{"paper_id":"77d99afa-afc9-4fb8-9002-4b45d629ca7f","body_text":"Endometriosis is characterized by the presence of tissue\nresembling the uterine lining outside the uterus ( 1 ). Although the precise cause of endometriosis remains elusive,\nits high prevalence among women worldwide underscores\nthe need for further research into its pathogenesis, contributing factors, and the identification of additional biomarkers ( 2 ). Although various factors have been associated with\nsusceptibility to endometriosis, the relationship between\nwomen's fertility status and this condition remains unclear.\nWhile endometriosis itself does not directly induce cancer, it has been associated with an increased risk of ovarian clear cell carcinoma (OCCC) ( 3 ). OCCC is a specific\nsubtype of epithelial ovarian cancer that has been linked\nto endometriosis in epidemiological and molecular studies. Since infertility frequently accompanies advanced\nendometriosis, we hypothesized that comparing gene\nexpression profiles between fertile and infertile women\nwith endometriosis, followed by intersection with OCCC\n-associated genes, might reveal shared molecular features.\nThis approach aims to generate hypotheses regarding\nbiomarkers that may be associated with both infertility\n-related endometriosis and OCCC, potentially informing\nfuture risk assessment and early detection strategies. We\nacknowledge that direct comparisons between fertile and\ninfertile women with endometriosis would be more appropriate for isolating fertility-specific effects, and such\nanalyses are suggested for future studies ( 4 ). The identification of biomarkers linking women’s fertility status\nwith endometriosis, and subsequently represent a pivotal\nadvancement in the early detection of this cancer and in\ndistinguishing it from endometriosis.\nThis bioinformatic study seeks to address this knowledge gap. Using bioinformatics tools, we aimed to identify potential biomarkers that distinguish between fertile\nand infertile women with stage IV endometriosis. Furthermore, we evaluated these biomarkers to investigate\ntheir potential association with OCCC. By exploring the\nmolecular landscape of endometriosis in relation to fertility status, we aimed to provide insights that may improve\nrisk assessment and potentially contribute to the development of novel therapeutic approaches for OCCC, particularly in women with endometriosis.\n\nThis bioinformatics study was approved by the Ethics\nCommittee of Islamic Azad University, Science and Research Branch (IR.IAU.SRB.REC.1402.008). Publicly\navailable datasets were retrieved from the Gene Expression Omnibus (GEO) database. As only anonymized, publicly available data were analyzed, no additional informed\nconsent was required.\nTwo publicly available datasets,  GSE122182  (miRNA\nexpression) and  GSE122183  (mRNA expression), were\nretrieved from the GEO ( https://www.ncbi.nlm.nih.gov/\ngeo/ ). The datasets were selected based on their public\navailability, relevance to OCCC, and the inclusion of clear\nclinical annotations, particularly fertility status. Samples\nwith missing clinical information or poor-quality RNA\nwere excluded. To minimize technical variability, batch\neffect correction and quantile normalization were applied.\nIt should be noted that  GSE122182  and  GSE122183  were\nused only for exploratory miRNA–mRNA integration\nanalyses and were not employed for differential expression, protein-protein interaction (PPI) network, hub gene,\nor survival analyses. The main discovery datasets were\n GSE120103  (endometriosis stratified by fertility status)\nand  GSE29450  (OCCC versus normal epithelium).\nTo establish an integrated view of molecular dysregulation, the following data linkage strategy was employed.\nFirst, differentially expressed miRNAs (DEmiRNAs)\nwere identified from the  GSE122182  dataset. Second, the\nmultiMiR database was used to predict high-confidence\ntarget genes of these DEmiRNAs. Third, these predicted\ntarget genes were intersected with differentially expressed\ngenes (DEGs) identified from the  GSE122183  mRNA\ndataset. This intersection yielded a list of candidate genes\npotentially regulated by miRNAs associated with endometriosis and infertility, providing an integrated perspective on miRNA-mRNA regulatory interactions.