Elucidating the role of transcription factors in molecular pathways underlying infertility in endometriosis: a bioinformatics approach

Objective Endometriosis is a multifactorial inflammatory disease characterized by the growth of endometrial-like tissue outside the uterus, frequently causing chronic pelvic pain and infertility. This study aimed to identify conserved differentially expressed genes (DEGs) and robust hub genes across heterogeneous cohorts to elucidate key molecular mechanisms in endometriosis pathogenesis.

Transcriptomic data from four independent microarray datasets (GSE7305, GSE120103, GSE23339, GSE51981) were analyzed using the Limma package in R to identify DEGs. Common DEGs were intersected across datasets, and a protein-protein interaction (PPI) network was constructed using STRING and visualized in Cytoscape (version 3.10.4). Hub genes were selected through a multi-metric approach in cytoHubba (MCC, Degree, EPC, DMNC). Functional enrichment (GO and pathway) and GeneMANIA network analysis were performed to explore biological roles.

Intersection of DEGs revealed conserved expression signatures despite cohort heterogeneity. A consensus set of 20 hub genes was identified, with 12 (60%) functioning as transcription factors. Enrichment analysis highlighted transcriptional regulation, DNA binding, developmental processes (e.g., tissue development, pattern specification, embryonic morphogenesis), and epithelial barrier components (e.g., tight junctions). Pathway analysis implicated nuclear receptor signaling, developmental biology pathways, and WNT signaling.GeneMANIA analysis confirmed strong co-expression and physical interactions among hub genes, particularly in transcriptional and developmental functions.

The conserved hub genes, enriched in transcription factors, suggest central roles for transcriptional dysregulation and developmental pathways in endometriosis across diverse populations. These findings provide robust candidates for further validation as potential biomarkers or therapeutic targets.

Biomarker, Endometriosis, Transcription factors, Hub gene, Developmental pathways Elucidating the role of transcription factors in molecular pathways underlying infertility in endometriosis: a bioinformatics approach Niloofar Shahgholi1, Zahra Noormohammadi1*, Ashraf Moini2,3,4 and Morteza Karimipoor5 Page 2 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29

Endometriosis is a frequently chronic inflammatory con - dition in women, characterized by the presence of ecto - pic endometrial-like tissue outside the uterus. These sites are primarily located in the pelvic region, including the ovaries, ligaments, peritoneal surfaces, and the bowel and bladder [1]. The primary symptoms of endometriosis include chronic pelvic pain, severely painful menstrual periods (dysmenorrhea), and infertility [ 2]. The theory of retrograde menstruation, also known as Sampson’s the - ory, suggests that menstrual blood, which contains endo - metrial cells, flows backward through the fallopian tubes into the peritoneal cavity. Once there, these cells have the potential to implant and proliferate [1]. Endometriosis is also a complex inflammatory condi - tion that impacts women’s reproductive health globally, from adolescence to menopause, crossing ethnic and socio-economic boundaries, and leading to significant health challenges [ 3]. Based on familial studies, the inci - dence of endometriosis is influenced by a genetic factor, accounting for approximately 50% of the susceptibility to the condition [ 4]. The malfunction of the genes involved in pathways such as steroidogenesis, sex hormone recep - tors, inflammation, immune response, tissue remodeling, angiogenesis, metabolism regulation, and DNA repair could be associated with endometriosis [ 5]. However, the precise genetic and pathophysiological basis of endome - triosis remains unclear [ 6]. Moreover, diagnosis of endo - metriosis is often delayed by 4 to 11 years from symptom onset until surgical confirmation. While laparoscopy is the gold standard for diagnosis, it is an invasive pro - cedure, highlighting the need for improved diagnostic

[ 7]. Using a biomarker or a set of biomarkers that can be easily measured, typically noninvasive, and may assist the clinician in diagnosing and tracking the treatment response is critical [ 8]. Current biomarkers recommended for endometriosis diagnosis include Can - cer Antigen 125 (CA-125), Cancer Antigen 199 (CA-199), Urocortin (UCN), and Interleukin-6 (IL-6). However, none of these new markers have yet been approved as an exclusive diagnostic biomarker for endometriosis [9]. The exact mechanisms by which endometriosis causes infertility are not fully understood. In patients with endometriosis, implantation failure occurs in the endo - metrium, contributing to infertility. One of the factors involved is progesterone resistance in the eutopic endo - metrium, which leads to the abnormal activation of the WNT/β-catenin signaling pathway. This activation

