Identification of key immune genes of endometriosis based on bioinformatics and machine learning

Identification of key immune genes of endometriosis based on bioinformatics and machine learning | 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 Identification of key immune genes of endometriosis based on bioinformatics and machine learning Ruiying Yuan, Fumin Gao, Xiaolong Li, Xianghong Ou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3551509/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Immunity and inflammation are involved in a multitude of reproductive metabolic processes, with a particular focus on endometriosis (EMT). The aim of this study is to employ bioinformatics methods to explore novel immune-related biomarkers and assess their predictive capabilities for EMT. Methods mRNA expression profiles were obtained from the GSE141549 and GSE7305 datasets in the Gene Expression Omnibus (GEO) database, while immune-related genes were sourced from the ImmPort database. Immune genes associated with EMT were filtered for differential analysis. Interrelationships between different immune-related genes (DIRGs) were characterized using protein-protein interaction (PPI) networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the functionality of DIRGs. Least Absolute Shrinkage and Selection Operation (LASSO) regression models and Boruta models were built to determine candidate genes for EMT, and the performance of the prediction models and candidate genes were verified using Receiver Operator Characterization curve (ROC) in GSE141549 and GSE7305. Finally, we structured the EMT prediction normogram on the basis of the five candidate DIRGs. Expression of the five candidate DIRGs in human samples was examined using PCR and Western Blot. The relative proportions of 22 immune cells were computed using the CIBERSORT algorithm, and the correlations between immune cells and candidate DIRGs were emphasized. Results Altogether 769 differentially expressed genes (DEGs) and 94 DIRGs were detected between ectopic and normal endometrium. These DIRGs were mainly concentrated in positive regulation of response to external stimulus, collagen-containing extracellular matrix, receptor ligand activity and signaling receptor activator activity. KEGG enrichment analysis mainly addressed Cytokine-cytokine receptor interaction and Neuroactive ligand-receptor interaction. Then, five key genes (SCG2, FOS, DES, GREM1, and PLA2G2A) were characterized using the GSE141549 dataset and used to build a prediction model for EMT. Conclusions Immunity and inflammation have a major role in the elaboration of EMT. SCG2, FOS, DES, GREM1 and PLA2G2A can serve as important biomarkers for EMT. endometriosis bioinformatics machine learning immunity diagnostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Endometriosis (EMT) is a disease in which endometrioid tissue grows anomalously beyond the womb and is marked by estrogen-dependent chronic inflammation[1]. It is common in women of reproductive age, with a morbidity rate of about 10 percent[2]. In addition, EMT is recognized by clinical signs such as dysmenorrhea, chronic pelvic pain and infertility, which gravely impacts the physical and mental health of fertile-age women[3]. The diagnosis of EMT is often markedly delayed owing to uncertain mechanisms of pathogenesis, lack of utilization of specific symptoms and non-invasive assays[4, 5]. Although diverse biomarkers such as IL-2, anti-PEP, CA125, and miR-150-5p have been investigated as clinical diagnostic instruments[6–9]. However, there is typically no monolithic metric that can provide a direct diagnosis of EMT. At present, laparoscopy combined with histopathologic examination continues to be the gold standard for the diagnosis of EMT, but laparoscopic surgery involves trauma, adhesions, and decreased fertility among other risks[5]. Therefore, it is crucial to obtain a deep understanding into the molecular principles of EMT and to seek out non-invasive diagnostic indicators with a superior sense of detail. The immune system exerts a primary function within the pelvic microenvironment, encompassing eliciting immune tolerance, dampening immune surveillance, and shunning phagocytosis conducted by immune cells[10]. Previous research has demonstrated that immune-related genes (IRGs) play a significant role in the complex regulatory network of tumors. They have been investigated as markers for tracking the development of tumor immunity and understanding the pathophysiological mechanisms of diseases like endometrial cancers[11]. Evidence from current studies reveals that not only the endometrial immune status is modified in EMT, but also their surrounding immune system, which facilitates the abnormality of the immune milieu in EMT by enlisting vast numbers of inflammatory factors, immune cells, and associated cytokines[12–14]. Nonetheless, the link between IRGs and the EMT is poorly understood and warrants deeper investigation. The current study utilized a bioinformatics approach to discuss the effects of immunity and inflammation in EMT. Further efforts were attempted to qualify IRGs as diagnostic biomarkers in patients with EMT, which may assist in the diagnosis and treatment of EMT. Furthermore, we probed the underlying connection between immune cells and EMT. 2. Methods 2.1 Data source Two EMT datasets were transferred from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ) database: GSE141549 and GSE7305[15]. The GSE141549 dataset, which consists of 198 ectopic endometrium samples and 210 normal endometrial samples, which was treated as the training data for research[16]. Additionally, the GSE7305 dataset received 10 ectopic endometrial samples and 10 normal endometrial samples and was considered as the validation data for the study[17]. IRGs were retrieved from the ImmPort database ( https://www.immport.org/shared/ ), resulting in a total of 1793 IRGs collected[18]. 2.2 Differential expression analysis With the R package "limma"[19], differentially expressed genes (DEGs) were identified between ectopic and normal endometrial samples in the GSE141549 dataset, at a threshold of adjusted p < 0.05. DEGs and IRGs were further crossed to yield differentially expressed immune-related genes (DIRGs). 2.3 Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses We performed GO and KEGG pathway enrichment analysis of the genes using the R package "ClusterProfiler"[20]. This analysis aimed to explore terms related to expansion in three distinct domains: cellular components, molecular functions, and biological processes, as well as the KEGG pathway. 2.4 Protein − protein interaction (PPI) analysis PPI networks were constructed making use of the STRING ( https://string-db.org/ ) database[21]. Then, it was made visible and optimized by Cytoscape 3.8.1 software ( https://cytoscape.org/ )[22]. The cytoHubba[23] plugin in Cytoscape software was employed to program the identification of hub genes. 2.5 Construction of an IRG prediction model for EMT Genes with |log2 fold change (FC)| >0.25 were sifted across all DIRGs and associations between these genes were analyzed using Spearman correlation. Minimum Absolute Shrinkage and Selection Operator (LASSO)[24] and Boruta[25] algorithms were deployed to evaluate meaningful diagnostic biomarkers in EMT. LASSO was a regression analysis technique for cleaning parameters to guard against misfit, available through the R package "glmnet". The Boruta algorithm adopted a classifier wrapper approach built round a Random Forest filter. The algorithm is implemented in the R package "boruta" and is intended for feature correlation with random probes. Lastly, the overlap of the candidate genes from the aforementioned two algorithms with the DIRGs with |logFC|>0.25 was taken to generate the optimal biomarkers for EMT. Meanwhile, the accuracy and efficacy of the candidate markers were assessed by using the R software package "pROC" to plot the receiver operator characteristic curve (ROC) and calculate the area under the curve (AUC) values[26]. 2.6 Construction and validation of a nomogram model for EMT diagnosis Univariate and multivariate logistic regressions were run on the above candidate genes, which were employed to reaffirm the identification of biomarkers independently relevant to the diagnosis. Simultaneously, in order to forecast the onset of EMT, we created a nomogram containing the final screened genes with the R software package "rms"[27]. 2.7 Ethics Statement Endometriosis tissues and normal endometriosis tissues were collected from patients in the Affiliated Guangdong Second Provincial General Hospital of Jinan University. All samples were collected with informed consent from patients, and all procedures were performed after the internal review and approval of the Ethics Committees of the Affiliated Guangdong Second Provincial General Hospital of Jinan University. 2.8 Gene expression of five important DIRGs in human tissue Total endometrial RNA was extracted by QIAamp RNA kit (Qiagen). After the concentration, purity and integrity of the total RNA were evaluated, and the total RNA was reversely tran-scribed into complementary DNA using PrimeScript™ RT reagent Kit (Takara). Second, quantitative polymerase chain reaction (qPCR) was performed by using KODSYBR®qPCR Mix (Toyobo). GAPDH was used as an internal reference. Finally, the results were calculated by the 2 −ΔΔCt formula. 2.9 Western Bolt A protein extraction kit was used to extract the proteins, and cells were lysed in protease inhibitors inhibitors. The BCA protein kit was used to determine the protein concentrations. The proteins were loaded onto SDS gels, separated by electrophoresis for 35 min, and then transferred onto PVDF membranes for 30 min. Finally, they were blocked with milk and incubated with primary antibodies overnight. After washing with TBST thrice, incubation with the secondary antibodies was conducted for 60 min at room temperature, and the protein was added to an ECL chromogenic solution. The results were analyzed with Image J software, and the gray values of serum protein bands were calculated. 2.10 Exploration of the immune microenvironment in EMT CIBERSORT is an analytical method that estimates the abundance of each cell type within a mixed cell population through linear support vector regression (SVR), using gene expression data[28]. Similar to the study by Gao et al[29], we made use of the CIBERSORT computational tool to comply with the GSE141549 dataset to prepare a comparison of the distribution of the 22 immune cell types between ectopic and normal endometrial samples. Furthermore, spearman correlation analysis was used to explore the relationship between immune cells and DIRGs in ectopic endometrium. 3. Result 3.1 Expression of DIRGs in EMT Using the R package "limma", 769 DEGs between ectopic and normal endometrial samples were obtained in the GSE1141549 dataset as measured by a corrected p-value of less than 0.05. Furthermore, we performed an intersection analysis between the 769 DEGs and the 1793 IRGs obtained from the ImmPort database, leading to the identification of 94 DIRGs (Fig. 1 A). Figure 1 B showed the difference in IRGs expression between ectopic and normal endometrial samples in the form of a volcano plot. In addition, Fig. 1 C displayed the selected |log2 FC| >0.25 of 8 DIRGs including SCG2, CCN1, FOS, DES, THBS1, GREM1, IL6, and PLA2G2A with a heatmap. 