Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq

Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq Nooshin Ghahramani , View ORCID Profile Ali Hashemi , Seçil Eroğlu , Elaheh Esmaeili Kordlar doi: https://doi.org/10.1101/2025.08.10.669560 Nooshin Ghahramani 1 Department of Animal Science, Division of Animal Breeding and Genetics, Faculty of Agriculture, Urmia University , Urmia, Iran Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ali Hashemi 1 Department of Animal Science, Division of Animal Breeding and Genetics, Faculty of Agriculture, Urmia University , Urmia, Iran Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ali Hashemi For correspondence: a.hashemi50{at}gmail.com Seçil Eroğlu 2 Department of Medical Biology, Faculty of Medicine, Gaziantep Islam Science and Technology University , Gaziantep, Turkey Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elaheh Esmaeili Kordlar 3 Chemical Sciences, Faculty of Chemistry, University of Padova , Padova, Italy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract Endometriosis (EMs) is a complex and prevalent gynecological disorder with a significant genetic component, posing a major clinical challenge in reproductive medicine due to multifactorial inheritance patterns and the involvement of gene–environment interactions in pathophysiology. However, despite extensive research, reliable diagnostic biomarkers for EMs have yet to be identified. We utilized bulk transcriptome sequencing data obtained from the Gene Expression Omnibus to identify hub genes involved in EMs . This study was conducted using a system biology analysis, incorporating differential gene expression, meta-analysis of transcriptomic data, functional enrichment analysis, construction of gene co-expression networks, and comprehensive topological analysis to identify key regulatory genes. Bulk RNA -seq analysis revealed significant differential gene expression between healthy and EMs groups. Overall, 603 and 443 meta-genes were discovered using the Fisher and Invorm P-value combination methods, respectively. A total of 427 meta-genes were subjected to functional enrichment analysis, which revealed significant enrichment in several KEGG pathways related to EMs including “Adherens junction,” “p53 signaling pathway,” and “AMPK signaling pathway.” Additionally, Gene Ontology analysis revealed key processes including “Regulation of Anatomical Structure Morphogenesis,” Acetylglucosaminyltransferase Activity” and “Positive Regulation of Intracellular Signal”. Co-expression network analysis identified the turquoise module as a critical functional module, within this significant module, the genes IGFBP7, IGFBP3, and NKAP were identified as EMs hub genes based on high connectivity and central roles in the network. The constructed protein–protein interaction network further highlighted STAR, PLCD3, RPAP2, MSI2, MAS1, TBX1, LIPT1, and SVIL, as key genes. These genes represented high centrality within the network, suggesting potential regulatory and functional significance in the molecular mechanisms underlying EMs . Notably, miR-143-3p, miR-340-5p, miR-410-3p, and miR-302b-5p were implicated in EMs -associated regulatory networks. This integrative approach significantly enhances our understanding of the molecular mechanisms underlying EMs and provides a robust foundation for the development of diagnostic biomarkers. Introduction Endometriosis (EMs) is a painful condition in which tissue that is similar to the inner lining of the uterus grows outside the uterus. EMs often affect the ovaries, fallopian tubes and the tissue lining the pelvis ( 1 ). Those with the condition often suffer from dysmenorrhea, dyspareunia, infertility, and pelvic pain, which negatively impact patients’ quality of life ( 2 ). EMs carries an increased risk of developing ovarian cancer, particularly clear cell carcinomas and ovarian endometrioid carcinomas ( 1 ). EMs is a multifactorial disease with complex host-immune interactions which causes Recurrent Implantation Failure (RIF) , affecting the endometrial receptivity and the embryo implantation process, leading to infertility and pregnancy failure ( 3 ). The immune system plays an important role in the beginning and progression of EMs . In particular, immune cells of the innate and acquired immune system play a key role in the survival and proliferation of endometrial cells outside the uterine cavity ( 4 ). However, its precise prevalence in the population is difficult to determine because it is asymptomatic or subclinical in most cases ( 5 ). Identifying molecular markers associated with EMs is of great significance for improving patient prognosis. Previous studies have suggested that several factors are involved in EMs , including genetic, and immune changes ( 1 , 6 ). Previous researches indicated that Next-generation sequencing (NGS) may provide new clues in the pathogenesis of EMs ( 7 ). High-throughput RNA -sequencing (RNA-Seq) has provided new insight into the contribution of gene expression in disease contexts. Gene