in the overexpression of WNT target genes such as Homeobox A10 (HOXA10) and Matrix Metallopro - teinases 9 and 2 (MMP-9 and MMP-2). These changes may impair endometrial receptivity during the critical window of implantation [ 10]. Emerging evidence also highlights the potential of targeting angiogenic pathways, such as VEGFR-2 signaling, to improve oocyte quality in women with endometriosis [ 11]. Accordingly, the iden - tification of major pathways implicated in endometri - osis-related infertility could facilitate the development of targeted therapies to improve fertility outcomes in affected women. In the present study, we analyzed differentially expressed genes derived from independent endometrio - sis cohorts with heterogeneous populations and demo - graphic backgrounds. By intersecting DEGs across these datasets, we aimed to identify conserved gene expression signatures and pathway regulation patterns that are con - sistently associated with endometriosis despite popula - tion diversity. Furthermore, we sought to characterize key network pathways and hub genes as robust candidate biomarkers in the pathogenesis of endometriosis.

Data acquisition We downloaded 4 endometriosis-associated datasets (GSE51981, GSE7305, GSE23339, and GSE120103) from the Gene Expression Omnibus (GEO) database. Based on the GPL570 platform [HG-U133_Plus_2], Affymetrix Human Genome U133 Plus 2.0 Array with 54,675 entries, GSE51981 contains the gene expression profiles of 148 American women with and without endometriosis, and GSE7305 involves 20 endometrium samples of Cauca - sian women with ovarian endometriosis and with normal conditions. Additionally, GSE23339 was on the GPL6102 platform (Illumina human-6 v2.0 expression bead chip) with 48,702 entries, which included endometrial sam - ples from American women (10 cases of endometrioma and 9 cases of non-endometriosis). GSE120103 was on the GPL6480 platform, Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F (Probe Name ver - sion), with 41,108 entries, including 36 samples from Indian women with and without endometriosis. Differentially expressed genes identification Raw microarray data from four independent endome - triosis cohorts (GSE7305, GSE51981, GSE23339, and GSE120103) were retrieved from GEO using the GEO - query package. Datasets were generated on different plat- forms (Affymetrix, Illumina, Agilent), requiring careful handling of cross-platform heterogeneity. Probe identi - fiers were mapped to official gene symbols, and multiple probes per gene were collapsed using the median. Gene- level matrices were merged across studies, and missing values were imputed with the median. Batch effects at the dataset level were corrected using limma’s removeBatchEffect function, while platform- level correction was not applied due to confounding between platform and dataset. PCA confirmed effec - tive batch correction, preserving biological differences Page 3 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 between endometriosis and control samples (Supplemen- tary Figure S1). Figure S1. PCA of combined gene expression data from four microarray datasets after batch effect correc - tion. Samples separate clearly by disease status (triangles: endometriosis; circles: control) along PC1, rather than by dataset (GSE120103: pink, GSE23339: green, GSE51981: blue, GSE7305: purple), indicating successful removal of technical variation and highlighting conserved biological differences. Differential expression analysis was performed for each dataset using limma, retaining platform-specific normal - ization (e.g., RMA for Affymetrix). Genes with |log₂ fold change| >1.5 and adjusted P < 0.05 were considered sig - nificant. Robust DEGs were defined as those consistently detected across all four cohorts, prioritizing reproduc - ible, disease-associated expression changes over dataset- specific effects. Construction of differential gene core modules and screening of hub genes The STRING database (Version 12.0) ( h t t p s : / / c n . s t r i n g - d b . o r g / ) was used to provide comprehensive information on experimental and predicted findings about protein- protein interactions between intersecting genes of the four selected microarray datasets. The network obtained from STRING was imported into Cytoscape software (Version 3.10.4) to visualize the interactions between proteins. The cytoHubba plugin is utilized to identify hub genes by calculating topological measurements within the protein network. Gene ontology enrichment analysis Enrichment analysis was conducted to elucidate the bio - logical processes of overlapping differentially expressed genes (DEGs) using the online platform Web-based Gene Set Analysis Toolkit (WebGestalt, h t t p s : / / w w w . w e b g e s t a l t . o r g / ) . The list of gene names was uploaded to WebGe - stalt, and the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/) d a t a b a s e was selected to identify the biological and functional path - ways associated with these genes, using a cutoff point of FDR < 0.25. Network construction of hub genes The online tool GeneMANIA ( https://genemania.org/) was used to construct the hub genes’ interaction net - work. The network weighting method was employed for this construction, where the weighting method was automatically selected. The maximum number of resul - tant genes and resultant attributes was set to 20 and 10, respectively. Identification of key transcription factors in gene regulation networks The human transcription factor dataset [ 12], comprising 1,639 transcription factors (TF), was utilized to identify which of the selected hub genes function as transcription factors.

