Differential Gene Expression Profiling of Ectopic Endometrium Reveals Key Molecular Pathways in Endometriosis Pathogenesis

Differential Gene Expression Profiling of Ectopic Endometrium Reveals Key Molecular Pathways in Endometriosis Pathogenesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Differential Gene Expression Profiling of Ectopic Endometrium Reveals Key Molecular Pathways in Endometriosis Pathogenesis Gunjan Rai, Ashish Ashish, Shivani Mishra, Sangeeta Rai, Surbhi Singh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8011817/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Molecular Biology Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Endometriosis is a chronic, estrogen-dependent inflammatory disorder characterized by ectopic endometrial-like tissue. We investigated gene expression changes in ectopic endometrium from 221 women with histologically confirmed endometriosis and compared them with eutopic endometrium from 27 fertile controls. Using quantitative real-time PCR, we analysed 35 candidate genes involved in epigenetic regulation, development (HOX genes), inflammation, angiogenesis and hormonal signalling. A total of 18 genes were upregulated and 17 downregulated in ectopic tissue (adjusted p < 0.05, Benjamini–Hochberg FDR). Upregulated genes included DNMT1, DNMT3A, VEGFA, TNF-α and MMP2, whereas HOXA10, PGR and ESR1 were among the downregulated genes. Gene ontology (GO) and protein–protein interaction (PPI) analyses (STRING, ShinyGO) revealed enrichment in DNA-methylation and inflammatory pathways. Clinical and biochemical measures (CA-125, CRP, pelvic pain) increased with rASRM stage. These results identify coordinated epigenetic and inflammatory alterations in ectopic endometrium, suggesting potential molecular targets for diagnosis and therapy. Endometriosis Gene expression DNA methylation Inflammation HOX genes Epigenetic regulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Endometriosis is a complex, estrogen-dependent, chronic inflammatory disorder affecting approximately 10% of women of reproductive age and is now recognized as a systemic condition rather than merely a localized gynecological disease( 1 ). It is characterized by the presence of endometrial-like glands and stroma outside the uterine cavity, most commonly on the ovaries, pelvic peritoneum, and other pelvic organs( 2 ). This ectopic implantation triggers a cascade of chronic inflammation, aberrant angiogenesis, fibrosis, and hormonal resistance, leading to pain, infertility, and impaired quality of life( 3 ). The multifactorial etiology of the disease encompasses genetic, epigenetic, immunological, and environmental influences( 4 ). One of the most accepted theories for its origin, retrograde menstruation, fails to fully explain lesion establishment and persistence, indicating that cellular and molecular alterations in endometrial tissue play a critical role in disease progression( 5 ). In this study, molecular progression refers to the transformation of eutopic endometrial cells that acquire invasive and hormone-resistant characteristics when forming ectopic lesions. To elucidate these mechanisms, we analyzed gene expression profiles in ectopic endometrial tissues obtained from women with surgically and histologically confirmed endometriosis, compared with eutopic endometrium from healthy fertile women( 6 ). The genes selected for investigation were chosen based on prior evidence implicating them in major pathways central to endometriosis pathophysiology, including epigenetic regulation (DNMT1, DNMT3A, DNMT3B, G9A/EHMT2, PRDM16), developmental and HOX transcription factors (HOXA and HOXB families), inflammatory mediators (TNF-α, IL-6, COX-2, NF-κB), angiogenic and extracellular matrix remodeling factors (VEGFA, MMP2, MMP9, CD44), and hormone signaling regulators (ESR1, PGR, FOXO1, HAND2, GATA2)( 7 , 8 ). Evaluating these genes offers insights into how interconnected epigenetic, inflammatory, and hormonal networks drive lesion persistence and infertility( 9 ). Furthermore, the classification of patients according to the revised American Society for Reproductive Medicine (rASRM) system provided a clinically relevant framework for correlating gene expression profiles with disease severity, thereby linking molecular findings with phenotypic stages( 10 ). Through this integrative approach, the study aimed to elucidate molecular signatures in ectopic endometrium that underlie the pathogenesis and progression of endometriosis. Classification Systems of Endometriosis To enable standardized assessment and correlation of gene expression with disease severity, endometriosis cases were classified according to the revised American Society for Reproductive Medicine (rASRM) system, which grades disease into four stages (minimal, mild, moderate, severe) based on lesion number, size, depth, and extent of adhesions( 11 ). This classification provides a consistent clinical framework to evaluate how molecular dysregulation varies with disease progression, supporting the study’s aim to identify stage-associated gene expression patterns. Materials and methods Ethical Approval The study received ethical approval under reference number Dean/2021/EC/2397. All participants, including those with endometriosis and healthy individuals, provided written informed consent, ensuring they were fully informed about the study's purpose, procedures, risks, and benefits. Confidentiality and anonymity were maintained throughout the research. Study Subjects The study was conducted at the MRU Lab in collaboration with the Department of Obstetrics and Gynaecology, Sir Sunderlal Hospital, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India, between January 2022 and June 2024. Data were collected from 27 fertile women and 221 infertile women with endometriosis, with details on lifestyle, habits, and familial history recorded. Endometriosis patients were classified using the revised American Society for Reproductive Medicine (rASRM) system. Participants were of Indian ethnicity from eastern Uttar Pradesh and Bihar. Informed consent was obtained, and women aged 18-50 with endometriosis diagnosed through hysteroscopy and laparoscopy, and a BMI < 32 kg/m², were included. Those with chronic pelvic pain due to other causes were excluded. Inclusion Exclusion Criteria: Inclusion: Infertile women aged 18–50 with laparoscopic/histopathological diagnosis and clinical evaluation of endometriosis; no hormonal/anti-inflammatory treatment in past 3 months; Infertility due to endometriosis. None of the participants were on active hormonal contraceptive therapy at the time of sample collection. A small proportion of fertile control women reported prior contraceptive use, but this was not ongoing during the study period. Exclusion: Obesity (BMI > 30), known metabolic and lipidomic abnormalities, Pregnancy/lactation; recent surgery/Chronic/ acute infection; immunosuppressant/antioxidant use; history of cancer, diabetes, (cardiovascular disorders) smoking/alcohol abuse; incomplete data. Clinical characteristics of the subject included: The study recorded the clinical characteristics of subjects, including age, residential setting, socioeconomic status, and infertility details (primary or secondary, and duration). Menstrual cycle information, such as age of onset, cycle frequency, flow, and symptoms like dysmenorrhea, dyspareunia, chronic pelvic pain, and urinary issues, was assessed. Physical exams included BMI, adnexal masses, uterine mobility, and tenderness. Imaging through transvaginal ultrasound (TVS) and MRI identified deep-infiltrating lesions, while diagnostic laparoscopy confirmed endometriosis staging using the revised AFS scoring system. This comprehensive approach aimed to understand endometriosis's presentation and impact. Tissue collection and RNA Extraction Endometrial tissue samples were obtained from two distinct groups of participants. For the endometriosis group, tissue was collected from ectopic endometrial lesions (including ovarian endometriomas, peritoneal implants, or deep infiltrating nodules) during laparoscopic or laparotomic surgery, and the diagnosis was confirmed by histopathological examination. For the control group, eutopic endometrial tissue was collected from healthy fertile women undergoing hysterectomy for benign, non-endometriotic conditions (such as fibroids or prolapse). Importantly, no eutopic endometrium from endometriosis patients was analyzed in this study; instead, the comparison was made between ectopic endometrial lesions from affected women and normal eutopic endometrium from controls. All tissue specimens were immediately placed in RNAlater (Ambion™, Invitrogen, Germany) to stabilize RNA and stored at −80°C until further processing for RNA extraction and quantitative real-time PCR (qPCR) analysis. RNA integrity and cDNA Synthesis Total RNA (1 µg) was reverse transcribed using M-MLV Reverse Transcriptase (Applied Biosystems) in 20 µL reactions. cDNA was diluted 1:5 with nuclease-free water for qPCR. qPCR reactions (25 µL) contained 12.5 µL 2× SYBR Green Master Mix (Applied Biosystems), 1 µL each of forward and reverse primers (10 µM), 5 µL diluted cDNA and nuclease-free water. Reactions were run on a CFX96 Touch (Bio-Rad) with cycling: 95°C 5 min; 40 cycles of 95°C 15 s, 60°C 30 s, followed by melt-curve analysis. Each sample was run in technical triplicate. GAPDH was used as the endogenous control; relative expression was calculated by the 2⁻ΔΔCt method using pooled control cDNA as calibrator. Samples with Ct > 35 or inconsistent triplicates were repeated or excluded. Differential expression was assessed by Student’s t-test (for two-group comparisons) and one-way ANOVA with Tukey post-hoc for multiple groups; normality was assessed by Kolmogorov–Smirnov test. For the gene panel, p-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR); q < 0.05 was considered significant. Gene Ontology Analysis The ShinyGO v0.74.1 was utilized to perform gene ontology (GO) enrichment analysis of both upregulated and downregulated genes (12). ENSEMBL gene IDs corresponding to these gene sets into the ShinyGO server was imported for the analysis(13). The analysis was specifically conducted for the human species. To ensure the robustness of the findings, false discovery rate (FDR) cutoff at set as 0.05. This cutoff is crucial as it helps control for multiple testing errors, allowing us to identify significant enrichment while minimizing the likelihood of false positives. ShinyGO calculates the FDR based on the nominal p-values derived from a hypergeometric test, which assesses the probability of observing the number of genes in a specific GO term by chance. STRING Network Analysis The significantly upregulated and downregulated genes were analyzed separately using the STRING database (https://string-db.org/) to construct protein–protein interaction (PPI) networks(14). A high-confidence interaction score threshold (≥0.7) was applied to ensure reliable associations. The generated PPI networks were then imported into Cytoscape (v3.x) for network visualization and topological analysis(15). Using the CytoHubba plugin, hub genes were identified based on the Maximal Clique Centrality (Degree) algorithm, and the top five upregulated and downregulated hub genes were selected for further analysis(16). Statistical analysis The study adhered to standard statistical practices, utilizing IBM SPSS Statistics for analysis, assessing normality with the Kolmogorov-Smirnov test, employing Tukey's test for mean separation under specific conditions, and reporting results in the Mean ± SD format. The significance of differences among groups was determined using one-way ANOVA, with clear thresholds