markers play an indispensable role in the prevention and diagnosis of diseases, offering novel perspectives for understanding the molecular mechanisms underlying these conditions and enabling the development of targeted therapeutics ( 8 ). In general, evaluating transcriptome datasets facilitates the assessment of overall gene functions and structures to investigate the molecular signatures predictive of certain diseases. Comparing the gene expression profiles of disease tissue to that of a normal healthy tissue is a powerful approach to understand the underlying cellular events in the etiology of any disease ( 9 ). Accordingly, previous studies have investigated differential gene expression through the use of diverse microarray platforms to explore molecular alterations associated with the EMs ( 10 , 11 ). A total of 1309 upregulated and 663 downregulated genes were identified through the analysis of the transcriptomes of eutopic and ectopic endometrial stromal cells ( 12 ). The gene expression levels of BCL6 and LITAF genes have been studied in the eutopic endometrial tissues of women with EMs in comparison with the normal endometrial samples ( 13 ). Significant expression differences were obtained for SPARC, MYC, IGFBP1 and MMP3 genes in the Ems ( 14 ). Identifying molecular diagnostic markers associated with EMs may allow for the early prediction of outcomes in patients and inform targeted treatment strategies, thereby substantially reducing the incidence of EMs ( 15 ). A large number of genes were identified in the occurrence of EMs , affecting immune system regulation, cell adhesion, and vascularization ( 2 ). However, there are crucial challenges of individual research gene-level studies, such as heterogeneity among datasets, high cost, and relatively small dataset size, which could provide false positive or negative findings ( 16 ). Meta-analysis as a traditional method was implemented to successfully deal with the genetic study’s challenges, to identify reliable EMs -related biomarkers ( 17 ). Thus far, the exact reason for EMs is still not clear and, therefore meta-data analysis may provide further knowledge to solve the molecular pathogenesis complexity of such conditions. The genome wide association (GWA) meta-analysis was identified 5 novel loci significantly associated with EMs risk ( 18 ). Furthermore, the biological processes that are involved with the differentially expressed genes (DEGs) and the functional enrichment analysis were also studied in EMs ( 19 ). Integrative-omics analysis identified critical roles of immune pathogenesis, Wnt signaling, differentiation, and migration of endometrial cells as hallmarks for EMs ( 19 ). Gene co-expression network analysis has been used to extract new information using DEGs ( 20 ). Using weighted gene co-expression network analysis (WGCNA), Yang et al. constructed gene co-expression networks for multiple cancer types and found that some prognostic modules were conserved across different cancer types ( 21 ). During the WGCNA analysis, 18 co-expression modules were identified. Among them, the pale turquoise module contained the hub genes FOSB, JUNB, ATF3, CXCL2 , and FOS, which showed a significant correlation with EMs ( 22 ). Protein-protein interaction (PPI) networks have been widely used to characterize the underlying mechanisms of genes associated with complex diseases ( 23 , 24 ). The majority of human diseases are caused by a group of correlated molecules or a network, rather than a single gene. Thus, identification and validation of biomarker networks is critical to disease diagnosis, prognosis and treatment ( 25 ). In this study, we analyzed the expression profiles of mRNA using bulk RNA sequencing and predicted the related functions of up and down-regulated meta- DE genes by functional enrichment analysis. This is the first study to construct mRNA co-expression network by analyzing meta- DE genes in EMs . The results of functional enrichment analysis indicated that meta-DE gene were mainly enriched in ’Adherens junction,’ ‘p53 signaling pathway,’ and ‘AMPK signaling pathway.’ This study aims to identify key genetic factors and underlying molecular mechanisms involved in the pathogenesis of EMs . By leveraging bulk RNA sequencing data and integrative bioinformatics analyses, we successfully identified critical genes associated with EMs . The findings of this research not only enhance our understanding of the molecular basis of EMs but also contribute to the identification of novel key genes. Ultimately, early detection of EMs risk through molecular profiling can facilitate more treatment strategies and help minimize unnecessary clinical interventions. Materials and Methods Data collection Bulk RNA-Seq datasets related to EMs were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds/ ). Three independent bulk RNA-Seq studies focusing on EMs were selected and analyzed. In the first dataset (GSE130435) , transcriptomic profiling of the endometrium was performed on samples collected from eleven women in the secretory