Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration

Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration | 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 Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration XueZhen Wang, Qin Zheng, Man Sun, Huan Zhang, WeiWei Ying This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2913831/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Oct, 2023 Read the published version in BMC Women's Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Endometriosis (EMS) occurs when normal uterine tissue grows outside the uterus, causing chronic pelvic pain and infertility. Endometriosis-associated infertility is thought to be caused by unknown mechanisms. In this study, using necroptosis-related genes, we developed and validated multi-gene joint signatures to diagnose EMS and explored their biological roles. Methods: We downloaded two databases (GSE7305 and GSE1169) from the Gene Expression Omnibus (GEO) database and 630 necroptosis-related genes from the GeneCards database and the GSEA database. The limma package in R software was used to identify differentially expressed genes (DEGs). We interleaved common differentially expressed genes (co-DEGs) and necroptosis-related genes (NRDEGs) in the endometriosis dataset. The DEGs functions were reflected by gene ontology analysis (GO), pathway enrichment analysis, and gene set enrichment analysis (GSEA). We used CIBERSORT to analyze immune microenvironment differences between EMS patients and controls. Furthermore, a correlation was found between necroptosis-related differentially expressed genes and infiltrating immune cells to understand the molecular immune mechanism better. Results: Compared to the control group, this study revealed 10 NRDEGs in EMS. There are two types of immune cell infiltration abundance (activated NK cells and M2 macrophage) in these two datasets, and the correlation between different groups of samples is statistically significant (P<0.05). MYO6 has a high correlation with activated NK cells in two datasets consistently. HOOK1 has a high correlation with M2 Macrophages in two datasets consistently. Conclusions: We identified 10 necroptosis-related genes in EMS and assessed their relationship to the immune microenvironment. MYO6 and HOOK1 may be novel biomarkers and treatment targets of the future. endometriosis necroptosis CIBERSORT immune microenvironment activated NK cells M2 macrophage MYO6 HOOK1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Endometriosis occurs when endometrial glands and stroma appear outside the uterine cavity. (1) The predicted prevalence of this disease at reproductive age is 10%. (2) Endometriosis reduces women's health-related quality of life (HRQOL), resulting in impairments in physical functioning, diminished social life, difficulties in intimate relationships, and decreased productivity. The endometriosis etiology is complex, involving multiple genetic and environmental risk factors. Although the endometriosis pathogenesis is relatively unclear, it is largely believed to be caused by retrograde menstruation leading to exfoliated endometrium implantation. However, there are limited studies on endometriosis development and endometrial-peritoneal attachments and invasions. Endometriosis remains difficult to diagnose in the biomarkers' absence for detecting or ruling out endometriosis. (3) Biomarkers and novel therapies that target the diverse physiological mechanisms associated with the onset, progression, and persistence of endometriosis symptoms are urgently required. (4) Necroptosis, also known as necroptosis, is a receptor-interacting serine/threonine protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-containing pseudo kinase (MLKL), but not Caspase-1 dependent. (5) Necroptosis has been implicated as a critical cell death pathway in cancers, Alzheimer's, other neurodegenerative diseases, and virus-infected cells. (6) Some studies have identified necroptotic modulators as possible prognostic biomarkers for cancer and certain diseases. (7, 8) Day et al. (9) found that BMI1 in ovarian cancer can participate in the PINK1-Park2-dependent mitochondrial pathway and induce a new type of non-apoptosis cell death mediated by necroptosis. The endometriosis severity is related to the apoptosis role, which usually destroys ectopic and heterotopic endometrial cells before forming necrotic tissue, thus inhibiting their migration and accumulation. (10, 11) Moreover, apoptotic mechanisms in the cytoplasm and cellular inflammasomes can further interact with ERβ-induced immune surveillance. However, necroptosis's correct mechanism and function in the endometriosis progression remains unclear. There is growing evidence that the immune system is vital in the pathophysiology and symptoms of EM. Immune cells such as natural killer (NK), macrophages, neutrophils, and CD4 T-helper cells are dysregulated in women with EM. (12, 13) Immune-related mechanisms have been described to be involved in the pathophysiology and symptomatology of EMS by contributing to the survival and persistence of endometriosis lesions. (14) Immune dysfunction is associated with implantation, proliferation, and apoptosis of ectopic endometrium. In women with endometriosis, however, it is unclear which subtypes of immune cells are present in ectopic endometrium. Analyzing the relationship between necroptosis-related and immune infiltration may help explore unknown mechanisms. Recently, in a meta-analysis of transcriptomes using the xCell algorithm, immune profiles in eutopic endometriosis and stages I–II, III–IV endometriosis were significantly different regardless of the hormone. (12) Therefore, exploring immune mechanisms in endometriosis is key to elucidating their role in endometriosis pathogenesis and generating unique insights for developing preventive and therapeutic strategies, innovative non-invasive diagnostic methods, and targeted therapies. This study explored potential biomarkers of endometriosis and its biological effects in the pathogenesis of endometriosis. We used the gene expression datasets GSE11691 and GSE7305 associated with normal and ectopic endometrium, which were extracted from the Gene Omnibus (GEO) database. Differential genes were screened and intersected with necroptosis-related genes. Subsequently, the immune microenvironment was compared between endometriosis patients and controls using CIBERSORT, and the immune cell association was calculated with NRDEGs for the first time. We performed a bioinformatics analysis of endometriosis to elucidate the endometriosis pathogenesis further. Materials And Methods 1.1 Downloading Data We downloaded the endometriosis-related datasets GSE7305 (15)and GSE1169 (15) from the GEO database (16) through the R package GEOquery. (17) The GSE7305 dataset, which comes from Homo Sapiens and the data platform is GPL570, contains 20 samples, including 10 endometriosis and 10 normal samples. Moreover, the GSE11691 dataset, from Homo Sapiens and data platform GPL96, contains 18 samples, nine of which are endometriosis and nine normal. All samples from the two datasets were included in this study. The GeneCards database (18) provides comprehensive information about human genes. Necroptosis-related genes were obtained using the word "necroptosis" as the search keyword in the GeneCards database and GSEA database. (19) A total of 630 necroptosis-related genes were obtained after merging and de-duplication, as shown in Table S1. 1.2 Analysis of differentially expressed genes associated with endometriosis To identify possible mechanisms and pathways associated with differential gene expression in endometriosis, R package limma was used to standardize data sets GSE7305 and GSE11691, and the expression profile data after processing were analyzed differently. DEGs between different subgroups were obtained from two endometriosis data sets, |logFC| > 0.5 and P.adj 0.5 and p.adj < 0.05 were upregulated DEGs. Genes with logFC < -0.5 and p.adj 0.5 and P.adj < 0.05 obtained from the differential analysis of dataset GSE7305 and dataset GSE11691 and plotted the Venn diagram to visualize the common differentially expressed genes of the dataset. Venn diagrams were then used to visualize the co-DEGs intersection and necroptosis-related genes between the two datasets. The difference analysis results were displayed by volcano map using R package ggplot2, and heatmap was drawn using R package pheatmap. 1.3 Functional Enrichment Analysis (GO) For large-scale functional enrichment studies, Gene Ontology (GO) (20) analysis is a common method, including biological process (BP) and molecular function (MF), as well as cellular component (CC). The R package clusterprofiler (21) was used for the GO analysis of NRDEGs. To qualify for entry, the screening criteria were a P value of 0.05 and an FDR value (Q value) of 0.05, which was considered statistically significant. The P value correction method was Benjamini-Hochberg (BH). 1.4 Gene Set Enrichment Analysis Gene Set Enrichment Analysis (GSEA) (22) evaluated the correlation between genes from a predefined Gene Set and phenotypes in the Gene Table to measure its phenotypic contribution. In this study, genes in the GSE7305 dataset (Table 2) and GSE11691 dataset (Table 3) were divided into high- and low-phenotypic correlation according to the ranking of phenotypic correlation degree. Then, R package clusterProfiler was used to enrich and analyze all DEGs in the two groups. Following are the parameters used in this GSEA: The seed was 2020, the number of calculations was 1000, the minimum number of genes in each gene set was 10, and the maximum number of genes was 500. The correction method of the P value was Benjamini-Hochberg (BH). Molecular Signatures Database (MSigDB) (23) Database provided the C2.cp.v7.2. symbols gene set, and the screening criteria for significant enrichment was P < 0.05 and FDR value (Q value) < 0.25. 1.5 PPI, mRNA-miRNA, mRNA-TF, mRNA-Drug interaction network Protein-protein interaction (PPI) is composed of individual proteins through their interactions. The STRING database (24) searches for interactions between proteins that have been predicted and those that have been known. In this study, we used the STRING database to construct the protein-protein interaction network related to differentially expressed genes (minimum required interaction score: Low confidence (0.150). The PPI network model was visualized with Cytoscape (25) software (version 3.9.1). With the Starbase (Version 3.0) database (26), we searched for microRNA targets by analyzing experimental data generated by CLIP-seq and degradation groups, providing various visual interfaces for exploring microRNA targets. The database contains abundant miRNA-ncRNA, miRNA-mRNA, miRNA-RNA, and RNA-RNA data. miRDB database (27) is used for miRNA target gene prediction and functional annotation. We used the Starbase and miRDB databases to predict miRNAs interacting with key genes (mRNAs) and then took the intersection part of the results from the two databases to draw the mRNA-miRNA interaction network with Cytoscape software. CHIPBase database (28) (version 2.0) (https://rna.sysu.edu.cn/chipbase/) from the DNA binding protein. ChIP-seq data identified thousands of combining base sequence matrices and binding sites; it also predicts millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. HTFtarget database (29) (http://bioinfo.life.hust.edu.cn/hTFtarget.) is a comprehensive database containing human TFs and their targets. We searched for TFs that bind to key genes through the CHIPBase and hTFtarget databases, extracted the intersection parts, and plotted the mRNA-TF interaction network with Cytoscape software. We also predicted the direct and indirect drug targets of NRDEGs through CTD (Comparative Toxicogenomics Database), (30) explored the interaction between NRDEGs and drugs, and used Cytoscape software to visualize the mRNA-drug interaction network. 1.6 Expression differences, chromosomal localization, and functional similarity analysis of NRDEGs We analyzed NRDEG expression in endometriosis datasets GSE7305 and GSE11691. To analyze the NRDEGs localization in 24 pairs of chromosomes, we first used the UCSC database (http://genome.ucsc.edu/) (31) to determine the start and stop sequences of NRDEGs. Subsequently, the R package RCircos (32) was used to draw the chromosome localization map. GoSemSim (33) is an R software package for calculating semantic similarity between gene products, gene clusters and GO terms. To analyze the functional correlations among key genes, the R package GOSemSim was used to calculate the functional correlations of key genes. 