for statistical and high statistical significance defined by p-values. The student’s t-test was employed to identify differentially expressed genes (DEGs) in endometriosis samples. Statistical significance was determined with a probability threshold of p < 0.05. For protein-protein interaction (PPI) analysis, a more stringent threshold of p < 0.01 was used to assess statistical significance(17). Results Clinical and biochemical characteristics of study participants A total of 248 women were included (27 fertile controls, 221 endometriosis cases). Demographic variables (age, BMI, residence) did not differ significantly between controls and cases (Table 1). Clinical symptoms (dysmenorrhea, dyspareunia, pelvic pain) and infertility duration increased progressively with rASRM stage (p < 0.001). Serum CA-125 and CRP were significantly elevated in cases compared with controls (p < 0.001) and rose with increasing stage(18). LH/FSH ratio showed a modest but significant increase with disease stage (p = 0.004), whereas hemoglobin decreased (p = 0.003), consistent with chronic menstrual blood loss and inflammation. Ovarian endometriomas were the most frequent lesion type (~53%), followed by peritoneal (~30%) and deep infiltrating endometriosis (~17%). These clinical and biochemical changes parallel the molecular dysregulation observed in ectopic tissues . Table 1: (Values are Mean ± SD or n (%); statistical significance determined by one-way ANOVA or χ² test; p < 0.05 significant) * p < 0.05; ** p < 0.001. DIE = deep infiltrating endometriosis. Parameter Control (n = 27) Stage I (n = 19) Stage II (n = 80) Stage III (n = 75) Stage IV (n = 47) p-value Age (years) 30.8 ± 4.7 31.2 ± 5.6 32.1 ± 5.0 31.6 ± 4.8 32.4 ± 5.3 0.79 (ns) BMI (kg/m²) 21.9 ± 2.1 21.7 ± 2.3 22.3 ± 2.2 22.0 ± 2.4 21.8 ± 2.1 0.21 (ns) Residence — Urban, n (%) 11 (40.7) 7 (36.8) 21 (26.3) 28 (37.3) 16 (34.0) 0.48 (ns) Menstrual irregularities, n (%) 2 (7.4) 6 (31.6) 32 (40.0) 38 (50.7) 31 (66.0) <0.001 ** Dysmenorrhea (VAS 0–10) 1.3 ± 0.6 3.5 ± 1.0 4.6 ± 1.2 6.2 ± 1.3 7.4 ± 1.6 <0.001 ** Dyspareunia, n (%) 0 (0) 5 (26.3) 25 (31.3) 36 (48.0) 32 (68.1) <0.001 ** Infertility duration (years) — 2.1 ± 0.8 2.7 ± 1.0 3.5 ± 1.2 4.1 ± 1.5 <0.001 ** Type of endometriosis — Ovarian 9 (47.4) / Peritoneal 6 (31.6) / DIE 4 (21.0) Ovarian 42 (52.5) / Peritoneal 24 (30.0) / DIE 14 (17.5) Ovarian 39 (52.0) / Peritoneal 23 (30.7) / DIE 13 (17.3) Ovarian 27 (57.4) / Peritoneal 13 (27.7) / DIE 7 (14.9) — Serum CA-125 (U/mL) 16.2 ± 5.4 28.4 ± 8.6 42.7 ± 11.3 56.2 ± 14.5 79.4 ± 18.7 <0.001 ** LH/FSH ratio 1.12 ± 0.21 1.21 ± 0.25 1.36 ± 0.27 1.49 ± 0.31 1.62 ± 0.34 0.004 * Hemoglobin (g/dL) 12.9 ± 1.1 12.3 ± 1.2 11.7 ± 1.3 11.2 ± 1.4 10.9 ± 1.5 0.003 * CRP (mg/L) 3.1 ± 1.2 5.8 ± 1.4 7.9 ± 2.1 9.4 ± 2.3 11.2 ± 2.7 <0.001 ** Pelvic pain (VAS 0–10) 0.8 ± 0.4 2.4 ± 0.9 3.8 ± 1.1 5.6 ± 1.3 7.1 ± 1.5 <0.001 ** Gene expression data and Identification of DEGs Gene expression analysis was performed to compare ectopic endometrial lesions obtained from women with surgically and histologically confirmed endometriosis with eutopic endometrial tissue from healthy fertile women. Among the examined genes, DNMT1, DNMT3A, DNMT3B, G9A/EHMT2, VENTX, PRDM16, HOXA1, HOXB6, VEGFA, TNF-α, IL-6, COX-2, CYP19A1, MMP2, MMP9, NFKB1, TGF-β, and CD44 showed significant upregulation in ectopic lesions compared with control endometrium, indicating enhanced activity in epigenetic modification, inflammation, and angiogenesis. Conversely, HOXA3, HOXA4, HOXA5, HOXA7, HOXA10, HOXA11, MLL2, ESR1, PGR, FOXO1, HAND2, GATA2, KLF9, PTEN, ARID1A, IL-10, and RECK were markedly downregulated, suggesting suppression of genes involved in tissue differentiation, hormonal responsiveness, and immune regulation. Table 2 Differentially Expressed Genes (DEGs) observed in endometriosis on the basis of fold change. Upregulated Genes: Sr. No. Genes: Ensemble id Log 2-Fold Change p-value DNMT1 ENSG00000130816 +10.40364159785399 0.00000287 DNMT3A ENSG00000119772 +6.65209010709126 0.0000264 DNMT3B ENSG00000088305 +3.40364159785399 0.00000109 G9A/ EHMT2 ENSG00000227333 +8.60880520296 0.00000256 VENTX ENSG00000151650 +7.252169566569 0.0000432 PRDM16 ENSG00000142611 +9.61468319933 0.0000169 HOXA1 ENSG00000105991 +8.437526418983 0.00012508 HOXB6 ENSG00000108511 +10.883578787644 0.00000534 VEGFA ENSG00000112715 +8.119331782 0.00000932 TNF-α ENSG00000232810 +7.82312542 0.00000764 IL-6 ENSG00000136244 +6.69647823 0.000098231 COX-2 ENSG00000073756 +7.424875214 0.000203411 CYP19A1 ENSG00000137869 +7.965987251 0.00088703 MMP2 ENSG00000087245 +9.332994586 0.008134795 MMP-9 ENSG00000100985 +10.025793148 0.004786423 NFKB1 ENSG00000109320 +10.75873654 0.002721006 TGF-β ENSG00000105329 +9.532114761 0.03581536 CD44 ENSG00000026508 +8.225914735 0.053472822 Downregulated Genes HOXA3 ENSG00000105997 -9.2054215 0.00000315 HOXA4 ENSG00000197576 -4.13938515 0.002019634 HOXA5 ENSG00000106004 -5.06037373895 0.000652623 HOXA7 ENSG00000122592 -7.06037373895 0.0000319 HOXA10 ENSG00000253293 -3.12240210716539 0.00013732 HOXA11 ENSG00000005073 -9.89018318037401 0.0000543 MLL2/ KMT2D ENSG00000167548 -4.82218813483675 0.00010954 ESR1 ENSG00000091831 -8.876404779 0.00103009 PGR ENSG00000082175 -8.210288179 0.0003601477 FOXO1 ENSG00000150907 -8.199871156 0.004888741 HAND2 ENSG00000164107 -7.370939934 0.0167114789 ARID1A ENSG00000117713 -7.514008656 0.04047928 KLF9 ENSG00000119138 -7.6062fmeth063 0.032654642 PTEN ENSG00000171862 -7.734825738 0.03300244 GATA2 ENSG00000179348 -7.850139264 0.00002591 IL-10 ENSG00000136634 -6.524862125 0.00202008 RECK ENSG00000122707 -6.225456943 0.002019039 A total of 35 differentially expressed genes (DEGs) were identified, comprising 18 upregulated and 17 downregulated transcripts (p < 0.05). These DEGs are summarized in Table 2 and visualized through a volcano plot (Figure 1) and a hierarchical heat map (Figure 2). The distinct clustering patterns observed between the ectopic and control groups confirm clear transcriptional divergence associated with endometriotic pathology. Construction of Interaction Networks for Differentially Expressed Genes (DEGs) The protein–protein interaction (PPI) network for differentially expressed genes (DEGs) was constructed using STRING (https://string-db.org/cgi/input)[9]. Lists of both upregulated and downregulated genes were imported separately into the STRING server. The “multiple proteins” feature of STRING was utilized for PPI network construction. Homo sapiens was specified as the organism to ensure the relevance of the interaction data. Various sources of information, including experimental data, computational predictions, and curated databases, are integrated by STRING to provide a comprehensive view of the interactions among the proteins encoded by the DEGs. This approach allows for the visualization of complex relationships between these proteins and the identification of potential pathways and networks involved in the biological processes of interest. Gene Ontology (GO) The GO analysis identified enriched biological processes (BP), molecular functions (MF), and cellular components (CC) for both upregulated and downregulated genes. The analysis for upregulated genes revealed significant enrichment in processes such as response to C-5 methylation of cytosine, DNA methylation of cytosine, negative regulation of histone H3-K9 methylation, and DNA methylation involved in embryo development. In contrast, the top-enriched biological processes for downregulated genes included embryonic skeletal system morphogenesis, anterior/posterior pattern specification, and skeletal system morphogenesis. For cellular components, the GO analysis indicated that NF-kappaB and heterochromatin were the most enriched for upregulated genes, while the brahma complex and transcription preinitiation complex were identified as the most enriched components for downregulated genes Table 3 .The highest-enriched molecular functions associated with upregulated genes included DNA cytosine-5 methyltransferase activity and DNA methyltransferase activity Figure 3 , 4 . For downregulated genes, transcription coactivator binding and steroid hormone receptor activity were found to be the highest-enriched molecular functions. Pathway analysis showed that cysteine and methionine metabolism was significantly enriched for upregulated genes, whereas thyroid hormone signaling pathways were significantly enriched for downregulated genes [12]. Table 3 Association of disease analysis of DEGs associated with Differentially Expressed Genes. GO Description Count % Log10(P) Log10(q) C0341858 Endometriosis of uterus 17 49.00 -30.00 -25.00 C0269102 Endometrioma 19 54.00 -29.00 -25.00 C0014170 Endometrial Neoplasms 17 49.00 -26.00 -22.00 C1153706 Endometrial adenocarcinoma 15 43.00 -23.00 -19.00 C0156369 Uterine Polyp 10 29.00 -21.00 -17.00 C0023267 Fibroid Tumor 16 46.00 -20.00 -17.00 C0205643 Carcinoma, Cribriform 12 34.00 -20.00 -17.00 C0205641 Adenocarcinoma, Basal Cell 12 34.00 -20.00 -17.00 C0398650 Immune thrombocytopenic purpura 15 43.00 -20.00 -17.00 C1536148 Chocolate cyst of ovary 10 29.00 -20.00 -17.00 C0162871 Aortic Aneurysm, Abdominal 17 49.00 -20.00 -16.00 C0205645 Adenocarcinoma, Tubular 12 34.00 -20.00 -16.00 C0940937 precancerous lesions 14 40.00 -20.00 -16.00 C0205642 Adenocarcinoma, Oxyphilic 12 34.00 -20.00 -16.00 Upregulated Genes The functional enrichment analysis of the upregulated genes demonstrated significant involvement in key biological processes, cellular components, and molecular functions. The top biological processes included ovulation, regulation of inflammatory response, regulation of cellular process, negative regulation of cellular process, heterochromatin organization, cellular response to reactive oxygen species, and metabolic processes. Prominent cellular component terms were chromatin, nucleoplasm, and nucleus. Key molecular function enrichments involved DNA (cytosine-5)-methyltransferase and S-adenosylmethionine-dependent methyltransferase activities. Protein-protein interaction (PPI) network analysis revealed a tightly interconnected module, with 15 nodes and 35 edges, significantly more than the 11 edges expected by chance (PPI enrichment p-value: 1.05e-08). The average node degree was 4.67 and the average clustering coefficient was 0.612, indicating strong network connectivity. Core hub genes identified in the upregulated set included HOXA5, HOXA10, ESR1, PGR, and FOXO1, all exhibiting robust interactions and regulatory potential. Downregulated Genes For downregulated genes, enrichment analysis revealed major roles in chromatin organization, nucleoplasm, nucleus, transcription regulation, and hormone-related biological processes including gland and uterus development. The molecular functions most strongly represented were transcription factor activity, RNA polymerase II-specific DNA-binding, and hormone binding. The cellular component categories highlighted chromatin, nucleoplasm, and nucleus as central to the downregulated gene set. The corresponding PPI network analysis displayed pronounced interconnectivity, comprising 17 nodes and 45 edges, whereas only 6 were expected at random (PPI enrichment p-value: < 1.0e-16). The average node degree was 5.29 and the clustering coefficient 0.692, further supporting the significance of networked interactions. Central hub genes identified among the downregulated genes included IL6, NFKB1, DNMT1, MMP9, and CD44, marking them as potential key players in the underlying biological mechanisms. These results illustrate the distinct functional profiles and hub gene networks observed between the upregulated and downregulated gene groups, underscoring their respective roles in the relevant biological pathways and processes