3.2 PPI analysis Protein interactions of 94 DIRGs were organized using the STRING database to yield a PPI network with interaction scores greater than 0.4 (Fig. 2 A). The up-regulated genes were marked in red and down-regulated genes were marked in blue in the figure. The network genes were then clustered using the cytoHubba plug-in in Cytoscape software. The top 10 nodes in the MCC were clustered, comprising ICAM1, IL6, JUN, PTGS2, VCAM1, CCL2, CCN2, CXCL8, ESR1, and FOS (Fig. 2 B). 3.3 GO and KEGG pathway enrichment analyses To comprehend the inflammation and immune regimes of EMT, the enrichment pathways and functions of these 94 DIRGs were exploited further. These genetic biological processes were mainly concentrated in the positive regulation of the response to external stimulus, leukocyte migration and leukocyte migration. The cellular components of the genes were mostly localized to the collagen-containing extracellular matrix. The molecular functions of the genes were mainly related to signaling receptor activator activity and receptor ligand activity (Figs. 3 A–C). The KEGG enrichment analysis indicated that the 94 DIRGs were mainly involved in the cytokine − cytokine receptor interaction, the neuroactive ligand − receptor interaction, and the TNF signaling pathway (Fig. 3 D). These findings indicate a significant association between EMT and immunity as well as inflammation. 3.4 Correlations between the expression levels of the DIRGs in EMT To criticize the association between the expression levels of DIRGs, we conducted correlation analysis on the expression levels of eight DIRGs with |log2 FC| > 0.25 and demonstrated the findings outlined using network diagrams (Fig. 4 A) and correlation heatmaps (Fig. 4 B). Figures 4 C-F depict scatter plots representing the four groups of genes displaying the most robust correlations with EMT. The findings suggested that CCN1 was strongly correlated with FOS and IL6, and IL6 was strongly related to FOS and THBS1. 3.5 Construction and assessment of the LASSO and Boruta models LASSO algorithm and Boruta algorithm were used to identify important prognostic biomarkers in EMT. The best λ was selected by 10-fold cross-validation as shown in Fig. 5 A-B. By LASSO analysis, we selected 32 DIRGs as candidate genes from all DIRGs. Meanwhile, 71 DIRGs were selected as candidate genes using the Boruta algorithm (Fig. 5 C). To continue narrowing down the DIRGs, the candidate genes in the LASSO regression model and Boruta model were intersected with the DIRGs with |logFC| > 0.25, and finally five DIRGs (SCG2, FOS, DES, GREM1, and PLA2G2A) were obtained and were used as the best biomarker candidates for EMT (Fig. 5 D). 3.6 Further analysis of the five important DIRGs All five significant DIRGs could be found to be highly expressed and statistically significant in EMT in the GSE141549 dataset (Fig. 6 A). In addition, except for the high expression of GREM1 in EMT which was not statistically significant, the results of the remaining 4 DIRGs were also validated in the GSE7305 dataset (Fig. 6 B). The diagnostic predictive performance of these five candidate genes was subsequently evaluated in both the training group (GSE141549) and the validation group (GSE7305). We found that the AUC values of the ROC curves of the five DIRGs (GSE141549, multigene, AUC = 0.945; GSE7305, multigene, AUC = 0.990) demonstrated that they were more stable in their ability to predict EMT than single genes (Fig. 6 C-D). Subsequently, we constructed a nomogram associated with the risk of developing EMT (Fig. 6 E), which can be used to distinguish between healthy samples and EMT patients based on the scores. 3.7 Genes and proteins are expressed in human samples In this study, the expression of SCG2, FOS, DES, GREM1, and PLA2G2A genes in human EMT samples was higher than that in normal endometrial tissues by PCR detection (Fig. 7 A). Meanwhile, Western Blot analysis showed that the expressions of SCG2, FOS, DES, GREM1, and PLA2G2A proteins in human EMT samples were higher than those in normal endometrial tissues (Fig. 7 B). 3.8 Immune microenvironment To better understand the immune microenvironment of EMT, we compared the infiltration of 22 immune cells between ectopic and normal endometrial samples (Fig. 8 A) and discovered that most immune cells differed between the two groups. Further exploration of the correlation of immune cells in EMT with five important DIRGs (Fig. 8 B) revealed that T cells regulatory (Tregs) was significantly negatively correlated with FOS, DES, and GREM1, NK cells activated was significantly negatively correlated with SCG2 and PLA2G2A, and Mast cells resting was significantly positively correlated with DES and GREM1. 4. Discussion EMT are one of the principal sources of sterility in women of childbearing age, incurring a substantial healthcare system burden worldwide. Recent studies have introduced a variety of novel biomarkers for diagnosing EMT, for instance TGFBI[30] and Hsa-mir-135a[31]. Based on the immune and Inflammation profiles of EMT, there are also no past reports to strategically investigate which IRGs can be used as prospective biomarkers for EMT. In addition, machine learning techniques and nomogram generation have not been employed in EMT diagnosis. In this study, we conducted a comprehensive set of integrated bioinformatics analyses and utilized machine learning methods to construct a nomogram, allowing us to assess the diagnostic potential of IRGs in individuals with EMT. The most significant discovery was the identification of five pivotal immune-related candidate genes (SCG2, FOS, DES, GREM1, and PLA2G2A), along with the creation of a diagnostic nomogram for individuals with EMT. SCG2 is a novel biomarker characterized in our own study of patients diagnosed with EMT. It is a member of the tyrosine sulfated granule protein family expressed in endocrine, neuroendocrine, and neuronal tissues[32] and has been implicated in the formation of secretory vesicles and the packaging of peptide hormones into vesicles [33]. SCG2 has a critical function in augmenting endothelial cell proliferation, migration, and angiogenesis[34, 35]. It has been found that SCG-derived peptides, such as secretin (SN) and EM66, are helpful markers for neuroendocrine tumors[36]. Moreover, SCG2 was recognized as a stroma-associated gene and polluted poor outcomes in patients with colorectal cancer[37]. However, there are no studies on SCG2 in EMT, and we believe that SCG2 is highly expressed in EMT and has excellent efficacy for diagnosing patients with EMT. Thus, our findings provide a foundation for further exploration of SCG2 in EMT in the future. FOS is an early transcription factor that can be implicated in the modulation of transcriptional processes through a nucleoprotein complex called AP-1[38, 39]. Under inflammatory conditions, many pro-inflammatory factors seem to be actively modulated by FOS[40]. Furthermore, the FOS gene has been implicated in estradiol-dependent cell proliferation[39], encoding a nuclear-associated protein that amplifies estrogenic messages by triggering the activation of transcription of other genes that control cell division. An estrogen-responsive element has been identified in the promoter area of the human FOS gene, a discovery that bolsters the hypothesis that estrogen occurs through direct irritation of FOS gene transcription to increase FOS mRNA levels[41]. In addition, it has been demonstrated in a preceding study that both FOS gene and protein expression are evoked in the human endometrium during the proliferative phase of the human cycle, and that FOS gene expression correlates markedly with cyclic estradiol concentrations[42]. Moreover, DES is a major filament in the intermediate that is particularly pronounced in cardiac, skeletal, and smooth muscle, and acts in cytoarchitecture, force transmission, and mitochondrial function[43]. DES is as well expressed in the uterus, but its role is not clear. It is expressed in the myometrium during the formation of the myometrium and is induced by estradiol in the endometrium[44, 45]. Likewise, our study argues that FOS and DES genes are overexpressed in EMT. Hence, we speculated that FOS or DES may guide the production of ectopic endometrium under abnormal estrogenic state. They can also be employed as diagnostic biomarkers. GREM1, a highly conserved secreted protein that has a critical position in diverse aspects of early embryonic development and differentiation, antagonizes the activity of bone morphogenetic proteins by heterodimerizing with specific bone morphogenetic proteins and blocking the interaction of bone morphogenetic proteins with the TGF-β receptor[46, 47]. Angiogenesis holds a vital place in the insertion of ectopic endometrium and the subsequent formation of pathology. Groothuis et al. attributed the emergence of a new system of vasculature to the endothelium originating from either the human peritoneum or from the mouse host, which supplies oxygen and nutrients to the outgrowth of the ectopic endometriotic grafts[48]. In addition, multiple studies have also illustrated the participation of GREM1 in the early stages of embryonic development as well as in various stem cell sources, thus explicating the theory that it is involved in abnormal endometrial angiogenesis[49–53]. Sha et al.[54] exhibited that GREM1 was expressed at significantly higher mRNA and protein levels in the ectopic endothelium of patients with EMT than in healthy control women, which is compatible with the findings of our study. As such, GREM1 deserves to be further pursued as a prospective serum biomarker for EMT. PLA2G2A is a secreted enzyme that hydrolyzes the sn-2 ester bond of glycerophospholipids to liberate free fatty acids and lysophospholipids[55]. The arachidonic acid and other polyunsaturated fatty acids derived therefrom are critical substrates for the eicosanoids (including prostaglandins), which are potent inflammatory mediators that incite cell growth and proliferation[56]. Equally, lysophospholipids are effective lipid mediators that are implicated in cell proliferation, survival, migration, and angiogenesis[57]. These mediators are exceptionally essential for the occurrence and development of EMT. To date, there were scant reports on PLA2G2A mRNA levels in endometriosis. In 11 patients with EMT, Eyster et al. recorded that PLA2G2A was the most up-regulated gene in ectopic endometrium compared to normal endometrium[58]. Lousse et al. similarly found that PLA2G2A mRNA levels were elevated 52-fold in 40 cases of peritoneal endometriotic lesions when compared with matched normal endometrium[59]. Our finding was in agreement with previously studies, and it could be inferred that PLA2G2A was instrumental in causing EMT. We constructed a diagnostic model for EMT based on the aforementioned genes and plotted ROC curves to validate its performance in multiple datasets. The model offered a high diagnostic merit, so we built a nomogram model for EMT risk prediction relying on these genes. In clinical work, it is convenient to procure blood and tissue samples from hospitalized patients for disease diagnosis and determination of disease changes. Consequently, the nomogram may have some clinical value for EMT risk screening. However, our study has several limitations. First, despite the fact that we pooled a single large dataset of EMT, the samples are still sparse, and the diagnostic value of the column line drawings awaits further validation due to the limited sample size. Second, our dataset involved the use of samples from the endometrium rather than peripheral blood samples, which still leaves a way to go in our pursuit of a non-invasive diagnostic index. Therefore, in the future, we need to validate these findings in peripheral blood specimens to realize the translational value of these biomarkers for clinical applications. Finally, although the five candidate hub genes are mainly recruited to modulate the immune pathway, their in-depth regulatory regimes for EMT still demand further basic research to fulfill. 