1.7 Analysis of immune infiltration CIBERSORTx (34) is an immune infiltration analysis algorithm based on linear support vector regression to deconvolve the transcriptome expression matrix to estimate the composition and immune cell abundance in mixed cells. We uploaded data gene expression matrix to CIBERSORTx online website (https://cibersortx.stanford.edu/), combined with Homo sapiens gene matrix (Homo sapiens) characteristics, data screening immune cell enrichment score greater than zero. Finally, the specific results of the immune cell infiltration abundance matrix were obtained and demonstrated. The difference in the proportion of immune cells between endometriosis samples (group: endometriosis) and normal samples (group: normal) in the endometriosis dataset was calculated by the Wilcoxon test, and the P value < 0.05 was considered statistically different. The correlation of immune cells between different groups was calculated by Spearman and visualized by R package ggplot2. Then, we combined the gene expression matrix of the dataset to calculate the correlation between immune cells and NRDEGs and drew the correlation heatmap by R package pheatmap. Results 2.1 Technical Roadmap 2.2Analysis of endometriosis-related differentially expressed genes Using the limma package, we first normalized the expression profile data of the endometriosis datasets GSE7305 and GSE11691. The data distribution before and after standardized treatment was shown by box plot (Figures 2A–D). We found that the data after standardized treatment tended to be consistent in expression level. To analyze the gene expression values in endometriosis data set samples (group: endometriosis) relative to normal samples (group: endometriosis), we used R package limma to analyze the differences between dataset GSE7305 and dataset GSE11691 and obtained the differentially expressed genes of the two data sets. The results were as follows: Data set GSE7305 got 20247 DEGs, in which 3480 genes meet the | logFC | > 0.5 and P.adj < 0.05 threshold. At this threshold, the number of highly expressed (low expression in the normal group, positive logFC, upregulated genes) in the endometriosis group was 1760, and the number of low expressed (high expression in the normal group, negative logFC, downregulated genes) in the endometriosis group was 1720. The volcano map was drawn based on the different analysis results of this dataset (Figure 3A). Data set GSE11691 got 12376 DEGs, 610 genes meet the | logFC | > 0.5 and P.adj < 0.05 threshold, under the threshold, the number of highly expressed (low expression in the normal group, positive logFC, upregulated genes) in the endometriosis group was 396. The number of low expressed (high expression in the normal group, negative logFC, downregulated genes) in the endometriosis group was 214. We drew a volcano map based on the differential analysis results on the GSE11691 dataset (Figure 3B). To obtain the NRDEGs, we intersected the DEGs from GSE7305 and GSE11691 with |logFC| > 0.5 and P.adj < 0.05, 330 common differentially expressed genes (co-DEGs) of the endometriosis dataset were obtained, and Venn diagram was drawn (Figure 3C). We also examined the intersection between co-DEGs and necroptosis-related genes using the endometriosis dataset. A total of 10 NRDEGs from the endometriosis data set were obtained, and a Venn diagram was drawn (Figure 3D), which were C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74. According to the results obtained by the Venn diagram, the expression differences of 10 NRDEGs in the GSE7305 dataset (Figure 3E) and GSE11691 dataset (Figure 3F) among different sample groups were analyzed respectively, and the R package pheatmap was used to draw heatmap to show the analysis results (Figures 3E and F). The results showed that PKP3, GJB1, HOOK1, TUFM, and MYO6 were up-regulated genes (low expression in the normal group, positive logFC, yellow in the figure), while C7, AHR, GSN, CLEC7A, and CD74 were down-regulated genes (high expression in the normal group, blue in the figure, logFC is negative). 2.3Functional Enrichment Analysis (GO) To evaluate the biological process, molecular function, cell component, biological pathway, and endometriosis of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74), we first conducted GO (Gene Ontology) analysis for NRDEGs (Table1). The screening criteria for enrichment items were P Value < 0.05 and FDR value (Q value) < 0.05, which were considered statistically significant. The results showed that 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) were mainly enriched in biological processes like regulation of lymphocyte activation in endometriosis, response to alcohol, regulation of multi-organism process and cellular components (CC) such as endocytic vesicle, clathrin-coated endocytic vesicle, clathrin-coated vesicle membrane. It was also enriched in molecular function (MF), including MHC protein binding, actin binding, and actin filament binding. We demonstrated the results of GO functional enrichment analysis by bubble diagram (Figure 4A). Furthermore, GO genes' functional enrichment analysis results were also presented in the network diagram (Figure 4B). Subsequently, we conducted GO enrichment analysis on the 10 NRDEGs combined with logFC. Moreover, based on the enrichment analysis, we calculated each molecule's corresponding Z score by the molecule's logFC value in the differential analysis result of the provided molecule in the endometriosis data set. We presented the GO enrichment analysis results of combined logFC through a chord diagram (Figure 4C). The GO enrichment analysis results are displayed in the form of a Sankey diagram (Figure 3D), including BP, CC, MF (Biological Process, Cellular Component, Molecular Function), and their corresponding function or pathway ID (ID) and category ID (ID) including the relationship between the gene names (gene).2.4 Gene set enrichment analysis To determine the impact of the expression levels of all genes related to endometriosis metabolism on the occurrence of endometriosis, we evaluated the gene expression profile and the biological processes involved in the GSE7305 dataset and GSE11691 dataset by GSEA (Gene Set Enrichment Analysis) enrichment analysis, respectively. Links between cellular components and the molecular functions they perform. P < 0.05 and FDR value (Q value) < 0.25 were considered as significant enrichment screening criteria. The results showed that differentially expressed genes in dataset GSE7305 were significantly enriched in IL1 and megakaryocytes in obesity (Figure 5B), photodynamic therapy-induced NFKB survival signaling (Figure 5C), IL5 pathway (Figure 5D), MAPK signaling pathway (Figure 5E) and other pathways (Figure 5A–E, Table 2). However, differentially expressed genes in dataset GSE11691 were significantly enriched in photodynamic therapy-induced NFKB survival signaling (Figure 5G), Wnt signaling (Figure 5H), IL8 CXCR2 pathway (Figure 5I), focal adhesion PI3K-AKT mTOR signaling pathway (Figure 5J). 2.5 Construction of PPI, mRNA-miRNA, mRNA-TF, and mRNA-drug regulatory networks First, protein-protein interaction analysis was conducted using the STRING database with a minimum required interaction score greater than 0.150. Low confidence (0.150) was used as the standard to construct a PPI network of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74). The interactions were visualized using Cytoscape software (Figure 6A). There are only seven NRDEGs in the PPI interaction network, which are C7, AHR, TUFM, GSN, MYO6, CLEC7A, and CD74. Second, miRNAs related to NRDEGs were obtained from the StarBase and miRDB databases. To visualize the mRNA-miRNA regulatory network, Cytoscape was applied (Figure 6B), which contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) and 26 miRNA molecules. The specific names of miRNA molecules are shown in Table S2. Then, the TFs combined with NRDEGs were obtained by ChIPBase and hTFtarget databases. With Cytoscape software, we structured and visualized a network of mRNA-TF interactions (Figure 6C). It contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) and 100 transcription factors. The specific TF molecule names are shown in Table S3. Finally, the CTD database was used to identify potential drugs or molecular compounds of NRDEGs. The mRNA-drug network was constructed and visualized by Cytoscape software, which contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, LEC7A, and CD74) and 41 drugs or molecular compounds. The names of specific drugs or molecular compounds are shown in Table S4. 2.6 Expression differences, chromosomal localization, and functional similarity analysis of NRDEGs To further verify the expression difference of NRDEGs in the endometriosis data set, 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74) were shown by group comparison (Figures 7A and B). CD74 expression analysis was performed for GSE7305 and GSE11691 in the endometriosis and normal groups. The difference results of dataset GSE7305 (Figure 7A) showed that all NRDEGs were statistically significant: The expression levels of C7, HOOK1, PKP3, AHR, GJB1, and MYO6 in different groups of endometriosis dataset GSE7305 were statistically significant (P value < 0.001). There was a highly statistically significant difference in the levels of expression of TUFM, GSN, and CLEC7A between groups (P value < 0.01). The expression of CD74 in different groups was statistically significant (P value < 0.05). The difference results of dataset GSE11691 (Figure 7B) showed that all NRDEGs are statistically significant: AHR, TUFM, and MYO6 expression levels in different groups of datasets GSE76885 were statistically significant (P value < 0.001). The expressions of HOOK1, GJB1, CLEC7A, and CD74 in different groups were highly statistically significant (P value < 0.01). The expression levels of C7, PKP3, and GSN in different groups were statistically significant (P value < 0.05). Then we mapped the chromosomal location of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) (Figure 7C). The results showed that gene C7 and gene CD74 were placed on chromosome 5, the HOOK1 gene was placed on chromosome 1, the MYO6 gene was placed on chromosome 6, the AHR gene was placed on chromosome 7, GSN gene was placed on chromosome 9, and PKP3 gene was placed on chromosome 11. The CLEC7A gene was placed on chromosome 12, and the TUFM gene was placed on chromosome 12. Based on the scores, Friends analyzes genes involved in endometriosis lesions and displays them as bar graphs (Figure 7D) and rain-cloud graphs (Figure 7E). The results showed that PKP3, GSN, MYO6, CLEC7A, and CD74 played important roles in this process. 2.7 Immune infiltration analysis We sorted out the expression profile data of GSE7305 and GSE11691 in the endometriosis dataset and uploaded it to CIBERSORTx online website and used the CIBERSORTx algorithm to calculate 22 immune cells and endometriosis samples in the endometriosis dataset (group: endometriosis) and the expression profile data of normal samples (group: normal). According to immune infiltration analysis results, we plotted the immune cell infiltration of each sample of 22 kinds of immune cells in the GSE7305 and GSE11691 datasets in bar graphs (Figures 8A and C). We also presented group comparison maps to illustrate the correlation between immune cell infiltration abundance and different groups in GSE7305 and GSE11691 datasets (Figures 8B and D). We demonstrated the correlation between the abundance of six immune cell infiltrates in the GSE7305 and GSE11691 datasets using correlation heat maps (Figures 9A and B). The results showed that after excluding the immune cells that had no difference analysis significance after grouping, there were statistically significant differences in the infiltration abundance of four types of immune cells in data set GSE7305 (Figure 8B) and the correlation between samples in different groups (P < 0.05). These four immune cells were: rest memory CD4+ T cells, follicular helper T cells, activated NK cells, and M2 Macrophages. In data set GSE11691 (Figure 8D), there were five types of immune cells, and the correlation between the infiltration abundance and samples in different groups was statistically different (P < 0.05). These five immune cells were: plasma cells, gamma-delta T cells, resting NK cells, activated NK cells, and M2 Macrophages. Activated NK cells and M2 Macrophages were statistically significant in both datasets. To analyze the correlation between the expression levels of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) in endometriosis datasets GSE7305, GSE11691 with infiltration abundance of two immune cells (activated NK cells and M2 macrophages). We showed the infiltration abundance of two immune cells (activated NK cells and M2 macrophages) and 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) by lollipop figure. (Figures 9C–F). As can be seen from the figure, the corresponding gene MYO6 has high correlation and consistency in the two datasets (r = 0.708 in GSE7305, P < 0.001; GSE11691 r = 0.686, P < 0.001); M2 Macrophages has higher correlation and consistency in the two datasets for the corresponding genes HOOK1 (r = -0.760 in GSE7305, P < 0.001; r = -0.726 in GSE11691, P < 0.001), GJB1 (in GSE7305) r = -0.679, P < 0.001; r = -0.626, P < 0.01 in GSE11691), MYO6 (r =-0.780, P < 0.001 in GSE7305; r = -0.633, P < 0.01 in GSE11691). Discussion Most gynecologists detect ovarian endometriosis with sonography, the most common form of endometriosis. (35) In recent years, microarray and high-throughput sequencing technologies have enabled bioinformatics analysis of endometriosis. However, most studies are now based on invasive methods and single arrays, resulting in poor acceptance and a lack of cohorts for multiple combined studies. Its goal is to discover new diagnostic methods and safe treatments for endometriosis by exploring its biological mechanisms and searching for meaningful molecular markers. Therefore, we analyzed patients with and without endometriosis and performed enrichment analysis of necroptosis-related genes to determine their role in endometriosis. Disease onset and progression are associated with necroptosis, according to increasing research. Necroptosis, a programmed necrotic cell death, is vital in the host's defense