visualized in the enrichment and PPI analyses. Construction of Interaction Networks for Differentially Expressed Genes (DEGs) A total of 35 genes, comprising 18 upregulated and 17 downregulated genes, were imported into the STRING server for the construction of the interaction network. The interaction network for the upregulated genes, as constructed by STRING, included 15 nodes and 35 edges, with an average node degree of 4.67, an expected number of edges of 11, and a PPI enrichment p-value of 1.05e-08. For the downregulated genes, the network displayed 17 nodes and 45 edges, with an average node degree of 5.29, an expected number of edges of 6, and a PPI enrichment p-value of <1.0e-16. Additionally, clustering analysis was performed separately on both upregulated and downregulated genes to identify groups within the datasets, thereby prioritizing genes for experimental validation. The K-means clustering method was employed to form the clusters, resulting in a network with high clustering coefficients of 0.692 for downregulated genes and 0.612 for upregulated genes. These high clustering coefficients indicate that the networks likely represent communities involved in similar functions. Three clusters were formed for both upregulated and downregulated DEGs, with cluster sizes ranging from a maximum of 9 genes to a minimum of 1. Notably, Cluster 3 for both gene sets contained only a single gene. The genes in Cluster 1 of the upregulated genes were associated with C5 methylation of cytosine, while those in Cluster 2 were linked to the cysteine switch. Similarly, the genes in Cluster 1 of the downregulated set were related to prostate gland development, whereas Cluster 2 was involved in embryonic skeletal system morphogenesis Figure 5,6 . Conclusion This study elucidates the molecular landscape of endometriosis by analyzing gene expression alterations in ectopic endometrial tissues and validating key pathways through bioinformatics approaches. Differential expression profiling revealed significant dysregulation of genes associated with epigenetic regulation (DNMT1, KLF9, HOXA10), inflammatory response (IL6, TNFα), and angiogenesis (VEGFA, MMP9)(19). These molecular alterations suggest a coordinated disruption of pathways that govern cellular proliferation, adhesion, and immune tolerance, contributing to the establishment and persistence of ectopic lesions(20,21). Gene Ontology and protein–protein interaction network analyses identified enrichment in biological processes related to cell adhesion, extracellular matrix organization, hormonal response, and immune signaling(22). The integrated analysis pinpointed several hub genes (DNMT1, IL6, VEGFA, MMP9, and CD44) that play central roles in disease pathophysiology(23). Together, these findings highlight a complex interaction between epigenetic modification, inflammation, and angiogenesis that underlies the pathogenesis of endometriosis. The study underscores the potential of these hub genes as biomarkers and therapeutic targets, paving the way for improved diagnostic and treatment strategies. Further validation in larger cohorts and through multi-omics approaches will strengthen these insights and facilitate their clinical translation (24). Table 4. Categorization of differentially expressed genes (DEGs) in endometriosis Functional Category Upregulated Genes Downregulated Genes Epigenetic Regulation DNMT1, DNMT3A, DNMT3B, G9A/EHMT2, PRDM16 ARID1A, KLF9, MLL2/KMT2D, PTEN Developmental / HOX Genes HOXA1 HOXA3, HOXA4, HOXA5, HOXA7, HOXA10, HOXA11 Inflammatory Mediators TNF-α, IL-6, COX-2, NFKB1 IL-10 Angiogenesis & ECM Remodeling VEGFA, MMP2, MMP9, CD44 RECK Hormone Signaling CYP19A1, TGF-β ESR1, PGR, FOXO1, HAND2, GATA2 Discussion The GO analysis revealed significant enrichment of upregulated genes in processes like C-5 methylation of cytosine and DNA methylation related to embryo development (25). These epigenetic changes may contribute to the dysregulated behavior of endometrial cells in endometriosis (26). In contrast, downregulated genes were enriched in processes such as embryonic skeletal system morphogenesis, suggesting developmental impacts on reproductive organs (27). Key cellular components, including NF-kappaB among upregulated genes, highlight the role of inflammation, while downregulated genes related to the brahma complex indicate disruptions in transcriptional regulation (28). Molecular functions associated with upregulated genes, such as DNA methyltransferase activities, reinforce the significance of methylation dysregulation. Additionally, the involvement of downregulated genes in steroid hormone receptor activity suggests hormonal signaling disruptions(29). Pathway analysis revealed that upregulated genes were significantly linked to cysteine and methionine metabolism, potentially affecting cellular signaling and inflammation(30). Conversely, downregulated genes were associated with thyroid hormone signaling pathways, indicating a complex interplay between hormonal regulation and endometriosis (31,32). The STRING interaction network showed robust connectivity, with high clustering coefficients indicating functional communities among the genes. Notably, clusters revealed that upregulated genes associated with C5 methylation and downregulated genes linked to prostate gland development may reflect shared pathways affected by endometriosis (33). Overall, our findings highlight the intricate biological landscape of endometriosis, emphasizing the roles of epigenetic modifications, inflammation, and hormonal disruptions. These insights could inform future therapeutic strategies aimed at improving patient outcomes. The differential expression of HOX genes in the ectopic endometrium compared to control tissues provides crucial insight into the disease's pathogenesis. HOX genes, known for their fundamental roles in embryogenesis, are also implicated in carcinogenesis, stem cell differentiation, and cellular processes such as proliferation, migration, and apoptosis (34). In the context of endometriosis, the dysregulated expression of HOXA1, HOXA5, HOXA7, HOXA10, HOXA3, HOXA4, and HOXA11 may contribute to abnormal tissue proliferation and inflammation, particularly in ectopic endometrial regions (35). This is supported by prior studies linking HOXA1 with breast cancer and suggesting broader roles of HOX gene dysregulation in stem cell differentiation and cancer progression (36) . The suppression of critical regulators such as HOXA10 and HOXA11, key genes in reproductive tissue integrity and function, in ectopic endometrial tissues highlights a disruption in normal cellular processes (37). These genes, when downregulated, may lead to impaired differentiation and tissue remodeling, which are essential in maintaining normal endometrial homeostasis. Similarly, the upregulation of pro-inflammatory genes, including VEGFA, TNF-α, and IL-6, signals a heightened inflammatory state that may exacerbate tissue damage and promote ectopic lesion establishment in endometriosis(38). The aberrant expression of these HOX genes, combined with altered epigenetic regulatory factors, suggests that the ectopic endometrial environment favors disease persistence and progression (39). Epigenetic modifications, especially DNA methylation, play a significant role in regulating gene expression in endometriosis. DNMT1, DNMT3A, and DNMT3B, key enzymes involved in DNA methylation, are significantly upregulated in the ectopic endometrium, as previously observed in various cancers and inflammatory conditions (40). DNMT1 has been linked to pro-inflammatory pathways, contributing to chronic inflammation and tissue remodeling, processes central to the pathophysiology of endometriosis (41). The correlation between DNMT1 and decreased PPAR-γ expression, a key anti-inflammatory mediator, further supports the role of epigenetic dysregulation in sustaining inflammation in the disease (42). The methyltransferase G9A/EHMT2 and genes like VENTX and PRDM16 also emerge as key players in the ectopic endometrium. G9A promotes cancer recurrence through repression of pro-inflammatory genes, while VENTX is involved in myeloid cell differentiation and has been linked to myeloid leukemia (43). These findings suggest that similar epigenetic and inflammatory mechanisms could be at play in the ectopic lesions of women with endometriosis. Moreover, the MLL2/KMT2D gene, frequently mutated in cancers, and PRDM proteins, which regulate cancer invasion and metastasis, are differentially expressed in endometriotic tissues (44). These proteins may play a pivotal role in endometrial cell proliferation, migration, and invasive potential, mirroring processes observed in malignancies. Thus, the dysregulation of HOX genes, along with epigenetic modifiers such as DNMTs, likely contributes to the abnormal cellular environment observed in endometriosis. The abnormal expression of these genes may drive aberrant cell proliferation, differentiation, and inflammation in the ectopic endometrium, potentially serving as biomarkers for disease progression. Understanding these pathways offers promising avenues for the development of targeted therapies aimed at controlling inflammation, abnormal cell growth, and tissue remodeling in endometriosis. Strengths and Limitations This study provides an integrative approach combining targeted gene expression profiling and bioinformatics network analysis to elucidate the molecular mechanisms underlying endometriosis. The inclusion of well-characterized patients with histologically confirmed ovarian, peritoneal, and deep infiltrating lesions enhances the clinical relevance of the findings. The use of both quantitative PCR validation and functional enrichment analyses (GO and STRING) strengthens the interpretation of the biological pathways involved, particularly highlighting the interplay of epigenetic, inflammatory, and angiogenic factors. However, certain limitations should be acknowledged. The study focused on a selected panel of candidate genes, which may not capture the full transcriptomic complexity of endometriosis. The sample size was relatively small, and stage-specific molecular variations could not be extensively analyzed. Additionally, functional validation at the protein level and longitudinal follow-up were beyond the scope of the current work. Future studies employing multi-omics approaches and larger cohorts are warranted to validate these findings and establish their clinical applicability as diagnostic or therapeutic targets. Declarations Acknowledgement We want to extend our sincere gratitude to Seed Grant (IoE, BHU) and Multi-Disciplinary Research Units (MRUs) Laboratory, a grant by ICMR-Department of Health Research. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics statement Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Institutional Ethical Committee, Institute of Medical Sciences, Banaras Hindu University (protocol code No. Dean/2021/EC/2397 and date of approval 15 February 2021). Consent to Participate All research procedures were approved by and in accordance with relevant guidelines and regulations. Consent for publication Not available. Data Availability The detailed datasets analyzed during the current study are available with the corresponding author. In the future, it will be made available on reasonable request. Data are however available from the authors upon reasonable request. Funding Not available. Author contributions RS, SR and GR conceived and designed the project. AA, SM and SS performed all operations. AA, SS analyzed the data and drew the figures. SM and AA wrote the manuscript. RS, SR and GR revised the manuscript. All authors contributed to the article and approved the submitted version. References Petraglia F, Vannuccini S, Donati C, Jeljeli M, Bourdon M, Chapron C. Endometriosis and comorbidities: molecular mechanisms and clinical implications. Trends Mol Med [Internet]. 