phase of the menstrual cycle. Among them, six women were diagnosed with EMs (stage I-IV), and five control subjects had no evidence of the disease at the time of surgery for benign gynecologic disorders. The mean ages of the EMs and control groups were 37 and 42 years old, respectively (range 23–49 years). None of the patients had used hormonal therapy for at least three months prior to sample collection. Endometrial biopsies were obtained through the University of California San Francisco (UCSF) NIH Human Endometrial Tissue and DNA Bank, following approval by the UCSF Committee on Human Research (IRB#10-02786) , with written informed consent obtained from all participants. The primary aim of this analysis was to elucidate proinflammatory phenotypes of macrophages within the eutopic endometrium of women with EMs , with particular focus on a potential infectious contribution to disease etiology. DEG analysis was conducted between macrophages isolated from affected and unaffected women, revealing the involvement of distinct biological and signaling pathways. The altered macrophage phenotypes were associated with dysregulated gene expression in the eutopic endometrium, contributing to the establishment of a proinflammatory microenvironment. These changes are implicated in the pathogenesis of EMs and may underlie impaired reproductive outcomes observed in affected women. In the second dataset (GSE134056) , transcriptomic profiling of the endometrium was performed on samples obtained from 38 women aged between 18 and 49 years. Among them, 16 were diagnosed with EMs and 22 served as control. All participants underwent laparoscopic procedures, during which informed consent was obtained in accordance with Institutional Review Board (IRB) protocols. Endometrial biopsies were collected prior to surgery using suction pipelles (Cooper Surgical Uterine Explora Model I) under general anesthesia. Each biopsy yielded ≥250 mg of tissue, which was subsequently processed for high-throughput mRNA sequencing using the Illumina NextSeq platform . Samples were sourced from three different institutions: (1) Women’s and Children’s Hospital, University of Missouri; (2) Boone Hospital, Columbia, MO; and (3) University of California, San Francisco. To distinguish EMs cases from controls based on transcriptomic profiles, various supervised machine learning (ML) techniques were applied, including decision trees, partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and random forests. Furthermore, a generalized linear model (GLM), followed by a likelihood ratio test, was used to identify DEGs among the 14,154 genes analyzed. This analysis revealed 28 DEGs , of which 5 were upregulated and 23 were downregulated in EMs samples. Several of these genes were proposed as potential biomarkers for the disease. In the GSE212787 study, two cohorts were analyzed to investigate the molecular mechanisms underlying EMs, with a particular focus on ubiquitination and its role in fibrosis development. All participants had normal menstrual cycles and had not received hormonal therapy in the three months prior to surgery. Cohort 1 included: Six control endometrial (NC) samples from non-endometriosis patients, six eutopic endometrial (EU) samples and ten ectopic endometrial (EC) samples from ten patients diagnosed with ovarian EMs . These samples underwent integrated transcriptomic and proteomic analyses using RNA sequencing . Samples were collected at the Department of Obstetrics and Gynecology, First Affiliated Hospital of Xiamen University, with ethical approval number KY2021-03. Cohort 2 consisted of: Five NC samples from non- EMs patients, Paired EU and EC endometrial samples from six patients with ovarian EMs. Label-free quantitative ubiquitylomics analysis was performed on these samples. They were collected from the Department of Obstetrics and Gynecology, First Affiliated Hospital of Fujian Medical University (Ethical approval: MRCTA, ECFAH of FMU [2021]484). For transcriptomic data analysis, DEGs was assessed using DESeq2 with Benjamini– Hochberg false discovery rate (FDR) correction. Genes were considered significantly differentially expressed if they met the criteria of: Adjusted p-value (FDR) 2. To explore the biological significance of DEGs , Gene Ontology (GO) and KEGG pathway enrichment analyses were conducted. These analyses helped to identify key functional changes involved in the pathogenesis of EMs , particularly those associated with ubiquitination and fibrosis. Integrating these datasets through meta-analysis increases statistical power, reduces dataset-specific bias, and enables the identification of consistently dysregulated genes and pathways across diverse patient populations and experimental conditions. Table 1 summarized the sample size, accession number, platform, Tissues, and references submitted to messenger RNA-Seq . View this table: View inline View popup Download powerpoint Table 1. Bulk RNA-Seq datasets for transcriptomics analyses of endometriosis. Data processing and identification of