{"paper_id":"295c27ef-6c46-4974-8d1c-fd5508cb91c5","body_text":"Hamidiye Med J \nORI GI NAL AR TIC LE\nCopyright© 2025 The Author. Published by Galenos Publishing House on behalf of University of Health Sciences Türkiye, Hamidiye Faculty of Medicine. \nThis is an open access article under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) International License.\n1Adıyaman University Faculty of Medicine, Department of Obstetrics and Gynecology, Adıyaman, Türkiye\n2University of Health Sciences Türkiye, Hamidiye University Faculty of Medicine, Department of Immunology, İstanbul, Türkiye\n Gürkan Özbey1,  Duygu Kırkık2\nAddress for Correspondence: Gürkan Özbey, Adıyaman University Faculty of Medicine, Department of Obstetrics and Gynecology, Adıyaman, Türkiye \nE-mail: ozbeyg@hotmail.com ORCID ID: orcid.org/0000-0001-7961-0087\nReceived: 19.04.2025  Accepted: 06.08.2025  Epub: 02.09.2025\nCite this article as: Özbey G, Kırkık D . Possible functional impact of ESR1 and GREB1 variants in endometriosis: an in silico approach. Hamidiye Med J.   [Epub Ahead of \nPrint]\nPossible Functional Impact of ESR1 and GREB1 Variants in \nEndometriosis: an in silico Approach\nEndometrioziste ESR1 ve GREB1 Varyantlarının Olası Fonksiyonel Etkisi:  \nin siliko Yaklaşım\nBackground: Endometriosis is a chronic, estrogen-dependent inflammatory disorder that affects a significant proportion of women \nof reproductive age. Although the pathophysiology of the disease remains incompletely understood, genetic and hormonal factors \nare believed to play key roles. T wo genes of particular interest in this context are Estrogen Receptor 1 (ESR1) and Growth Regulation \nby Estrogen in Breast Cancer 1 (GREB1), both of which are integral to estrogen signaling and cell proliferation. This study aimed to \ninvestigate the potential contribution of missense Single Nucleotide Polymorphisms (SNPs) in the ESR1 and GREB1 genes to the \npathogenesis of endometriosis using an in silico approach.\nMaterials and Methods: Publicly available data from National Center for Biotechnology Information and SNP database were used to \nidentify missense variants in ESR1 and GREB1. The functional impact of each variant was predicted using six bioinformatics tools: \nSorting Intolerant From T olerant, Polymorphism Phenotyping v2, Protein Variation Effect Analyzer, SNPs and Gene Ontology, Protein \nAnalysis Through Evolutionary Relationships, and PredictSNP . Protein-protein interaction networks were constructed via the Search \nT ool for the Retrieval of Interacting Genes/Proteins and Gene Multiple Association Network Integration Algorithm platforms, and \ndisease and pathway associations were analyzed using the Kyoto Encyclopedia of Genes and Genomes and DISEASES databases.\nResults: ESR1 was found to be a central node in estrogen signaling, with strong predicted interactions with GREB1 and other \nhormone-regulated genes. Several SNPs in both genes were consistently classified as deleterious across all predictive tools. Disease \nenrichment analysis further linked these genes to endometriosis, as well as to other estrogen-responsive conditions such as breast \nand ovarian cancers.\nConclusion: This study identifies potentially high-risk ESR1 and GREB1 variants and highlights their involvement in key estrogen-\nregulated pathways. These findings support the role of genetic variation in the molecular pathogenesis of endometriosis and lay the \ngroundwork for future experimental validation.\nKeywords: GREB1, ESR1, in silico, endometriosis, immunoinformatics\nAmaç: Endometriozis, üreme çağındaki kadınların önemli bir kısmını etkileyen, kronik ve östrojene bağımlı enflamatuvar  bir \nhastalıktır. Hastalığın patofizyolojisi tam olarak aydınlatılamamış olmakla birlikte, genetik ve hormonal faktörlerin önemli rol \noynadığı düşünülmektedir. Bu bağlamda özellikle dikkat çeken iki gen, östrojen sinyal iletimi ve hücre proliferasyonu açısından \nkritik olan Östrojen Reseptörü 1 (ESR1) ve Meme Kanserinde Östrojenle Düzenlenen Büyüme Geni 1’dir (GREB1). Bu çalışma, in silico bir \nyaklaşımla ESR1 ve GREB1 genlerindeki anlamsal (missense) T ek Nükleotid Polimorfizmlerinin (SNP’ler) endometriozis patogenezine \nolası katkısını araştırmayı amaçlamıştır.