\nThe endometriosis gene expression dataset\n( GSE120103 ), which includes tissue samples from\n9 disease-free fertile women, 9 disease-free infertile\nwomen, 9 fertile women with stage IV endometriosis,\nand 9 infertile women with stage IV endometriosis, was\nselected and used as the discovery dataset to identify\nDEGs. Diagnosis and staging of endometriosis in this\ndataset were confirmed according to the American Society for Reproductive Medicine (ASRM) criteria by experienced gynecologists and pathologists, as reported in\nthe original study ( 5 ). This dataset was generated using\nthe Agilent-014850 Whole Human Genome Microarray\n4x44K G4112F platform.\nThe OCCC gene expression dataset ( GSE29450 ), which\nconsisted of tissue samples from 10 primary OCCC tumors and 10 normal ovarian surface epithelia, was downloaded to identify DEGs. This dataset was generated using the Affymetrix Human Genome U133 Plus 2.0 Array\n[HG-U133_Plus_2] platform.\nThe raw microarray datasets were preprocessed to ensure data quality and compatibility for downstream analyses. First, quantile normalization was applied to adjust\npotential systematic biases between samples and ensure\ncomparability across arrays. Quantile normalization is\nwidely used to ensure that the distribution of probe intensities is similar across all arrays, thereby reducing technical variation.\nAfter quantile normalization, batch effect correction\nwas applied to reduce technical variation across platforms.\nAll downstream analyses were performed on the normalized expression matrices ( 6 ). This step was essential because the data were obtained from different experimental\nplatforms, and correcting for batch effects was critical to\navoid false-positive associations.\nIn addition, genes with consistently low expression levels across all samples were excluded using a predefined\nthreshold. This filtering step reduced background noise\nand focused the analysis on genes with potential biological significance, thereby improving the sensitivity of\ndownstream analyses, including differential expression\nand pathway enrichment analyses.\nDEGs were identified using the limma package in R\n(version 4.4.0). Adjusted P values were used to reduce the\noccurrence of false-positive results. Genes with |log2 fold\nchange (FC)| ≥ 2 and an adjusted P<0.01 were considered\ndifferentially expressed between the study groups. Multi\nple testing correction was performed using the Benjami\nni-Hochberg false discovery rate (FDR) method. A Venn\ndiagram was used to visualize the identified DEGs ( http://\nbioinformatics.psb.ugent.be/webtools/Venn/ ).\nFunctional and pathway enrichment analyses were\nperformed to identify biological processes and signaling\npathways associated with DEGs. Gene Ontology (GO)\nand Kyoto Encyclopedia of Genes and Genomes (KEGG)\npathway enrichment analyses were performed using the\nEnrichR tool ( https://maayanlab.cloud/Enrichr/ ), which\nintegrates multiple gene set enrichment databases to characterize the biological functions and signaling pathways\nassociated with the identified DEGs. GO terms provide\ninsight into biological processes, molecular functions, and\ncellular components, allowing gene products to be categorized based on shared functions, whereas KEGG pathways provide a systems-level understanding of molecular\ninteractions within biological pathways. Enrichment analyses were conducted using the default background gene\nset provided by EnrichR, and statistical significance was\ndefined as P<0.05.\nThe STRING database (http://string.embl.de/) was used\nto construct a PPI network of DEGs based on known and\npredicted interactions. A confidence score ≥0.7 was used\nfor to select high-confidence interactions among the DEGs\n( 6 ). Subsequently, the network was visualized using Cytoscape software (version 3.5.1, Cytoscape Consortium,\nSan Diego, CA, USA). Furthermore, the Molecular Complex Detection (MCODE) plug-in was used to identify the\nsignificant modules in the PPI network, with the following parameters: degree cutoff=2, K-Core=2, and Nnode\nscore cutoff=0.2.\nFor hub gene identification, separate PPI networks were\nconstructed for Group A (shared DEGs between infertile\nendometriosis and OCCC) and group B (shared DEGs between fertile endometriosis and OCCC). For each group,\nthe complete list of shared DEGs, including both upregulated and downregulated genes, was used as input. The\ncytoHubba plug-in was then applied, and nine topological algorithms (DNMC, MNC, Degree, EPC, BottleNeck,\nCloseness, Radiality, Betweenness, and Stress) were used\nto rank the nodes. The top five genes identified by each\nalgorithms were selected for hub gene identification.