Differential gene identification from microarray datasets This study aimed to identify differentially expressed genes (DEGs) from four microarray datasets (GSE120103, GSE7305, GSE51981, and GSE23339) (Table  1). Based on the Limma package, we identified 49 DEGs in GSE7305, 396 in GSE23339, 205 in GSE51981, and 70 in GSE120103. Each dataset’s upregulated and downregu - lated genes were visualized using volcano plots (Fig. 1). Comparison of overlapped genes among the four GEO datasets Among the up-regulated DEGs, three common genes were identified between GSE120103 and GSE23339, two common genes between GSE120103 and GSE51981, one gene between GSE7305 and GSE23339, and one gene between GSE120103 and GSE7305. Among the down- regulated DEGs, 189 common genes were identified between GSE23339 and GSE51981, six common genes between GSE23339, GSE51981, and GSE7305, and one gene between GSE23339, GSE51981, and GSE120103 (Table S1). Venn diagram analysis identified genes con - sistently shared across all four datasets, including 7 com - monly up-regulated and 196 commonly down-regulated genes (Fig. 2). Table 1 The information related to the analyzed GEO datasets in this study Dataset Platform Method Tissue Type Menstrual Phase Normal (No.) Endome- triosis (No.) Total (No.) PMID GSE7305 GPL570 Microarray Eutopic endometrium Secretory phase 10 10 20 17,640,886 GSE120103 GPL6480 Microarray Eutopic endometrium Proliferative and secretory 18 18 36 30,760,267 GSE23339 GPL6102 Microarray Eutopic endometrium Not specified 9 10 19 21,436,257 GSE51981 GPL570 Microarray Eutopic endometrium Proliferative, early secretory, mid-secretory 34 114 148 25,243,856 Page 4 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 Integrated analysis of differentially expressed genes: construction and evaluation of PPI networks A total of 202 differentially expressed genes (DEGs) were identified across the four datasets. Protein-pro - tein interaction (PPI) information for these DEGs was obtained from the STRING database (version 12.0) with a minimum required interaction score of 0.4 (medium confidence). The resulting network was imported into Cytoscape (version 3.10.4) for visualization and analysis, comprising 142 nodes and 307 edges (Figure S2). Hub genes were identified using the cytoHubba plu - gin. The top 20 genes were independently ranked by four algorithms in cytoHubba: MCC, Degree, Edge Percolated Component (EPC), and Density of Maximum Neigh - borhood Component (DMNC). Given the high overlap among these rankings, a consensus set of 20 hub genes Fig. 1 Volcano plots depicting the differentially expressed genes (DEGs) identified from GSE7305, GSE120103, GSE23339, and GSE51981. The criteria used for identifying DEGs were |logFC2| > 1.5 and p-value < 0.05. Upregulated genes are highlighted in red, while downregulated genes are marked in blue Page 5 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 was selected based on consistent high performance across methods (Fig.  3). These hub genes include ESR1, EPCAM , MUC1, MMP9, CLDN7 , MET, RAB25 , PRSS8 , PGR, DKK1, FOXA2, MSX1, HOXB4, HOXA9, HOXA10, PAX8, GATA2, SOX17, RUNX1, and MAL2. Enrichment analysis of endometriosis-related hub genes Pathway and Gene Ontology (GO) enrichment analyses were performed using WebGestalt to explore the func - tional roles of the 20 hub genes, with pathways sourced from KEGG and Reactome databases. GO analysis was conducted across three categories: biological process (BP), molecular function (MF), and cellular component (CC). KEGG pathway analysis indicated enrichment in cancer-related pathways, including pathways in cancer, proteoglycans in cancer, and tight junction (Fig.  4-A). In contrast, Reactome analysis highlighted significant involvement in developmental biology pathways, such as signaling by nuclear receptors, RUNX1-regulated tran - scription, WNT signaling, gastrulation, formation of definitive endoderm, estrogen-dependent gene expres - sion, and ESR-mediated signaling (Fig. 4-A). GO biological process analysis revealed that hub genes are predominantly associated with tissue development, regionalization, pattern specification processes, epi - thelium development, embryonic morphogenesis, and positive regulation of transcription/DNA-templated transcription/RNA biosynthetic processes by RNA poly - merase II (Fig.  4-B). Molecular function analysis showed enrichment in transcription regulatory region nucleic acid/DNA binding, sequence-specific double-stranded DNA binding, and DNA-binding transcription acti - vator activity (Fig.  4-B). For cellular component, hub genes were mainly localized to transcription regulator complexes, protein-DNA complexes, chromatin/chro - mosomes, tight junctions, and plasma membrane com - ponents (apical/basal/lateral) (Fig. 4-B). These results suggest that the hub genes contribute to endometriosis pathogenesis through transcriptional regulation, disruption of epithelial barrier function (e.g., tight junctions), and dysregulation of developmental processes, consistent with the hormonal and structural abnormalities observed in the disease. The GeneMANIA database was used to construct a functional association network for the 20 hub genes, illustrating their potential interactions and shared bio - logical roles (Fig.  5). The network incorporates multiple types of evidence, including co-expression, physical interactions, predicted interactions, genetic interactions, co-localization, shared protein domains, and pathway associations, with line colors indicating the type of evi - dence (as shown in the legend). Node size and pie chart composition reflect the relative contribution of each evi - dence type to the gene’s connectivity, highlighting genes with stronger functional associations. Genes with higher connectivity and involvement in multiple shared functions include FOXA2, SOX17, PAX8, HOXA9, HOXA10 , HOXB4 , HOXB7 , HOXB8 , GATA2 , RUNX1, and DKK1 (Fig.  5). These genes participate in enriched processes such as pattern specification, region - alization, embryonic morphogenesis, embryonic organ development, and positive regulation of transcription by RNA polymerase II, consistent with their roles in endo - metrial development and transcriptional regulation. Fig. 2 A Venn diagram illustrating the overlap of DEGs across the datasets. A Upregulated overlapped DEGs. B Downregulated overlapped DEGs Page 6 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 Identification of hub genes functioning as transcription factors Among the 20 identified hub genes, 12 were found to act as transcription factors. A Venn diagram illustrating the overlap between the 1,639 human transcription factors and the identified hub genes is presented in Fig. 6.