DE Gs Initially, read counts from patient and control groups were collected for transcriptomic analysis. Subsequently, Ensembl IDs within the count matrix were converted to GeneIDs . DEGs were identified using the DESeq2 package (v1.28.1) implemented in R ( 29 ). DESeq2 utilizes negative binomial generalized linear models to estimate gene-specific dispersion parameters. The statistical significance of DEGs was evaluated using the Wald test implemented within the DESeq2 framework ( 30 ). Multiple testing correction was applied using the FDR method. Genes with a fold change ≥ |2| and an adjusted P-value ≤ 0.05 were considered significantly differentially expressed ( 31 ). Meta-Analysis of Datasets Meta-analysis has been extensively utilized in genetic research, particularly for the identification of genes associated with various diseases ( 32 ). Subsequent to the identification of DEGs , a meta-analysis approach was employed to systematically integrate and synthesize data derived from multiple independent transcriptomic studies. This methodology enhanced the overall statistical power and analytical robustness, thereby facilitating the identification of meta-genes that demonstrate significant associations with the EMs . To perform the meta-analysis, two widely P-value combination methods: Fisher’s method and the inverse normal method were implemented using the metaRNASeq package (v1.0.5) from the Bioconductor ( 33 ). Biological Pathway Enrichment Analysis Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from omics experiments. This method identifies specific biological pathways (BPs), molecular functions (MFs), cellular component (CCs) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) that are enriched in a group of DEGs ( 34 , 35 ). For enrichment analysis, the list of meta-genes was imported to Enrichr database. Detecting over-represented biological and molecular pathways donates valued comprehension of biological mechanisms in EMs . Construction of gene co-expression networks The WGCNA Bioconductor R package (v3.5.1) was employed to construct gene co-expression networks, detect the correlation patterns among genes and identify important modules across bulk RNA-Seq samples ( 36 ). The expression values of meta-genes were normalized using the variance-stabilizing transformation function (vst) , in order to generate a matrix of values for which variance is constant across the range of mean values ( 37 ). The stringsAsFactors function was used while checking for missing values in the dataset. Hierarchical clustering using the hclust function was applied to detect outlier samples. The scale-free topology features of biological networks were incorporated through the application of the pickSoftThreshold function. The adjacency matrix was constructed by employing Pearson correlations methods across meta-genes ( 20 , 38 ). Afterwards, the adjacency matrix was converted into a Topological Overlap Matrix (TOM) and corresponding dissimilarity matrix (1−TOM) for the identification of gene modules for each pair of genes with strong interconnectivity ( 36 ). Finally, the cutreeDynamic function, along with average linkage hierarchical clustering, was used to clustered genes into modules based on similar expression patterns. To determine the effect of hub genes on the modules, they were imported to Cytoscape . Network Topological Analysis Topological data analysis represents a systems biology-oriented approach that has emerged as one of the most powerful methodologies for the investigation and interpretation of complex biological networks ( 39 ). The topological analysis of Protein-protein interactions (PPIs) network assist to explore the molecular mechanisms and pathways regulated by the essential genes in an organism ( 40 ). PPI among meta-genes will be analyzed and visualized using Cytoscape software to construct gene interaction networks. Hub genes will be identified based on their degree of connectivity and their relative biological significance compared to other genes within the network ( 41 ). In the network the nodes correspond to proteins and the edges to the interactions between each protein. We then used the CentiScaPe Cytoscape plug-in to calculate the node degree and betweenness centrality of each protein. The nodes (proteins) that had a degree centrality and a betweenness centrality greater than or equal to the mean were identified as key proteins more likely to modulate symptoms of EMs . Prediction of miRNAs targeting the hub genes Computational prediction of miRNAs plays a pivotal role in the early stages of research, as it provides essential insights into the complex regulatory networks governed by these small noncoding RNAs . Candidate miRNAs targeting the hub genes were systematically predicted using the miRDB database. Results Identification of DEGs The step-by-step workflow of the systems biology approach used in this study is presented in Fig 1 . Initially, a comprehensive analysis was conducted on a total of 58 endometrial tissue samples, from three independent