\nTo evaluate the prognostic relevance of the identified\nhub genes, overall survival analysis was performed using\nthe OncoLnc platform ( http://www.oncolnc.org ), which\nintegrates The Cancer Genome Atlas (TCGA) survival\ndata with gene expression profiles. The cancer type was\nset to \"OV\" (ovarian serous cystadenocarcinoma). Survival analysis was performed by comparing patients in\nthe upper 20% (high-expression) and lower 20% (low-\nexpression) groups for each hub gene. Statistical significance was defined as P<0.05.\n\nOur analysis identified distinct sets of DEGs among\nthe study groups. In the  GSE120103  dataset, which included tissue samples from 9 disease-free fertile women, 9 disease-free infertile women, 9 fertile women with\nstage IV endometriosis, and 9 infertile women with\nstage IV endometriosis, a significantly higher number\nof DEGs was identified in infertile women with stage\nIV endometriosis (4,223 genes) than in fertile women\nwith stage IV endometriosis (1390 genes) ( Table 1 ). In\nthe  GSE29450  dataset, which included tissue samples\nfrom 10 primary OCCC tumors and 10 normal ovarian surface epithelia, comparison of OCCC tissues with\nnormal ovarian surface epitheliaidentified2,762 DEGs\n( Table 1 ). To further examine the relationship among\nthese gene sets, a Venn diagram was generated to illustrate the overlapping and unique DEGs identified\nin each comparison. In addition, volcano plots, heatmaps, and comprehensive lists of DEGs and hub genes\nare provided in  Tables S1, S2 , and Figures S1-S6 \n(See Supplementary Online Information at  www.ijfs.\nir ) confirming the distinct expression profiles observed\namong the study groups.\nCharacteristics of the GSE dataset included in this study and the\nnumbers of differentially expressed genes (DEGs)\nThe enriched KEGG pathways included Wnt, Notch,\nMAPK, PI3K/Akt, and TGFB signaling pathways. GO\nenrichment analysis showed that these DEGs were significantly associated with immune response, cell proliferation, angiogenesis, cell adhesion, and cell migration\n(Figes. 1 - 3 ).\nFunctional enrichment analysis of DEGs in fertile women with stage IV endometriosis. DEGs identified infertile women with stage IV endometriosis\nfrom the  GSE120103  dataset (n=9) were subjected to functional enrichment analysis.  A.  KEGG pathway enrichment analysis.  B.  GO biological process enrichment analysis.  C.  GO molecular function enrichment analysis. DEGs; Differentially expressed genes, KEGG; Kyoto Encyclopedia of Genes and Genomes,\nand GO; Gene Ontology.\nFunctional enrichment analysis of DEGs in infertile women with stage IV endometriosis. DEGs identified in infertile women with stage IV endometriosis from the  GSE120103  dataset (n=9) were subjected to functional enrichment analysis.  A.  KEGG pathway enrichment analysis.  B.  GO biological\nprocess enrichment analysis.  C.  GO molecular function enrichment analysis. DEGs; Differentially expressed genes, KEGG; Kyoto Encyclopedia of Genes and\nGenomes, and GO; Gene Ontology.\nFunctional enrichment analysis of DEGs in ovarian clear cell carcinoma. DEGs identified between ovarian clear cell carcinoma tissues (n=10) and\nnormal ovarian surface epithelium samples (n=10) from the  GSE29450  dataset were subjected to functional enrichment analysis.  A.  KEGG pathway enrichment analysis.  B.  GO biological process enrichment analysis.  C.  GO molecular function enrichment analysis. DEGs; Differentially expressed genes, KEGG;\nKyoto Encyclopedia of Genes and Genomes, and GO; Gene Ontology.\nFor further analysis, two gene groups were selected.\nGroup A consisted of 481 genes shared between the\nDEGs from infertile women with endometriosis in\n GSE120103  and the DEGs from  GSE29450 , whereas Group B consisted of 123 genes shared between\nthe DEGs from fertile women with endometriosis in\n GSE120103  and the DEGs from  GSE29450 . KEGG\nand GO enrichment analyses revealed distinct functional characteristics for the two groups. Group A\ngenes were enriched in cancer-related, inflammatory\nand metabolic pathways, including the PI3K-Akt signaling pathway. Group B genes were primarily enriched\nin immune response, stem cell signaling, and cancer\n-related pathways (Figes .4 ,  5 ).