\nGereç ve Yöntemler: ESR1 ve GREB1 genlerindeki anlamsal varyantları belirlemek için Ulusal Biyoteknoloji Bilgi Merkezi ve T ek \nNükleotid Polimorfizmi Veri Tabanı gibi halka açık veri tabanları kullanılmıştır. Her bir varyantın fonksiyonel etkisi; T olere Edilemeyen \nDeğişiklikleri Ayırma Aracı, Polimorfizm Fenotipleme Aracı, Versiyon 2, Protein Varyasyonu Etki Analizörü, SNPs ve Gen Ontolojisi Aracı, \nEvrimsel İlişkiler Üzerinden Protein Analizi ve PredictSNP olmak üzere altı farklı biyoinformatik aracıyla tahmin edilmiştir. Protein-\nABSTRACTÖZ\nDOI: 10.4274/hamidiyemedj.galenos.2025.98698\n\n \nIntroduction\nEndometriosis is a chronic, estrogen-dependent \ninflammatory disorder characterized by the presence of \nfunctional endometrial tissue outside the uterine cavity. \nAlthough the ectopic endometrial lesions are most frequently \nlocated within the pelvic region, affecting structures such \nas the ovaries, pouch of Douglas, sacrouterine ligaments, \npelvic peritoneum, rectovaginal septum, and cervix, \nthere are documented cases of extra-pelvic involvement. \nRarely, a comma is included endometriotic foci have been \nidentified in organs including the lungs, pleura, diaphragm, \nintestines, gallbladder, kidneys, ureters, umbilicus, skin, \ncentral nervous system, and extremities (1,2).\nThe prevalence of endometriosis among women of \nreproductive age ranges from 3% to 37%, and despite \nits high frequency and significant impact on quality of \nlife and fertility, the pathogenesis of the disease remains \nincompletely understood (3). One of the major contributing \nfactors to this knowledge gap is the complex nature \nof its genetic background. Current evidence suggests a \npolygenic and multifactorial inheritance pattern, wherein \ndisease development results from a combination of genetic \npredisposition and environmental influences (4). \nIdentifying specific genetic contributors is complicated \nby several factors. The necessity for invasive procedures, \nsuch as laparoscopy or laparotomy, for definitive diagnosis \nlimits early detection and may result in underdiagnosis (5). \nFurthermore, endometriosis is now considered a \nheterogeneous condition encompassing multiple \nsubtypes such as superficial peritoneal lesions, ovarian \nendometriomas, and deeply infiltrating endometriosis, \neach with potentially distinct genetic and molecular \ncharacteristics. Environmental exposures, particularly to \nendocrine-disrupting chemicals like dioxins, may further \ninfluence disease development and expression (6,7).\nIn this study, the investigation of genes such as Estrogen \nReceptor 1 (ESR1) and Growth Regulation by Estrogen in \nBreast Cancer 1 (GREB1)  has gained attention due to their \npivotal roles in estrogen signaling, cell proliferation, and \nendometrial receptivity, all of which are relevant in the \netiology and progression of endometriosis (7-11). This study \naims to explore the potential contribution of missense \nSingle Nucleotide Polymorphisms (SNPs)  in the ESR1 and \nGREB1 genes to the pathogenesis of endometriosis using \na comprehensive in silico  bioinformatics approach. By \nevaluating the functional impact of these genetic variants, \nmapping protein-protein interactions (PPIs), and analyzing \ndisease-associated pathways, we seek to identify high-risk \nmutations and elucidate possible molecular mechanisms \nthrough which these genes may influence the development \nand progression of endometriosis.