2025 Oct 2 [cited 2025 Nov 2]; Available from: https://www.sciencedirect.com/science/article/pii/S1471491425002114 Smolarz B, Szyłło K, Romanowicz H. 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Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Molecular Biology Reports → Version 1 posted Editorial decision: Revision requested 19 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor assigned by journal 03 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 02 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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07:16:55","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146522,"visible":true,"origin":"","legend":"","description":"","filename":"493b1926a6c448aba7cfb49bfe67d7a31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/c489ca51868d18f464b8b18e.xml"},{"id":95927122,"identity":"699f5483-fb39-4b73-84d2-cb43e66bbd22","added_by":"auto","created_at":"2025-11-14 13:43:03","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":160268,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/3bd8e1524921efcda477f507.html"},{"id":95927091,"identity":"cb66fae8-d77c-4044-b59f-9c987a8ac9fb","added_by":"auto","created_at":"2025-11-14 13:43:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111479,"visible":true,"origin":"","legend":"\u003cp\u003eA volcano plot illustrating the differential expression of genes between case and control groups. The x-axis represents the log₂ fold change (log₂FC) in gene expression, while the y-axis indicates the -log₁₀ of the adjusted p-value (FDR-corrected), reflecting statistical significance. Genes significantly upregulated in the case group are shown in red (log₂FC \u0026gt; 2 and adjusted p \u0026lt; 0.05), while significantly downregulated genes are shown in blue (log₂FC \u0026lt; -2 and adjusted p \u0026lt; 0.05). Grey points represent genes that were not significantly differentially expressed (adjusted p ≥ 0.05). Selected genes of interest are labeled in red text. Notable upregulated genes include VEGFA, DNMT1, HOX, and IL6, while HOXA3, GATA2, and PGR are among the downregulated genes.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/bb2e30ed38de3701e349f544.png"},{"id":96242782,"identity":"3656822e-f22e-4024-b3ad-1effcf49a2f2","added_by":"auto","created_at":"2025-11-19 07:14:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":261922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap: \u003c/strong\u003eThe \u0026nbsp;heatmap illustrates hierarchical clustering of endometriosis samples based on \u0026nbsp;their gene expression profiles. A total of 35 genes, identified as \u0026nbsp;differentially expressed (fold change ≥ 3.0, adjusted p-value ≤ 0.05), were \u0026nbsp;organized into distinct clusters of upregulated and downregulated genes. Red \u0026nbsp;shading indicates upregulation, while orange represents downregulation. The \u0026nbsp;x-axis lists the differentially expressed genes, while the y-axis displays the \u0026nbsp;corresponding sample IDs. This clustering clearly distinguishes expression \u0026nbsp;patterns among the samples, providing a visual representation of the gene \u0026nbsp;expression changes associated with endometriosis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/4f511779c5ed47689ff522ca.png"},{"id":95927092,"identity":"a099fda2-2a6d-44d4-be56-294ac7cfc772","added_by":"auto","created_at":"2025-11-14 13:43:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":388623,"visible":true,"origin":"","legend":"\u003cp\u003eThe GO enrichment analysis of upregulated genes, highlighting top-enriched \u003cstrong\u003e(A)\u003c/strong\u003eBiological processes, \u003cstrong\u003e(B)\u003c/strong\u003e Molecular functions, \u003cstrong\u003e(C)\u003c/strong\u003e cellular components and \u003cstrong\u003e(D)\u003c/strong\u003e signalling pathways\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/2adf07122fd86bf0acb3877a.png"},{"id":95927093,"identity":"a043581b-672f-446c-8fe0-2ffff6269a50","added_by":"auto","created_at":"2025-11-14 13:43:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":411867,"visible":true,"origin":"","legend":"\u003cp\u003eThe GO enrichment analysis of downregulated genes, highlighting top-enriched (A)Top Hub Upregulated Gene (B) Top Hub Downregulated Gene Biological Processes Molecular functions and cellular components (C)Upregulated Biological processes Molecular functions and cellular components (D Downregulated Biological Processes Molecular functions and cellular components.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/5e65ab4b05e50aff9c23de8f.png"},{"id":95927096,"identity":"7fdadcdc-1941-424f-b9b6-6ae5c8d0a329","added_by":"auto","created_at":"2025-11-14 13:43:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":457913,"visible":true,"origin":"","legend":"\u003cp\u003eThe cluster analysis of upregulated genes using STRING. The identified clusters are coloured in (A) red (B) green and (C) blue\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/972f47f9ea166b2f5ecc88bd.png"},{"id":96244902,"identity":"d69c1356-3e75-42af-a196-c8274a9c3d31","added_by":"auto","created_at":"2025-11-19 07:19:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297837,"visible":true,"origin":"","legend":"\u003cp\u003eThe cluster analysis of downregulated genes using STRING. The identified clusters are coloured in (A) red (B) green and (C) blue\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/fbbe44fad851c4975e8970c3.png"},{"id":102234104,"identity":"2d846bed-a77b-4d6d-8b09-feec34e18e90","added_by":"auto","created_at":"2026-02-09 16:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2920804,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8011817/v1/e91c04bb-acd5-40b0-95c9-714620df9fae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential Gene Expression Profiling of Ectopic Endometrium Reveals Key Molecular Pathways in Endometriosis Pathogenesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis is a complex, estrogen-dependent, chronic inflammatory disorder affecting approximately 10% of women of reproductive age and is now recognized as a systemic condition rather than merely a localized gynecological disease(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is characterized by the presence of endometrial-like glands and stroma outside the uterine cavity, most commonly on the ovaries, pelvic peritoneum, and other pelvic organs(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This ectopic implantation triggers a cascade of chronic inflammation, aberrant angiogenesis, fibrosis, and hormonal resistance, leading to pain, infertility, and impaired quality of life(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The multifactorial etiology of the disease encompasses genetic, epigenetic, immunological, and environmental influences(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). One of the most accepted theories for its origin, retrograde menstruation, fails to fully explain lesion establishment and persistence, indicating that cellular and molecular alterations in endometrial tissue play a critical role in disease progression(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In this study, molecular progression refers to the transformation of eutopic endometrial cells that acquire invasive and hormone-resistant characteristics when forming ectopic lesions. To elucidate these mechanisms, we analyzed gene expression profiles in ectopic endometrial tissues obtained from women with surgically and histologically confirmed endometriosis, compared with eutopic endometrium from healthy fertile women(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The genes selected for investigation were chosen based on prior evidence implicating them in major pathways central to endometriosis pathophysiology, including epigenetic regulation (DNMT1, DNMT3A, DNMT3B, G9A/EHMT2, PRDM16), developmental and HOX transcription factors (HOXA and HOXB families), inflammatory mediators (TNF-α, IL-6, COX-2, NF-κB), angiogenic and extracellular matrix remodeling factors (VEGFA, MMP2, MMP9, CD44), and hormone signaling regulators (ESR1, PGR, FOXO1, HAND2, GATA2)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Evaluating these genes offers insights into how interconnected epigenetic, inflammatory, and hormonal networks drive lesion persistence and infertility(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Furthermore, the classification of patients according to the revised American Society for Reproductive Medicine (rASRM) system provided a clinically relevant framework for correlating gene expression profiles with disease severity, thereby linking molecular findings with phenotypic stages(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Through this integrative approach, the study aimed to elucidate molecular signatures in ectopic endometrium that underlie the pathogenesis and progression of endometriosis.\u003c/p\u003e\n\u003ch3\u003eClassification Systems of Endometriosis\u003c/h3\u003e\n\u003cp\u003eTo enable standardized assessment and correlation of gene expression with disease severity, endometriosis cases were classified according to the revised American Society for Reproductive Medicine (rASRM) system, which grades disease into four stages (minimal, mild, moderate, severe) based on lesion number, size, depth, and extent of adhesions(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This classification provides a consistent clinical framework to evaluate how molecular dysregulation varies with disease progression, supporting the study\u0026rsquo;s aim to identify stage-associated gene expression patterns.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval under reference number Dean/2021/EC/2397. All participants, including those with endometriosis and healthy individuals, provided written informed consent, ensuring they were fully informed about the study\u0026apos;s purpose, procedures, risks, and benefits. Confidentiality and anonymity were maintained throughout the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at the MRU Lab in collaboration with the Department of Obstetrics and Gynaecology, Sir Sunderlal Hospital, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India, between January 2022 and June 2024. Data were collected from 27 fertile women and 221 infertile women with endometriosis, with details on lifestyle, habits, and familial history recorded. Endometriosis patients were classified using the revised American Society for Reproductive Medicine (rASRM) system. Participants were of Indian ethnicity from eastern Uttar Pradesh and Bihar. Informed consent was obtained, and women aged 18-50 with endometriosis diagnosed through hysteroscopy and laparoscopy, and a BMI \u0026lt; 32 kg/m\u0026sup2;, were included. Those with chronic pelvic pain due to other causes were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Exclusion Criteria:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion:\u003c/strong\u003e Infertile women aged 18\u0026ndash;50 with laparoscopic/histopathological diagnosis and clinical evaluation of endometriosis; no hormonal/anti-inflammatory treatment in past 3 months; Infertility due to endometriosis. None of the participants were on active hormonal contraceptive therapy at the time of sample collection. A small proportion of fertile control women reported prior contraceptive use, but this was not ongoing during the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion:\u003c/strong\u003e Obesity (BMI \u0026gt; 30), known metabolic and lipidomic abnormalities, Pregnancy/lactation; recent surgery/Chronic/ acute infection; immunosuppressant/antioxidant use; history of cancer, diabetes, (cardiovascular disorders) smoking/alcohol abuse; incomplete data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical characteristics of the subject included:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study recorded the clinical characteristics of subjects, including age, residential setting, socioeconomic status, and infertility details (primary or secondary, and duration). Menstrual cycle information, such as age of onset, cycle frequency, flow, and symptoms like dysmenorrhea, dyspareunia, chronic pelvic pain, and urinary issues, was assessed. Physical exams included BMI, adnexal masses, uterine mobility, and tenderness. Imaging through transvaginal ultrasound (TVS) and MRI identified deep-infiltrating lesions, while diagnostic laparoscopy confirmed endometriosis staging using the revised AFS scoring system. This comprehensive approach aimed to understand endometriosis\u0026apos;s presentation and impact.