5. Conclusions In summary, we explored the prospective relevance between immune inflammation and the incidence of EMT by machine learning and noted a robust link between the pair. Some IRGs such as SCG2, FOS, DES, GREM1, and PLA2G2A have promising diagnostic value in EMT and are worthy of intensive inquiry in the future. Declarations Data Availability The datasets generated and/or analysed during the current study are not publicly available due privacy reasons but are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Ethics approval and consent to participate This research ethics is approved by the Scientific Research Ethics Committee of the Second People's Hospital of Guangdong Province (No.2021-KZ-006-01). Consent for publication The datasets generated and/or analysed during the current study are not publicly available due privacy reasons but are available from the corresponding author on reasonable request. Author Contribution R.Y. and F.G. wrote the main manuscript text and X.L. and X.O. prepared figures and tables. All authors reviewed the manuscript. References Taylor HS, Kotlyar AM, Flores VA: Endometriosis is a chronic systemic disease: clinical challenges and novel innovations . Lancet (London, England) 2021, 397 (10276):839-852. Bulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, Kohlmeier A, Yin P, Milad M, Wei J: Endometriosis . Endocrine reviews 2019, 40 (4):1048-1079. Agarwal SK, Chapron C, Giudice LC, Laufer MR, Leyland N, Missmer SA, Singh SS, Taylor HS: Clinical diagnosis of endometriosis: a call to action . American journal of obstetrics and gynecology 2019, 220 (4):354.e351-354.e312. Chapron C, Marcellin L, Borghese B, Santulli P: Rethinking mechanisms, diagnosis and management of endometriosis . Nature reviews Endocrinology 2019, 15 (11):666-682. Falcone T, Flyckt R: Clinical Management of Endometriosis . Obstetrics and gynecology 2018, 131 (3):557-571. Kyama CM, Mihalyi A, Simsa P, Mwenda JM, Tomassetti C, Meuleman C, D'Hooghe TM: Non-steroidal targets in the diagnosis and treatment of endometriosis . Current medicinal chemistry 2008, 15 (10):1006-1017. Joshi SG, Zamah NM, Raikar RS, Buttram VC, Jr., Henriques ES, Gordon M: Serum and peritoneal fluid proteins in women with and without endometriosis . Fertility and sterility 1986, 46 (6):1077-1082. Dochez V, Caillon H, Vaucel E, Dimet J, Winer N, Ducarme G: Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review . Journal of ovarian research 2019, 12 (1):28. Moustafa S, Burn M, Mamillapalli R, Nematian S, Flores V, Taylor HS: Accurate diagnosis of endometriosis using serum microRNAs . American journal of obstetrics and gynecology 2020, 223 (4):557.e551-557.e511. Patel BG, Lenk EE, Lebovic DI, Shu Y, Yu J, Taylor RN: Pathogenesis of endometriosis: Interaction between Endocrine and inflammatory pathways . Best practice & research Clinical obstetrics & gynaecology 2018, 50 :50-60. Chen P, Yang Y, Zhang Y, Jiang S, Li X, Wan J: Identification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer . Aging 2020, 12 (4):3371-3387. Wang Y, Nicholes K, Shih IM: The Origin and Pathogenesis of Endometriosis . Annual review of pathology 2020, 15 :71-95. Vallvé-Juanico J, Houshdaran S, Giudice LC: The endometrial immune environment of women with endometriosis . Human reproduction update 2019, 25 (5):564-591. Abramiuk M, Grywalska E, Małkowska P, Sierawska O, Hrynkiewicz R, Niedźwiedzka-Rystwej P: The Role of the Immune System in the Development of Endometriosis . Cells 2022, 11 (13). Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M et al : NCBI GEO: archive for functional genomics data sets--update . Nucleic acids research 2013, 41 (Database issue):D991-995. Gabriel M, Fey V, Heinosalo T, Adhikari P, Rytkönen K, Komulainen T, Huhtinen K, Laajala TD, Siitari H, Virkki A et al : A relational database to identify differentially expressed genes in the endometrium and endometriosis lesions . Scientific data 2020, 7 (1):284. Hever A, Roth RB, Hevezi P, Marin ME, Acosta JA, Acosta H, Rojas J, Herrera R, Grigoriadis D, White E et al : Human endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator . Proceedings of the National Academy of Sciences of the United States of America 2007, 104 (30):12451-12456. Bhattacharya S, Dunn P, Thomas CG, Smith B, Schaefer H, Chen J, Hu Z, Zalocusky KA, Shankar RD, Shen-Orr SS et al : ImmPort, toward repurposing of open access immunological assay data for translational and clinical research . Scientific data 2018, 5 :180015. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic acids research 2015, 43 (7):e47. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L et al : clusterProfiler 4.0: A universal enrichment tool for interpreting omics data . Innovation (Cambridge (Mass)) 2021, 2 (3):100141. Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S et al : The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest . Nucleic acids research 2023, 51 (D1):D638-d646. Doncheva NT, Morris JH, Gorodkin J, Jensen LJ: Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data . Journal of proteome research 2019, 18 (2):623-632. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY: cytoHubba: identifying hub objects and sub-networks from complex interactome . BMC systems biology 2014, 8 Suppl 4 (Suppl 4):S11. Waldmann P, Mészáros G, Gredler B, Fuerst C, Sölkner J: Evaluation of the lasso and the elastic net in genome-wide association studies . Frontiers in genetics 2013, 4 :270. Yang C, Ren J, Li B, Jin C, Ma C, Cheng C, Sun Y, Shi X: Identification of gene biomarkers in patients with postmenopausal osteoporosis . Molecular medicine reports 2019, 19 (2):1065-1073. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M: pROC: an open-source package for R and S+ to analyze and compare ROC curves . BMC bioinformatics 2011, 12 :77. Zhang S, Tong YX, Zhang XH, Zhang YJ, Xu XS, Xiao AT, Chao TF, Gong JP: A novel and validated nomogram to predict overall survival for gastric neuroendocrine neoplasms . Journal of Cancer 2019, 10 (24):5944-5954. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA: Robust enumeration of cell subsets from tissue expression profiles . Nature methods 2015, 12 (5):453-457. Gao Y, Chen L, Cai G, Xiong X, Wu Y, Ma D, Li SC, Gao Q: Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study . Oncoimmunology 2020, 9 (1):1760067. Janša V, Pušić Novak M, Ban Frangež H, Rižner TL: TGFBI as a candidate biomarker for non-invasive diagnosis of early-stage endometriosis . Human reproduction (Oxford, England) 2023, 38 (7):1284-1296. Perricos A, Proestling K, Husslein H, Kuessel L, Hudson QJ, Wenzl R, Yotova I: Hsa-mir-135a Shows Potential as A Putative Diagnostic Biomarker in Saliva and Plasma for Endometriosis . Biomolecules 2022, 12 (8). Troger J, Theurl M, Kirchmair R, Pasqua T, Tota B, Angelone T, Cerra MC, Nowosielski Y, Mätzler R, Troger J et al : Granin-derived peptides . Progress in neurobiology 2017, 154 :37-61. Beuret N, Stettler H, Renold A, Rutishauser J, Spiess M: Expression of regulated secretory proteins is sufficient to generate granule-like structures in constitutively secreting cells . The Journal of biological chemistry 2004, 279 (19):20242-20249. Albrecht-Schgoer K, Schgoer W, Holfeld J, Theurl M, Wiedemann D, Steger C, Gupta R, Semsroth S, Fischer-Colbrie R, Beer AG et al : The angiogenic factor secretoneurin induces coronary angiogenesis in a model of myocardial infarction by stimulation of vascular endothelial growth factor signaling in endothelial cells . Circulation 2012, 126 (21):2491-2501. Hannon PR, Duffy DM, Rosewell KL, Brännström M, Akin JW, Curry TE, Jr.: Ovulatory Induction of SCG2 in Human, Nonhuman Primate, and Rodent Granulosa Cells Stimulates Ovarian Angiogenesis . Endocrinology 2018, 159 (6):2447-2458. Guillemot J, Thouënnon E, Guérin M, Vallet-Erdtmann V, Ravni A, Montéro-Hadjadje M, Lefebvre H, Klein M, Muresan M, Seidah NG et al : Differential expression and processing of secretogranin II in relation to the status of pheochromocytoma: implications for the production of the tumoral marker EM66 . Journal of molecular endocrinology 2012, 48 (2):115-127. Liu JW, Yu F, Tan YF, Huo JP, Liu Z, Wang XJ, Li JM: Profiling of Tumor Microenvironment Components Identifies Five Stroma-Related Genes with Prognostic Implications in Colorectal Cancer . Cancer biotherapy & radiopharmaceuticals 2022, 37 (10):882-892. Shaulian E, Karin M: AP-1 in cell proliferation and survival . Oncogene 2001, 20 (19):2390-2400. Crowe DL, Brown TN, Kim R, Smith SM, Lee MK: A c-fos/Estrogen receptor fusion protein promotes cell cycle progression and proliferation of human cancer cell lines . Molecular cell biology research communications : MCBRC 2000, 3 (4):243-248. Lee YN, Tuckerman J, Nechushtan H, Schutz G, Razin E, Angel P: c-Fos as a regulator of degranulation and cytokine production in FcepsilonRI-activated mast cells . Journal of immunology (Baltimore, Md : 1950) 2004, 173 (4):2571-2577. Weisz A, Rosales R: Identification of an estrogen response element upstream of the human c-fos gene that binds the estrogen receptor and the AP-1 transcription factor . Nucleic acids research 1990, 18 (17):5097-5106. Reis FM, Maia AL, Ribeiro MF, Spritzer PM: Progestin modulation of c-fos and prolactin gene expression in the human endometrium . Fertility and sterility 1999, 71 (6):1125-1132. Paulin D, Li Z: Desmin: a major intermediate filament protein essential for the structural integrity and function of muscle . Experimental cell research 2004, 301 (1):1-7. Glasser SR, Lampelo S, Munir MI, Julian J: Expression of desmin, laminin and fibronectin during in situ differentiation (decidualization) of rat uterine stromal cells . Differentiation; research in biological diversity 1987, 35 (2):132-142. Mehasseb MK, Bell SC, Habiba MA: The effects of tamoxifen and estradiol on myometrial differentiation and organization during early uterine development in the CD1 mouse . Reproduction (Cambridge, England) 2009, 138 (2):341-350. Khokha MK, Hsu D, Brunet LJ, Dionne MS, Harland RM: Gremlin is the BMP antagonist required for maintenance of Shh and Fgf signals during limb patterning . Nature genetics 2003, 34 (3):303-307. Merino R, Rodriguez-Leon J, Macias D, Gañan Y, Economides AN, Hurle JM: The BMP antagonist Gremlin regulates outgrowth, chondrogenesis and programmed cell death in the developing limb . Development (Cambridge, England) 1999, 126 (23):5515-5522. Groothuis PG, Nap AW, Winterhager E, Grümmer R: Vascular development in endometriosis . Angiogenesis 2005, 8 (2):147-156. Stabile H, Mitola S, Moroni E, Belleri M, Nicoli S, Coltrini D, Peri F, Pessi A, Orsatti L, Talamo F et al : Bone morphogenic protein antagonist Drm/gremlin is a novel proangiogenic factor . Blood 2007, 109 (5):1834-1840. Kueh J, Richards M, Ng SW, Chan WK, Bongso A: The search for factors in human feeders that support the derivation and propagation of human embryonic stem cells: preliminary studies using transcriptome profiling by serial analysis of gene expression . Fertility and sterility 2006, 85 (6):1843-1846. Frank NY, Kho AT, Schatton T, Murphy GF, Molloy MJ, Zhan Q, Ramoni MF, Frank MH, Kohane IS, Gussoni E: Regulation of myogenic progenitor proliferation in human fetal skeletal muscle by BMP4 and its antagonist Gremlin . The Journal of cell biology 2006, 175 (1):99-110. Taylor HS: Endometrial cells derived from donor stem cells in bone marrow transplant recipients . Jama 2004, 292 (1):81-85. Carmeliet P: Mechanisms of angiogenesis and arteriogenesis . Nature medicine 2000, 6 (4):389-395. Sha G, Zhang Y, Zhang C, Wan Y, Zhao Z, Li C, Lang J: Elevated levels of gremlin-1 in eutopic endometrium and peripheral serum in patients with endometriosis . Fertility and sterility 2009, 91 (2):350-358. Murakami M, Taketomi Y, Girard C, Yamamoto K, Lambeau G: Emerging roles of secreted phospholipase A2 enzymes: Lessons from transgenic and knockout mice . Biochimie 2010, 92 (6):561-582. Wang D, Dubois RN: Eicosanoids and cancer . Nature reviews Cancer 2010, 10 (3):181-193. Panupinthu N, Lee HY, Mills GB: Lysophosphatidic acid production and action: critical new players in breast cancer initiation and progression . British journal of cancer 2010, 102 (6):941-946. Eyster KM, Klinkova O, Kennedy V, Hansen KA: Whole genome deoxyribonucleic acid microarray analysis of gene expression in ectopic versus eutopic endometrium . Fertility and sterility 2007, 88 (6):1505-1533. Lousse JC, Defrère S, Colette S, Van Langendonckt A, Donnez J: Expression of eicosanoid biosynthetic and catabolic enzymes in peritoneal endometriosis . Human reproduction (Oxford, England) 2010, 25 (3):734-741. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3551509","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":249991647,"identity":"895d1208-cdd2-4d92-99b7-fc375ceb302f","order_by":0,"name":"Ruiying Yuan","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Ruiying","middleName":"","lastName":"Yuan","suffix":""},{"id":249991648,"identity":"b4a61261-6c7e-4485-ba9a-1d5bcd26c7e0","order_by":1,"name":"Fumin Gao","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Fumin","middleName":"","lastName":"Gao","suffix":""},{"id":249991649,"identity":"824c8a1a-671d-4646-825e-87d02447a391","order_by":2,"name":"Xiaolong Li","email":"","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Li","suffix":""},{"id":249991650,"identity":"1e957606-37fa-4359-9f43-a7dcbaf96ecf","order_by":3,"name":"Xianghong Ou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYHACNoYPDMxyBiRpYZzBwGxMmhZmHgbmxA1Eq+eXyDF7bPPLmnE7A/Oxj18Y7PIIapGckZZunNuXzmzZwJY8W4YhuZigFoMbycekc3sOsxkc4DFmlmA4kNhAWEtim7Rlz2EeUrQAbWH4cVgCpIXxAzFaJHuepUn2NqQbGBxmS2ZmMEgmrIWfPcdM4scf6/oNx5sPM/6osCOsBQwY24AEMxDxEB+hf6BafxCtYxSMglEwCkYSAAAPCDfW5+AAEwAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":true,"prefix":"","firstName":"Xianghong","middleName":"","lastName":"Ou","suffix":""}],"badges":[],"createdAt":"2023-11-03 12:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3551509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3551509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46806721,"identity":"f0401040-dc8d-40f5-8f35-8adb1d630c96","added_by":"auto","created_at":"2023-11-20 21:26:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241924,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expression analysis of IRGs. \u003cstrong\u003e(A)\u003c/strong\u003e The intersection of DEGs in GSE141549 dataset and IRGs downloaded from ImmPort database contains 94 DIRGs. \u003cstrong\u003e(B) \u003c/strong\u003eA volcano plot of the 94 DIRGs. \u003cstrong\u003e(C)\u003c/strong\u003e A heatmap of the DIRGs identified in the GSE141549 dataset.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/5f383113854261f62555565b.jpeg"},{"id":46807986,"identity":"63a01744-97fb-4f44-b47f-af56d955549e","added_by":"auto","created_at":"2023-11-20 21:42:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":397046,"visible":true,"origin":"","legend":"\u003cp\u003eThe PPI analysis. \u003cstrong\u003e(A)\u003c/strong\u003e The PPI network analysis of the 94 DIRGs. \u003cstrong\u003e(B) \u003c/strong\u003ePPI network diagram of hub genes obtained by MCC algorithm.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/7e4aba4e06ded1cd54af3ec3.jpeg"},{"id":46806717,"identity":"0247cc07-77c0-4319-a97c-620007fdc67c","added_by":"auto","created_at":"2023-11-20 21:26:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186763,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment of DIRGs. \u003cstrong\u003e(A)\u003c/strong\u003e BP annotation of DIRGs. \u003cstrong\u003e(B)\u003c/strong\u003e CC annotation of DIRGs. \u003cstrong\u003e(C)\u003c/strong\u003e MF annotation of DIRGs. \u003cstrong\u003e(D)\u003c/strong\u003e KEGG annotation of DIRGs.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/65f7462e0065bb6f85a93b55.jpeg"},{"id":46807612,"identity":"e0ad664d-8da8-4d41-a2ee-8c0b23ab88c2","added_by":"auto","created_at":"2023-11-20 21:34:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":223646,"visible":true,"origin":"","legend":"\u003cp\u003eExpression correlation of DIRGs. \u003cstrong\u003e(A)\u003c/strong\u003e Heatmap of 8 DIRGs expression correlations. \u003cstrong\u003e(B) \u003c/strong\u003eDIRGs expression correlation heatmap. \u003cstrong\u003e(C–F)\u003c/strong\u003e Scatter plot of some highly correlated DIRGs.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/311a7678cfb092bffc623dd6.jpeg"},{"id":46806723,"identity":"251d51c7-1279-42aa-8abb-5510d541d3b0","added_by":"auto","created_at":"2023-11-20 21:26:52","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192741,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment the model. \u003cstrong\u003e(A)\u003c/strong\u003e LASSO regression coefficient profiles of the 94 DIRGs. Each curve represents the changing trajectory of each DIRG. \u003cstrong\u003e(B)\u003c/strong\u003e Partial likelihood deviance versus log (λ)was drawn using LASSO regression model. \u003cstrong\u003e(C) \u003c/strong\u003eImportance ranking of genes calculated by Boruta algorithm. \u003cstrong\u003e(D)\u003c/strong\u003e Venn plots show the candidate genes by overlapping the candidate genes selected from the LASSO regression model, the Boruta model and the top 8 DIRGs.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/6365c9aeb14bc5f9da3386fd.jpeg"},{"id":46806722,"identity":"ca2cf6f5-fdef-4641-b50d-87b3296c03a9","added_by":"auto","created_at":"2023-11-20 21:26:52","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":334729,"visible":true,"origin":"","legend":"\u003cp\u003eFurther analysis of five important IRGs. \u003cstrong\u003e(A-B)\u003c/strong\u003e The relative expression level of five important IRGs between ectopic and normal endometrial samples from GSE141549 and GSE7305. \u003cstrong\u003e(C-D)\u003c/strong\u003e ROC curves validated the performances of five important DIRGs for the prediction of OP in GSE141549 and GSE7305 datasets. \u003cstrong\u003e(E) \u003c/strong\u003eDiagnostic Nomogram.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/610345bffc207387528a51ed.jpeg"},{"id":46806718,"identity":"01d8408c-aaec-467c-ba00-d283cd1a3a98","added_by":"auto","created_at":"2023-11-20 21:26:51","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":121264,"visible":true,"origin":"","legend":"\u003cp\u003eGenes and proteins are expressed in human samples.\u003cstrong\u003e (A)\u003c/strong\u003e Column graph exhibiting the PCR results on the gene expressions in human samples (n = 3). \u003cstrong\u003e(B) \u003c/strong\u003eWestern Bolt analysis detected the protein expressions of the five important IRGs in human samples.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/2df87796b81129a3ef353123.jpg"},{"id":46806724,"identity":"8de0034b-ba88-4da5-b962-6b16f554627b","added_by":"auto","created_at":"2023-11-20 21:26:52","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":239852,"visible":true,"origin":"","legend":"\u003cp\u003eExploration of immune microenvironment. \u003cstrong\u003e(A)\u003c/strong\u003e Differences of the infiltrate immune cells between the ectopic and normal endometrial group. \u003cstrong\u003e(B)\u003c/strong\u003e Correlations between five important DIRGs and immune cells.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/de7995cc53c2e3722400d67d.jpeg"},{"id":63275904,"identity":"0afe7458-1f75-41d0-94b8-39b0b49424bf","added_by":"auto","created_at":"2024-08-26 12:15:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3918227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3551509/v1/0125d6e4-4704-42c8-ba1e-0140d78c0150.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of key immune genes of endometriosis based on bioinformatics and machine learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEndometriosis (EMT) is a disease in which endometrioid tissue grows anomalously beyond the womb and is marked by estrogen-dependent chronic inflammation[1]. It is common in women of reproductive age, with a morbidity rate of about 10 percent[2]. In addition, EMT is recognized by clinical signs such as dysmenorrhea, chronic pelvic pain and infertility, which gravely impacts the physical and mental health of fertile-age women[3]. The diagnosis of EMT is often markedly delayed owing to uncertain mechanisms of pathogenesis, lack of utilization of specific symptoms and non-invasive assays[4, 5]. Although diverse biomarkers such as IL-2, anti-PEP, CA125, and miR-150-5p have been investigated as clinical diagnostic instruments[6\u0026ndash;9]. However, there is typically no monolithic metric that can provide a direct diagnosis of EMT. At present, laparoscopy combined with histopathologic examination continues to be the gold standard for the diagnosis of EMT, but laparoscopic surgery involves trauma, adhesions, and decreased fertility among other risks[5]. Therefore, it is crucial to obtain a deep understanding into the molecular principles of EMT and to seek out non-invasive diagnostic indicators with a superior sense of detail.\u003c/p\u003e \u003cp\u003eThe immune system exerts a primary function within the pelvic microenvironment, encompassing eliciting immune tolerance, dampening immune surveillance, and shunning phagocytosis conducted by immune cells[10]. Previous research has demonstrated that immune-related genes (IRGs) play a significant role in the complex regulatory network of tumors. They have been investigated as markers for tracking the development of tumor immunity and understanding the pathophysiological mechanisms of diseases like endometrial cancers[11]. Evidence from current studies reveals that not only the endometrial immune status is modified in EMT, but also their surrounding immune system, which facilitates the abnormality of the immune milieu in EMT by enlisting vast numbers of inflammatory factors, immune cells, and associated cytokines[12\u0026ndash;14].