\nFunctional enrichment analysis of shared DEGs between infertile endometriosis and ovarian clear cell carcinoma. Common DEGs between infertile\nwomen with stage IV endometriosis from  GSE120103  (n=9) and ovarian clear cell carcinoma samples from  GSE29450  (n=10) were subjected to functional\nenrichment analysis.  A.  KEGG pathway enrichment analysis.  B.  GO biological process enrichment analysis.  C.  GO molecular function enrichment analysis.\nDEGs; Differentially expressed genes, KEGG; Kyoto Encyclopedia of Genes and Genomes, and GO; Gene Ontology.\nFunctional enrichment analysis of shared DEGs between fertile endometriosis and ovarian clear cell carcinoma. Common DEGs between fertile\nwomen with stage IV endometriosis from  GSE120103  (n=9) and ovarian clear cell carcinoma samples from  GSE29450  (n=10) were subjected to functional\nenrichment analysis.  A.  KEGG pathway enrichment analysis.  B.  GO biological process enrichment analysis.  C.  GO molecular function enrichment analysis.\nDEGs; Differentially expressed genes, KEGG; Kyoto Encyclopedia of Genes and Genomes, and GO; Gene Ontology.\nPPI network analysis revealed distinct module organizations between the two groups. In group A, eight significant modules were identified, including five modules\ncontaining three nodes each, 2 two modules containing\nfour nodes each, and one module containing five nodes.\nGroup B exhibited six significant modules, consisting\nof one module with three nodes, two modules with four\nnodes each, one module with 12 nodes, and one module\nwith 10 nodes. These findings indicate differences in network organization between the two groups, suggesting\npotential variations in the underlying biological processes\nand regulatory mechanisms ( Figes.S3, S4 , See Supplementary Online Information at  www.ijfs.ir ).\nHub gene analysis identified 17 hub genes in group A,\nincluding  IGF1, FGF1, FGFR3, EPHX2, INA, BIRC5,\nPPIG, KIF20A, CYP2C9, NES, TLR4, RHO, SOX2, LY96,\nFCGR2A, S100B,  and e In group B, 12 hub genes\nwere identified, including  SLC17A8, HMGA2, CCND2,\nMSRB3, NPY, SFRP1, MDM2, CYP19A1, NCOA3, CD86,\nCDC25A,  and  CXCL13  ( Tables S1  and\n S2 , See Supplementary Online Information at  www.ijfs.ir ).\nSurvival analysis revealed that two of the 17 hub genes\nin Group A ( BIRC5  and  EPHX2 ) and one of 12 hub genes\nin Group B ( NCOA3 ) were significantly associated with\noverall survival in the OV cohort ( Figes.S5,6 , See Supplementary Online Information at  www.ijfs.ir ). These\nfindings suggest that these hub genes may have potential\nprognostic relevance and warrant further validation in independent cohorts and experimental studies\n\nEndometriosis is chronic gynecological disease characterized by the presence of endometrial-like tissue outside the uterus ( 1 ). It affects approximately 10-15% of\nwomen of reproductive age and up to 30-50% of women\nwith pelvic pain or infertility ( 8 ). Clinical presentation is\nhighly variable, and some affected women remain asymptomatic, contributing to delays in diagnosis ( 8 ). Given the\nabsence of accurate noninvasive diagnostic methods, the\nidentification of reliable biomarkers remains an important\nresearch priority.\nOCCC represents a distinct subtype of epithelial ovarian cancer, accounting for only approximately 5-10% of\nall cases ( 4 ). Unlike the more common high-grade serous\nsubtype, OCCC is thought to arise from a different cell\nof origin in the ovary and exhibits distinct clinicopathological characteristics ( 9 ). Although less common, OCCC\ncan be an aggressive form of ovarian cancer and is often\ndiagnosed at an advanced stage due to its relatively subtle clinical presentation. The exact etiology of OCCC is\nunclear. However, several studies suggest a possible association with endometriosis ( 10 ). However, given that\nOCCC can be more resistant to standard chemotherapy\nthan other subtypes of ovarian cancer, early detection is\ncritical for improving treatment outcomes ( 11 ).