\nMaterials and Methods\nRetrieval of Protein Sequences and Missense Variants for \nESR1 and GREB1 Genes\nPublicly available data from the National Center for \nBiotechnology Information (NCBI) and the NCBI Single \nNucleotide Polymorphism database (dbSNP) were used to \ninvestigate the ESR1 and GREB1 genes associated with \nendometriosis. Protein sequences and known SNPs for \nboth genes were retrieved and analyzed. The focus was \non missense mutations, as these variants result in amino \nacid changes that may alter the protein’s structure and \nimpair its normal biological function. Such changes can \naffect processes like hormone binding or gene regulation, \nwhich are critical in the pathogenesis of endometriosis. \nBioinformatics tools were then applied to evaluate the \npotential effects of these mutations on protein function \n(12,13).\nInteraction Analysis of GREB1 and ESR1\nT o explore the functional and physical interactions \ninvolving the GREB1 and ESR1 genes, the Search T ool for the \nRetrieval of Interacting Genes/Proteins (STRING) database \nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\nprotein etkileşim ağları etkileşimli gen/proteinleri bulma aracı ve gen çoklu ilişki ağlarını entegre etme algoritması platformları \naracılığıyla oluşturulmuş, hastalık ve yolak ilişkileri Kyoto genler ve genomlar ansiklopedisi ve DISEASES veri tabanı kullanılarak \nanaliz edilmiştir.\nBulgular: ESR1’nin, östrojen sinyal yolaklarında merkezi bir düğüm olduğu ve GREB1 ile diğer hormonla düzenlenen genlerle \ngüçlü etkileşimler gösterdiği tespit edilmiştir. Her iki gendeki bazı SNP’ler, tüm tahmin araçlarında tutarlı şekilde zararlı olarak \nsınıflandırılmıştır. Hastalık zenginleştirme analizleri, bu genleri endometriozis ile birlikte meme ve over kanseri gibi diğer östrojen \nduyarlı hastalıklarla da ilişkilendirmiştir.\nSonuç: Bu çalışma, ESR1 ve GREB1 genlerindeki potansiyel yüksek riskli varyantları ortaya koymuş ve bu genlerin östrojenle \ndüzenlenen temel yolaklardaki rolüne dikkat çekmiştir. Bulgular, genetik varyasyonların endometriozisin moleküler patogenezindeki \nrolünü desteklemekte ve ileri deneysel doğrulama çalışmaları için bir temel oluşturmaktadır.\nAnahtar Kelimeler: GREB1, ESR1, in silico, endometriozis, immünoinformatik\nÖZ\n\n \n(version 11.5) was employed using a medium confidence \ninteraction score threshold (≥0.4). This platform was used \nto build a comprehensive PPI network and to predict \nassociations based on known and predicted interactions. In \nparallel, the Gene Multiple Association Network Integration \nAlgorithm (GeneMANIA) tool (version 3.5.2) was used to \nfurther investigate gene-gene relationships and to identify \nadditional genes functionally linked to GREB1 and ESR1. \nThis analysis included co-expression, shared pathways, \nco-localization, and physical interaction data. The results \nobtained from GeneMANIA were cross-referenced with the \nSTRING analysis to confirm the consistency and biological \nrelevance of the predicted interactions. All computational \nanalyses were conducted between February 2 and 8, 2025, \nensuring up-to-date and reliable data integration (14,15).      \nIdentifying the Most Deleterious SNPs\nT o assess the potential functional consequences of \nnon-synonymous SNPs identified in the ESR1 and GREB1 \ngenes, six independent in silico  prediction tools were \nemployed: Sorting Intolerant From T olerant (SIFT) (https://\nsift.jcvi.org), Protein ANalysis THrough Evolutionary \nRelationships (PANTHER) (https://www.pantherdb.org/\ntools), Polymorphism Phenotyping v2 (PolyPhen-2) (https://\ngenetics.bwh.harvard.edu/pph2/), SNPs&Gene Ontology \n(GO) (https://snps.biofold.org/snps-and-go/), Protein \nVariation Effect Analyzer (PROVEAN) (https://provean.\njcvi.org), and PredictSNP (https://loschmidt.chemi.muni.