\u003c/p\u003e \u003cp\u003eNonetheless, the link between IRGs and the EMT is poorly understood and warrants deeper investigation. The current study utilized a bioinformatics approach to discuss the effects of immunity and inflammation in EMT. Further efforts were attempted to qualify IRGs as diagnostic biomarkers in patients with EMT, which may assist in the diagnosis and treatment of EMT. Furthermore, we probed the underlying connection between immune cells and EMT.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eTwo EMT datasets were transferred from the Gene Expression Omnibus (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database: GSE141549 and GSE7305[15]. The GSE141549 dataset, which consists of 198 ectopic endometrium samples and 210 normal endometrial samples, which was treated as the training data for research[16]. Additionally, the GSE7305 dataset received 10 ectopic endometrial samples and 10 normal endometrial samples and was considered as the validation data for the study[17]. IRGs were retrieved from the ImmPort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.immport.org/shared/\u003c/span\u003e\u003cspan address=\"https://www.immport.org/shared/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), resulting in a total of 1793 IRGs collected[18].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eWith the R package \"limma\"[19], differentially expressed genes (DEGs) were identified between ectopic and normal endometrial samples in the GSE141549 dataset, at a threshold of adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DEGs and IRGs were further crossed to yield differentially expressed immune-related genes (DIRGs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses\u003c/h2\u003e \u003cp\u003eWe performed GO and KEGG pathway enrichment analysis of the genes using the R package \"ClusterProfiler\"[20]. This analysis aimed to explore terms related to expansion in three distinct domains: cellular components, molecular functions, and biological processes, as well as the KEGG pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Protein\u0026thinsp;\u0026minus;\u0026thinsp;protein interaction (PPI) analysis\u003c/h2\u003e \u003cp\u003ePPI networks were constructed making use of the STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database[21]. Then, it was made visible and optimized by Cytoscape 3.8.1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[22]. The cytoHubba[23] plugin in Cytoscape software was employed to program the identification of hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction of an IRG prediction model for EMT\u003c/h2\u003e \u003cp\u003eGenes with |log2 fold change (FC)| \u0026gt;0.25 were sifted across all DIRGs and associations between these genes were analyzed using Spearman correlation. Minimum Absolute Shrinkage and Selection Operator (LASSO)[24] and Boruta[25] algorithms were deployed to evaluate meaningful diagnostic biomarkers in EMT. LASSO was a regression analysis technique for cleaning parameters to guard against misfit, available through the R package \"glmnet\". The Boruta algorithm adopted a classifier wrapper approach built round a Random Forest filter. The algorithm is implemented in the R package \"boruta\" and is intended for feature correlation with random probes. Lastly, the overlap of the candidate genes from the aforementioned two algorithms with the DIRGs with |logFC|\u0026gt;0.25 was taken to generate the optimal biomarkers for EMT. Meanwhile, the accuracy and efficacy of the candidate markers were assessed by using the R software package \"pROC\" to plot the receiver operator characteristic curve (ROC) and calculate the area under the curve (AUC) values[26].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Construction and validation of a nomogram model for EMT diagnosis\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate logistic regressions were run on the above candidate genes, which were employed to reaffirm the identification of biomarkers independently relevant to the diagnosis. Simultaneously, in order to forecast the onset of EMT, we created a nomogram containing the final screened genes with the R software package \"rms\"[27].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Ethics Statement\u003c/h2\u003e \u003cp\u003eEndometriosis tissues and normal endometriosis tissues were collected from patients in the Affiliated Guangdong Second Provincial General Hospital of Jinan University. All samples were collected with informed consent from patients, and all procedures were performed after the internal review and approval of the Ethics Committees of the Affiliated Guangdong Second Provincial General Hospital of Jinan University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene expression of five important DIRGs in human tissue\u003c/h2\u003e \u003cp\u003eTotal endometrial RNA was extracted by QIAamp RNA kit (Qiagen). After the concentration, purity and integrity of the total RNA were evaluated, and the total RNA was reversely tran-scribed into complementary DNA using PrimeScript\u0026trade; RT reagent Kit (Takara). Second, quantitative polymerase chain reaction (qPCR) was performed by using KODSYBR\u0026reg;qPCR Mix (Toyobo). GAPDH was used as an internal reference. Finally, the results were calculated by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e formula.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Western Bolt\u003c/h2\u003e \u003cp\u003eA protein extraction kit was used to extract the proteins, and cells were lysed in protease inhibitors inhibitors. The BCA protein kit was used to determine the protein concentrations. The proteins were loaded onto SDS gels, separated by electrophoresis for 35 min, and then transferred onto PVDF membranes for 30 min. Finally, they were blocked with milk and incubated with primary antibodies overnight. After washing with TBST thrice, incubation with the secondary antibodies was conducted for 60 min at room temperature, and the protein was added to an ECL chromogenic solution. The results were analyzed with Image J software, and the gray values of serum protein bands were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Exploration of the immune microenvironment in EMT\u003c/h2\u003e \u003cp\u003eCIBERSORT is an analytical method that estimates the abundance of each cell type within a mixed cell population through linear support vector regression (SVR), using gene expression data[28]. Similar to the study by Gao et al[29], we made use of the CIBERSORT computational tool to comply with the GSE141549 dataset to prepare a comparison of the distribution of the 22 immune cell types between ectopic and normal endometrial samples. Furthermore, spearman correlation analysis was used to explore the relationship between immune cells and DIRGs in ectopic endometrium.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Expression of DIRGs in EMT\u003c/h2\u003e\n\u003cp\u003eUsing the R package \"limma\", 769 DEGs between ectopic and normal endometrial samples were obtained in the GSE1141549 dataset as measured by a corrected p-value of less than 0.05. Furthermore, we performed an intersection analysis between the 769 DEGs and the 1793 IRGs obtained from the ImmPort database, leading to the identification of 94 DIRGs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB showed the difference in IRGs expression between ectopic and normal endometrial samples in the form of a volcano plot. In addition, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC displayed the selected |log2 FC| \u0026gt;0.25 of 8 DIRGs including SCG2, CCN1, FOS, DES, THBS1, GREM1, IL6, and PLA2G2A with a heatmap.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 PPI analysis\u003c/h2\u003e\n\u003cp\u003eProtein interactions of 94 DIRGs were organized using the STRING database to yield a PPI network with interaction scores greater than 0.4 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The up-regulated genes were marked in red and down-regulated genes were marked in blue in the figure. The network genes were then clustered using the cytoHubba plug-in in Cytoscape software. The top 10 nodes in the MCC were clustered, comprising ICAM1, IL6, JUN, PTGS2, VCAM1, CCL2, CCN2, CXCL8, ESR1, and FOS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 GO and KEGG pathway enrichment analyses\u003c/h2\u003e\n\u003cp\u003eTo comprehend the inflammation and immune regimes of EMT, the enrichment pathways and functions of these 94 DIRGs were exploited further. These genetic biological processes were mainly concentrated in the positive regulation of the response to external stimulus, leukocyte migration and leukocyte migration. The cellular components of the genes were mostly localized to the collagen-containing extracellular matrix. The molecular functions of the genes were mainly related to signaling receptor activator activity and receptor ligand activity (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C). The KEGG enrichment analysis indicated that the 94 DIRGs were mainly involved in the cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction, the neuroactive ligand\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, and the TNF signaling pathway (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). These findings indicate a significant association between EMT and immunity as well as inflammation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Correlations between the expression levels of the DIRGs in EMT\u003c/h2\u003e\n\u003cp\u003eTo criticize the association between the expression levels of DIRGs, we conducted correlation analysis on the expression levels of eight DIRGs with |log2 FC| \u0026gt; 0.25 and demonstrated the findings outlined using network diagrams (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) and correlation heatmaps (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Figures\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC-F depict scatter plots representing the four groups of genes displaying the most robust correlations with EMT. The findings suggested that CCN1 was strongly correlated with FOS and IL6, and IL6 was strongly related to FOS and THBS1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Construction and assessment of the LASSO and Boruta models\u003c/h2\u003e\n\u003cp\u003eLASSO algorithm and Boruta algorithm were used to identify important prognostic biomarkers in EMT. The best \u0026lambda; was selected by 10-fold cross-validation as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-B. By LASSO analysis, we selected 32 DIRGs as candidate genes from all DIRGs. Meanwhile, 71 DIRGs were selected as candidate genes using the Boruta algorithm (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). To continue narrowing down the DIRGs, the candidate genes in the LASSO regression model and Boruta model were intersected with the DIRGs with |logFC| \u0026gt; 0.25, and finally five DIRGs (SCG2, FOS, DES, GREM1, and PLA2G2A) were obtained and were used as the best biomarker candidates for EMT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6 Further analysis of the five important DIRGs\u003c/h2\u003e\n\u003cp\u003eAll five significant DIRGs could be found to be highly expressed and statistically significant in EMT in the GSE141549 dataset (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). In addition, except for the high expression of GREM1 in EMT which was not statistically significant, the results of the remaining 4 DIRGs were also validated in the GSE7305 dataset (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). The diagnostic predictive performance of these five candidate genes was subsequently evaluated in both the training group (GSE141549) and the validation group (GSE7305). We found that the AUC values of the ROC curves of the five DIRGs (GSE141549, multigene, AUC\u0026thinsp;=\u0026thinsp;0.945; GSE7305, multigene, AUC\u0026thinsp;=\u0026thinsp;0.990) demonstrated that they were more stable in their ability to predict EMT than single genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). Subsequently, we constructed a nomogram associated with the risk of developing EMT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE), which can be used to distinguish between healthy samples and EMT patients based on the scores.