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue collection and RNA Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndometrial tissue samples were obtained from two distinct groups of participants. For the endometriosis group, tissue was collected from ectopic endometrial lesions (including ovarian endometriomas, peritoneal implants, or deep infiltrating nodules) during laparoscopic or laparotomic surgery, and the diagnosis was confirmed by histopathological examination. For the control group, eutopic endometrial tissue was collected from healthy fertile women undergoing hysterectomy for benign, non-endometriotic conditions (such as fibroids or prolapse). Importantly, no eutopic endometrium from endometriosis patients was analyzed in this study; instead, the comparison was made between ectopic endometrial lesions from affected women and normal eutopic endometrium from controls. All tissue specimens were immediately placed in RNAlater (Ambion\u0026trade;, Invitrogen, Germany) to stabilize RNA and stored at \u0026minus;80\u0026deg;C until further processing for RNA extraction and quantitative real-time PCR (qPCR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA integrity and cDNA Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA (1 \u0026micro;g) was reverse transcribed using M-MLV Reverse Transcriptase (Applied Biosystems) in 20 \u0026micro;L reactions. cDNA was diluted 1:5 with nuclease-free water for qPCR. qPCR reactions (25 \u0026micro;L) contained 12.5 \u0026micro;L 2\u0026times; SYBR Green Master Mix (Applied Biosystems), 1 \u0026micro;L each of forward and reverse primers (10 \u0026micro;M), 5 \u0026micro;L diluted cDNA and nuclease-free water. Reactions were run on a CFX96 Touch (Bio-Rad) with cycling: 95\u0026deg;C 5 min; 40 cycles of 95\u0026deg;C 15 s, 60\u0026deg;C 30 s, followed by melt-curve analysis. Each sample was run in technical triplicate. GAPDH was used as the endogenous control; relative expression was calculated by the 2⁻\u0026Delta;\u0026Delta;Ct method using pooled control cDNA as calibrator. Samples with Ct \u0026gt; 35 or inconsistent triplicates were repeated or excluded. Differential expression was assessed by Student\u0026rsquo;s t-test (for two-group comparisons) and one-way ANOVA with Tukey post-hoc for multiple groups; normality was assessed by Kolmogorov\u0026ndash;Smirnov test. For the gene panel, p-values were adjusted using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR); q \u0026lt; 0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ShinyGO v0.74.1 was utilized to perform gene ontology (GO) enrichment analysis of both upregulated and downregulated genes (12). ENSEMBL gene IDs corresponding to these gene sets into the ShinyGO server was imported for the analysis(13). The analysis was specifically conducted for the human species. To ensure the robustness of the findings, false discovery rate (FDR) cutoff at set as 0.05. This cutoff is crucial as it helps control for multiple testing errors, allowing us to identify significant enrichment while minimizing the likelihood of false positives. ShinyGO calculates the FDR based on the nominal p-values derived from a hypergeometric test, which assesses the probability of observing the number of genes in a specific GO term by chance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTRING Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significantly upregulated and downregulated genes were analyzed separately using the STRING database (https://string-db.org/) to construct protein\u0026ndash;protein interaction (PPI) networks(14). A high-confidence interaction score threshold (\u0026ge;0.7) was applied to ensure reliable associations. The generated PPI networks were then imported into Cytoscape (v3.x) for network visualization and topological analysis(15). Using the CytoHubba plugin, hub genes were identified based on the Maximal Clique Centrality (Degree) algorithm, and the top five upregulated and downregulated hub genes were selected for further analysis(16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adhered to standard statistical practices, utilizing IBM SPSS Statistics for analysis, assessing normality with the Kolmogorov-Smirnov test, employing Tukey\u0026apos;s test for mean separation under specific conditions, and reporting results in the Mean \u0026plusmn; SD format. The significance of differences among groups was determined using one-way ANOVA, with clear thresholds for statistical and high statistical significance defined by p-values. The student\u0026rsquo;s t-test was employed to identify differentially expressed genes (DEGs) in endometriosis samples. Statistical significance was determined with a probability threshold of p \u0026lt; 0.05. For protein-protein interaction (PPI) analysis, a more stringent threshold of p \u0026lt; 0.01 was used to assess statistical significance(17).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical and biochemical characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 248 women were included (27 fertile controls, 221 endometriosis cases). Demographic variables (age, BMI, residence) did not differ significantly between controls and cases \u003cstrong\u003e(Table 1).\u003c/strong\u003e Clinical symptoms (dysmenorrhea, dyspareunia, pelvic pain) and infertility duration increased progressively with rASRM stage (p \u0026lt; 0.001). Serum CA-125 and CRP were significantly elevated in cases compared with controls (p \u0026lt; 0.001) and rose with increasing stage(18). LH/FSH ratio showed a modest but significant increase with disease stage (p = 0.004), whereas hemoglobin decreased (p = 0.003), consistent with chronic menstrual blood loss and inflammation. Ovarian endometriomas were the most frequent lesion type (~53%), followed by peritoneal (~30%) and deep infiltrating endometriosis (~17%). These clinical and biochemical changes parallel the molecular dysregulation observed in ectopic tissues\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1:\u003c/strong\u003e (Values are Mean \u0026plusmn; SD or n (%); statistical significance determined by one-way ANOVA or \u0026chi;\u0026sup2; test; p \u0026lt; 0.05 significant) * p \u0026lt; 0.05; ** p \u0026lt; 0.001. DIE = deep infiltrating endometriosis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"904\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl (n = 27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage I (n = 19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage II (n = 80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage III (n = 75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage IV (n = 47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.8 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.2 \u0026plusmn; 5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.1 \u0026plusmn; 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.6 \u0026plusmn; 4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.4 \u0026plusmn; 5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79 (ns)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.9 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.7 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.3 \u0026plusmn; 2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.0 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.8 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21 (ns)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence \u0026mdash; Urban, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.48 (ns)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenstrual irregularities, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (50.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDysmenorrhea (VAS 0\u0026ndash;10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyspareunia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfertility duration (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of endometriosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOvarian 9 (47.4) / Peritoneal 6 (31.6) / DIE 4 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOvarian 42 (52.5) / Peritoneal 24 (30.0) / DIE 14 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOvarian 39 (52.0) / Peritoneal 23 (30.7) / DIE 13 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOvarian 27 (57.4) / Peritoneal 13 (27.7) / DIE 7 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum CA-125 (U/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.2 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.4 \u0026plusmn; 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.7 \u0026plusmn; 11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.2 \u0026plusmn; 14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.4 \u0026plusmn; 18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLH/FSH ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.12 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.36 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.62 \u0026plusmn; 0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin (g/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.3 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.7 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.2 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.9 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003 *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.9 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.4 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.2 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePelvic pain (VAS 0\u0026ndash;10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.4 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.6 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGene expression data and Identification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression analysis was performed to compare ectopic endometrial lesions obtained from women with surgically and histologically confirmed endometriosis with eutopic endometrial tissue from healthy fertile women. Among the examined genes, DNMT1, DNMT3A, DNMT3B, G9A/EHMT2, VENTX, PRDM16, HOXA1, HOXB6, VEGFA, TNF-\u0026alpha;, IL-6, COX-2, CYP19A1, MMP2, MMP9, NFKB1, TGF-\u0026beta;, and CD44 showed significant upregulation in ectopic lesions compared with control endometrium, indicating enhanced activity in epigenetic modification, inflammation, and angiogenesis. Conversely, HOXA3, HOXA4, HOXA5, HOXA7, HOXA10, HOXA11, MLL2, ESR1, PGR, FOXO1, HAND2, GATA2, KLF9, PTEN, ARID1A, IL-10, and RECK were markedly downregulated, suggesting suppression of genes involved in tissue differentiation, hormonal responsiveness, and immune regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Differentially Expressed Genes (DEGs) observed in endometriosis on the basis of fold change.