bulk RNA-Seq datasets. These samples included 35 cases diagnosed with EMs and 23 control samples from healthy individuals. Download figure Open in new tab Fig 1. Workflow of the systems biology methodology in this project After pre-processing of the bulk RNA -seq data of each studies, we created datasets containing the genes of 1117, 1005, 3123 in GSE130435, GSE134056 , and GSE212787 , respectively ( Fig 2 ) . We performed differential analysis using the GLM followed by likelihood ratio test on 5245 genes and found 2005 upregulated and 3240 downregulated genes overall. Download figure Open in new tab Fig 2. Identification of DEGs Across Three Datasets via Venn Diagram Analysis Meta-analysis of DEGs Overall, 603 and 443 meta-genes, were discovered in response to EMs using the Fisher and Invorm methods, respectively. Among these, 427 common meta-genes considered for identifying correlation patterns. Fig 3 displays the outcomes of meta-analysis of bulk RNA-Seq datasets. Download figure Open in new tab Fig 3. Identification of 427 Meta-Genes Using Fisher and Invorm Functional Enrichment Analysis of meta-genes Functional enrichment analysis was performed on the identified meta-genes to elucidate associated BPs, MFs, CCs, and signaling pathways. The results of this comprehensive analysis are illustrated in Fig 4 and Table 2 , highlighting the key functional categories and enriched pathways potentially implicated in the underlying biological mechanisms. Download figure Open in new tab Fig 4: Functional Enrichment Analysis of Meta-Genes across BPs, MFs, and CCs View this table: View inline View popup Download powerpoint Table 2: The most significant pathways identified through the analysis of meta-genes in EMs Construction of the gene co-expression network In order to comprehensively investigate the functional relationships and co-regulatory patterns among the identified meta-genes associated with EMs , we employed WGCNA software. A crucial step in the construction of network topology is the selection of an appropriate soft-thresholding power (β), which influences the strength of correlation between gene pairs. We selected β = 20 as the optimal power, since it was the lowest value at which the scale free topology fit index (R²) reached 0.9, indicating a strong scale-free topology ( Fig 5 ) ( 36 ). Download figure Open in new tab Fig 5: Scale independence and mean connectivity of network topology for different soft-thresholding Meta-genes with similar expression patterns were clustered into co-expression modules that were displayed in different colors. A total of four distinct gene co-expression modules were identified. The largest module in the network is significant and contains 205 genes, while the non-significant modules consist of 170, 41, and 11 genes ( Fig 6A ). The relevance between each gene co-expression module and clinical information was further explored through module–trait relationship analysis. The resulting visualizes the strength and direction of these correlations, with each cell representing the Pearson correlation coefficient and the associated P-value ( Fig. 6B ). Among these, the turquoise module, containing 205 genes, was found to have a significant correlation with EMs (R = 0.36, P = 0.005). This module was considered for downstream analyses, including the identification of hub genes and the exploration of potential regulatory mechanisms involved in EM -related pathways. To explore the biological relevance of modules, the module–trait relationships were assessed by correlating the module eigengenes with the phenotypic trait ‘weight’. Additionally, gene significance (GS) for weight was calculated and integrated into the visualization, allowing the identification of modules most strongly associated with the trait ( Fig 6C ). To visualize the topological structure of the gene co-expression network, a network heatmap plot was constructed based on the TOM. Prominent blocks of darker coloration along the diagonal were observed, indicating regions of high interconnectivity that correspond to modules identified through hierarchical clustering. These coherent clusters reflect elevated intramodular connectivity and suggest that genes within each module may be functionally related or co-regulated ( Fig. 6D ). Download figure Open in new tab Download figure Open in new tab Fig 6: A) The sizes of determined modules based on the number of involved genes. B) Determination of module-trait relationship of EMs and identification of the most clinically relevant modules; each row indicates a module eigengene, and each column represents a clinical trait. C) Cluster dendrogram of the meta-genes. The branches and color bands demonstrate the specific module. D) TOM plot; light color symbolizes low overlap, and progressively darker red color symbolizes higher overlap between common genes. Blocks of darker colors along the diagonal correspond to modules. Using the density-based clustering non-parametric algorithm (DBSCAN), we analyzed the 20 hub genes identified within the turquoise