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e3.7 Genes and proteins are expressed in human samples\u003c/h2\u003e\n\u003cp\u003eIn this study, the expression of SCG2, FOS, DES, GREM1, and PLA2G2A genes in human EMT samples was higher than that in normal endometrial tissues by PCR detection (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Meanwhile, Western Blot analysis showed that the expressions of SCG2, FOS, DES, GREM1, and PLA2G2A proteins in human EMT samples were higher than those in normal endometrial tissues (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e3.8 Immune microenvironment\u003c/h2\u003e\n\u003cp\u003eTo better understand the immune microenvironment of EMT, we compared the infiltration of 22 immune cells between ectopic and normal endometrial samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA) and discovered that most immune cells differed between the two groups. Further exploration of the correlation of immune cells in EMT with five important DIRGs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB) revealed that T cells regulatory (Tregs) was significantly negatively correlated with FOS, DES, and GREM1, NK cells activated was significantly negatively correlated with SCG2 and PLA2G2A, and Mast cells resting was significantly positively correlated with DES and GREM1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEMT are one of the principal sources of sterility in women of childbearing age, incurring a substantial healthcare system burden worldwide. Recent studies have introduced a variety of novel biomarkers for diagnosing EMT, for instance TGFBI[30] and Hsa-mir-135a[31]. Based on the immune and Inflammation profiles of EMT, there are also no past reports to strategically investigate which IRGs can be used as prospective biomarkers for EMT. In addition, machine learning techniques and nomogram generation have not been employed in EMT diagnosis. In this study, we conducted a comprehensive set of integrated bioinformatics analyses and utilized machine learning methods to construct a nomogram, allowing us to assess the diagnostic potential of IRGs in individuals with EMT. The most significant discovery was the identification of five pivotal immune-related candidate genes (SCG2, FOS, DES, GREM1, and PLA2G2A), along with the creation of a diagnostic nomogram for individuals with EMT.\u003c/p\u003e \u003cp\u003eSCG2 is a novel biomarker characterized in our own study of patients diagnosed with EMT. It is a member of the tyrosine sulfated granule protein family expressed in endocrine, neuroendocrine, and neuronal tissues[32] and has been implicated in the formation of secretory vesicles and the packaging of peptide hormones into vesicles [33]. SCG2 has a critical function in augmenting endothelial cell proliferation, migration, and angiogenesis[34, 35]. It has been found that SCG-derived peptides, such as secretin (SN) and EM66, are helpful markers for neuroendocrine tumors[36]. Moreover, SCG2 was recognized as a stroma-associated gene and polluted poor outcomes in patients with colorectal cancer[37]. However, there are no studies on SCG2 in EMT, and we believe that SCG2 is highly expressed in EMT and has excellent efficacy for diagnosing patients with EMT. Thus, our findings provide a foundation for further exploration of SCG2 in EMT in the future.\u003c/p\u003e \u003cp\u003eFOS is an early transcription factor that can be implicated in the modulation of transcriptional processes through a nucleoprotein complex called AP-1[38, 39]. Under inflammatory conditions, many pro-inflammatory factors seem to be actively modulated by FOS[40]. Furthermore, the FOS gene has been implicated in estradiol-dependent cell proliferation[39], encoding a nuclear-associated protein that amplifies estrogenic messages by triggering the activation of transcription of other genes that control cell division. An estrogen-responsive element has been identified in the promoter area of the human FOS gene, a discovery that bolsters the hypothesis that estrogen occurs through direct irritation of FOS gene transcription to increase FOS mRNA levels[41]. In addition, it has been demonstrated in a preceding study that both FOS gene and protein expression are evoked in the human endometrium during the proliferative phase of the human cycle, and that FOS gene expression correlates markedly with cyclic estradiol concentrations[42]. Moreover, DES is a major filament in the intermediate that is particularly pronounced in cardiac, skeletal, and smooth muscle, and acts in cytoarchitecture, force transmission, and mitochondrial function[43]. DES is as well expressed in the uterus, but its role is not clear. It is expressed in the myometrium during the formation of the myometrium and is induced by estradiol in the endometrium[44, 45]. Likewise, our study argues that FOS and DES genes are overexpressed in EMT. Hence, we speculated that FOS or DES may guide the production of ectopic endometrium under abnormal estrogenic state. They can also be employed as diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eGREM1, a highly conserved secreted protein that has a critical position in diverse aspects of early embryonic development and differentiation, antagonizes the activity of bone morphogenetic proteins by heterodimerizing with specific bone morphogenetic proteins and blocking the interaction of bone morphogenetic proteins with the TGF-β receptor[46, 47]. Angiogenesis holds a vital place in the insertion of ectopic endometrium and the subsequent formation of pathology. Groothuis et al. attributed the emergence of a new system of vasculature to the endothelium originating from either the human peritoneum or from the mouse host, which supplies oxygen and nutrients to the outgrowth of the ectopic endometriotic grafts[48]. In addition, multiple studies have also illustrated the participation of GREM1 in the early stages of embryonic development as well as in various stem cell sources, thus explicating the theory that it is involved in abnormal endometrial angiogenesis[49\u0026ndash;53]. Sha et al.[54] exhibited that GREM1 was expressed at significantly higher mRNA and protein levels in the ectopic endothelium of patients with EMT than in healthy control women, which is compatible with the findings of our study. As such, GREM1 deserves to be further pursued as a prospective serum biomarker for EMT.\u003c/p\u003e \u003cp\u003ePLA2G2A is a secreted enzyme that hydrolyzes the sn-2 ester bond of glycerophospholipids to liberate free fatty acids and lysophospholipids[55]. The arachidonic acid and other polyunsaturated fatty acids derived therefrom are critical substrates for the eicosanoids (including prostaglandins), which are potent inflammatory mediators that incite cell growth and proliferation[56]. Equally, lysophospholipids are effective lipid mediators that are implicated in cell proliferation, survival, migration, and angiogenesis[57]. These mediators are exceptionally essential for the occurrence and development of EMT. To date, there were scant reports on PLA2G2A mRNA levels in endometriosis. In 11 patients with EMT, Eyster et al. recorded that PLA2G2A was the most up-regulated gene in ectopic endometrium compared to normal endometrium[58]. Lousse et al. similarly found that PLA2G2A mRNA levels were elevated 52-fold in 40 cases of peritoneal endometriotic lesions when compared with matched normal endometrium[59]. Our finding was in agreement with previously studies, and it could be inferred that PLA2G2A was instrumental in causing EMT.\u003c/p\u003e \u003cp\u003eWe constructed a diagnostic model for EMT based on the aforementioned genes and plotted ROC curves to validate its performance in multiple datasets. The model offered a high diagnostic merit, so we built a nomogram model for EMT risk prediction relying on these genes. In clinical work, it is convenient to procure blood and tissue samples from hospitalized patients for disease diagnosis and determination of disease changes. Consequently, the nomogram may have some clinical value for EMT risk screening.\u003c/p\u003e \u003cp\u003eHowever, our study has several limitations. First, despite the fact that we pooled a single large dataset of EMT, the samples are still sparse, and the diagnostic value of the column line drawings awaits further validation due to the limited sample size. Second, our dataset involved the use of samples from the endometrium rather than peripheral blood samples, which still leaves a way to go in our pursuit of a non-invasive diagnostic index. Therefore, in the future, we need to validate these findings in peripheral blood specimens to realize the translational value of these biomarkers for clinical applications. Finally, although the five candidate hub genes are mainly recruited to modulate the immune pathway, their in-depth regulatory regimes for EMT still demand further basic research to fulfill.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, we explored the prospective relevance between immune inflammation and the incidence of EMT by machine learning and noted a robust link between the pair. Some IRGs such as SCG2, FOS, DES, GREM1, and PLA2G2A have promising diagnostic value in EMT and are worthy of intensive inquiry in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due privacy reasons but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research ethics is approved by the Scientific Research Ethics Committee of the Second People\u0026apos;s Hospital of Guangdong Province (No.2021-KZ-006-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due privacy reasons but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.Y. and F.G. wrote the main manuscript text and X.L. and X.O. prepared figures and tables. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaylor HS, Kotlyar AM, Flores VA: \u003cstrong\u003eEndometriosis is a chronic systemic disease: clinical challenges and novel innovations\u003c/strong\u003e. \u003cem\u003eLancet (London, England) \u003c/em\u003e2021, \u003cstrong\u003e397\u003c/strong\u003e(10276):839-852.