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 651px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated Genes:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSr. No.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenes:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnsemble id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog 2-Fold Change\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT1\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000130816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+10.40364159785399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000119772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+6.65209010709126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0000264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eDNMT3B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000088305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+3.40364159785399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eG9A/ EHMT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000227333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+8.60880520296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"5\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eVENTX\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000151650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+7.252169566569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0000432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePRDM16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000142611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+9.61468319933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0000169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"7\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000105991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+8.437526418983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00012508\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"8\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXB6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000108511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+10.883578787644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"9\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eVEGFA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000112715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+8.119331782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"10\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eTNF-\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000232810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+7.82312542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"11\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eIL-6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000136244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+6.69647823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.000098231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"12\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eCOX-2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000073756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+7.424875214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.000203411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"13\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eCYP19A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000137869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+7.965987251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00088703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"14\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMMP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000087245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+9.332994586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.008134795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"15\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMMP-9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000100985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+10.025793148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.004786423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"16\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eNFKB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000109320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+10.75873654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.002721006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"17\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eTGF-\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000105329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+9.532114761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.03581536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"18\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eCD44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000026508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e+8.225914735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.053472822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 651px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownregulated Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"19\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000105997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-9.2054215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00000315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"20\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000197576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-4.13938515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.002019634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"21\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000106004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-5.06037373895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.000652623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"22\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000122592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.06037373895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0000319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"23\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000253293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-3.12240210716539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00013732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"24\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHOXA11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000005073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-9.89018318037401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0000543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"25\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eMLL2/ KMT2D\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000167548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-4.82218813483675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00010954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"26\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eESR1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000091831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-8.876404779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00103009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"27\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePGR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000082175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-8.210288179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0003601477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"28\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eFOXO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000150907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-8.199871156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.004888741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"29\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eHAND2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000164107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.370939934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.0167114789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"30\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eARID1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000117713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.514008656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.04047928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"31\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eKLF9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000119138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.6062fmeth063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.032654642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"32\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000171862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.734825738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.03300244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"33\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eGATA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000179348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-7.850139264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00002591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"34\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eIL-10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000136634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-6.524862125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.00202008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\u0026nbsp;\u003col start=\"35\" type=\"1\"\u003e\n \u003cli\u003e\u0026nbsp;\u003c/li\u003e\n \u003c/ol\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cem\u003eRECK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eENSG00000122707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e-6.225456943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.002019039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA total of 35 differentially expressed genes (DEGs) were identified, comprising 18 upregulated and 17 downregulated transcripts (p \u0026lt; 0.05). These DEGs are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e and visualized through a volcano plot \u003cstrong\u003e(Figure 1)\u003c/strong\u003e and a hierarchical heat map \u003cstrong\u003e(Figure 2).\u003c/strong\u003e The distinct clustering patterns observed between the ectopic and control groups confirm clear transcriptional divergence associated with endometriotic pathology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Interaction Networks for Differentially Expressed Genes (DEGs)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protein\u0026ndash;protein interaction (PPI) network for differentially expressed genes (DEGs) was constructed using STRING (https://string-db.org/cgi/input)[9]. Lists of both upregulated and downregulated genes were imported separately into the STRING server. The \u0026ldquo;multiple proteins\u0026rdquo; feature of STRING was utilized for PPI network construction. Homo sapiens was specified as the organism to ensure the relevance of the interaction data. Various sources of information, including experimental data, computational predictions, and curated databases, are integrated by STRING to provide a comprehensive view of the interactions among the proteins encoded by the DEGs. This approach allows for the visualization of complex relationships between these proteins and the identification of potential pathways and networks involved in the biological processes of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology (GO)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GO analysis identified enriched biological processes (BP), molecular functions (MF), and cellular components (CC) for both upregulated and downregulated genes.