against certain pathogen incursions. Inflammatory diseases also result from necroptosis deregulation. (5) However, its function in the immune response to endometriosis remains elusive. In this study, there were 330 DEGs between 19 endometriosis samples and 19 normal samples in two expression profile datasets (GSE7305 and GSE1169). We intersected the common differentially expressed genes (co-DEGs) and necroptosis-related genes in two endometriosis data sets to obtain 10 NRDEGs from endometriosis data sets. The 10 NRDEGs are C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74. Among them, PKP3, whose protein belongs to the plakophilin family, promotes tissue integrity. The plakophilins link desmosomal cadherins to intermediate filaments at desmosome junctions, and in common with other catenins, they perform additional functions, including in the nucleus. (16) Gene GJB1 encodes the transmembrane channel protein connexin 32 (Cx32), a member of the Cxs family. (36) Previous studies have shown that GJB1 exerts anti-apoptotic and pro-tumor effects by interacting with. (36, 37) HOOK1, encodes a member of the hook family of coiled-coil proteins that bind to microtubules and organelles via their N-and C-terminal domains, respectively. In our study, HOOK1 expression was upregulated. Notably, the N-segment of Hook1 has a cytoskeletal protein binding site involved in cell migration and intracellular vesicle trafficking. (38) The mitochondrial translation elongation factor is encoded by the TUFM gene. Chang-Yong Cho et al. suggest that TUFM may be important in CASP8 inhibition via autophagy activation. (39) Actin-based myosins (MYO6) move cargo towards the minus ends of actin filaments using their actin-based motor proteins. Since it is the only myosin with this directionality, it is vital in many biological processes. (40) MYO6 is involved in various physiological processes in vivo , and its expression has been reported to be increased in humans and mice for different diseases, including cancer and hearing loss. (41, 42) The enrichment analysis included GO terms for functional enrichment and GSEA enrichment. In addition to being enriched in immune response and activation, these genes were significantly associated with immune-cell interaction. In recent decades, accumulating evidence has demonstrated an immune imbalance in endometriosis. (43) Compared to normal endometrium, endometriosis patients have more immune cells, particularly NK and M2 cells. However, the immune cell activation pattern in endometriosis remains unclear. The role of immune cell infiltration in endometriosis needs to be further investigated; we performed a comprehensive evaluation of immune infiltration in endometriosis using CIBERSORT. Our study showed the correlation between the infiltration abundance of NK cells and M2 macrophage immune cells. Moreover, genes were statistically different in datasets GSE7305 and GSE1169. Several previous studies have reported the downregulation of NK cells in endometriosis patients; our results are consistent with theirs. (44, 45) Lagana AS et al. (46) found that the number of M1 and M2 macrophages was significantly higher in the endometriosis group than in controls, regardless of stage. Moreover, M2 macrophages might inhibit the immunological response of NK cells. Reduced NK cell number and function results in reduced cytotoxicity and reduced elimination of ectopic endometrial cells. (47, 48) In our study, the relative gene of NK cells activated was MYO6 and that of macrophages. M2 was HOOK1 in the two datasets. MYO6 and HOOK1 serve a limited impact on the immune system in endometriosis. A macromolecular antigen, ovalbumin, showed high permeability to Rmc monolayers lacking myo6. It still induced strong T-cell activation since it retained antigenicity. In a study by Yu-wei Liao et al. (49), MyO6-deficient RMC monolayers demonstrated high permeability, retaining the ovalbumin antigenicity, thereby activating T cells. Our findings suggest that MYO6 and HOOK1 are associated with immune infiltration in endometriosis and can be used as novel potential biomarkers and predictors of immune cell infiltration in endometriosis. Necroptosis represents the newly discovered immunogenic cell death (ICD) forms. There is evidence that necroptosis modulates the immune system, primarily composed of natural killer (NK) cells, macrophages, dendritic cells (DC), and T and B lymphocytes. (50) To investigate whether MYO6 and HOOK1 contribute to the infiltration of immune cells, we explored the correlation between those two factors. It appears that necroptosis and the immune response are interconnected in endometriosis, as indicated by their significant association with immune cells. Molecular mechanisms underlying the complex interactions between these genes and immune cells must be elucidated in further studies. In this study, we obtained 26 miRNAs associated with NRDEGs from the StarBase and miRDB databases. In endometriosis, miRNAs are associated with genetic, epigenetic, and angiogenic factors, hormones, cytokines, chemokines, oxidative stress (OS) markers, inflammation mediators, hypoxia, angiogenesis, and altered immune system, which contribute to its pathogenesis. (51) A total of 100 Transcription factors (TFS) combined with NRDEGs were obtained from the ChIPBase and hTFtarget databases. It has been demonstrated in previous studies that microRNA miR-106a-5p inhibits the proliferation, migration, and invasion of ectopic endometrial stromal cells by targeting the forkhead box transcription factor FOXC1 via the PI3K/Akt/mTOR signaling pathway. (52) Due to the lack of effective therapeutic drugs for EMS, (53) based on the CTD database, 41 molecular compounds, and drugs that are potentially effective against EM were identified by NRDEGs. Inevitably, our study has some limitations. First, we conducted a comprehensive bioinformatics analysis to identify the association between NRDEGs and endometriosis. We need to conduct further in vitro and in vivo studies to validate necroptotic-related genes' role in endometriosis and to gain a deeper understanding of its pathogenesis. Second, the study was retrospective; therefore, important clinical information could not be obtained. Third, the specimens in this study were from endometriotic tissue; therefore, the biomarkers could not be used to diagnose the early stage of the disease, and more research on blood biomarkers is required. Conclusions Ten NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) may serve as diagnostic biomarkers for endometriosis have been found for the first time. MYO6 and HOOK1 can be used as potential biomarkers for endometriosis. A strong association was also found between two selected genes and immune cell infiltration to explore endometriosis' pathogenesis, which could provide a rationale for future treatment. These findings increase our knowledge of necroptosis genes in EMS patients. However, the role of these necroptosis genes in EMS needs to be validated in the future. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE Not applicable CONSENT FOR PUBLICATION Not applicable DATA AVAILABILITY STATEMENT The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s. AUTHOR CONTRIBUTIONS All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XZW, QZ, MS, HZ and WWY. The first draft of the manuscript was written by XZW and all authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version. FUNDING This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online CONFLICT OF INTEREST Not applicable ACKNOWLEDGEMENT Not applicable References Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Vigano P. Endometriosis. Nat Rev Dis Primers. 2018;4(1):9. Shafrir AL, Farland LV, Shah DK, Harris HR, Kvaskoff M, Zondervan K, et al. Risk for and consequences of endometriosis: A critical epidemiologic review. Best Pract Res Clin Obstet Gynaecol. 2018;51:1-15. Nisenblat V, Bossuyt PM, Shaikh R, Farquhar C, Jordan V, Scheffers CS, et al. Blood biomarkers for the non-invasive diagnosis of endometriosis. Cochrane Database Syst Rev. 2016(5):CD012179. Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med. 2020;382(13):1244-56. 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Endometriosis, infertility and MicroRNA's: A review. J Gynecol Obstet Hum Reprod. 2021;50(9):102157. Zhou X, Chen Z, Pei L, Sun J. MicroRNA miR-106a-5p targets forkhead box transcription factor FOXC1 to suppress the cell proliferation, migration, and invasion of ectopic endometrial stromal cells via the PI3K/Akt/mTOR signaling pathway. Bioengineered. 2021;12(1):2203-13. Falcone T, Flyckt R. Clinical Management of Endometriosis. Obstet Gynecol. 2018;131(3):557-71. Tables Table 1. GO enrichment analysis results of necroptosis-related differentially expressed genes. Ontology ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0051249 regulation of lymphocyte activation 4/10 485/18670 8.34e-05 0.008 0.004 BP GO:0097305 response to alcohol 3/10 233/18670 2.16e-04 0.014 0.007 BP GO:0043900 regulation of multi-organism process 3/10 405/18670 0.001 0.032 0.016 CC GO:0030139 endocytic vesicle 3/10 303/19717 3.98e-04 0.019 0.010 CC GO:0045334 clathrin-coated endocytic vesicle 2/10 63/19717 4.45e-04 0.019 0.010 CC GO:0030665 clathrin-coated vesicle membrane 2/10 115/19717 0.001 0.042 0.021 MF GO:0042287 MHC protein binding 2/9 40/17697 1.78e-04 0.011 0.004 MF GO:0003779 actin binding 3/9 431/17697 0.001 0.032 0.013 MF GO:0051015 actin filament binding 2/9 198/17697 0.004 0.034 0.014 GO, Gene Ontology; BP, biological process; CC: cell component; MF, molecular function. Table 2. GSEA analysis of dataset GSE7305. Description setSize enrichmentScore NES pvalue p.adjust REACTOME_COMPLEMENT_CASCADE 56 0.822144048 2.431471926 0.001862197 0.028091787 KEGG_COMPLEMENT_AND_COAGULATION_CASCADES 67 0.762017649 2.333823306 0.001841621 0.028091787 WP_HUMAN_COMPLEMENT_SYSTEM 92 0.709058749 2.296694499 0.001795332 0.028091787 WP_COMPLEMENT_AND_COAGULATION_CASCADES 57 0.776932609 2.293240512 0.001879699 0.028091787 WP_COMPLEMENT_ACTIVATION 20 0.883248685 2.184014586 0.001984127 0.028091787 REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT 23 0.845953791 2.156923481 0.001964637 0.028091787 KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 50 0.739155934 2.155146117 0.001855288 0.028091787 BIOCARTA_COMP_PATHWAY 17 0.900396432 2.141644522 0.002 0.028091787 WP_TYROBP_CAUSAL_NETWORK 59 0.705926715 2.111012452 0.001821494 0.028091787 BIOCARTA_LAIR_PATHWAY 17 0.873581328 2.077863261 0.002 0.028091787 WP_CELLS_AND_MOLECULES_INVOLVED_IN_LOCAL_ACUTE_INFLAMMATORY_RESPONSE 17 0.873581328 2.077863261 0.002 0.028091787 WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY 24 0.736409022 1.875075348 0.001972387 0.028091787 WP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_SIGNALING 35 0.644242533 1.754669341 0.005859375 0.048273954 BIOCARTA_IL5_PATHWAY 10 0.829191458 1.693843996 0.006012024 0.048273954 KEGG_MAPK_SIGNALING_PATHWAY 254 0.408313605 1.480258186 0.005016722 0.045404266 GSEA, Gene Set Enrichment Analysis. Table 3. GSEA analysis of dataset GSE11691. Description setSize enrichmentScore NES pvalue p.adjust WP_TYROBP_CAUSAL_NETWORK 50 0.701769525 2.23878553 0.001658375 0.11042735 REACTOME_SMOOTH_MUSCLE_CONTRACTION 35 0.722865333 2.195371633 0.001644737 0.11042735 NABA_CORE_MATRISOME 188 0.556594657 2.179478164 0.00136612 0.11042735 REACTOME_ELASTIC_FIBRE_FORMATION 38 0.688839129 2.119760258 0.001628664 0.11042735 REACTOME_MOLECULES_ASSOCIATED_WITH_ELASTIC_FIBRES 33 0.692726707 2.086001585 0.001644737 0.11042735 NABA_ECM_GLYCOPROTEINS 127 0.552642372 2.048692518 0.001490313 0.11042735 REACTOME_MUSCLE_CONTRACTION 175 0.52131301 2.020357098 0.001371742 0.11042735 KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS 49 0.631792413 2.009084444 0.001666667 0.11042735 REACTOME_CHEMOKINE_RECEPTORS_BIND_CHEMOKINES 52 0.618962401 1.988331531 0.001658375 0.11042735 REACTOME_ECM_PROTEOGLYCANS 70 0.581720599 1.98573096 0.001587302 0.11042735 REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION 255 0.492682565 1.981455275 0.001329787 0.11042735 PID_IL8_CXCR2_PATHWAY 32 0.631225305 1.875950683 0.003372681 0.118114144 WP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_SIGNALING 34 0.549039654 1.662008258 0.016583748 0.217999152 WP_WNT_SIGNALING 95 0.41447078 1.483747047 0.017160686 0.221492947 WP_FOCAL_ADHESIONPI3KAKTMTORSIGNALING_PATHWAY 278 0.326507177 1.324520451 0.019556714 0.227236181 GSEA, Gene Set Enrichment Analysis. Additional Declarations No competing interests reported. Supplementary Files TableS1necroptosisrelatedgenes.xlsx TableS2miRNAmiRNA.docx TableS3TFmRNA.xlsx TableS4drugmRNA.docx Cite Share Download PDF Status: Published Journal Publication published 11 Oct, 2023 Read the published version in BMC Women's Health → Version 1 posted Editorial decision: Major revision 30 Jun, 2023 Reviews received at journal 29 Jun, 2023 Reviewers agreed at journal 26 Jun, 2023 Reviews received at journal 25 Jun, 2023 Reviewers agreed at journal 15 Jun, 2023 Reviewers invited by journal 14 Jun, 2023 Editor assigned by journal 14 Jun, 2023 Editor invited by journal 02 Jun, 2023 Submission checks completed at journal 02 Jun, 2023 First submitted to journal 09 May, 2023 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. 