\ncz/predictsnp). These tools were used to evaluate the \nlikelihood of deleterious effects caused by each amino acid \nsubstitution. Variants that were consistently classified as \ndamaging by all six tools were considered to be high-risk \nmutations with strong potential to impair protein function. \nEach tool applies a different algorithm to determine the \npathogenicity of SNPs. SIFT utilizes sequence homology \nto determine whether an amino acid change is tolerated, \nflagging substitutions with a probability score below \n0.05 as deleterious. PANTHER evaluates evolutionary \nconservation and functional domains to estimate the effect \nof substitutions. PolyPhen-2 predicts the potential structural \nand functional consequences of amino acid changes based \non multiple sequence alignments and protein structure \nfeatures. SNPs&GO integrates gene ontology data with \nmachine learning (support vector machine-based) models \nto associate mutations with disease. PROVEAN applies a \nsequence-based approach to assess whether amino acid \nsubstitutions are functionally disruptive, using a cutoff \nscore of -2.5 to classify variants. Lastly, PredictSNP combines \npredictions from several algorithms (including SIFT, \nPolyPhen-2, Multivariate Analysis of Protein Polymorphism, \nScreening for Non-Acceptable Polymorphisms, and Predictor \nof Human Deleterious-SNP) to generate a consensus \nassessment of each SNP’s deleterious potential.\nPathway and Disease Association Analysis of GREB1 and \nESR1\nPathway and disease analyses for the GREB1 and ESR1 \ngenes were performed using the Kyoto Encyclopedia of \nGenes and Genomes (KEGG) database to explore their \nroles in essential molecular pathways, particularly those \nassociated with hormone signaling and estrogen-responsive \nmechanisms relevant to endometriosis. Access to the KEGG \npathway data was facilitated through the KEGG application \nprogramming interface, allowing systematic mapping of \ngene functions in biological processes such as estrogen \nsignaling, cell proliferation, and transcriptional regulation.\nT o complement these findings, disease associations were \nextracted from the DISEASES database (JensenLab, 2024 \nversion), which provided insight into the clinical relevance \nof GREB1 and ESR1 in endometriosis and other hormone-\nrelated disorders. Additionally, the STRING database was \nused to construct PPI networks, further validating the \ninvolvement of these genes in interconnected regulatory \nsystems. This integrated bioinformatics approach revealed \nkey functional pathways and disease links associated with \nGREB1 and ESR1 (16-18).\nStatistical Analysis\nAll bioinformatics and in silico  statistical analyses \nwere conducted using integrated online platforms and \ncomputational tools. Functional predictions of missense \nvariants were obtained from SIFT, PolyPhen-2, PROVEAN, \nPANTHER, SNPs&GO, and PredictSNP web servers. Protein-\nprotein interaction networks were analyzed via STRING \n(version 11.5; European Molecular Biology Laboratory, \nHeidelberg, Germany) and GeneMANIA (version 3.5.2; \nUniversity of T oronto, T oronto, Canada). Pathway and \ndisease enrichment analyses were performed using the \nKEGG database (KEGG, Kyoto University, Kyoto, Japan) and \nDISEASES database (JensenLab, Copenhagen, Denmark). All \nanalyses were performed between February 2 and February \n10, 2025, and descriptive statistics were automatically \ncalculated by the respective bioinformatics servers.\nResults\nIdentifying the Most Deleterious SNPs\nAlthough this study primarily focused on missense \nvariants, all listed GREB1 SNPs are intronic and were \nincluded due to their potential regulatory relevance \nas supported by prior literature. These variants were \ntherefore excluded from functional prediction analyses. \nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\n\n \nThe initial step of our analysis involved the identification \nand curation of SNPs within the GREB1 and ESR1 genes, \nboth of which are implicated in estrogen signaling and \nhave been associated with hormone-dependent conditions \nincluding endometriosis. Table 1 presents the complete list \nof selected