\nEnrichment analysis of DEGs identified in fertile women with stage IV endometriosis showed significant associations with various biological processes and molecular\nfunctions. KEGG pathway analysis revealed several enriched pathways, including the Wnt signaling pathway\nand steroid hormone biosynthesis. Dysregulation of these\npathways may influence cell proliferation, differentiation,\nand migration, thereby contributing to the abnormal endometrial growth. The Wnt signaling pathway, which plays a\nfundamental role in tissue development and homeostasis,\nhas been implicated in endometriosis Similarly, steroid\nhormone biosynthesis is essential for regulating the menstrual cycle and endometrial function, and alterations in\nthis pathway can contribute to disease development. GO\nenrichment analysis further indicated the involvement of\nthese genes in epithelial structure maintenance, digestive\nepithelium maintenance, nervous system development,\nimmune system regulation, steroid hormones biosynthesis, cell communication, response to stimuli, and growthrelated processes. Molecular function analysis also highlighted their involvement in hormone-related activities.\nEnrichment analysis of DEGs identified in infertile\nwomen with stage IV endometriosis revealed multiple\nbiological pathways potentially associated with endometriosis, infertility, and OCCC. KEGG enrichment analysis\nrevealed several enriched pathways, including cytokine-cytokine receptor interaction, chemokine signaling,\nPI3K-Akt, MAPK, and Notch signaling pathways. Cytokine-cytokine receptor interaction and chemokine signaling play essential roles in immune cell recruitment and\nactivation and are closely associated with chronic inflammatory responses, a hallmark of both endometriosis and\ninfertility. The PI3K-Akt and MAPK signaling pathways\nregulate cell growth, proliferation, and differentiation and\nhave been implicated in the pathogenesis of both endometriosis and OCCC. GO enrichment further indicated\nthe involvement of these genes in angiogenesis, immune\nresponse, cellular processes related to proliferation and\nadhesion. Molecular function analysis also highlighted\nenrichment in growth factor activity, cytokine receptor\nbinding, hormone activity, and angiogenesis.\nKEGG enrichment analysis of DEGs between OCCC\nand normal ovarian surface epithelia identified several\nsignificantly enriched pathways, including pathways\nin cancer, thePI3K-Akt signaling pathway, the TGFβsignaling pathway, cell adhesion molecules, extracellular matrix–receptor (ECM-receptor) interaction, the cell\ncycle, the p53 signaling pathway, and focal adhesion.\nGenes involved in the PI3K-Akt and TGF-βsignaling\npathways play important roles in cell growth, proliferation, survival, and immune regulation. In addition, genes\ninvolved in cell adhesion molecules, ECM-receptor interaction, and focal adhesion contribute to cell-cell and\nECM interactions and may promote ovarian cancer cell\nmigration and metastasis. GO biological process enrichment further indicated associations with cell cycle regulation, cell adhesion, and cell migration, response to estrogen, and immune response regulation.\nThe  BIRC5  gene encodes the survivin protein, which is\ninvolved in cell division and cell survival ( 12 ). It plays\nan essential role in mitosis, by ensuring accurate chromosome segregation during cell division ( 12 ). However, survivin exerts anti-apoptotic functions that may promote the\nsurvival of damaged or abnormal cells, thereby contributing to tumor development. ( 11 ). The role of  BIRC5  in\nfertility remains under investigation. Dysregulated BIRC5\nexpression has been associated with issues related to oocyte maturation and embryo implantation, suggesting a\npotential role in female fertility ( 13 - 15 ). Nevertheless, the\nunderlying mechanisms linking  BIRC5  to human fertility\nhave not yet been fully elucidated.\nIncreased  BIRC5  expression has been reported in ectopic endometrial tissues from patients with endometriosis,\nsuggesting a potential role in the abnormal proliferation\nand maintenance of these displaced endometrial lesions\n( 16 ). Furthermore, the anti-apoptotic function of  BIRC5 \nmay contribute to the resistance of these ectopic lesions\nto therapeutic interventions ( 16 ). Elevated  BIRC5  expression has also been associated with a more aggressive\nOCCC phenotype and poorer prognosis ( 17 ). Its ability\nto promote cell survival and inhibit apoptosis may facilitate tumor progression and resistance to therapeutic interventions ( 17 ,  18 ). Further investigation of the molecular\nmechanisms through which  BIRC5  contributes to OCCC\ndevelopment and progression may provide insights into\npotential therapeutic strategies.