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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-2913831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":206212885,"identity":"6331ecc3-a33d-4226-8d93-d31fafc42fc6","order_by":0,"name":"XueZhen Wang","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"XueZhen","middleName":"","lastName":"Wang","suffix":""},{"id":206212886,"identity":"76510e0a-0ca9-4128-94bd-ce29df4c25f4","order_by":1,"name":"Qin Zheng","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zheng","suffix":""},{"id":206212887,"identity":"fd136f5e-855f-45e0-9abb-d31959743867","order_by":2,"name":"Man Sun","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Sun","suffix":""},{"id":206212888,"identity":"13db2e22-b982-43eb-8c2f-ae6857b85bde","order_by":3,"name":"Huan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACCTB5AIgZGw58MLDh4edvIFoLc+PDGRVpMpIzDhCthb3ZmOfMYRuDhgT8OuRnNz97+IXhTmL/7MY2Cd628zwGDAcYP3zMwa2Fcc4xc2MZhmeJM+4cbJOQbLvNY87cwCw5cxtuLcwSCWbSEgyHExtuJLZJGAK1WDYcYGPmxaOFTSL9G1jLfJCWxLZzPAYHEvBr4ZHIMZP8ANSy4UZis8GBMwcIa5GQyCmTZmA4bLzxRmLjw4aKZB7JGQeb8fpFfkb6NskfDIdl591If3D4j4GdPT9/88EPH/FoAQcB7z8UPmMDfvUgJT8IKhkFo2AUjIIRDQC641jAQd0XEwAAAABJRU5ErkJggg==","orcid":"","institution":"Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Huan","middleName":"","lastName":"Zhang","suffix":""},{"id":206212889,"identity":"90ca3990-9224-4425-b7e4-bf2a42075028","order_by":4,"name":"WeiWei Ying","email":"","orcid":"","institution":"Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"WeiWei","middleName":"","lastName":"Ying","suffix":""}],"badges":[],"createdAt":"2023-05-10 01:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2913831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2913831/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12905-023-02668-7","type":"published","date":"2023-10-11T15:02:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":38095605,"identity":"484288e6-2241-40e7-95e4-70024109af37","added_by":"auto","created_at":"2023-06-06 13:55:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239243,"visible":true,"origin":"","legend":"\u003cp\u003eTechnology Roadmap\u003c/p\u003e","description":"","filename":"figure1600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/011e5379f2141bc18fb3160a.png"},{"id":38093579,"identity":"1042040d-8f5a-44d6-a1be-84e60a5e176d","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255480,"visible":true,"origin":"","legend":"\u003cp\u003ePresentation of results of standardized processing of the endometriosis dataset A–B. Endometriosis dataset GSE7105 is displayed in the data box before (A) and after (B) standardized treatment. C–D. Endometriosis dataset GSE1169 is displayed in the data box before (C) and after (D) standardized treatment.\u003c/p\u003e","description":"","filename":"figure2600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/e7800875f0a951519acad9fe.png"},{"id":38093583,"identity":"671e6ecb-2c75-45cf-86d1-3b34b4fbdf26","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":693641,"visible":true,"origin":"","legend":"\u003cp\u003eEndometriosis-related differentially expressed genes analysis\u003c/p\u003e\n\u003cp\u003e(A)–(B). Volcano map of differentially expressed genes analysis between Endometriosis (group: Endometriosis) and normal endometrial tissue (group: normal) in GSE7305 dataset (A) and GSE11691 dataset (B). (C). Venn diagram of differentially expressed genes in the GSE7305 dataset and GSE11691 dataset. (D). Venn diagram of co-DEGs and necroptosis-related genes in the dataset. E–F. Complex numerical heat maps of NRDEGs in GSE7305 dataset (E). GSE11691 dataset (F). Co-DEGs, common differentially expressed genes. NRDEGs, necroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure3600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/f94ce655c79335613e710fdc.png"},{"id":38095013,"identity":"4ced4784-9419-465d-a164-bcc975f4b1b7","added_by":"auto","created_at":"2023-06-06 13:47:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":798341,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment Analysis of NRDEGs (GO)\u003c/p\u003e\n\u003cp\u003e(A) . Bubble diagram of GO functional enrichment analysis results of NRDEGs. (B). Network diagram of GO functional enrichment analysis results of NRDEGs. In the circular network diagram (B), yellow dots represent specific genes, and blue circles represent specific pathways. (C). Chord plots of GO functional enrichment combined with logFC analysis results of NRDEGs. (D). Sankey diagram showing the results of GO functional enrichment analysis of NRDEGs. GO, Gene Ontology; BP, biological process; CC, cell component; MF, Molecular Function. The screening criteria for GO enrichment items were P value \u0026lt; 0.05 and FDR value (Q value) \u0026lt; 0.05. NRDEGs, necroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure4600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/4f8114df7bde94a84aca0f17.png"},{"id":38095011,"identity":"3daf8667-6a49-4c8b-9d41-4bc4dbcd7adb","added_by":"auto","created_at":"2023-06-06 13:47:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":493603,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA enrichment analysis of the endometriosis dataset\u003c/p\u003e\n\u003cp\u003e(A), Four main biological characteristics of GSEA enrichment analysis for the GSE7305 dataset. The differentially expressed genes in B-E. GSE7305 were significantly enriched in WP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY (B), WP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_SIGNALING (C),BIOCARTA_IL5_PATHWAY (D),KEGG_MAPK_SIGNALING_PATHWAY (E) and other pathways. F. Four main biological characteristics of GSEA analysis in the GSE11691 dataset. The differentially expressed genes in G-J. GSE11691 data set were significantly enriched in WP_PHOTODYNAMIC_THERAPYINDUCED_ NFKB_SURVIVAL_SIGNALI NG (G), WP_WNT_SIGNALING (H), WP_FOCAL_ADHESIONPI3KAKTMTO RSIGNALING_PATHWAY PID_IL8_CXCR2_PATHWAY (I), (J). The significant enrichment screening criteria for GSEA enrichment analysis were P \u0026lt; 0.05 and FDR value (Q value) \u0026lt; 0.25. GSEA, Gene Set Enrichment Analysis.\u003c/p\u003e","description":"","filename":"figure5600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/df287cdd3da1502d173f049f.png"},{"id":38095014,"identity":"bd1fe1fb-8c0b-45da-b3a2-57eccbdefb8a","added_by":"auto","created_at":"2023-06-06 13:47:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1128987,"visible":true,"origin":"","legend":"\u003cp\u003ePPI, TF-mRNA, mRNA-miRNA, and mRNA-Drug regulatory networks\u003c/p\u003e\n\u003cp\u003e(A) . NRDEGs PPI Network. (B). mRNA-miRNA regulatory network: blue oval is mRNA, and green diamond is miRNA. (C). mRNA-TF regulatory network: the blue rectangle is mRNA; the green oval is TF. (D). mRNA-drug regulatory network: the blue rectangle is mRNA; the pink diamond is a drug. TF, Transcription factor. PPI, protein-protein interaction; NRDEGs, necroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure6600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/6084863f01ebc33beb7d1829.png"},{"id":38093591,"identity":"37c14640-2fd2-492b-8760-45a662ab9113","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":501253,"visible":true,"origin":"","legend":"\u003cp\u003eExpression differential analysis, chromosomal localization analysis, and functional similarity analysis of NRDEGs were demonstrated\u003c/p\u003e\n\u003cp\u003e(A)–(B). Group comparison of NRDEGs expression differential analysis results in dataset GSE7305 (A) and dataset GSE11691 (B). (C). Display of chromosome localization results of NRDEGs. (D)–(E). Functional similarity analysis results of NRDEGs are shown in the bar chart (D) and rain-cloud chart (E). * represents P value \u0026lt; 0.05, which is statistically significant; ** represents P value \u0026lt; 0.01, which is highly statistically significant; *** represents P value \u0026lt; 0.001, which is highly statistically significant. NRDEGs, necroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure7600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/d495c7711a97ad2a6a528b87.png"},{"id":38093582,"identity":"7dd04d34-a5be-4fcb-be89-0c29463b9a84","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":430023,"visible":true,"origin":"","legend":"\u003cp\u003eEndometriosis analysis of immune infiltration in GSE7305 and GSE11691 datasets（CIBERSORTx）\u003cstrong\u003e(\u003c/strong\u003eA)–(B). Results of 22 types of immune cell infiltration in the GSE7305 dataset are shown in the bar chart (A) and the group comparison chart (B). (C)–(D). Results of 22 immune cell infiltration in the GSE11691 data set are shown in the bar chart (C) and the group comparison chart (D). The symbol NS means P ≥0.05, which is not statistically significant. The symbol * is equivalent to P \u0026lt; 0.05, which is statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant; The symbol *** is equivalent to P \u0026lt; 0.001, which is highly statistically significant.\u003c/p\u003e","description":"","filename":"figure8600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/4bd4e98ff8e540c1d688fdc7.png"},{"id":38095016,"identity":"05bf2b71-3602-45ff-839b-47eb8d55e233","added_by":"auto","created_at":"2023-06-06 13:47:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":532122,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of correlation presents immune infiltration results of GSE7305 and GSE11691 data sets and correlation analysis between two kinds of immune cells and NRDEGs \u003cstrong\u003e(\u003c/strong\u003eA)–(B). Heat map showing the correlation of 22 immune cell infiltration results in GSE7305 dataset (A) and GSE11691 dataset (B); (C)–(D). Correlation analysis between immune cells activated Nk. cells and NRDEGs in GSE7305 (C) and GSE11691 (D) data sets; (E)–(F). M2 Macrophages show the correlation between NRDEGs and GSE7305 (E) and GSE11691 (F). The Y-axis of the lollipop figure represents the specific gene, and the X-axis represents the correlation size. The circle size in the lollipop graph represents the correlation degree; the higher the degree of correlation, the larger the circle, and the different colors of the circle represent the P value obtained by the statistical correlation method. The higher the bar (distance from 0), the higher the degree of correlation (positive numbers mean positive correlations, negative numbers mean negative correlations). The symbol * is equivalent to P \u0026lt; 0.05, statistically significant; The symbol ** is equivalent to P \u0026lt; 0.01, which is highly statistically significant. NRDEGs, necroptosis-related differentially expressed genes.\u003c/p\u003e","description":"","filename":"figure9600dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/930db559dc1ecb24e927fd49.png"},{"id":44699929,"identity":"c325e8b9-863f-4b04-9739-f45aa98003c2","added_by":"auto","created_at":"2023-10-16 15:09:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3653555,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/fcf1304a-9822-4a5d-b961-4073e64a0487.pdf"},{"id":38093588,"identity":"64fb0024-dd2e-4d30-810d-4b99b30e3708","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":17947,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1necroptosisrelatedgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/133743dfd25a46cd852c4fa4.xlsx"},{"id":38095606,"identity":"6864575c-4d28-4c91-9991-598611dd745c","added_by":"auto","created_at":"2023-06-06 13:55:09","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":13621,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2miRNAmiRNA.docx","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/5dac43a18ea8c3f6fcd4fa85.docx"},{"id":38095607,"identity":"68155a6b-fb8f-4723-aace-5cad7f4ed8fb","added_by":"auto","created_at":"2023-06-06 13:55:09","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":13352,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3TFmRNA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/9508d577da74f56f6f5dc711.xlsx"},{"id":38093586,"identity":"a26a39b3-a482-4ed3-a333-999b99615c87","added_by":"auto","created_at":"2023-06-06 13:39:09","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":13638,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4drugmRNA.docx","url":"https://assets-eu.researchsquare.com/files/rs-2913831/v1/83beccdfeec0f1216c5d6a0f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis occurs when endometrial glands and stroma appear outside the uterine cavity.\u0026nbsp;(1)\u0026nbsp;The predicted prevalence of this disease at reproductive age is 10%.\u0026nbsp;(2)\u0026nbsp;Endometriosis reduces women\u0026apos;s health-related quality of life (HRQOL), resulting in impairments in physical functioning, diminished social life, difficulties in intimate relationships, and decreased productivity. The endometriosis etiology is complex, involving multiple genetic and environmental risk factors.\u0026nbsp;Although the endometriosis pathogenesis is relatively unclear, it is largely believed to be caused by retrograde menstruation leading to exfoliated endometrium implantation. However, there are limited studies on endometriosis development and endometrial-peritoneal attachments and invasions. Endometriosis remains difficult to diagnose in the biomarkers\u0026apos; absence for detecting or ruling out endometriosis.\u0026nbsp;(3)\u0026nbsp;Biomarkers and novel therapies that target the diverse physiological mechanisms associated with the onset, progression, and persistence of endometriosis symptoms are urgently required.\u0026nbsp;(4)\u003c/p\u003e\u003cp\u003eNecroptosis, also known as necroptosis, is a receptor-interacting serine/threonine protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-containing pseudo kinase (MLKL), but not Caspase-1 dependent.