module of co-expression network. These genes included NKAP, ZFTA, OGN, CEP112, TEF, JCAD, IGFBP3, SCD5, OLFML1, CC2D2A, XYLT2, ME3, ANK2, KRBA1, NLGN3, PLCD3, LRRC17, PRKG1, ZFP2, and PTPRB . The STRING database was employed to assess network among these hub genes. PPI of the significant module Using the Cytoscape platform, a comprehensive PPI network was constructed based on the 205 DE -meta genes identified. The value of the Betweenness Centrality (BC) is between 0 and 1. The node size in the networks represents the centrality of the corresponding nodes. As shown in Fig 7 , the PPI network was observed to represent the central hub positions of several genes. Each node was designated to represent a protein, and each edge between them was used to indicate an interaction between two proteins. Smaller and lighter-colored nodes correspond to proteins with fewer interactions, which may perform more specific and limited roles in biological processes. Collectively, these genes are expected to contribute to key biological processes including signal transduction, transcriptional regulation, cellular metabolism, and structural remodeling. Proteins such as RPAP2, TBX1, LRRC49, MAS1, TTTL7, MSI2, SERPINE, TONSL, STAR, SVIL, PLCD3, LIPT1, TPH1 and PIPOX were commonly identified as hub proteins due to their extensive interactions with numerous other proteins within the network. These proteins, which were characterized by a higher degree of interconnectedness and putative co-involvement in discrete biological pathways, were systematically partitioned into functionally relevant clusters, as delineated through integrative genetic network analyses. These hub proteins were found to play crucial roles in maintaining network integrity and regulating essential biological processes. Hub proteins have been proposed as critical regulators in endometriosis, highlighting their potential functional significance in the underlying biological processes. For example, MSI2 has been implicated in RNA binding and stem cell maintenance ( 42 ), whereas PLCD3 is involved in phosphoinositide signaling a pathway frequently related to cell proliferation and migration ( 43 ). Additionally, the presence of transcriptional regulators such as RPAP2 and chromatin-modifying enzymes like KAT14 suggests potential epigenetic modulation within the disease context ( 44 ). The identification of STAR and MAS1 , are known to participate in steroidogenesis and hormonal signaling, further indicates the possible involvement of endocrine regulatory mechanisms ( 45 ). Download figure Open in new tab Fig 7. Protein–protein interaction network for the meta-genes using Cytoscape . Construction of the miRNAs Regulatory Network Using the miRDB database, we identified 39, 53, 9, 516, 35, 43, and 101 miRNAs targeting TBX1, RPAP2, MAS1, MSI2, STAR, PLCD3, and SVIL, respectively. These findings suggest that the identified miRNAs may regulate distinct regulatory networks by targeting hub genes, thereby modulating critical signaling pathways and biological processes involved in the progression of EMs . The hub gene MSI2 was found to share twenty-nine common miRNAs with SVIL , as illustrated in Fig 8 , highlighting a substantial overlap in their post-transcriptional regulatory networks and suggesting that these two genes may be co-regulated by a similar set of miRNA molecules. Download figure Open in new tab Fig. 8. Network visualization of MSI2 & SVIL and their common associated miRNAs , constructed in Cytoscape . Discussion The availability of bulk RNA-seq has enabled a more comprehensive characterization of molecular alterations underlying various diseases. EMs remains a significant challenge in reproductive medicine. This study aims to identify key hub genes involved in EMs using a bulk RNA-seq analysis approach. Initially, relevant datasets were obtained from the GEO database. After normalization, DEGs associated with EMs were identified. To achieve sufficient statistical power and gain novel insights into the relationships and expression patterns of key regulatory genes, a meta-analysis was conducted. This approach aimed to identify meta-genes consistently dysregulated across multiple datasets, which may serve as potential biomarkers. To further explore the functional implications of these genes, pathway and GO enrichment analyses were performed, aiming to uncover the critical biological functions and pathways involved in EMs . Subsequently, we employed WGCNA to construct gene co-expression networks and identify gene modules associated with the disease. In addition, a disease-specific interaction network for EMs was constructed. This comprehensive approach allowed us to pinpoint hub genes and potential biomarkers relevant to the diagnosis and prognosis of EMs . Unlike previous studies that primarily relied on microarray-based datasets, our research integrates bulk RNA-seq data with differential expression analysis, meta-analysis, WGCNA , and PPI network analysis. Through this integrative approach, we identified key genes under this specific