\u003c/li\u003e\n\u003cli\u003eBulun SE, Yilmaz BD, Sison C, Miyazaki K, Bernardi L, Liu S, Kohlmeier A, Yin P, Milad M, Wei J: \u003cstrong\u003eEndometriosis\u003c/strong\u003e. \u003cem\u003eEndocrine reviews \u003c/em\u003e2019, \u003cstrong\u003e40\u003c/strong\u003e(4):1048-1079.\u003c/li\u003e\n\u003cli\u003eAgarwal SK, Chapron C, Giudice LC, Laufer MR, Leyland N, Missmer SA, Singh SS, Taylor HS: \u003cstrong\u003eClinical diagnosis of endometriosis: a call to action\u003c/strong\u003e. \u003cem\u003eAmerican journal of obstetrics and gynecology \u003c/em\u003e2019, \u003cstrong\u003e220\u003c/strong\u003e(4):354.e351-354.e312.\u003c/li\u003e\n\u003cli\u003eChapron C, Marcellin L, Borghese B, Santulli P: \u003cstrong\u003eRethinking mechanisms, diagnosis and management of endometriosis\u003c/strong\u003e. \u003cem\u003eNature reviews Endocrinology \u003c/em\u003e2019, \u003cstrong\u003e15\u003c/strong\u003e(11):666-682.\u003c/li\u003e\n\u003cli\u003eFalcone T, Flyckt R: \u003cstrong\u003eClinical Management of Endometriosis\u003c/strong\u003e. \u003cem\u003eObstetrics and gynecology \u003c/em\u003e2018, \u003cstrong\u003e131\u003c/strong\u003e(3):557-571.\u003c/li\u003e\n\u003cli\u003eKyama CM, Mihalyi A, Simsa P, Mwenda JM, Tomassetti C, Meuleman C, D\u0026apos;Hooghe TM: \u003cstrong\u003eNon-steroidal targets in the diagnosis and treatment of endometriosis\u003c/strong\u003e. \u003cem\u003eCurrent medicinal chemistry \u003c/em\u003e2008, \u003cstrong\u003e15\u003c/strong\u003e(10):1006-1017.\u003c/li\u003e\n\u003cli\u003eJoshi SG, Zamah NM, Raikar RS, Buttram VC, Jr., Henriques ES, Gordon M: \u003cstrong\u003eSerum and peritoneal fluid proteins in women with and without endometriosis\u003c/strong\u003e. \u003cem\u003eFertility and sterility \u003c/em\u003e1986, \u003cstrong\u003e46\u003c/strong\u003e(6):1077-1082.\u003c/li\u003e\n\u003cli\u003eDochez V, Caillon H, Vaucel E, Dimet J, Winer N, Ducarme G: \u003cstrong\u003eBiomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review\u003c/strong\u003e. \u003cem\u003eJournal of ovarian research \u003c/em\u003e2019, \u003cstrong\u003e12\u003c/strong\u003e(1):28.\u003c/li\u003e\n\u003cli\u003eMoustafa S, Burn M, Mamillapalli R, Nematian S, Flores V, Taylor HS: \u003cstrong\u003eAccurate diagnosis of endometriosis using serum microRNAs\u003c/strong\u003e. \u003cem\u003eAmerican journal of obstetrics and gynecology \u003c/em\u003e2020, \u003cstrong\u003e223\u003c/strong\u003e(4):557.e551-557.e511.\u003c/li\u003e\n\u003cli\u003ePatel BG, Lenk EE, Lebovic DI, Shu Y, Yu J, Taylor RN: \u003cstrong\u003ePathogenesis of endometriosis: Interaction between Endocrine and inflammatory pathways\u003c/strong\u003e. \u003cem\u003eBest practice \u0026amp; research Clinical obstetrics \u0026amp; gynaecology \u003c/em\u003e2018, \u003cstrong\u003e50\u003c/strong\u003e:50-60.\u003c/li\u003e\n\u003cli\u003eChen P, Yang Y, Zhang Y, Jiang S, Li X, Wan J: \u003cstrong\u003eIdentification of prognostic immune-related genes in the tumor microenvironment of endometrial cancer\u003c/strong\u003e. \u003cem\u003eAging \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e(4):3371-3387.\u003c/li\u003e\n\u003cli\u003eWang Y, Nicholes K, Shih IM: \u003cstrong\u003eThe Origin and Pathogenesis of Endometriosis\u003c/strong\u003e. \u003cem\u003eAnnual review of pathology \u003c/em\u003e2020, \u003cstrong\u003e15\u003c/strong\u003e:71-95.\u003c/li\u003e\n\u003cli\u003eVallv\u0026eacute;-Juanico J, Houshdaran S, Giudice LC: \u003cstrong\u003eThe endometrial immune environment of women with endometriosis\u003c/strong\u003e. \u003cem\u003eHuman reproduction update \u003c/em\u003e2019, \u003cstrong\u003e25\u003c/strong\u003e(5):564-591.\u003c/li\u003e\n\u003cli\u003eAbramiuk M, Grywalska E, Małkowska P, Sierawska O, Hrynkiewicz R, Niedźwiedzka-Rystwej P: \u003cstrong\u003eThe Role of the Immune System in the Development of Endometriosis\u003c/strong\u003e. \u003cem\u003eCells \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(13).\u003c/li\u003e\n\u003cli\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNCBI GEO: archive for functional genomics data sets--update\u003c/strong\u003e. \u003cem\u003eNucleic acids research \u003c/em\u003e2013, \u003cstrong\u003e41\u003c/strong\u003e(Database issue):D991-995.\u003c/li\u003e\n\u003cli\u003eGabriel M, Fey V, Heinosalo T, Adhikari P, Rytk\u0026ouml;nen K, Komulainen T, Huhtinen K, Laajala TD, Siitari H, Virkki A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA relational database to identify differentially expressed genes in the endometrium and endometriosis lesions\u003c/strong\u003e. \u003cem\u003eScientific data \u003c/em\u003e2020, \u003cstrong\u003e7\u003c/strong\u003e(1):284.\u003c/li\u003e\n\u003cli\u003eHever A, Roth RB, Hevezi P, Marin ME, Acosta JA, Acosta H, Rojas J, Herrera R, Grigoriadis D, White E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eHuman endometriosis is associated with plasma cells and overexpression of B lymphocyte stimulator\u003c/strong\u003e. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America \u003c/em\u003e2007, \u003cstrong\u003e104\u003c/strong\u003e(30):12451-12456.\u003c/li\u003e\n\u003cli\u003eBhattacharya S, Dunn P, Thomas CG, Smith B, Schaefer H, Chen J, Hu Z, Zalocusky KA, Shankar RD, Shen-Orr SS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImmPort, toward repurposing of open access immunological assay data for translational and clinical research\u003c/strong\u003e. \u003cem\u003eScientific data \u003c/em\u003e2018, \u003cstrong\u003e5\u003c/strong\u003e:180015.\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: \u003cstrong\u003elimma powers differential expression analyses for RNA-sequencing and microarray studies\u003c/strong\u003e. \u003cem\u003eNucleic acids research \u003c/em\u003e2015, \u003cstrong\u003e43\u003c/strong\u003e(7):e47.\u003c/li\u003e\n\u003cli\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eclusterProfiler 4.0: A universal enrichment tool for interpreting omics data\u003c/strong\u003e. \u003cem\u003eInnovation (Cambridge (Mass)) \u003c/em\u003e2021, \u003cstrong\u003e2\u003c/strong\u003e(3):100141.\u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest\u003c/strong\u003e. \u003cem\u003eNucleic acids research \u003c/em\u003e2023, \u003cstrong\u003e51\u003c/strong\u003e(D1):D638-d646.\u003c/li\u003e\n\u003cli\u003eDoncheva NT, Morris JH, Gorodkin J, Jensen LJ: \u003cstrong\u003eCytoscape StringApp: Network Analysis and Visualization of Proteomics Data\u003c/strong\u003e. \u003cem\u003eJournal of proteome research \u003c/em\u003e2019, \u003cstrong\u003e18\u003c/strong\u003e(2):623-632.\u003c/li\u003e\n\u003cli\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY: \u003cstrong\u003ecytoHubba: identifying hub objects and sub-networks from complex interactome\u003c/strong\u003e. \u003cem\u003eBMC systems biology \u003c/em\u003e2014, \u003cstrong\u003e8 Suppl 4\u003c/strong\u003e(Suppl 4):S11.\u003c/li\u003e\n\u003cli\u003eWaldmann P, M\u0026eacute;sz\u0026aacute;ros G, Gredler B, Fuerst C, S\u0026ouml;lkner J: \u003cstrong\u003eEvaluation of the lasso and the elastic net in genome-wide association studies\u003c/strong\u003e. \u003cem\u003eFrontiers in genetics \u003c/em\u003e2013, \u003cstrong\u003e4\u003c/strong\u003e:270.\u003c/li\u003e\n\u003cli\u003eYang C, Ren J, Li B, Jin C, Ma C, Cheng C, Sun Y, Shi X: \u003cstrong\u003eIdentification of gene biomarkers in patients with postmenopausal osteoporosis\u003c/strong\u003e. \u003cem\u003eMolecular medicine reports \u003c/em\u003e2019, \u003cstrong\u003e19\u003c/strong\u003e(2):1065-1073.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, M\u0026uuml;ller M: \u003cstrong\u003epROC: an open-source package for R and S+ to analyze and compare ROC curves\u003c/strong\u003e. \u003cem\u003eBMC bioinformatics \u003c/em\u003e2011, \u003cstrong\u003e12\u003c/strong\u003e:77.\u003c/li\u003e\n\u003cli\u003eZhang S, Tong YX, Zhang XH, Zhang YJ, Xu XS, Xiao AT, Chao TF, Gong JP: \u003cstrong\u003eA novel and validated nomogram to predict overall survival for gastric neuroendocrine neoplasms\u003c/strong\u003e. \u003cem\u003eJournal of Cancer \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e(24):5944-5954.\u003c/li\u003e\n\u003cli\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA: \u003cstrong\u003eRobust enumeration of cell subsets from tissue expression profiles\u003c/strong\u003e. \u003cem\u003eNature methods \u003c/em\u003e2015, \u003cstrong\u003e12\u003c/strong\u003e(5):453-457.\u003c/li\u003e\n\u003cli\u003eGao Y, Chen L, Cai G, Xiong X, Wu Y, Ma D, Li SC, Gao Q: \u003cstrong\u003eHeterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study\u003c/strong\u003e. \u003cem\u003eOncoimmunology \u003c/em\u003e2020, \u003cstrong\u003e9\u003c/strong\u003e(1):1760067.\u003c/li\u003e\n\u003cli\u003eJan\u0026scaron;a V, Pu\u0026scaron;ić Novak M, Ban Frangež H, Rižner TL: \u003cstrong\u003eTGFBI as a candidate biomarker for non-invasive diagnosis of early-stage endometriosis\u003c/strong\u003e. \u003cem\u003eHuman reproduction (Oxford, England) \u003c/em\u003e2023, \u003cstrong\u003e38\u003c/strong\u003e(7):1284-1296.\u003c/li\u003e\n\u003cli\u003ePerricos A, Proestling K, Husslein H, Kuessel L, Hudson QJ, Wenzl R, Yotova I: \u003cstrong\u003eHsa-mir-135a Shows Potential as A Putative Diagnostic Biomarker in Saliva and Plasma for Endometriosis\u003c/strong\u003e. \u003cem\u003eBiomolecules \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(8).\u003c/li\u003e\n\u003cli\u003eTroger J, Theurl M, Kirchmair R, Pasqua T, Tota B, Angelone T, Cerra MC, Nowosielski Y, M\u0026auml;tzler R, Troger J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGranin-derived peptides\u003c/strong\u003e. \u003cem\u003eProgress in neurobiology \u003c/em\u003e2017, \u003cstrong\u003e154\u003c/strong\u003e:37-61.\u003c/li\u003e\n\u003cli\u003eBeuret N, Stettler H, Renold A, Rutishauser J, Spiess M: \u003cstrong\u003eExpression of regulated secretory proteins is sufficient to generate granule-like structures in constitutively secreting cells\u003c/strong\u003e. \u003cem\u003eThe Journal of biological chemistry \u003c/em\u003e2004, \u003cstrong\u003e279\u003c/strong\u003e(19):20242-20249.\u003c/li\u003e\n\u003cli\u003eAlbrecht-Schgoer K, Schgoer W, Holfeld J, Theurl M, Wiedemann D, Steger C, Gupta R, Semsroth S, Fischer-Colbrie R, Beer AG\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe angiogenic factor secretoneurin induces coronary angiogenesis in a model of myocardial infarction by stimulation of vascular endothelial growth factor signaling in endothelial cells\u003c/strong\u003e. \u003cem\u003eCirculation \u003c/em\u003e2012, \u003cstrong\u003e126\u003c/strong\u003e(21):2491-2501.\u003c/li\u003e\n\u003cli\u003eHannon PR, Duffy DM, Rosewell KL, Br\u0026auml;nnstr\u0026ouml;m M, Akin JW, Curry TE, Jr.: \u003cstrong\u003eOvulatory Induction of SCG2 in Human, Nonhuman Primate, and Rodent Granulosa Cells Stimulates Ovarian Angiogenesis\u003c/strong\u003e. \u003cem\u003eEndocrinology \u003c/em\u003e2018, \u003cstrong\u003e159\u003c/strong\u003e(6):2447-2458.