\u0026nbsp;(5)\u0026nbsp;Necroptosis has been implicated as a critical cell death pathway in cancers, Alzheimer\u0026apos;s, other neurodegenerative diseases, and virus-infected cells.\u0026nbsp;(6)\u0026nbsp;Some studies have identified necroptotic modulators as possible prognostic biomarkers for cancer and certain diseases.\u0026nbsp;(7, 8)\u0026nbsp;Day et al.\u0026nbsp;(9)\u0026nbsp;found that BMI1 in ovarian cancer can participate in the PINK1-Park2-dependent mitochondrial pathway and induce a new type of non-apoptosis cell death mediated by necroptosis.\u0026nbsp;The endometriosis severity is related to the apoptosis role, which usually destroys ectopic and heterotopic endometrial cells before forming necrotic tissue, thus inhibiting their migration and accumulation.\u0026nbsp;(10, 11)\u0026nbsp;Moreover, apoptotic mechanisms in the cytoplasm and cellular inflammasomes can further interact with ER\u0026beta;-induced immune surveillance. However, necroptosis\u0026apos;s correct mechanism and function in the endometriosis progression remains unclear.\u003c/p\u003e\u003cp\u003eThere is growing evidence that the immune system is vital in the pathophysiology and symptoms of EM. Immune cells such as natural killer (NK), macrophages, neutrophils, and CD4 T-helper cells are dysregulated in women with EM.\u0026nbsp;(12, 13)\u0026nbsp;Immune-related mechanisms have been described to be involved in the pathophysiology and symptomatology of EMS by contributing to the survival and persistence of endometriosis lesions.\u0026nbsp;(14)\u0026nbsp;Immune dysfunction is associated with implantation, proliferation, and apoptosis of ectopic endometrium.\u0026nbsp;In women with endometriosis, however, it is unclear which subtypes of immune cells are present in ectopic endometrium. Analyzing the relationship between necroptosis-related and immune infiltration may help explore unknown mechanisms. Recently,\u0026nbsp;in a meta-analysis of transcriptomes using the xCell algorithm, immune profiles in eutopic endometriosis and stages I\u0026ndash;II, III\u0026ndash;IV endometriosis were significantly different regardless of the hormone.\u0026nbsp;(12)\u0026nbsp;Therefore, exploring immune mechanisms in endometriosis is key to elucidating their role in endometriosis pathogenesis and generating unique insights for developing preventive and therapeutic strategies, innovative non-invasive diagnostic methods, and targeted therapies.\u003c/p\u003e\u003cp\u003eThis study explored potential biomarkers of endometriosis and its biological effects in the pathogenesis of endometriosis. We used the gene expression datasets GSE11691 and GSE7305 associated with normal and ectopic endometrium, which were extracted from the Gene Omnibus (GEO) database. Differential genes were screened and intersected with necroptosis-related genes. Subsequently, the immune microenvironment was compared between endometriosis patients and controls using CIBERSORT, and the immune cell association was calculated with NRDEGs for the first time. We performed a bioinformatics analysis of endometriosis to elucidate the endometriosis pathogenesis further.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Downloading Data\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe downloaded the endometriosis-related datasets GSE7305\u0026nbsp;(15)and GSE1169\u0026nbsp;(15)\u0026nbsp;from the GEO database\u0026nbsp;(16)\u0026nbsp;through the R package GEOquery.\u0026nbsp;(17)\u0026nbsp;The GSE7305 dataset, which comes from Homo Sapiens and the data platform is GPL570, contains 20 samples, including 10 endometriosis and 10 normal samples. Moreover, the GSE11691 dataset, from Homo Sapiens and data platform GPL96, contains 18 samples, nine of which are endometriosis and nine normal. All samples from the two datasets were included in this study.\u003c/p\u003e\u003cp\u003eThe GeneCards database\u0026nbsp;(18)\u0026nbsp;provides comprehensive information about human genes. \u0026nbsp;Necroptosis-related genes were obtained using the word \u0026quot;necroptosis\u0026quot; as the search keyword in the GeneCards database and GSEA database.\u0026nbsp;(19)\u0026nbsp;A total of 630 necroptosis-related genes were obtained after merging and de-duplication, as shown in Table S1.\u003c/p\u003e\u003cp\u003e1.2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Analysis of differentially expressed genes associated with endometriosis\u003c/p\u003e\u003cp\u003eTo identify possible mechanisms and pathways associated with differential gene expression in endometriosis, R package limma was used to standardize data sets GSE7305 and GSE11691, and the expression profile data after processing were analyzed differently. DEGs between different subgroups were obtained from two endometriosis data sets, |logFC| \u0026gt; 0.5 and P.adj \u0026lt; 0.05, used as standards to further screen out DEGs involved in this study. Among them, the genes with logFC \u0026gt; 0.5 and p.adj \u0026lt; 0.05 were upregulated DEGs. Genes with logFC \u0026lt; -0.5 and p.adj \u0026lt; 0.05 were downregulated DEGs.\u003c/p\u003e\u003cp\u003eTo obtain the necroptosis-related differentially expressed genes (NRDEGs) of endometriosis, we first intersected all the differentially expressed genes with |logFC| \u0026gt; 0.5 and P.adj \u0026lt; 0.05 obtained from the differential analysis of dataset GSE7305 and dataset GSE11691 and plotted the Venn diagram to visualize the common differentially expressed genes of the dataset.\u0026nbsp;Venn diagrams were then used to visualize the co-DEGs intersection and necroptosis-related genes between the two datasets. The difference analysis results were displayed by volcano map using R package ggplot2, and heatmap was drawn using R package pheatmap.\u003c/p\u003e\u003cp\u003e1.3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Functional Enrichment Analysis (GO)\u003c/p\u003e\u003cp\u003eFor large-scale functional enrichment studies, Gene Ontology (GO)\u0026nbsp;(20)\u0026nbsp;analysis is a common method, including biological process (BP) and molecular function (MF), as well as cellular component (CC).\u0026nbsp;The R package clusterprofiler\u0026nbsp;(21)\u0026nbsp;was used for the GO analysis of NRDEGs.\u0026nbsp;To qualify for entry, the screening criteria were a P value of 0.05 and an FDR value (Q value) of 0.05, which was considered statistically significant. The P value correction method was Benjamini-Hochberg (BH).\u003c/p\u003e\u003cp\u003e1.4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u0026nbsp;(22)\u0026nbsp;evaluated the correlation between genes from a predefined Gene Set and phenotypes in the Gene Table to measure its phenotypic contribution. In this study, genes in the GSE7305 dataset (Table 2) and GSE11691 dataset (Table 3) were divided into high- and low-phenotypic correlation according to the ranking of phenotypic correlation degree. Then, R package clusterProfiler was used to enrich and analyze all DEGs in the two groups. Following are the parameters used in this GSEA: The seed was 2020, the number of calculations was 1000, the minimum number of genes in each gene set was 10, and the maximum number of genes was 500. The correction method of the P value was Benjamini-Hochberg (BH). Molecular Signatures Database (MSigDB)\u0026nbsp;(23)\u0026nbsp;Database provided the C2.cp.v7.2. symbols gene set, and the screening criteria for significant enrichment was P \u0026lt; 0.05 and FDR value (Q value) \u0026lt; 0.25.\u003c/p\u003e\u003cp\u003e1.5\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;PPI, mRNA-miRNA, mRNA-TF, mRNA-Drug interaction network\u003c/p\u003e\u003cp\u003eProtein-protein interaction (PPI) is composed of individual proteins through their interactions. The STRING database\u0026nbsp;(24)\u0026nbsp;searches for interactions between proteins that have been predicted and those that have been known. In this study, we used the STRING database to construct the protein-protein interaction network related to differentially expressed genes (minimum required interaction score: Low confidence (0.150). The PPI network model was visualized with Cytoscape\u0026nbsp;(25)\u0026nbsp;software (version 3.9.1).\u003c/p\u003e\u003cp\u003eWith the Starbase (Version 3.0) database\u0026nbsp;(26),\u0026nbsp;we searched for microRNA targets by analyzing experimental data generated by CLIP-seq and degradation groups, providing various visual interfaces for exploring microRNA targets. The database contains abundant miRNA-ncRNA, miRNA-mRNA, miRNA-RNA, and RNA-RNA data. miRDB database\u0026nbsp;(27)\u0026nbsp;is used for miRNA target gene prediction and functional annotation. We used the Starbase and miRDB databases to predict miRNAs interacting with key genes (mRNAs) and then took the intersection part of the results from the two databases to draw the mRNA-miRNA interaction network with Cytoscape software.\u003c/p\u003e\u003cp\u003eCHIPBase database\u0026nbsp;(28)\u0026nbsp;(version 2.0) (https://rna.sysu.edu.cn/chipbase/) from the DNA binding protein. ChIP-seq data identified thousands of combining base sequence matrices and binding sites; it also predicts millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. HTFtarget database\u0026nbsp;(29)\u0026nbsp;(http://bioinfo.life.hust.edu.cn/hTFtarget.) is a comprehensive database containing human TFs and their targets. We searched for TFs that bind to key genes through the CHIPBase and hTFtarget databases, extracted the intersection parts, and plotted the mRNA-TF interaction network with Cytoscape software.\u003c/p\u003e\u003cp\u003eWe also predicted the direct and indirect drug targets of NRDEGs through CTD (Comparative Toxicogenomics Database),\u0026nbsp;(30)\u0026nbsp;explored the interaction between NRDEGs and drugs, and used Cytoscape software to visualize the mRNA-drug interaction network.\u003c/p\u003e\u003cp\u003e1.6\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Expression differences, chromosomal localization, and functional similarity analysis of NRDEGs\u003c/p\u003e\u003cp\u003eWe analyzed NRDEG expression in endometriosis datasets GSE7305 and GSE11691. To analyze the NRDEGs localization in 24 pairs of chromosomes, we first used the UCSC database (http://genome.ucsc.edu/)\u0026nbsp;(31)\u0026nbsp;to determine the start and stop sequences of NRDEGs. \u0026nbsp;Subsequently, the R package RCircos\u0026nbsp;(32)\u0026nbsp;was used to draw the chromosome localization map. \u0026nbsp; GoSemSim\u0026nbsp;(33)\u0026nbsp;is an R software package for calculating semantic similarity between gene products, gene clusters and GO terms. To analyze the functional correlations among key genes, the R package GOSemSim was used to calculate the functional correlations of key genes.\u003c/p\u003e\u003cp\u003e1.7\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Analysis of immune infiltration\u003c/p\u003e\u003cp\u003eCIBERSORTx (34) is an immune infiltration analysis algorithm based on linear support vector regression to deconvolve the transcriptome expression matrix to estimate the composition and immune cell abundance in mixed cells. We uploaded data gene expression matrix to CIBERSORTx online website (https://cibersortx.stanford.edu/), combined with Homo sapiens gene matrix (Homo sapiens) characteristics, data screening immune cell enrichment score greater than zero. Finally, the specific results of the immune cell infiltration abundance matrix were obtained and demonstrated. The difference in the proportion of immune cells between endometriosis samples (group: endometriosis) and normal samples (group: normal) in the endometriosis dataset was calculated by the Wilcoxon test, and the P value \u0026lt; 0.05 was considered statistically different. The correlation of immune cells between different groups was calculated by Spearman and visualized by R package ggplot2. Then, we combined the gene expression matrix of the dataset to calculate the correlation between immune cells and NRDEGs and drew the correlation heatmap by R package pheatmap.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 \u0026nbsp; \u0026nbsp; \u0026nbsp; Technical Roadmap\u003c/p\u003e\n\u003cp\u003e2.2Analysis of endometriosis-related differentially expressed genes\u003c/p\u003e\n\u003cp\u003eUsing the limma package, we first normalized the expression profile data of the endometriosis datasets GSE7305 and GSE11691. The data distribution before and after standardized treatment was shown by box plot (Figures 2A\u0026ndash;D). We found that the data after standardized treatment tended to be consistent in expression level. To analyze the gene expression values in endometriosis data set samples (group: endometriosis) relative to normal samples (group: endometriosis), we used R package limma to analyze the differences between dataset GSE7305 and dataset GSE11691 and obtained the differentially expressed genes of the two data sets. The results were as follows: Data set GSE7305 got 20247 DEGs, in which 3480 genes meet the | logFC | \u0026gt; 0.5 and P.adj \u0026lt; 0.05 threshold. At this threshold, the number of highly expressed (low expression in the normal group, positive logFC, upregulated genes) in the endometriosis group was 1760, and the number of low expressed (high expression in the normal group, negative logFC, downregulated genes) in the endometriosis group was 1720. The volcano map was drawn based on the different analysis results of this dataset (Figure 3A). Data set GSE11691 got 12376 DEGs, 610 genes meet the | logFC | \u0026gt; 0.5 and P.adj \u0026lt; 0.05 threshold, under the threshold, the number of highly expressed (low expression in the normal group, positive logFC, upregulated genes) in the endometriosis group was 396. The number of low expressed (high expression in the normal group, negative logFC, downregulated genes) in the endometriosis group was 214. We drew a volcano map based on the differential analysis results on the GSE11691 dataset (Figure 3B).