Endometriosis is a chronic inflammatory disease that predominantly affects women of reproductive age. It is characterized by symptoms such as irregular menstrua - tion, menorrhagia, and infertility [13]. Although the exact mechanism of the disease remains unclear, substantial evidence supports its multifactorial nature, influenced by anatomical, hormonal, immunological, estrogenic, genetic, epigenetic, and environmental factors [ 14]. Numerous genes have been identified as playing roles in the pathogenesis of endometriosis, many of which have been discovered through experimental studies. How - ever, microarray technologies and RNA sequencing have emerged as powerful tools for identifying potential bio - markers in endometriosis research. This allows for a com- prehensive analysis of expression profiles by identifying differentially expressed genes (DEGs) between endome - triosis patients and healthy controls [15]. In this study, an integrative analysis of four indepen - dent microarray datasets identified 20 hub genes that were consistently altered across cohorts, providing a robust molecular signature of endometriosis. Among these, 12 hub genes function as transcription fac - tors (TFs )—including FOXA2, SOX17, PAX8, HOXA9, HOXA10, HOXB4, HOXB7, RUNX1, GATA2, ESR1, PGR, and MSX1— highlighting a central role of transcriptional regulation in endometrial homeostasis and pathology [16– 18]. Functional enrichment analyses of these hub genes revealed coordinated roles in transcriptional regulation, tissue development, and epithelial structure maintenance. Gene Ontology (GO) molecular function terms indicated that these TFs primarily act as DNA-binding activators, regulating downstream genes essential for uterine gland formation and endometrial receptivity. Transcription fac- tors can lead to abnormal biological outcomes in endo - metriosis, such as increased estrogen levels, immune system inflammation, and enhanced angiogenesis [ 19]. For instance, FOXA2 acts as a pioneer TF modulating Fig. 3 Protein-protein interaction (PPI) network of 20 consensus hub genes in endometriosis. Node size and color reflect degree centrality (larger/darker nodes indicate higher connectivity); edges represent STRING interaction confidence (thicker/darker edges indicate higher score) Page 7 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 chromatin accessibility and glandular development, while SOX17 ensures epithelial identity and proper glandular morphogenesis [ 20]. PAX8 regulates epithelial differen - tiation and is essential for the homeostatic regeneration and maintenance of both luminal and glandular endo - metrial epithelium [21], and members of the HOX family (such as HOXA10 and HOXA11) are pivotal in uterine tissue patterning and endometrial development [22]. Endometriosis is a complex condition that involves hormonal, neurological, and immunological factors [ 23]. The imbalance of ovarian steroid hormones, specifically Progesterone (P4) and Estrogen (E2), along with the dys - regulation of their downstream signaling targets, plays Fig. 4 Dot plots of enrichment and Gene Ontology (GO) analysis of the top 60 hub genes. A Enriched pathway enrichment analysis conducted through KEGG and Reactome. B Gene Ontology (GO) analysis for the top 20 hub genes Page 8 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 a crucial role in the development and persistence of the disorder [ 24]. Hormonal regulation through ESR1 and PGR, also identified among hub genes, reinforces the crucial interplay between steroid signaling and transcrip - tional control. Dysregulation of these nuclear receptors likely disrupts downstream transcriptional networks, contributing to abnormal proliferation, inflammation, and compromised endometrial receptivity—hallmarks of endometriosis [25, 26]. Protein-protein interaction and GeneMANIA analy - ses further revealed extensive co-expression and physi - cal interactions among hub genes, forming a cohesive functional module enriched for developmental processes such as pattern specification, regionalization, embryonic morphogenesis, and organ development. These obser - vations suggest that transcriptional dysregulation and altered developmental signaling may affect adenogenesis and epithelial barrier integrity, processes that have been previously implicated in ectopic lesion establishment and infertility in endometriosis [ 27– 29]. GO cellular component terms related to tight junctions and plasma membrane structures support the relevance of these hub genes in maintaining epithelial integrity, where disrup - tion may promote lesion establishment and invasion [30]. Importantly, our results underscore the interconnected nature of transcriptional, developmental, and hormonal pathways in endometriosis. Hub genes such as HOXA10, HOXA9, HOXB4, FOXA2, SOX17, PAX8, RUNX1, and GATA2 appear to act synergistically, orchestrating gene networks critical for endometrial morphogenesis, gland formation, and hormone responsiveness. The consistent differential expression of these genes across multiple datasets strengthens their candidacy as potential bio - markers or therapeutic targets. While literature supports the functional roles of FOXA2, SOX17, PAX8, and HOX family members in Fig. 5 Gene interaction network of the top 20 hub genes visualized using GeneMANIA Page 9 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 uterine development and fertility [ 17, 18]. Our integra - tive transcriptomic analysis across multiple independent cohorts identified a set of shared differentially expressed hub genes relevant to endometriosis. These shared genes were selected based on consistent up- or downregulation across datasets, irrespective of the ancestral background or population heterogeneity of individual study partici - pants. Consequently, this approach emphasizes genes with robust involvement in endometriosis pathogenesis rather than population-specific effects. Deeper stratified analyses (e.g., by disease stage, phenotype, or infertility status) were not feasible due to the lack of harmonized clinical metadata across the included GEO datasets. Nonetheless, limitations include the absence of direct functional validation, a lack of detailed patient fertility status data, and the need for proteomic corroboration.

