors for endometriosis near estrogen \nreceptor 1 and coexpression of genes in this region in endometrium. Mol \nHum Reprod 27(1):gaaa082\n 58. Zhou X, Gu Y, Wang DN, Ni S, Yan J (2013) Eight functional polymor‑\nphisms in the estrogen receptor 1 gene and endometrial cancer risk: a \nmeta‑analysis. PLoS ONE 8(4):e60851\n 59. Paskulin DD, Cunha‑Filho JS, Paskulin LD, Souza CA, Ashton‑Prolla P (2013) \nESR1 rs9340799 is associated with endometriosis‑related infertility and \nin vitro fertilization failure. Dis Markers 35(6):907–913\n 60. Carow B, Rottenberg ME (2014) SOCS3, a major regulator of infection and \ninflammation. Front Immunol 5:58\n 61. Dimitriadis E, Stoikos C, Tan YL, Salamonsen LA (2006) Interleukin 11 \nsignaling components signal transducer and activator of transcription 3 \n(STAT3) and suppressor of cytokine signaling 3 (SOCS3) regulate human \nendometrial stromal cell differentiation. Endocrinology 147(8):3809–3817\n 62. Dimitriadis E, Stoikos C, Stafford‑Bell M, Clark I, Paiva P , Kovacs G et al \n(2006) Interleukin‑11, IL‑11 receptoralpha and leukemia inhibitory factor \nare dysregulated in endometrium of infertile women with endometriosis \nduring the implantation window. J Reprod Immunol 69(1):53–64\n 63. Linjawi S, Li TC, Tuckerman EM, Blakemore AI, Laird SM (2004) Expression \nof interleukin‑11 receptor alpha and interleukin‑11 protein in the endo‑\nmetrium of normal fertile women and women with recurrent miscar‑\nriage. J Reprod Immunol 64(1–2):145–155\n 64. Berthiaume AA, Grant RI, McDowell KP , Underly RG, Hartmann DA, Levy M \net al (2018) Dynamic remodeling of pericytes in vivo maintains capillary \ncoverage in the adult mouse brain. Cell Rep 22(1):8–16\n 65. Corliss BA, Ray HC, Mathews C, Fitzgerald K, Doty RW, Smolko CM et al \n(2019) Myh11 lineage corneal endothelial cells and ASCs populate cor‑\nneal endothelium. Invest Ophthalmol Vis Sci 60(15):5095–5103\n 66. Wang T, Jiang R, Yao Y, Qian L, Zhao Y, Huang X (2021) Identification of \nendometriosis‑associated genes and pathways based on bioinformatic \nanalysis. Medicine (Baltimore) 100(27):e26530\n 67. Tuckey RC (2005) Progesterone synthesis by the human placenta. Pla‑\ncenta 26(4):273–281\n 68. Miller WL, Auchus RJ (2011) The molecular biology, biochemistry, and \nphysiology of human steroidogenesis and its disorders. Endocr Rev \n32(1):81–151\n 69. Bulun SE, Zeitoun KM, Takayama K, Sasano H (2000) Estrogen biosynthesis \nin endometriosis: molecular basis and clinical relevance. J Mol Endocrinol \n25(1):35–42\n 70. Zhang H, Wu J, Li Y, Jin G, Tian Y, Kang S (2022) Identification of key \ndifferentially methylated/expressed genes and pathways for ovarian \nendometriosis by bioinformatics analysis. Reprod Sci 29(5):1630–1643\n 71. Rosenberg ME, Silkensen J (1995) Clusterin: physiologic and pathophysi‑\nologic considerations. Int J Biochem Cell Biol 27(7):633–645\n 72. Shannan B, Seifert M, Leskov K, Willis J, Boothman D, Tilgen W et al (2006) \nChallenge and promise: roles for clusterin in pathogenesis, progression \nand therapy of cancer. Cell Death Differ 13(1):12–19\n 73. Konrad L, Hackethal A, Oehmke F, Berkes E, Engel J, Tinneberg HR \n(2016) Analysis of clusterin and clusterin receptors in the endometrium \nand clusterin levels in cervical mucus of endometriosis. Reprod Sci \n23(10):1371–1380\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in pub‑\nlished maps and institutional affiliations.","source_license":"CC0","license_restricted":false}