\u003c/p\u003e\n\u003cp\u003eTo obtain the NRDEGs, we intersected the DEGs from GSE7305 and GSE11691 with |logFC| \u0026gt; 0.5 and P.adj \u0026lt; 0.05, 330 common differentially expressed genes (co-DEGs) of the endometriosis dataset were obtained, and Venn diagram was drawn (Figure 3C). We also examined the intersection between co-DEGs and necroptosis-related genes using the endometriosis dataset. A total of 10 NRDEGs from the endometriosis data set were obtained, and a Venn diagram was drawn (Figure 3D), which were C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74. \u0026nbsp;According to the results obtained by the Venn diagram, the expression differences of 10 NRDEGs in the GSE7305 dataset (Figure 3E) and GSE11691 dataset (Figure 3F) among different sample groups were analyzed respectively, and the R package pheatmap was used to draw heatmap to show the analysis results (Figures 3E and F). The results showed that PKP3, GJB1, HOOK1, TUFM, and MYO6 were up-regulated genes (low expression in the normal group, positive logFC, yellow in the figure), while C7, AHR, GSN, CLEC7A, and CD74 were down-regulated genes (high expression in the normal group, blue in the figure, logFC is negative).\u003c/p\u003e\n\u003cp\u003e2.3Functional Enrichment Analysis (GO)\u003c/p\u003e\n\u003cp\u003eTo evaluate the biological process, molecular function, cell component, biological pathway, and endometriosis of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74), we first conducted GO (Gene Ontology) analysis for NRDEGs (Table1). The screening criteria for enrichment items were P Value \u0026lt; 0.05 and FDR value (Q value) \u0026lt; 0.05, which were considered statistically significant. The results showed that 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) were mainly enriched in biological processes like regulation of lymphocyte activation in endometriosis, response to alcohol, regulation of multi-organism process and cellular components (CC) such as endocytic vesicle, clathrin-coated endocytic vesicle, clathrin-coated vesicle membrane. It was also enriched in molecular function (MF), including MHC protein binding, actin binding, and actin filament binding. We demonstrated the results of GO functional enrichment analysis by bubble diagram (Figure 4A). Furthermore, GO genes\u0026apos; functional enrichment analysis results were also presented in the network diagram (Figure 4B). Subsequently, we conducted GO enrichment analysis on the 10 NRDEGs combined with logFC. Moreover, based on the enrichment analysis, we calculated each molecule\u0026apos;s corresponding Z score by the molecule\u0026apos;s logFC value in the differential analysis result of the provided molecule in the endometriosis data set. We presented the GO enrichment analysis results of combined logFC through a chord diagram (Figure 4C). The GO enrichment analysis results are displayed in the form of a Sankey diagram (Figure 3D), including BP, CC, MF (Biological Process, Cellular Component, Molecular Function), and their corresponding function or pathway ID (ID) and category ID (ID) including the relationship between the gene names (gene).2.4 Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eTo determine the impact of the expression levels of all genes related to endometriosis metabolism on the occurrence of endometriosis, we evaluated the gene expression profile and the biological processes involved in the GSE7305 dataset and GSE11691 dataset by GSEA (Gene Set Enrichment Analysis) enrichment analysis, respectively. Links between cellular components and the molecular functions they perform. P \u0026lt; 0.05 and FDR value (Q value) \u0026lt; 0.25 were considered as significant enrichment screening criteria. The results showed that differentially expressed genes in dataset GSE7305 were significantly enriched in IL1 and megakaryocytes in obesity (Figure 5B), photodynamic therapy-induced NFKB survival signaling (Figure 5C), IL5 pathway (Figure 5D), MAPK signaling pathway (Figure 5E) and other pathways (Figure 5A\u0026ndash;E, Table 2). However, differentially expressed genes in dataset GSE11691 were significantly enriched in photodynamic therapy-induced NFKB survival signaling (Figure 5G), Wnt signaling (Figure 5H), IL8 CXCR2 pathway (Figure 5I), focal adhesion PI3K-AKT mTOR signaling pathway (Figure 5J).\u003c/p\u003e\n\u003cp\u003e2.5 Construction of PPI, mRNA-miRNA, mRNA-TF, and mRNA-drug regulatory networks\u003c/p\u003e\n\u003cp\u003eFirst, protein-protein interaction analysis was conducted using the STRING database with a minimum required interaction score greater than 0.150. Low confidence (0.150) was used as the standard to construct a PPI network of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74). The interactions were visualized using Cytoscape software (Figure 6A). \u0026nbsp;There are only seven NRDEGs in the PPI interaction network, which are C7, AHR, TUFM, GSN, MYO6, CLEC7A, and CD74. Second, miRNAs related to NRDEGs were obtained from the StarBase and miRDB databases. To visualize the mRNA-miRNA regulatory network, Cytoscape was applied (Figure 6B), which contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) and 26 miRNA molecules. The specific names of miRNA molecules are shown in Table S2. Then, the TFs combined with NRDEGs were obtained by ChIPBase and hTFtarget databases. With Cytoscape software, we structured and visualized a network of mRNA-TF interactions (Figure 6C). It contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) and 100 transcription factors. The specific TF molecule names are shown in Table S3. Finally, the CTD database was used to identify potential drugs or molecular compounds of NRDEGs. The mRNA-drug network was constructed and visualized by Cytoscape software, which contained 10 mRNA key genes (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, LEC7A, and CD74) and 41 drugs or molecular compounds. The names of specific drugs or molecular compounds are shown in Table S4.\u003c/p\u003e\n\u003cp\u003e2.6 Expression differences, chromosomal localization, and functional similarity analysis of NRDEGs\u003c/p\u003e\n\u003cp\u003eTo further verify the expression difference of NRDEGs in the endometriosis data set, 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, CD74) were shown by group comparison (Figures 7A and B). CD74 expression analysis was performed for GSE7305 and GSE11691 in the endometriosis and normal groups. The difference results of dataset GSE7305 (Figure 7A) showed that all NRDEGs were statistically significant: The expression levels of C7, HOOK1, PKP3, AHR, GJB1, and MYO6 in different groups of endometriosis dataset GSE7305 were statistically significant (P value \u0026lt; 0.001). There was a highly statistically significant difference in the levels of expression of TUFM, GSN, and CLEC7A between groups (P value \u0026lt; 0.01). The expression of CD74 in different groups was statistically significant (P value \u0026lt; 0.05). The difference results of dataset GSE11691 (Figure 7B) showed that all NRDEGs are statistically significant: AHR, TUFM, and MYO6 expression levels in different groups of datasets GSE76885 were statistically significant (P value \u0026lt; 0.001). The expressions of HOOK1, GJB1, CLEC7A, and CD74 in different groups were highly statistically significant (P value \u0026lt; 0.01). The expression levels of C7, PKP3, and GSN in different groups were statistically significant (P value \u0026lt; 0.05). Then we mapped the chromosomal location of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) (Figure 7C). The results showed that gene C7 and gene CD74 were placed on chromosome 5, the HOOK1 gene was placed on chromosome 1, the MYO6 gene was placed on chromosome 6, the AHR gene was placed on chromosome 7, GSN gene was placed on chromosome 9, and PKP3 gene was placed on chromosome 11. The CLEC7A gene was placed on chromosome 12, and the TUFM gene was placed on chromosome 12.\u0026nbsp;Based on the scores, Friends analyzes genes involved in endometriosis lesions and displays them as bar graphs (Figure 7D) and rain-cloud graphs (Figure 7E). The results showed that PKP3, GSN, MYO6, CLEC7A, and CD74 played important roles in this process.\u003c/p\u003e\n\u003cp\u003e2.7 Immune infiltration analysis\u003c/p\u003e\n\u003cp\u003eWe sorted out the expression profile data of GSE7305 and GSE11691 in the endometriosis dataset and uploaded it to CIBERSORTx online website and used the CIBERSORTx algorithm to calculate 22 immune cells and endometriosis samples in the endometriosis dataset (group: endometriosis) and the expression profile data of normal samples (group: normal). According to immune infiltration analysis results, we plotted the immune cell infiltration of each sample of 22 kinds of immune cells in the GSE7305 and GSE11691 datasets in bar graphs (Figures 8A and C). We also presented group comparison maps to illustrate the correlation between immune cell infiltration abundance and different groups in GSE7305 and GSE11691 datasets (Figures 8B and D). We demonstrated the correlation between the abundance of six immune cell infiltrates in the GSE7305 and GSE11691 datasets using correlation heat maps (Figures 9A and B). The results showed that after excluding the immune cells that had no difference analysis significance after grouping, there were statistically significant differences in the infiltration abundance of four types of immune cells in data set GSE7305 (Figure 8B) and the correlation between samples in different groups (P \u0026lt; 0.05). These four immune cells were: rest memory CD4+ T cells, follicular helper T cells, activated NK cells, and M2 Macrophages. In data set GSE11691 (Figure 8D), there were five types of immune cells, and the correlation between the infiltration abundance and samples in different groups was statistically different (P \u0026lt; 0.05). These five immune cells were: plasma cells, gamma-delta T cells, resting NK cells, activated NK cells, and M2 Macrophages. Activated NK cells and M2 Macrophages were statistically significant in both datasets. To analyze the correlation between the expression levels of 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) in endometriosis datasets GSE7305, GSE11691 with infiltration abundance of two immune cells (activated NK cells and M2 macrophages). We showed the infiltration abundance of two immune cells (activated NK cells and M2 macrophages) and 10 NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) by lollipop figure. (Figures 9C\u0026ndash;F). As can be seen from the figure, the corresponding gene MYO6 has high correlation and consistency in the two datasets (r = 0.708 in GSE7305, P \u0026lt; 0.001; GSE11691 r = 0.686, P \u0026lt; 0.001); M2 Macrophages has higher correlation and consistency in the two datasets for the corresponding genes HOOK1 (r = -0.760 in GSE7305, P \u0026lt; 0.001; r = -0.726 in GSE11691, P \u0026lt; 0.001), GJB1 (in GSE7305) r = -0.679, P \u0026lt; 0.001; r = -0.626, P \u0026lt; 0.01 in GSE11691), MYO6 (r =-0.780, P \u0026lt; 0.001 in GSE7305; r = -0.633, P \u0026lt; 0.01 in GSE11691).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost gynecologists detect ovarian endometriosis with sonography, the most common form of endometriosis.\u0026nbsp;(35)\u0026nbsp;In recent years, microarray and high-throughput sequencing technologies have enabled bioinformatics analysis of endometriosis. However, most studies are now based on invasive methods and single arrays, resulting in poor acceptance and a lack of cohorts for multiple combined studies. Its goal is to discover new diagnostic methods and safe treatments for endometriosis by exploring its biological mechanisms and searching for meaningful molecular markers. Therefore, we analyzed patients with and without endometriosis and performed enrichment analysis of necroptosis-related genes to determine their role in endometriosis.\u0026nbsp;\u003c/p\u003e\u003cp\u003eDisease onset and progression are associated with necroptosis, according to increasing research. Necroptosis, a programmed necrotic cell death, is vital in the host\u0026apos;s defense against certain pathogen incursions. Inflammatory diseases also result from necroptosis deregulation.\u0026nbsp;(5)\u0026nbsp;However, its function in the immune response to endometriosis remains elusive. In this study,\u0026nbsp;there were 330 DEGs between 19 endometriosis samples and 19 normal samples in two expression profile datasets (GSE7305 and GSE1169). We intersected the common differentially expressed genes (co-DEGs) and necroptosis-related genes in two endometriosis data sets to obtain 10 NRDEGs from endometriosis data sets. The 10 NRDEGs are C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74. Among them, PKP3, whose protein belongs to the plakophilin family, promotes tissue integrity. The plakophilins link desmosomal cadherins to intermediate filaments at desmosome junctions, and in common with other catenins, they perform additional functions, including in the nucleus. (16)\u0026nbsp;Gene GJB1 encodes the transmembrane channel protein connexin 32 (Cx32), a member of the Cxs family.