\nThe  EPHX2  gene encodes the enzyme epoxide hydrolase 2, which plays a crucial role in the metabolism of\nepoxide compounds generated during cellular processes\n( 19 ). Impaired metabolism of these compounds may\nlead to DNA damage and disruption of cellular functions\n( 19 ).  EPHX2  contributes to the detoxification of these\nmetabolites, thereby helping to maintain cellular homeostasis. The association between  EPHX2  and infertility remains incompletely understood. Genetic variations in the\n EPHX2  gene has been associated with an elevated risk\nof infertility in females ( 20 ). These variants may impair\nepoxide metabolism, potentially affecting oocyte quality or embryo development ( 21 ). However, further studies are needed to validate this association and clarify the\nunderlying mechanisms. Altered  EPHX2  expression has\nalso been reported in ectopic endometrial tissue compared\nwith normal endometrium ( 22 ), suggesting potential role\nin the pathogenesis of endometriosis. However, the biological significance of this finding remains unclear and\nrequires further investigation. A better understanding of\nthe role of  EPHX2  may provide insights into its potential\ndiagnostic and therapeutic relevance in OCCC.\nThe nuclear receptor coactivator 3 (NCOA3), also\nknown as AIB1, SRC-3, and TRAM-1, is a transcriptional coactivator protein ( 23 ). Elevated levels of NCOA3\nhave been widely reported in hormone-dependent cancers\nsuch as breast cancer (BRCA), prostate adenocarcinoma\n(PRAD), and ovarian cancer (OVCA) ( 24 ). NCOA3 has\nbeen implicated in the proliferation of both hormonedependent and hormone -independent prostate and breast\ncancer cells ( 25 - 27 ). Increased expression of this coactivator has also been associated with recurrence following\nhormone-deprivation therapy through activation of the\nMAPK signaling pathway ( 28 ). These findings support\nthe potential relevance of NCOA3 expression as a candidate biomarker and warrant further investigation.\nCollectively, the dysregulation of  BIRC5, EPHX2 , and\n NCOA3  may represent candidate molecular features shared\nbetween infertility-associated endometriosis and OCCC.\nHowever, direct comparisons between fertile and infertile\nendometriosis samples would be more appropriate for isolating fertility-specific effects. Such analyses are recommended in future studies to better distinguish baseline fertility-related transcriptional differences from disease-specific alterations. The overlapping genes identified in this\nstudy should be regarded as hypothesis-generating findings\nthat suggest potential shared molecular pathways, rather\nthan evidence of causality or disease progression.\nSeveral limitations should be considered when interpreting these findings. The relatively small sample size\nlimited clinical information, and lack of independent validation and experimental confirmation may have affected\nthe robustness of the results. The identification of common DEGs across independently analyzed datasets from\ndifferent conditions does not support inferences regarding\nbiological progression or causality. In addition, because\nthe TCGA-OV cohort includes mixed ovarian cancer\nhistological subtypes and lacks fertility-related clinical\ninformation, the survival analysis should be interpreted\nonly as an assessment of the general prognostic relevance\nof hub genes in ovarian cancer rather than OCCC-specific\nor fertility-related outcomes. Therefore, further validation\nin appropriate experimental models and independent clinical cohorts is required before any diagnostic, prognostic,\nor biological conclusions can be drawn.\n\nThis study provides preliminary bioinformatics-based\nidentification of DEGs and hub genes potentially linking\nendometriosis and OCCC, with particular consideration\nof fertility status. Key genes such as  NCOA3, BIRC5,  and\n EPHX2  were highlighted as candidates for further investigation due to their potential roles in cell survival, hormone\nsignaling, and epoxide metabolism. The findings should\nbe considered hypothesis-generating and interpreted with\nappropriate caution. Future studies involving larger, multi-center cohorts, experimental functional validation, and\ndirect comparisons between fertile and infertile women\nwith endometriosis are warranted to validate and extend\nthese findings.","source_license":"CC0","license_restricted":false}