variants, annotated with reference SNP cluster \nIDs, allelic composition, ancestral alleles, Human Genome \nVariation Society nomenclature-compliant transcript-based \nnomenclature, chromosomal positions, and minor allele \nfrequencies (MAFs). Importantly, all variants listed under \nESR1 are exonic and classified as missense mutations, \nthus, eligible for functional prediction analysis via in silico \ntools such as SIFT, PolyPhen-2, and PROVEAN. In contrast, \nall GREB1 variants in our dataset are located in intronic \nregions, rendering them non-coding and thereby outside the \nscope of classical missense-based prediction algorithms. \nNevertheless, these GREB1 variants were retained due to \ntheir high population frequency and potential regulatory \nroles, as suggested by previous genome-wide association \nand transcriptomic studies linking GREB1 expression to \nestrogen-mediated proliferation in endometrial tissues.\nAmong the ESR1 variants, rs753014570 (c.728G>A) \nand rs779180038 (c.727C>T) occur in close proximity \nwithin the coding sequence, possibly affecting the same \nfunctional domain, and may act in tandem as a multi-\nnucleotide polymorphism in certain haplotypes. Variant \nrs773500294 also appears as a duplicated entry in public \ndatabases, with different reported alternative alleles (C>A \nand C>G), which requires cautious interpretation due to \npossible annotation inconsistencies. The low MAFs (<0.01) \nof several ESR1 variants suggest they may represent \nrare, potentially pathogenic alterations with relevance to \ndisease susceptibility. These prioritized SNPs served as the \nfoundation for downstream analyses, including PPI mapping \nand disease association profiling.\nInteraction Analysis of GREB1 and ESR1\nPPI analysis revealed that ESR1 occupies a central \nposition within the interaction network, engaging in \nnumerous functional associations with other proteins \nrelevant to estrogen signaling and transcriptional regulation. \nNotably, GREB1 and its paralog GREB1L demonstrated \nstrong connectivity with ESR1, supporting their known \nroles as estrogen-responsive genes. The presence of thick \ninteraction lines indicates high-confidence associations, \nsuggesting a direct regulatory relationship. Similarly, a \nprominent interaction was observed between ESR1 and \nprogesterone receptor (PGR), highlighting the interplay \nbetween estrogen and progesterone pathways in hormone-\nregulated tissues (Figure 1).\nThe corresponding interaction network is presented \nin Figure 1. In the GeneMANIA-derived visualization, \ndifferent edge colors represent distinct types of functional \nassociations: pink lines indicate co-expression, blue lines \ndenote physical interactions, green lines correspond to co-\nlocalization, and orange lines reflect predicted interactions. \nThese integrated networks provide evidence for the \nfunctional linkage between ESR1 and GREB1, particularly \nwithin estrogen-responsive signaling pathways.\nDisease association analysis performed using the \nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\nTable 1. Summary of selected SNPs in ESR1 and GREB1 genes, including their HGVS nomenclature, genomic location, ancestral and \nalternative alleles, and MAF. All GREB1 variants listed are intronic and not eligible for functional prediction via missense-specific tools\nSource rs ID Allele Ancestral HGVS name Location MAF\nGREB1\nrs13394619 A/G A ENST00000234142.9: c.1160-1365G>A Chromosome 2:11587381 0.50\nrs11674184 A/T T ENST00000234142.9: c.901+577T>A Chromosome 2:11581409 0.37 \nrs12470971 A/G G ENST00000234142.9: c.902-46G>A Chromosome 2:11585115 0.50\nrs11686574 C/G C ENST00000381483.6: c.