\u0026nbsp;(36)\u0026nbsp;Previous studies have shown that GJB1 exerts anti-apoptotic and pro-tumor effects by interacting with.\u0026nbsp;(36, 37)\u0026nbsp;HOOK1, encodes a member of the hook family of coiled-coil proteins that bind to microtubules and organelles via their N-and C-terminal domains, respectively. In our study, HOOK1 expression was upregulated. Notably, the N-segment of Hook1 has a cytoskeletal protein binding site involved in cell migration and intracellular vesicle trafficking.\u0026nbsp;(38)\u0026nbsp;The mitochondrial translation elongation factor is encoded by the TUFM gene. Chang-Yong Cho et al. suggest that TUFM may be important in CASP8 inhibition via autophagy activation.\u0026nbsp;(39)\u0026nbsp;Actin-based myosins (MYO6) move cargo towards the minus ends of actin filaments using their actin-based motor proteins. Since it is the only myosin with this directionality, it is vital in many biological processes.\u0026nbsp;(40)\u0026nbsp;MYO6 is involved in various physiological processes\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e, and its expression has been reported to be increased in humans and mice for different diseases, including cancer and hearing loss.\u0026nbsp;(41, 42)\u0026nbsp;The enrichment analysis included GO terms for functional enrichment and GSEA enrichment. In addition to being enriched in immune response and activation, these genes were significantly associated with immune-cell interaction.\u0026nbsp;\u003c/p\u003e\u003cp\u003eIn recent decades, accumulating evidence has demonstrated an immune imbalance in endometriosis.\u0026nbsp;(43)\u0026nbsp;Compared to normal endometrium, endometriosis patients have more immune cells, particularly NK and M2 cells. However, the immune cell activation pattern in endometriosis remains unclear. The role of immune cell infiltration in endometriosis needs to be further investigated; we performed a comprehensive evaluation of immune infiltration in endometriosis using CIBERSORT.\u0026nbsp;Our study showed the correlation between the infiltration abundance of NK cells and M2 macrophage immune cells. Moreover, genes were statistically different in datasets GSE7305 and GSE1169. Several previous studies have reported the downregulation of NK cells in endometriosis patients; our results are consistent with theirs.\u0026nbsp;(44, 45)\u0026nbsp;Lagana AS et al.\u0026nbsp;(46)\u0026nbsp;found that the number of M1 and M2 macrophages was significantly higher in the endometriosis group than in controls, regardless of stage. Moreover, M2 macrophages might inhibit the immunological response of NK cells. Reduced NK cell number and function results in reduced cytotoxicity and reduced elimination of ectopic endometrial cells.\u0026nbsp;(47, 48)\u0026nbsp;In our study, the relative gene of NK cells activated was MYO6 and that of macrophages. M2 was HOOK1 in the two datasets. MYO6 and HOOK1 serve a limited impact on the immune system in endometriosis. A macromolecular antigen, ovalbumin, showed high permeability to Rmc monolayers lacking myo6. It still induced strong T-cell activation since it retained antigenicity. In a study by Yu-wei Liao et al.\u0026nbsp;(49),\u0026nbsp;MyO6-deficient RMC monolayers demonstrated high permeability, retaining the ovalbumin antigenicity, thereby activating T cells. Our findings suggest that MYO6 and HOOK1 are associated with immune infiltration in endometriosis and can be used as novel potential biomarkers and predictors of immune cell infiltration in endometriosis.\u003c/p\u003e\u003cp\u003eNecroptosis represents the newly discovered immunogenic cell death (ICD) forms.\u0026nbsp;There is evidence that necroptosis modulates the immune system, primarily composed of natural killer (NK) cells, macrophages, dendritic cells (DC), and T and B lymphocytes.\u0026nbsp;(50)\u0026nbsp;To investigate whether MYO6 and HOOK1 contribute to the infiltration of immune cells, we explored the correlation between those two factors. It appears that necroptosis and the immune response are interconnected in endometriosis, as indicated by their significant association with immune cells. Molecular mechanisms underlying the complex interactions between these genes and immune cells must be elucidated in further studies.\u0026nbsp;\u003c/p\u003e\u003cp\u003eIn this study, we obtained 26 miRNAs associated with NRDEGs from the StarBase and miRDB databases.\u0026nbsp;In endometriosis, miRNAs are associated with genetic, epigenetic, and angiogenic factors, hormones, cytokines, chemokines, oxidative stress (OS) markers, inflammation mediators, hypoxia, angiogenesis, and altered immune system, which contribute to its pathogenesis.\u0026nbsp;(51)\u0026nbsp;A total of 100 Transcription factors (TFS) combined with NRDEGs were obtained from the ChIPBase and hTFtarget databases. It has been demonstrated in previous studies that microRNA miR-106a-5p inhibits the proliferation, migration, and invasion of ectopic endometrial stromal cells by targeting the forkhead box transcription factor FOXC1 via the PI3K/Akt/mTOR signaling pathway.\u0026nbsp;(52)\u0026nbsp;Due to the lack of effective therapeutic drugs for EMS,\u0026nbsp;(53)\u0026nbsp;based on the CTD database, 41 molecular compounds, and drugs that are potentially effective against EM were identified by NRDEGs.\u003c/p\u003e\u003cp\u003eInevitably, our study has some limitations. First,\u0026nbsp;we conducted a comprehensive bioinformatics analysis to identify the association between NRDEGs and endometriosis. We need to conduct further\u0026nbsp;\u003cem\u003ein vitro\u003c/em\u003e and\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e studies to validate necroptotic-related genes\u0026apos; role in endometriosis and to gain a deeper understanding of its pathogenesis. Second, the study was retrospective; therefore, important clinical information could not be obtained. Third, the specimens in this study were from endometriotic tissue; therefore, the biomarkers could not be used to diagnose the early stage of the disease, and more research on blood biomarkers is required.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTen NRDEGs (C7, HOOK1, PKP3, AHR, TUFM, GJB1, GSN, MYO6, CLEC7A, and CD74) may serve as diagnostic biomarkers for endometriosis have been found for the first time. MYO6 and HOOK1 can be used as potential biomarkers for endometriosis. A strong association was also found between two selected genes and immune cell infiltration to explore endometriosis\u0026apos; pathogenesis, which could provide a rationale for future treatment. These findings increase our knowledge of necroptosis genes in EMS patients. However, the role of these necroptosis genes in EMS needs to be validated in the future.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XZW, QZ, MS, HZ and WWY. The first draft of the manuscript was written by XZW and all authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSUPPLEMENTARY MATERIAL\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe Supplementary Material for this article can be found online\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Vigano P. Endometriosis. 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Methods Mol Biol. 2020;2117:207-15.\u003c/li\u003e\n\u003cli\u003eNewman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773-82.\u003c/li\u003e\n\u003cli\u003eHayashi S, Nakamura T, Motooka Y, Ito F, Jiang L, Akatsuka S, et al. Novel ovarian endometriosis model causes infertility via iron-mediated oxidative stress in mice. Redox Biol. 2020;37:101726.\u003c/li\u003e\n\u003cli\u003eXiang Y, Wang Q, Guo Y, Ge H, Fu Y, Wang X, et al. Cx32 exerts anti-apoptotic and pro-tumor effects via the epidermal growth factor receptor pathway in hepatocellular carcinoma. J Exp Clin Cancer Res. 2019;38(1):145.\u003c/li\u003e\n\u003cli\u003eZhao Y, Lai Y, Ge H, Guo Y, Feng X, Song J, et al. Non-junctional Cx32 mediates anti-apoptotic and pro-tumor effects via epidermal growth factor receptor in human cervical cancer cells. Cell Death Dis. 2017;8(5):e2773.\u003c/li\u003e\n\u003cli\u003eFu MM, Holzbaur EL. Integrated regulation of motor-driven organelle transport by scaffolding proteins. Trends Cell Biol. 2014;24(10):564-74.\u003c/li\u003e\n\u003cli\u003eChoi CY, Vo MT, Nicholas J, Choi YB. Autophagy-competent mitochondrial translation elongation factor TUFM inhibits caspase-8-mediated apoptosis. Cell Death Differ. 2022;29(2):451-64.\u003c/li\u003e\n\u003cli\u003ede Jonge JJ, Batters C, O'Loughlin T, Arden SD, Buss F. The MYO6 interactome: selective motor-cargo complexes for diverse cellular processes. FEBS Lett. 2019;593(13):1494-507.\u003c/li\u003e\n\u003cli\u003eXue Y, Hu X, Wang D, Li D, Li Y, Wang F, et al. Gene editing in a Myo6 semi-dominant mouse model rescues auditory function. Mol Ther. 2022;30(1):105-18.\u003c/li\u003e\n\u003cli\u003eLuan Y, Li X, Luan Y, Zhao R, Li Y, Liu L, et al. Circulating lncRNA UCA1 Promotes Malignancy of Colorectal Cancer via the miR-143/MYO6 Axis. Mol Ther Nucleic Acids. 2020;19:790-803.\u003c/li\u003e\n\u003cli\u003eZou G, Wang J, Xu X, Xu P, Zhu L, Yu Q, et al. Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing. Cell Biosci. 2021;11(1):98.\u003c/li\u003e\n\u003cli\u003eSciezynska A, Komorowski M, Soszynska M, Malejczyk J. NK Cells as Potential Targets for Immunotherapy in Endometriosis. J Clin Med. 2019;8(9).\u003c/li\u003e\n\u003cli\u003eUshiwaka T, Yamamoto S, Yoshii C, Hashimoto S, Tsuzuki T, Taniguchi K, et al. Peritoneal natural killer cell chemotaxis is decreased in women with pelvic endometriosis. Am J Reprod Immunol. 2022;88(3):e13556.\u003c/li\u003e\n\u003cli\u003eLagana AS, Salmeri FM, Ban Frangez H, Ghezzi F, Vrtacnik-Bokal E, Granese R. Evaluation of M1 and M2 macrophages in ovarian endometriomas from women affected by endometriosis at different stages of the disease. Gynecol Endocrinol. 2020;36(5):441-4.\u003c/li\u003e\n\u003cli\u003eGuo SW, Du Y, Liu X. Platelet-derived TGF-beta1 mediates the down-modulation of NKG2D expression and may be responsible for impaired natural killer (NK) cytotoxicity in women with endometriosis. Hum Reprod. 2016;31(7):1462-74.\u003c/li\u003e\n\u003cli\u003eMa J, Zhang L, Zhan H, Mo Y, Ren Z, Shao A, et al. Single-cell transcriptomic analysis of endometriosis provides insights into fibroblast fates and immune cell heterogeneity. Cell Biosci. 2021;11(1):125.\u003c/li\u003e\n\u003cli\u003eMidgley AC, Rogers M, Hallett MB, Clayton A, Bowen T, Phillips AO, et al. Transforming growth factor-beta1 (TGF-beta1)-stimulated fibroblast to myofibroblast differentiation is mediated by hyaluronan (HA)-facilitated epidermal growth factor receptor (EGFR) and CD44 co-localization in lipid rafts. J Biol Chem. 2013;288(21):14824-38.\u003c/li\u003e\n\u003cli\u003eNiu X, Chen L, Li Y, Hu Z, He F. Ferroptosis, necroptosis, and pyroptosis in the tumor microenvironment: Perspectives for immunotherapy of SCLC. Semin Cancer Biol. 2022.\u003c/li\u003e\n\u003cli\u003eRaja MHR, Farooqui N, Zuberi N, Ashraf M, Azhar A, Baig R, et al. Endometriosis, infertility and MicroRNA's: A review. J Gynecol Obstet Hum Reprod. 2021;50(9):102157.\u003c/li\u003e\n\u003cli\u003eZhou X, Chen Z, Pei L, Sun J. MicroRNA miR-106a-5p targets forkhead box transcription factor FOXC1 to suppress the cell proliferation, migration, and invasion of ectopic endometrial stromal cells via the PI3K/Akt/mTOR signaling pathway. Bioengineered. 2021;12(1):2203-13.\u003c/li\u003e\n\u003cli\u003eFalcone T, Flyckt R. Clinical Management of Endometriosis. Obstet Gynecol. 2018;131(3):557-71.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cbr /\u003e \u0026nbsp;\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. GO enrichment analysis results of necroptosis-related differentially expressed genes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"646\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eOntology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0051249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eregulation of lymphocyte activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e4/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e485/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e8.34e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0097305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eresponse to alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e233/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e2.16e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0043900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eregulation of multi-organism process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e405/18670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0030139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eendocytic vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e3/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e303/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e3.98e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0045334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eclathrin-coated endocytic vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e2/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e63/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e4.45e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0030665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eclathrin-coated vesicle membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e2/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e115/19717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0042287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eMHC protein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e2/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e40/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e1.78e-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0003779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eactin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e3/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e431/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.901081916537867%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.837712519319938%\"\u003e\n \u003cp\u003eGO:0051015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.77434312210201%\"\u003e\n \u003cp\u003eactin filament binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.828438948995363%\"\u003e\n \u003cp\u003e2/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e198/17697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.591962905718702%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.664605873261205%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.737248840803709%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGO, Gene Ontology; BP, biological process; CC: cell component; MF, molecular function.