conditions that have not been previously reported, offering novel targets for early diagnosis and therapeutic intervention in EMs . Overall, the meta-analysis approach detected 427 common meta-genes using the Fisher and Invorm methods, which missense mutations in the PTEN meta-gene have been reported to contribute to the molecular mechanisms underlying endometriosis, as well as the genotypic-phenotypic correlations observed in endometrial and ovarian cancers ( 46 ). Serial analysis of gene expression revealed differential expression of the B3GNT5 meta-gene between endometriosis and normal endometrium ( 47 ). Our enrichment analysis revealed that these meta-genes are predominantly associated with several key biological pathways and disease processes. The involvement of these pathways suggests potential mechanistic associations with EMs , particularly in relation to altered cellular metabolism, and immune dysregulation. Analysis of exosomal microRNAs revealed that anatomical structure morphogenesis is a significantly enriched biological process associated with the pathogenesis of EMs ( 48 ). The process of positive regulation of intracellular signal transduction significantly influences the molecular mechanisms underlying the development of EMs ( 49 ). Recent research highlighted the essential association between ubiquitination and EMs pathogenesis, ( 50 ), also, Acetylglucosaminyltransferase Activity is involved in glycosylation, which affects cell signaling, adhesion, and immune interaction, all processes relevant to Ems ( 51 ). The association of the p53 signaling pathway with EMs susceptibility has been investigated in a Taiwanese population ( 52 ). In EMs , dysregulation of the p53 pathway may contribute to enhanced cell survival, resistance to apoptosis, and the invasion of endometrial-like tissue beyond the uterine cavity ( 53 ). The AMPK signaling pathway plays a crucial role in regulating cellular metabolism, inflammation, and the immune response, the dysregulation of AMPK signaling has been implicated in several female reproductive disorders, including EMs , infertility, and reproductive ageing ( 54 ). Targeting EphA2 has been reported to inhibit the progression of EMs by modulating the AMPK signaling pathway ( 55 ). Adherens junctions have been reported to decline during the preimplantation period, potentially facilitating trophoblast invasion through the epithelial barrier ( 56 ). Dysfunction of the Adherens junction appears to contribute to the detachment of endometriotic cells, representing a key initial step in the development of ovarian EMs ( 57 ). In the current study, systems biology analysis revealed distinct topological characteristics within the co-expression network of EMs -related genes. Notably, IGFBP7, IGFBP3, and NKAP were grouped within a turquoise co-expression module, which was selected for discussion due to its potential relevance to endometriosis pathophysiology. The multifaceted functions of IGFBP7, including its regulatory roles in cell proliferation, apoptosis, and migration, were investigated to elucidate the underlying mechanistic pathways. Notably, IGFBP7 appears to play a crucial role in the pathogenesis of endometriosis ( 58 , 59 ). MicroRNA-210-3p has been identified as a critical post-transcriptional regulator in endometriosis, contributing to the development and progression of endometriotic lesions by directly targeting IGFBP3. Through the downregulation of IGFBP3, miR-210-3p may enhance cellular proliferation, migration, and survival within ectopic endometrial tissue, thereby promoting lesion establishment and maintenance ( 60 ). NF-κB , also known as NKAP, has been shown to be activated in peritoneal endometriosis in women, underscoring its pivotal role in promoting inflammation within ectopic lesions. This activation supports the notion of a distinct inflammatory microenvironment in endometriotic implants compared to surrounding normal tissue. The NF-κB signaling pathway is believed to contribute to the persistence and progression of endometriotic lesions by regulating pro-inflammatory cytokines, immune cell recruitment, and the expression of adhesion and survival-related genes ( 61 ). This study highlighted several genes including ZFTA, OGN, CEP112, TEF, JCAD, SCD5, OLFML1, CC2D2A, XYLT2, ME3, ANK2, KRBA1, NLGN3, PLCD3, LRRC17, PRKG1, ZFP2, and PTPRB that may represent novel molecular contributors or potential biomarkers for endometriosis. However, further research involving experimental validation and detailed functional analyses is necessary to clarify their precise roles in the disease’s pathophysiology. The gene network visualization of the DE -meta gene signatures are represented in Fig 7 . The significance level for the hub genes is set at BC ≥ 0.1. TBX1 was identified as an upregulated transcription factor expressed differential expression in EMs , ( 62 ), suggesting its potential involvement in the underlying molecular mechanisms of reproductive dysfunction. TBX1 is a transcription factor involved in tissue development and