-159+1064C>G Chromosome 2:11543881 0.47\nrs6740248 C/G C ENST00000234142.9: c.454+110C>G Chromosome 2:11566766  0.22\nrs2930961 C/T T ENST00000336148.10: c.305-20263A>G Chromosome 8:94431578 0.40\nrs1250248 A/G G ENST00000323926.10: c.1394-127T>C Chromosome 2:215422370 0.22\nESR1\nrs139960913 C/T C ENST00000206249.8: c.16C>T Chromosome 6:151807928 0.01\nrs746521050 G/A G ENST00000206249.8: c.269G>A Chromosome 6:151808181 < 0.01\nrs773500294 C/A C ENST00000206249.8: c.296C>A Chromosome 6:151808208 < 0.01\nrs149308960 G/A/C/T G ENST00000206249.8: c.478G>T Chromosome 6:151842622 0.01\nrs779180038 C/T C ENST00000206249.8: c.727C>T Chromosome 6:151880738 < 0.01\nrs753014570 G/A G ENST00000206249.8: c.728G>A Chromosome 6:151880739 < 0.01\nESR1: Estrogen Receptor 1, GREB1: Growth Regulation by Estrogen in Breast Cancer 1 Like, HGVS: Human Genome Variation Society, MAF: Minor allele frequencies, rs \nID: Reference SNP identification number, SNP: Single Nucleotide Polymorphism\n\n \nDISEASES database (JensenLab) revealed that both ESR1 and \nGREB1 are strongly linked to a variety of hormone-dependent \nand estrogen-responsive conditions. ESR1 showed high-\nconfidence associations with several diseases, most notably \nbreast cancer (Z: 9.0), carcinoma (Z: 7.4), endometriosis (Z: \n7.1), and ovarian cancer (Z: 6.6). These associations reflect \nESR1’s pivotal role in estrogen signaling, transcriptional \nregulation, and reproductive tissue homeostasis.\nSimilarly, GREB1—a gene regulated by ESR1 and known \nto mediate estrogen-stimulated cell proliferation—also \ndemonstrated associations with estrogen-sensitive \npathologies. The strongest connections were observed with \nbreast cancer (Z: 5.3), endometriosis (Z: 4.7), amelogenesis \nimperfecta type 1G (Z: 4.6); and various gynecologic \nmalignancies such as uterine cancer, ovarian cancer, and \nuterine fibroids (Figures 2 and 3).\nCollectively, these findings reinforce the functional \ninterplay between ESR1 and GREB1 in estrogen-regulated \npathways and highlight their shared involvement in the \npathogenesis of endometriosis and other hormone-related \ndisorders.\nFigure 4 shows the representation of the estrogen \nsignaling pathway based on the KEGG pathway map. The \npathway includes both membrane-initiated and nuclear-\ninitiated steroid signaling mechanisms. ESR1 acts as a \ncentral transcription factor activated by estrogen, leading to \ndownstream signaling events including activation of MAPK/\nERK and PI3K/AKT pathways. GREB1, indicated as a target \ngene, is transcriptionally regulated by ESR1 upon estrogen \nbinding, suggesting its role as a downstream effector in \nestrogen-dependent biological processes such as cell \nproliferation, differentiation, and survival.\nFigure 1. The PPI analysis was conducted using the STRING database (v11.5) and further supported by GeneMANIA (v3.5.2)\nCYP19A1: Cytochrome P450 Family 19 Subfamily A Member 1, ESR1: Estrogen Receptor 1, GeneMANIA: Gene Multiple Association Network Integration, GREB1: \nGrowth Regulation by Estrogen in Breast Cancer 1, GREB1L: Growth Regulation by Estrogen in Breast Cancer 1 Like, NCOA1: Nuclear Receptor Coactivator 1, PGR: \nProgesterone receptor, POLR2A: RNA Polymerase II Subunit A, PPI: Protein-protein interaction, SPDEF: SAM Pointed Domain Containing ETS Transcription Factor, \nSTC2: Stanniocalcin 2 STRING: Search Tool for the Retrieval of Interacting Genes/Proteins, TFF1: Trefoil Factor 1\nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\n\n \nDiscussion\nIn this study, a comprehensive in silico  analysis was \nperformed to investigate the potential contribution of \nmissense SNPs in the ESR1 and GREB1 genes to the \npathogenesis of endometriosis. These genes were selected \ndue to their critical roles in estrogen signaling, cell \nproliferation, and reproductive tissue regulation, all of \nwhich are highly relevant to the etiology of endometriosis \n(7-11). By integrating data from multiple bioinformatics \nplatforms—including SNP prediction tools, PPI networks, \nand disease association databases—we sought to identify \nhigh-risk variants that may influence disease susceptibility \nand progression.