\u003c/li\u003e\n\u003cli\u003eGuillemot J, Thou\u0026euml;nnon E, Gu\u0026eacute;rin M, Vallet-Erdtmann V, Ravni A, Mont\u0026eacute;ro-Hadjadje M, Lefebvre H, Klein M, Muresan M, Seidah NG\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDifferential expression and processing of secretogranin II in relation to the status of pheochromocytoma: implications for the production of the tumoral marker EM66\u003c/strong\u003e. \u003cem\u003eJournal of molecular endocrinology \u003c/em\u003e2012, \u003cstrong\u003e48\u003c/strong\u003e(2):115-127.\u003c/li\u003e\n\u003cli\u003eLiu JW, Yu F, Tan YF, Huo JP, Liu Z, Wang XJ, Li JM: \u003cstrong\u003eProfiling of Tumor Microenvironment Components Identifies Five Stroma-Related Genes with Prognostic Implications in Colorectal Cancer\u003c/strong\u003e. \u003cem\u003eCancer biotherapy \u0026amp; radiopharmaceuticals \u003c/em\u003e2022, \u003cstrong\u003e37\u003c/strong\u003e(10):882-892.\u003c/li\u003e\n\u003cli\u003eShaulian E, Karin M: \u003cstrong\u003eAP-1 in cell proliferation and survival\u003c/strong\u003e. \u003cem\u003eOncogene \u003c/em\u003e2001, \u003cstrong\u003e20\u003c/strong\u003e(19):2390-2400.\u003c/li\u003e\n\u003cli\u003eCrowe DL, Brown TN, Kim R, Smith SM, Lee MK: \u003cstrong\u003eA c-fos/Estrogen receptor fusion protein promotes cell cycle progression and proliferation of human cancer cell lines\u003c/strong\u003e. \u003cem\u003eMolecular cell biology research communications : MCBRC \u003c/em\u003e2000, \u003cstrong\u003e3\u003c/strong\u003e(4):243-248.\u003c/li\u003e\n\u003cli\u003eLee YN, Tuckerman J, Nechushtan H, Schutz G, Razin E, Angel P: \u003cstrong\u003ec-Fos as a regulator of degranulation and cytokine production in FcepsilonRI-activated mast cells\u003c/strong\u003e. \u003cem\u003eJournal of immunology (Baltimore, Md : 1950) \u003c/em\u003e2004, \u003cstrong\u003e173\u003c/strong\u003e(4):2571-2577.\u003c/li\u003e\n\u003cli\u003eWeisz A, Rosales R: \u003cstrong\u003eIdentification of an estrogen response element upstream of the human c-fos gene that binds the estrogen receptor and the AP-1 transcription factor\u003c/strong\u003e. \u003cem\u003eNucleic acids research \u003c/em\u003e1990, \u003cstrong\u003e18\u003c/strong\u003e(17):5097-5106.\u003c/li\u003e\n\u003cli\u003eReis FM, Maia AL, Ribeiro MF, Spritzer PM: \u003cstrong\u003eProgestin modulation of c-fos and prolactin gene expression in the human endometrium\u003c/strong\u003e. \u003cem\u003eFertility and sterility \u003c/em\u003e1999, \u003cstrong\u003e71\u003c/strong\u003e(6):1125-1132.\u003c/li\u003e\n\u003cli\u003ePaulin D, Li Z: \u003cstrong\u003eDesmin: a major intermediate filament protein essential for the structural integrity and function of muscle\u003c/strong\u003e. \u003cem\u003eExperimental cell research \u003c/em\u003e2004, \u003cstrong\u003e301\u003c/strong\u003e(1):1-7.\u003c/li\u003e\n\u003cli\u003eGlasser SR, Lampelo S, Munir MI, Julian J: \u003cstrong\u003eExpression of desmin, laminin and fibronectin during in situ differentiation (decidualization) of rat uterine stromal cells\u003c/strong\u003e. \u003cem\u003eDifferentiation; research in biological diversity \u003c/em\u003e1987, \u003cstrong\u003e35\u003c/strong\u003e(2):132-142.\u003c/li\u003e\n\u003cli\u003eMehasseb MK, Bell SC, Habiba MA: \u003cstrong\u003eThe effects of tamoxifen and estradiol on myometrial differentiation and organization during early uterine development in the CD1 mouse\u003c/strong\u003e. \u003cem\u003eReproduction (Cambridge, England) \u003c/em\u003e2009, \u003cstrong\u003e138\u003c/strong\u003e(2):341-350.\u003c/li\u003e\n\u003cli\u003eKhokha MK, Hsu D, Brunet LJ, Dionne MS, Harland RM: \u003cstrong\u003eGremlin is the BMP antagonist required for maintenance of Shh and Fgf signals during limb patterning\u003c/strong\u003e. \u003cem\u003eNature genetics \u003c/em\u003e2003, \u003cstrong\u003e34\u003c/strong\u003e(3):303-307.\u003c/li\u003e\n\u003cli\u003eMerino R, Rodriguez-Leon J, Macias D, Ga\u0026ntilde;an Y, Economides AN, Hurle JM: \u003cstrong\u003eThe BMP antagonist Gremlin regulates outgrowth, chondrogenesis and programmed cell death in the developing limb\u003c/strong\u003e. \u003cem\u003eDevelopment (Cambridge, England) \u003c/em\u003e1999, \u003cstrong\u003e126\u003c/strong\u003e(23):5515-5522.\u003c/li\u003e\n\u003cli\u003eGroothuis PG, Nap AW, Winterhager E, Gr\u0026uuml;mmer R: \u003cstrong\u003eVascular development in endometriosis\u003c/strong\u003e. \u003cem\u003eAngiogenesis \u003c/em\u003e2005, \u003cstrong\u003e8\u003c/strong\u003e(2):147-156.\u003c/li\u003e\n\u003cli\u003eStabile H, Mitola S, Moroni E, Belleri M, Nicoli S, Coltrini D, Peri F, Pessi A, Orsatti L, Talamo F\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBone morphogenic protein antagonist Drm/gremlin is a novel proangiogenic factor\u003c/strong\u003e. \u003cem\u003eBlood \u003c/em\u003e2007, \u003cstrong\u003e109\u003c/strong\u003e(5):1834-1840.\u003c/li\u003e\n\u003cli\u003eKueh J, Richards M, Ng SW, Chan WK, Bongso A: \u003cstrong\u003eThe search for factors in human feeders that support the derivation and propagation of human embryonic stem cells: preliminary studies using transcriptome profiling by serial analysis of gene expression\u003c/strong\u003e. \u003cem\u003eFertility and sterility \u003c/em\u003e2006, \u003cstrong\u003e85\u003c/strong\u003e(6):1843-1846.\u003c/li\u003e\n\u003cli\u003eFrank NY, Kho AT, Schatton T, Murphy GF, Molloy MJ, Zhan Q, Ramoni MF, Frank MH, Kohane IS, Gussoni E: \u003cstrong\u003eRegulation of myogenic progenitor proliferation in human fetal skeletal muscle by BMP4 and its antagonist Gremlin\u003c/strong\u003e. \u003cem\u003eThe Journal of cell biology \u003c/em\u003e2006, \u003cstrong\u003e175\u003c/strong\u003e(1):99-110.\u003c/li\u003e\n\u003cli\u003eTaylor HS: \u003cstrong\u003eEndometrial cells derived from donor stem cells in bone marrow transplant recipients\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2004, \u003cstrong\u003e292\u003c/strong\u003e(1):81-85.\u003c/li\u003e\n\u003cli\u003eCarmeliet P: \u003cstrong\u003eMechanisms of angiogenesis and arteriogenesis\u003c/strong\u003e. \u003cem\u003eNature medicine \u003c/em\u003e2000, \u003cstrong\u003e6\u003c/strong\u003e(4):389-395.\u003c/li\u003e\n\u003cli\u003eSha G, Zhang Y, Zhang C, Wan Y, Zhao Z, Li C, Lang J: \u003cstrong\u003eElevated levels of gremlin-1 in eutopic endometrium and peripheral serum in patients with endometriosis\u003c/strong\u003e. \u003cem\u003eFertility and sterility \u003c/em\u003e2009, \u003cstrong\u003e91\u003c/strong\u003e(2):350-358.\u003c/li\u003e\n\u003cli\u003eMurakami M, Taketomi Y, Girard C, Yamamoto K, Lambeau G: \u003cstrong\u003eEmerging roles of secreted phospholipase A2 enzymes: Lessons from transgenic and knockout mice\u003c/strong\u003e. \u003cem\u003eBiochimie \u003c/em\u003e2010, \u003cstrong\u003e92\u003c/strong\u003e(6):561-582.\u003c/li\u003e\n\u003cli\u003eWang D, Dubois RN: \u003cstrong\u003eEicosanoids and cancer\u003c/strong\u003e. \u003cem\u003eNature reviews Cancer \u003c/em\u003e2010, \u003cstrong\u003e10\u003c/strong\u003e(3):181-193.\u003c/li\u003e\n\u003cli\u003ePanupinthu N, Lee HY, Mills GB: \u003cstrong\u003eLysophosphatidic acid production and action: critical new players in breast cancer initiation and progression\u003c/strong\u003e. \u003cem\u003eBritish journal of cancer \u003c/em\u003e2010, \u003cstrong\u003e102\u003c/strong\u003e(6):941-946.\u003c/li\u003e\n\u003cli\u003eEyster KM, Klinkova O, Kennedy V, Hansen KA: \u003cstrong\u003eWhole genome deoxyribonucleic acid microarray analysis of gene expression in ectopic versus eutopic endometrium\u003c/strong\u003e. \u003cem\u003eFertility and sterility \u003c/em\u003e2007, \u003cstrong\u003e88\u003c/strong\u003e(6):1505-1533.\u003c/li\u003e\n\u003cli\u003eLousse JC, Defr\u0026egrave;re S, Colette S, Van Langendonckt A, Donnez J: \u003cstrong\u003eExpression of eicosanoid biosynthetic and catabolic enzymes in peritoneal endometriosis\u003c/strong\u003e. \u003cem\u003eHuman reproduction (Oxford, England) \u003c/em\u003e2010, \u003cstrong\u003e25\u003c/strong\u003e(3):734-741.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"endometriosis, bioinformatics, machine learning, immunity, diagnostic markers","lastPublishedDoi":"10.21203/rs.3.rs-3551509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3551509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eImmunity and inflammation are involved in a multitude of reproductive metabolic processes, with a particular focus on endometriosis (EMT). The aim of this study is to employ bioinformatics methods to explore novel immune-related biomarkers and assess their predictive capabilities for EMT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003emRNA expression profiles were obtained from the GSE141549 and GSE7305 datasets in the Gene Expression Omnibus (GEO) database, while immune-related genes were sourced from the ImmPort database. Immune genes associated with EMT were filtered for differential analysis. Interrelationships between different immune-related genes (DIRGs) were characterized using protein-protein interaction (PPI) networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the functionality of DIRGs. Least Absolute Shrinkage and Selection Operation (LASSO) regression models and Boruta models were built to determine candidate genes for EMT, and the performance of the prediction models and candidate genes were verified using Receiver Operator Characterization curve (ROC) in GSE141549 and GSE7305. Finally, we structured the EMT prediction normogram on the basis of the five candidate DIRGs. Expression of the five candidate DIRGs in human samples was examined using PCR and Western Blot. The relative proportions of 22 immune cells were computed using the CIBERSORT algorithm, and the correlations between immune cells and candidate DIRGs were emphasized.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAltogether 769 differentially expressed genes (DEGs) and 94 DIRGs were detected between ectopic and normal endometrium. These DIRGs were mainly concentrated in positive regulation of response to external stimulus, collagen-containing extracellular matrix, receptor ligand activity and signaling receptor activator activity. KEGG enrichment analysis mainly addressed Cytokine-cytokine receptor interaction and Neuroactive ligand-receptor interaction. Then, five key genes (SCG2, FOS, DES, GREM1, and PLA2G2A) were characterized using the GSE141549 dataset and used to build a prediction model for EMT.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eImmunity and inflammation have a major role in the elaboration of EMT. SCG2, FOS, DES, GREM1 and PLA2G2A can serve as important biomarkers for EMT.\u003c/p\u003e","manuscriptTitle":"Identification of key immune genes of endometriosis based on bioinformatics and machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-20 21:26:47","doi":"10.21203/rs.3.rs-3551509/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1788227f-7f5f-437f-a41b-2cfd8910988f","owner":[],"postedDate":"November 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T12:06:53+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-20 21:26:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3551509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3551509","identity":"rs-3551509","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}