In this study, an integrative analysis of four indepen - dent transcriptomic datasets identified a robust set of 20 hub genes that were consistently altered in endome - triosis. Among these, 12 transcription factors—includ - ing FOXA2, SOX17, PAX8, and members of the HOX family—play central roles in transcriptional regulation, uterine development, and endometrial homeostasis. Functional enrichment and network analyses revealed coordinated roles in developmental processes, gland formation, and epithelial integrity, highlighting mecha - nisms potentially contributing to infertility and lesion establishment in endometriosis. Importantly, the shared differential expression of these hub genes across cohorts was observed independently of participant ancestry, emphasizing disease-specific molecular signatures rather than population-specific effects. These findings provide a foundation for future functional studies and may inform the development of biomarkers or targeted therapies for the treatment of endometriosis. Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . 1 1 8 6 / s 4 3 0 4 3 - 0 2 6 - 0 0 3 1 0 - 8. Supplementary Material 1. Supplementary Material 2. Table S1. Overlapped DEGs among 4 datasets, GSE7305, GSE120103, GSE23339, and GSE51981. Supplementary Material 3. Figure S1. PCA of combined gene expression data from four microarray datasets after batch effect correction. Samples separate clearly by disease status (triangles: endometriosis; circles: control) along PC1, rather than by dataset (GSE120103: pink, GSE23339: green, GSE51981: blue, GSE7305: purple), indicating successful removal of techni- cal variation and highlighting conserved biological differences. Figure S2. Protein-protein interaction (PPI) network of the overlapped DEGs among the datasets. Authors’ contributions N.S. and Z.N. contributed to the Conceptualization, Methodology, and Investigation. Z.N., A.M., and M.K. provided Supervision and Project Administration. All authors were involved in Writing, reviewing, and Editing, and approved the final version of the manuscript for submission. Funding No grants were received to support the conduct of this study. Fig. 6 Venn Diagram showing the overlap between the list of human transcription factors (TFs) and the high-scoring genes involved in the gene interac- tion network Page 10 of 10 Shahgholi et al. Middle East Fertility Society Journal (2026) 31:29 Data availability No datasets were generated during the current study. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Received: 24 September 2025 / Accepted: 23 February 2026

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