\nOur PPI analysis revealed that ESR1 serves as a central \nhub within the estrogen signaling network, demonstrating \nstrong associations with GREB1 and other key genes such \nas PGR, CYP1B1, and CTNNB1 (14,15). These interactions \nsupport previous findings that ESR1 and GREB1 are not only \nco-expressed but also functionally interlinked in hormone-\nresponsive pathways (8,10,11).\nFurther connections between ESR1 and components \nof the RNA polymerase II complex (including POLR2A, \nPOLR2F, POLR2J, among others) emphasize its role in the \ntranscriptional activation of downstream target genes. \nAdditionally, interactions with genes such as CYP1B1, \nTFF1, CTNNB1, and SAFB reflect ESR1’s broad involvement \nin cellular processes including hormone metabolism, cell \nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\nFigure 2. Disease association of ESR1 based on text mining analysis from the DISEASES database\nESR1: Estrogen Receptor 1\nFigure 3. Disease association of GREB1 based on DISEASES database text mining\nGREB1: Growth Regulation by Estrogen in Breast Cancer 1 Like\n\n \nproliferation, and chromatin remodeling (14,16). In addition \nto the molecular pathway relevance of these genes, the \nclinical significance of the identified variants was also \nexamined. T o further contextualize the relevance of the \nidentified SNPs, we explored existing literature and variant \ndatabases to determine whether these polymorphisms have \npreviously been associated with endometriosis or other \nestrogen-dependent conditions. While none of the ESR1 or \nGREB1 variants listed in Table 1 has been directly linked \nto endometriosis in large genome-wide association studies, \nsome—such as ESR1 rs753014570 (c.728G>A)—have been \nimplicated in hormone-responsive cancers including breast \nand ovarian cancer, where dysregulated estrogen signaling \nis a common pathological feature (19,20). This overlap \nis noteworthy, given the shared molecular mechanisms \nbetween these diseases and endometriosis, including \nestrogen-driven proliferation, progesterone resistance, and \ninflammatory microenvironment remodeling. Additionally, \nthe low-frequency variants identified in ESR1 (e.g., \nrs779180038, rs746521050) may represent rare, potentially \nfunctional mutations that could alter receptor conformation, \nDNA binding affinity, or cofactor recruitment, ultimately \ninfluencing downstream gene transcription. Although the \nGREB1 variants identified in this study are intronic and \nhave not been directly associated with endometriosis, prior \nevidence suggests that regulatory SNPs in intronic regions \ncan affect gene expression via splicing efficiency, enhancer \ndisruption, or transcription factor binding site modulation \n(21,22). Therefore, these variants may contribute to altered \nGREB1 expression levels in estrogen-responsive tissues. \nFuture experimental validation and population-based \nassociation studies are required to assess the biological \nsignificance of these candidate variants in endometriosis \npathogenesis (23,24). The functional link between ESR1 and \nÖzbey and Kırkık. ESR1 and GREB1 Variants in Endometriosis\nFigure 4. Estrogen signaling pathway showing ESR1 activation and downstream regulation of GREB1 (adapted from KEGG)\nESR1: Estrogen Receptor 1, GREB1L: Growth Regulation by Estrogen in Breast Cancer 1 Like, KEGG: Kyoto Encyclopedia of Genes and Genomes\n\n \nGREB1, in particular, underscores a shared role in estrogen-\nmediated gene expression, suggesting that genetic variants \naffecting these proteins may contribute to the molecular \npathology of endometriosis (10,11). The rationale for \nselecting ESR1 and GREB1 in this study stems from their \nwell-established roles in estrogen signaling, which is central \nto the pathogenesis of endometriosis (25,26). ESR1 encodes \nEstrogen Receptor α (ERα), a nuclear hormone receptor that \nregulates the transcription of estrogen-responsive