\u0026nbsp;The analysis for upregulated genes revealed significant enrichment in processes such as response to C-5 methylation of cytosine, DNA methylation of cytosine, negative regulation of histone H3-K9 methylation, and DNA methylation involved in embryo development. In contrast, the top-enriched biological processes for downregulated genes included embryonic skeletal system morphogenesis, anterior/posterior pattern specification, and skeletal system morphogenesis.\u003c/p\u003e\n\u003cp\u003eFor cellular components, the GO analysis indicated that NF-kappaB and heterochromatin were the most enriched for upregulated genes, while the brahma complex and transcription preinitiation complex were identified as the most enriched components for downregulated genes\u003cstrong\u003e\u0026nbsp;Table 3\u003c/strong\u003e.The highest-enriched molecular functions associated with upregulated genes included DNA cytosine-5 methyltransferase activity and DNA methyltransferase activity\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e,\u003cstrong\u003e4\u003c/strong\u003e. For downregulated genes, transcription coactivator binding and steroid hormone receptor activity were found to be the highest-enriched molecular functions. Pathway analysis showed that cysteine and methionine metabolism was significantly enriched for upregulated genes, whereas thyroid hormone signaling pathways were significantly enriched for downregulated genes [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Association of disease analysis of DEGs associated with Differentially Expressed Genes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog10(P)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog10(q)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0341858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eEndometriosis of uterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e49.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-30.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0269102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eEndometrioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e54.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0014170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eEndometrial Neoplasms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e49.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC1153706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eEndometrial adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-23.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0156369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eUterine Polyp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-21.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0023267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eFibroid Tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e46.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0205643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eCarcinoma, Cribriform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0205641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eAdenocarcinoma, Basal Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0398650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eImmune thrombocytopenic purpura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC1536148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eChocolate cyst of ovary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e29.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-17.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0162871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eAortic Aneurysm, Abdominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e49.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0205645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eAdenocarcinoma, Tubular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0940937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eprecancerous lesions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eC0205642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eAdenocarcinoma, Oxyphilic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e-16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eUpregulated Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional enrichment analysis of the upregulated genes demonstrated significant involvement in key biological processes, cellular components, and molecular functions. The top biological processes included ovulation, regulation of inflammatory response, regulation of cellular process, negative regulation of cellular process, heterochromatin organization, cellular response to reactive oxygen species, and metabolic processes. Prominent cellular component terms were chromatin, nucleoplasm, and nucleus. Key molecular function enrichments involved DNA (cytosine-5)-methyltransferase and S-adenosylmethionine-dependent methyltransferase activities.\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction (PPI) network analysis revealed a tightly interconnected module, with 15 nodes and 35 edges, significantly more than the 11 edges expected by chance (PPI enrichment p-value: 1.05e-08). The average node degree was 4.67 and the average clustering coefficient was 0.612, indicating strong network connectivity. Core hub genes identified in the upregulated set included HOXA5, HOXA10, ESR1, PGR, and FOXO1, all exhibiting robust interactions and regulatory potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDownregulated Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor downregulated genes, enrichment analysis revealed major roles in chromatin organization, nucleoplasm, nucleus, transcription regulation, and hormone-related biological processes including gland and uterus development. The molecular functions most strongly represented were transcription factor activity, RNA polymerase II-specific DNA-binding, and hormone binding. The cellular component categories highlighted chromatin, nucleoplasm, and nucleus as central to the downregulated gene set.\u003c/p\u003e\n\u003cp\u003eThe corresponding PPI network analysis displayed pronounced interconnectivity, comprising 17 nodes and 45 edges, whereas only 6 were expected at random (PPI enrichment p-value: \u0026lt; 1.0e-16). The average node degree was 5.29 and the clustering coefficient 0.692, further supporting the significance of networked interactions. Central hub genes identified among the downregulated genes included IL6, NFKB1, DNMT1, MMP9, and CD44, marking them as potential key players in the underlying biological mechanisms.\u003c/p\u003e\n\u003cp\u003eThese results illustrate the distinct functional profiles and hub gene networks observed between the upregulated and downregulated gene groups, underscoring their respective roles in the relevant biological pathways and processes visualized in the enrichment and PPI analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Interaction Networks for Differentially Expressed Genes (DEGs)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 35 genes, comprising 18 upregulated and 17 downregulated genes, were imported into the STRING server for the construction of the interaction network. The interaction network for the upregulated genes, as constructed by STRING, included 15 nodes and 35 edges, with an average node degree of 4.67, an expected number of edges of 11, and a PPI enrichment p-value of 1.05e-08. For the downregulated genes, the network displayed 17 nodes and 45 edges, with an average node degree of 5.29, an expected number of edges of 6, and a PPI enrichment p-value of \u0026lt;1.0e-16. Additionally, clustering analysis was performed separately on both upregulated and downregulated genes to identify groups within the datasets, thereby prioritizing genes for experimental validation. The K-means clustering method was employed to form the clusters, resulting in a network with high clustering coefficients of 0.692 for downregulated genes and 0.612 for upregulated genes. These high clustering coefficients indicate that the networks likely represent communities involved in similar functions. Three clusters were formed for both upregulated and downregulated DEGs, with cluster sizes ranging from a maximum of 9 genes to a minimum of 1. Notably, Cluster 3 for both gene sets contained only a single gene. The genes in Cluster 1 of the upregulated genes were associated with C5 methylation of cytosine, while those in Cluster 2 were linked to the cysteine switch. Similarly, the genes in Cluster 1 of the downregulated set were related to prostate gland development, whereas Cluster 2 was involved in embryonic skeletal system morphogenesis\u003cstrong\u003e\u0026nbsp;Figure 5,6\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the molecular landscape of endometriosis by analyzing gene expression alterations in ectopic endometrial tissues and validating key pathways through bioinformatics approaches. Differential expression profiling revealed significant dysregulation of genes associated with epigenetic regulation (DNMT1, KLF9, HOXA10), inflammatory response (IL6, TNF\u0026alpha;), and angiogenesis (VEGFA, MMP9)(19). These molecular alterations suggest a coordinated disruption of pathways that govern cellular proliferation, adhesion, and immune tolerance, contributing to the establishment and persistence of ectopic lesions(20,21). Gene Ontology and protein\u0026ndash;protein interaction network analyses identified enrichment in biological processes related to cell adhesion, extracellular matrix organization, hormonal response, and immune signaling(22). The integrated analysis pinpointed several hub genes (DNMT1, IL6, VEGFA, MMP9, and CD44) that play central roles in disease pathophysiology(23). Together, these findings highlight a complex interaction between epigenetic modification, inflammation, and angiogenesis that underlies the pathogenesis of endometriosis. The study underscores the potential of these hub genes as biomarkers and therapeutic targets, paving the way for improved diagnostic and treatment strategies. Further validation in larger cohorts and through multi-omics approaches will strengthen these insights and facilitate their clinical translation\u0026nbsp;(24).