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. GSEA analysis of dataset GSE7305.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003esetSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003eenrichmentScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eREACTOME_COMPLEMENT_CASCADE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.822144048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.431471926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001862197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eKEGG_COMPLEMENT_AND_COAGULATION_CASCADES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.762017649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.333823306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001841621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_HUMAN_COMPLEMENT_SYSTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.709058749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.296694499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001795332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_COMPLEMENT_AND_COAGULATION_CASCADES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.776932609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.293240512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001879699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_COMPLEMENT_ACTIVATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.883248685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.184014586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001984127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eREACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.845953791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.156923481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001964637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eKEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.739155934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.155146117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001855288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eBIOCARTA_COMP_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.900396432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.141644522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_TYROBP_CAUSAL_NETWORK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.705926715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.111012452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001821494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eBIOCARTA_LAIR_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.873581328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.077863261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_CELLS_AND_MOLECULES_INVOLVED_IN_LOCAL_ACUTE_INFLAMMATORY_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.873581328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e2.077863261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_IL1_AND_MEGAKARYOCYTES_IN_OBESITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.736409022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e1.875075348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.001972387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.028091787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eWP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.644242533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e1.754669341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.005859375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.048273954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eBIOCARTA_IL5_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.829191458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e1.693843996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.006012024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.048273954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.53451043338684%\"\u003e\n \u003cp\u003eKEGG_MAPK_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001605136436597%\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.187800963081862%\"\u003e\n \u003cp\u003e0.408313605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.544141252006421%\"\u003e\n \u003cp\u003e1.480258186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\"\u003e\n \u003cp\u003e0.005016722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13804173354735%\"\u003e\n \u003cp\u003e0.045404266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment Analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. GSEA analysis of dataset GSE11691.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003esetSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003eenrichmentScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eWP_TYROBP_CAUSAL_NETWORK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.701769525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.23878553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001658375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11042735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eREACTOME_SMOOTH_MUSCLE_CONTRACTION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.722865333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.195371633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001644737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11042735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eNABA_CORE_MATRISOME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.556594657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.179478164\u003c/p\u003e\n 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width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11042735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eREACTOME_ECM_PROTEOGLYCANS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.581720599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.98573096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001587302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.11042735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n 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width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.631225305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.875950683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003372681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.118114144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eWP_PHOTODYNAMIC_THERAPYINDUCED_NFKB_SURVIVAL_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.549039654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.662008258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.016583748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.217999152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eWP_WNT_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.41447078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.483747047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.017160686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.221492947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.324324324324323%\" valign=\"bottom\"\u003e\n \u003cp\u003eWP_FOCAL_ADHESIONPI3KAKTMTORSIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.954954954954955%\" valign=\"bottom\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.063063063063062%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.326507177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.711711711711711%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.324520451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.019556714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.135135135135135%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.227236181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGSEA, Gene Set Enrichment\u0026nbsp;Analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"endometriosis, necroptosis, CIBERSORT, immune microenvironment, activated NK cells, M2 macrophage, MYO6, HOOK1","lastPublishedDoi":"10.21203/rs.3.rs-2913831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2913831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEndometriosis (EMS) occurs when normal uterine tissue grows outside the uterus, causing chronic pelvic pain and infertility. Endometriosis-associated infertility is thought to be caused by unknown mechanisms. In this study, using necroptosis-related genes, we developed and validated multi-gene joint signatures to diagnose EMS and explored their biological roles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe downloaded two databases (GSE7305 and GSE1169) from the Gene Expression Omnibus (GEO) database and 630 necroptosis-related genes from the GeneCards database and the GSEA database. The limma package in R software was used to identify differentially expressed genes (DEGs). We interleaved common differentially expressed genes (co-DEGs) and necroptosis-related genes (NRDEGs) in the endometriosis dataset. The DEGs functions were reflected by gene ontology analysis (GO), pathway enrichment analysis, and gene set enrichment analysis (GSEA). We used CIBERSORT to analyze immune microenvironment differences between EMS patients and controls. Furthermore, a correlation was found between necroptosis-related differentially expressed genes and infiltrating immune cells to understand the molecular immune mechanism better.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCompared to the control group, this study revealed 10 NRDEGs in EMS. There are two types of immune cell infiltration abundance (activated NK cells and M2 macrophage) in these two datasets, and the correlation between different groups of samples is statistically significant (P\u0026lt;0.05). MYO6 has a high correlation with activated NK cells in two datasets consistently. HOOK1 has a high correlation with M2 Macrophages in two datasets consistently.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eWe identified 10 necroptosis-related genes in EMS and assessed their relationship to the immune microenvironment. MYO6 and HOOK1 may be novel biomarkers and treatment targets of the future.\u003c/p\u003e","manuscriptTitle":"Signatures of necroptosis-related genes as diagnostic markers of endometriosis and their correlation with immune infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-06 13:39:04","doi":"10.21203/rs.3.rs-2913831/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-06-30T06:02:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-29T12:58:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"e90c64a9-65ee-4867-a710-a8302cd6549b","date":"2023-06-26T06:51:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-25T13:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"08630242-7471-4b88-af6b-c076cdb360cb","date":"2023-06-15T12:23:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-14T17:32:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-14T14:39:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-02T08:59:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-02T08:58:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Women's Health","date":"2023-05-10T01:05:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-womens-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmwh","sideBox":"Learn more about [BMC Women's Health](http://bmcwomenshealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmwh/default.aspx","title":"BMC Women's Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e96070ea-165d-43d1-8059-f7b5e0383987","owner":[],"postedDate":"June 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T15:06:00+00:00","versionOfRecord":{"articleIdentity":"rs-2913831","link":"https://doi.org/10.1186/s12905-023-02668-7","journal":{"identity":"bmc-womens-health","isVorOnly":false,"title":"BMC Women's Health"},"publishedOn":"2023-10-11 15:02:36","publishedOnDateReadable":"October 11th, 2023"},"versionCreatedAt":"2023-06-06 13:39:04","video":"","vorDoi":"10.1186/s12905-023-02668-7","vorDoiUrl":"https://doi.org/10.1186/s12905-023-02668-7","workflowStages":[]},"version":"v1","identity":"rs-2913831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2913831","identity":"rs-2913831","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}