genes \nupon ligand binding (27,28). GREB1 is one such early response \ngene directly upregulated by ESR1 via estrogen-bound \nERα complexes (29). Multiple studies have demonstrated \nthat GREB1 expression is tightly correlated with estrogen \nstimulation in hormone-responsive tissues including the \nendometrium and that it functions as a key mediator of \nestrogen-driven cellular proliferation and differentiation \n(30-32). Specifically, chromatin immunoprecipitation assays \nhave shown that ER α binds to enhancer regions within \nthe GREB1 gene locus, activating its transcription (33). \nThis regulatory axis is critical in endometrial biology, as \ndysregulation of estrogen signaling is known to promote \nthe ectopic growth and invasiveness characteristic of \nendometriotic lesions. Therefore, the functional interplay \nbetween ESR1 and GREB1 reflects a direct transcriptional \nhierarchy, wherein polymorphisms in either gene may \ndisrupt normal hormonal responses, leading to altered \ngene expression patterns that favor the development or \npersistence of endometriosis (8-34,35).\nSeveral missense mutations in both ESR1 and GREB1 were \nidentified, some of which were predicted to be deleterious \nacross multiple algorithms. Variants such as rs779180038 \nand rs753014570, although classified as multi-nucleotide \nvariants with ambiguous impact, highlight the complexity \nof interpreting in silico  predictions and the necessity for \nfuture experimental validation. These findings suggest \nthat specific SNPs may alter protein structure or function, \npotentially disrupting ER activity or its downstream gene \ntargets (12-13).\nPathway and disease enrichment analyses supported \nthese observations, linking ESR1 and GREB1 not only to \nendometriosis but also to other estrogen-dependent \nconditions such as breast cancer, ovarian cancer, and uterine \nfibroids (16,17). These overlapping associations underline \nthe shared molecular mechanisms underlying these \ndiseases and reinforce the importance of studying ESR1 and \nGREB1 in a broader hormonal context (7-9).\nCollectively, our results emphasize the value of \nintegrated bioinformatics approaches in identifying \ncandidate variants for further investigation. While in \nsilico predictions provide important insights, they should \nbe followed by functional assays and population-based \nstudies to validate the clinical relevance of the identified \nmutations. Understanding how these genes and their \nvariants contribute to estrogen signaling and endometrial \npathophysiology may ultimately aid in the development of \nmore personalized diagnostic and therapeutic strategies for \nendometriosis.\nConclusion\nAlthough silico -based approaches cannot fully replace \nexperimental validation, they serve as valuable tools \nfor prioritizing candidate variants for further functional \nand clinical research. The integration of these results \nwith future laboratory and population-level studies may \nenhance our understanding of endometriosis and facilitate \nthe development of targeted diagnostic and therapeutic \nstrategies.\nEthics\nEthics Committee Approval: Since this study was entirely \nbased on publicly available bioinformatics databases and \nperformed using in silico analyses, no ethical approval was \nrequired.\nInformed Consent: As no human participants or patient \ndata were involved in this in silico study, informed consent \nwas not applicable.\nFootnotes\nAuthorship Contributions\nSurgical and Medical Practices: G.Ö., D.K., Concept: G.Ö., \nD.K., Design: G.Ö., D.K., Data Collection or Processing: G.Ö., \nD.K., Analysis or Interpretation: G.Ö., Literature Search: G.Ö., \nD.K., Writing: G.Ö., D.K.\nConflict of Interest: No conflict of interest was declared \nby the authors.\nFinancial Disclosure:  The authors declared that this \nstudy received no financial support.\nREFERENCES\n1. 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