immune system regulation ( 63 ). Alterations in this gene may be observed in tissue abnormalities and endometriosis. RPAP2 has been implicated in the regulation of gene transcription, and its alteration may be associated with the dysregulation of genes involved in inflammation and tissue growth, ( 64 ) which can be considered relevant to endometriosis. It has been demonstrated that MAS1 is expressed in the eutopic proliferative endometrium of patients with ovarian endometriotic tissues. This suggests that MAS1 may be involved in the initiation of endometriosis, particularly in the migration of endometrial tissues from eutopic to ectopic sites ( 65 ). It was also observed that MSI2 is downregulated as a result of the overexpression of miR-145 , a molecule known to be dysregulated in endometriosis. This downregulation leads to alterations in cell behavior, including increased invasiveness, as demonstrated by the Matrigel Invasion Assay ( 66 ). The results demonstrate that aberrant expression of STAR in ectopic endometriotic tissues, resulting in increased peritoneal progesterone levels, is associated with the development of endometriosis ( 45 ). Current studies suggest that the endometriosis susceptibility locus on 17q21 may be associated with PLCD3 mapping and the involvement of PI-PLC δ3 in the disease ( 67 ). Gene expression analysis indicated that LIPT1 expression was downregulated in uterine corpus endometrial carcinoma (UCEC) and was identified as a cuproptosis-related prognostic marker ( 68 ). The observed expression patterns suggest that SVIL is consistently expressed a strong to moderate levels in both normal endometrial tissues and endometrial cancer (EC) tissues ( 69 ). hsa-miR-143-3p has been frequently reported as dysregulated in ectopic and eutopic endometrial tissues, and appears to contribute to the abnormal cell proliferation, migration, and invasion characteristic of Ems ( 70 ). Likewise, hsa-miR-340-5p shows altered expression in endometriotic lesions, and is supposed to participate in inflammatory and proliferative signaling pathways associated with disease progression ( 71 ). hsa-miR-410-3p is commonly downregulated in EMs and is thought to influence key pathways involved in cellular proliferation and immune responses ( 72 ). In addition, hsa-miR-302b-5p , implicated in endometrial stem cell regulation, has emerging evidence linking it to the pathophysiology of EMs ( 73 ). Conclusion Through integrative systems biology analysis, our study successfully identified hub genes that may serve as potential diagnostic biomarkers for EMs . These findings provide novel insights into the genetic and molecular mechanisms underlying EMs , helping to fill a critical gap in current research. By characterizing gene expression profiles and associated signaling pathways, our results establish a theoretical framework for early disease prediction and the development of personalized therapeutic strategies. However, several limitations must be acknowledged. First, the diagnostic potential and biological significance of the identified hub genes have not yet been validated through experimental studies or machine learning-based predictive modeling. Additionally, the specific molecular functions and regulatory roles of these genes in the pathogenesis and progression of EMs remain unclear and require further investigation. Moreover, as our conclusions are based on bioinformatics analysis, validation through in vitro, in vivo, or clinical studies is essential. Despite these limitations, the identification of robust genetic markers in this study holds promise for improving early diagnosis, optimizing treatment plans, and potentially reducing the incidence of EMs in the future. 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Share Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq Nooshin Ghahramani , Ali Hashemi , Seçil Eroğlu , Elaheh Esmaeili Kordlar bioRxiv 2025.08.10.669560; doi: https://doi.org/10.1101/2025.08.10.669560 Share This Article: Copy Citation Tools Gene co-expression network reveals key hub genes associated with endometriosis using bulk RNA-seq Nooshin Ghahramani , Ali Hashemi , Seçil Eroğlu , Elaheh Esmaeili Kordlar bioRxiv 2025.08.10.669560; doi: https://doi.org/10.1101/2025.08.10.669560 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7779) Biochemistry (18147) Bioengineering (14323) Bioinformatics (42971) Biophysics (21905) Cancer Biology (19008) Cell Biology (26045) Clinical Trials (138) Developmental Biology (13600) Ecology (20330) Epidemiology (2067) Evolutionary Biology (24794) Genetics (15819) Genomics (22919) Immunology (18150) Microbiology (41219) Molecular Biology (17468) Neuroscience (90554) Paleontology (679) Pathology (2898) Pharmacology and Toxicology (4933) Physiology (7855) Plant Biology (15429) Scientific Communication and Education (2063) Synthetic Biology (4402) Systems Biology (9970) Zoology (2311) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a18d15a34c9fdd69',t:'MTc4MzY2MDQ2Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();