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Categorization of differentially expressed genes (DEGs) in endometriosis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownregulated Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpigenetic Regulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDNMT1, DNMT3A, DNMT3B, G9A/EHMT2, PRDM16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARID1A, KLF9, MLL2/KMT2D, PTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDevelopmental / HOX Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHOXA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHOXA3, HOXA4, HOXA5, HOXA7, HOXA10, HOXA11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammatory Mediators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTNF-\u0026alpha;, IL-6, COX-2, NFKB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAngiogenesis \u0026amp; ECM Remodeling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVEGFA, MMP2, MMP9, CD44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRECK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormone Signaling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCYP19A1, TGF-\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eESR1, PGR, FOXO1, HAND2, GATA2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe GO analysis revealed significant enrichment of upregulated genes in processes like C-5 methylation of cytosine and DNA methylation related to embryo development (25). These epigenetic changes may contribute to the dysregulated behavior of endometrial cells in endometriosis (26). In contrast, downregulated genes were enriched in processes such as embryonic skeletal system morphogenesis, suggesting developmental impacts on reproductive organs (27). Key cellular components, including NF-kappaB among upregulated genes, highlight the role of inflammation, while downregulated genes related to the brahma complex indicate disruptions in transcriptional regulation (28). Molecular functions associated with upregulated genes, such as DNA methyltransferase activities, reinforce the significance of methylation dysregulation. Additionally, the involvement of downregulated genes in steroid hormone receptor activity suggests hormonal signaling disruptions(29). Pathway analysis revealed that upregulated genes were significantly linked to cysteine and methionine metabolism, potentially affecting cellular signaling and inflammation(30). Conversely, downregulated genes were associated with thyroid hormone signaling pathways, indicating a complex interplay between hormonal regulation and endometriosis (31,32). The STRING interaction network showed robust connectivity, with high clustering coefficients indicating functional communities among the genes. Notably, clusters revealed that upregulated genes associated with C5 methylation and downregulated genes linked to prostate gland development may reflect shared pathways affected by endometriosis (33). Overall, our findings highlight the intricate biological landscape of endometriosis, emphasizing the roles of epigenetic modifications, inflammation, and hormonal disruptions. These insights could inform future therapeutic strategies aimed at improving patient outcomes.\u003c/p\u003e\n\u003cp\u003eThe differential expression of HOX genes in the ectopic endometrium compared to control tissues provides crucial insight into the disease\u0026apos;s pathogenesis. HOX genes, known for their fundamental roles in embryogenesis, are also implicated in carcinogenesis, stem cell differentiation, and cellular processes such as proliferation, migration, and apoptosis (34). In the context of endometriosis, the dysregulated expression of HOXA1, HOXA5, HOXA7, HOXA10, HOXA3, HOXA4, and HOXA11 may contribute to abnormal tissue proliferation and inflammation, particularly in ectopic endometrial regions (35). This is supported by prior studies linking HOXA1 with breast cancer and suggesting broader roles of HOX gene dysregulation in stem cell differentiation and cancer progression (36) .\u003c/p\u003e\n\u003cp\u003eThe suppression of critical regulators such as HOXA10 and HOXA11, key genes in reproductive tissue integrity and function, in ectopic endometrial tissues highlights a disruption in normal cellular processes (37). These genes, when downregulated, may lead to impaired differentiation and tissue remodeling, which are essential in maintaining normal endometrial homeostasis. Similarly, the upregulation of pro-inflammatory genes, including VEGFA, TNF-\u0026alpha;, and IL-6, signals a heightened inflammatory state that may exacerbate tissue damage and promote ectopic lesion establishment in endometriosis(38). The aberrant expression of these HOX genes, combined with altered epigenetic regulatory factors, suggests that the ectopic endometrial environment favors disease persistence and progression (39).\u003c/p\u003e\n\u003cp\u003eEpigenetic modifications, especially DNA methylation, play a significant role in regulating gene expression in endometriosis. DNMT1, DNMT3A, and DNMT3B, key enzymes involved in DNA methylation, are significantly upregulated in the ectopic endometrium, as previously observed in various cancers and inflammatory conditions (40). DNMT1 has been linked to pro-inflammatory pathways, contributing to chronic inflammation and tissue remodeling, processes central to the pathophysiology of endometriosis (41). The correlation between DNMT1 and decreased PPAR-\u0026gamma; expression, a key anti-inflammatory mediator, further supports the role of epigenetic dysregulation in sustaining inflammation in the disease (42).\u003c/p\u003e\n\u003cp\u003eThe methyltransferase G9A/EHMT2 and genes like VENTX and PRDM16 also emerge as key players in the ectopic endometrium. G9A promotes cancer recurrence through repression of pro-inflammatory genes, while VENTX is involved in myeloid cell differentiation and has been linked to myeloid leukemia (43). These findings suggest that similar epigenetic and inflammatory mechanisms could be at play in the ectopic lesions of women with endometriosis. Moreover, the MLL2/KMT2D gene, frequently mutated in cancers, and PRDM proteins, which regulate cancer invasion and metastasis, are differentially expressed in endometriotic tissues (44). These proteins may play a pivotal role in endometrial cell proliferation, migration, and invasive potential, mirroring processes observed in malignancies.\u003c/p\u003e\n\u003cp\u003eThus, the dysregulation of HOX genes, along with epigenetic modifiers such as DNMTs, likely contributes to the abnormal cellular environment observed in endometriosis. The abnormal expression of these genes may drive aberrant cell proliferation, differentiation, and inflammation in the ectopic endometrium, potentially serving as biomarkers for disease progression. Understanding these pathways offers promising avenues for the development of targeted therapies aimed at controlling inflammation, abnormal cell growth, and tissue remodeling in endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides an integrative approach combining targeted gene expression profiling and bioinformatics network analysis to elucidate the molecular mechanisms underlying endometriosis. The inclusion of well-characterized patients with histologically confirmed ovarian, peritoneal, and deep infiltrating lesions enhances the clinical relevance of the findings. The use of both quantitative PCR validation and functional enrichment analyses (GO and STRING) strengthens the interpretation of the biological pathways involved, particularly highlighting the interplay of epigenetic, inflammatory, and angiogenic factors. However, certain limitations should be acknowledged. The study focused on a selected panel of candidate genes, which may not capture the full transcriptomic complexity of endometriosis. The sample size was relatively small, and stage-specific molecular variations could not be extensively analyzed. Additionally, functional validation at the protein level and longitudinal follow-up were beyond the scope of the current work. Future studies employing multi-omics approaches and larger cohorts are warranted to validate these findings and establish their clinical applicability as diagnostic or therapeutic targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to extend our sincere gratitude to Seed Grant (IoE, BHU) and Multi-Disciplinary Research Units (MRUs) Laboratory, a grant by ICMR-Department of Health Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Institutional Ethical Committee, Institute of Medical Sciences, Banaras Hindu University (protocol code No. Dean/2021/EC/2397 and date of approval 15 February 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll research procedures were approved by and in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed datasets analyzed during the current study are available with the corresponding author. In the future, it will be made available on reasonable request. Data are however available from the authors upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRS, SR and GR conceived and designed the project. AA, SM and SS performed all operations. AA, SS analyzed the data and drew the figures. SM and AA wrote the manuscript. RS, SR and GR revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePetraglia F, Vannuccini S, Donati C, Jeljeli M, Bourdon M, Chapron C. Endometriosis and comorbidities: molecular mechanisms and clinical implications. 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Available from: https://dx.doi.org/10.1210/endocr/bqab088\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Endometriosis, Gene expression, DNA methylation, Inflammation, HOX genes, Epigenetic regulation","lastPublishedDoi":"10.21203/rs.3.rs-8011817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8011817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEndometriosis is a chronic, estrogen-dependent inflammatory disorder characterized by ectopic endometrial-like tissue. We investigated gene expression changes in ectopic endometrium from 221 women with histologically confirmed endometriosis and compared them with eutopic endometrium from 27 fertile controls. Using quantitative real-time PCR, we analysed 35 candidate genes involved in epigenetic regulation, development (HOX genes), inflammation, angiogenesis and hormonal signalling. A total of 18 genes were upregulated and 17 downregulated in ectopic tissue (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Benjamini\u0026ndash;Hochberg FDR). Upregulated genes included DNMT1, DNMT3A, VEGFA, TNF-α and MMP2, whereas HOXA10, PGR and ESR1 were among the downregulated genes. Gene ontology (GO) and protein\u0026ndash;protein interaction (PPI) analyses (STRING, ShinyGO) revealed enrichment in DNA-methylation and inflammatory pathways. Clinical and biochemical measures (CA-125, CRP, pelvic pain) increased with rASRM stage. These results identify coordinated epigenetic and inflammatory alterations in ectopic endometrium, suggesting potential molecular targets for diagnosis and therapy.\u003c/p\u003e","manuscriptTitle":"Differential Gene Expression Profiling of Ectopic Endometrium Reveals Key Molecular Pathways in Endometriosis Pathogenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 13:42:58","doi":"10.21203/rs.3.rs-8011817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T12:16:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T12:29:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154148335816764656962148386668656271910","date":"2025-11-25T11:47:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214829169997018256498087190017755237319","date":"2025-11-05T05:57:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T13:43:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-04T03:45:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-04T03:44:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2025-11-02T14:59:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2588f4e1-20a1-4fac-97be-f0bdd1d166aa","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:03:23+00:00","versionOfRecord":{"articleIdentity":"rs-8011817","link":"https://doi.org/10.1007/s11033-026-11521-7","journal":{"identity":"molecular-biology-reports","isVorOnly":false,"title":"Molecular Biology Reports"},"publishedOn":"2026-02-03 15:57:52","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-11-14 13:42:58","video":"","vorDoi":"10.1007/s11033-026-11521-7","vorDoiUrl":"https://doi.org/10.1007/s11033-026-11521-7","workflowStages":[]},"version":"v1","identity":"rs-8011817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8011817","identity":"rs-8011817","version":["v1"]},"buildId":"k6vKHA0u1VdKjwwnw531e","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}