The analysis of the ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) using a metabolomics approach

The analysis of the ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) using a metabolomics approach | 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 The analysis of the ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) using a metabolomics approach Fang Zhang, Aijuan Yuan, Jia Bian, Minjie Liu, Chunhong Yan, Jie Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5817199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Recently, there has been a notable rise in the prevalence of intrauterine adhesions (IUA), exerting a substantial impact on female reproductive capacity. The phenomenon of ferroptosis is evident in IUA, yet the regulatory network associated with it remains unclear. Consequently, the objective of this study is to elucidate the metabolic regulatory network of ferroptosis in IUA through metabolomics, offering a fresh perspective for a more profound comprehension of the mechanisms underlying IUA. Concurrently, new active metabolites may emerge as potential targets for the prevention and treatment of IUA. Methods and Results : Uterine endometrial samples were collected from both healthy individuals and patients with IUA, with each endometrium pathologically confirmed. Liquid chromatography-mass spectrometry (LC-MS) was employed for sample analysis. A total of 6250 differential metabolites were identified, of which 102 were screened (VIP>1 and P<0.05). Among these, 29 showed upregulation, while 73 were downregulated. KEGG pathway analysis identified biological processes and metabolic pathways. Differentially regulated metabolic pathways included glucose metabolism, amino acid degradation, fatty acid metabolism, etc. Notably associated were pathways like AMPK signaling pathway, unsaturated fatty acid biosynthesis, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism. Joint pathway analysis identified six metabolite pathways (Glutathione metabolism, Arachidonic acid metabolism, Citrate cycle (TCA cycle), Lysine degradation, Glycerolipid metabolism, Cysteine and methionine metabolism) from the differentially expressed metabolites (DEMs). These pathways collectively constitute the Ferroptosis metabolic regulatory network in intrauterine adhesions (IUA). Conclusions: This study conducted a non-targeted metabolomics investigation on IUA . Taking the perspective of differential metabolites between normal and IUA, the study utilized metabolomics to reveal the metabolic regulatory network associated with iron death in IUA. ferroptosis in IUA is regulated by multiple metabolic pathways, including lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic pathways, by modulating the activity of key enzymes such as lipid peroxidase and glutathione peroxidase, impact the occurrence and progression of ferroptosis. The metabolic regulatory network of iron death in IUA is closely related to the occurrence of intrauterine adhesions (IUA). ferroptosis plays a crucial role in the pathological process of intrauterine adhesions by regulating the metabolic pathways of adhesive tissues, potentially contributing to the prevention and treatment of IUA. This research not only aids in a deeper understanding of the pathological mechanisms of IUA but also provides new targets and strategies for preventing and treating diseases related to IUA. intrauterine adhesions (IUA) ferroptosis metabolic regulatory network metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Intrauterine adhesion(IUA)is a condition where the uterine cavity becomes mutually adherent due to damage to the basal layer of the endometrium, resulting from uterine surgery or factors such as infection and radiation. It is a common uterine cavity disorder that significantly impacts the fertility [ 1 ] . The primary pathological change in IUA is the replacement of normal endometrial tissue by newly formed fibrous scar tissue after damage to the basal layer of the endometrium [ 2 ] . With the increasing prevalence of invasive uterine surgeries like induced abortion, fertility problems caused by uterine adhesions have become a focus of clinical research. For patients with moderate to severe IUA, the high recurrence rate after surgery, scarring of the uterine cavity, slow and asynchronous growth of the endometrium, and undesirable reproductive outcomes make uterine adhesion a global challenge, posing a serious threat to female reproductive health and function, thereby affecting family stability and societal harmony [ 3 ] . The formation of uterine adhesions has diverse causes, including uterine surgical procedures, infections, and genetic factors, with individual variations. Although various theories, such as neural reflex, trauma, and infection, exist regarding the mechanisms of IUA [ 4 – 6 ] , there is still no consensus on the exact pathogenesis. Currently, common methods for examining uterine adhesions include hysteroscopy, transvaginal ultrasound examination, hysterosalpingography, and uterotubal angiography. However, early prediction of the occurrence of uterine adhesions remains challenging. Given the limitations in the early prediction of uterine adhesions, researchers have been progressively exploring new methods for early diagnosis at the molecular level. The pathogenesis of IUA involves the imbalance of various molecular proteins related to adhesion formation9 [ 7 – 9 ] , such as transforming growth factor β1 (TGFβ1), hypoxia-inducible factor α (HIF1α), basic fibroblast growth factor (β-FGF), and platelet-derived growth factor (PDGF). Although these studies contribute to the diagnosis of uterine adhesions, they are insufficient for early prediction of the risk of uterine adhesion occurrence. In recent years, the concept of active metabolomics has broadened our previous understanding [ 10 ] : metabolomic analysis is no longer a simple biomarker identification tool but is considered a novel technology capable of exploring active driving factors in physiological and pathological processes. Metabolites not only serve as markers of the organism's phenotype but also influence the physiological function of cells by regulating other omics (genomics, epigenomics, transcriptomics, and proteomics). Metabolites are essential regulators of life activities, influencing protein function through various mechanisms such as epigenetic modifications, conformational regulation, and signal transduction, forming complex interaction networks [ 11 ] . Recent investigations have illuminated the intricate link between metabolism and fibrosis. Studies suggest that the metabolic reprogramming or dysregulation of cells plays a pivotal role in their profibrotic function, impacting conditions such as pulmonary fibrosis and other fibrotic diseasess [ 12 ] . The identification of metabolism-related subtypes has been associated with immune-metabolic interactions in idiopathic pulmonary fibrosis, unveiling a significant interplay between metabolism and immunity [ 13 ] . Recent studies have delved into immunometabolism changes in fibrosis, accentuating the cellular metabolic reprogramming experienced by immune cells during the development of various profibrotic diseases [ 14 ] . The evolving role of metabolism in fibrosis has become a rapidly expanding area of interest, implicating metabolism in multiple profibrotic conditions [ 15 ] . These findings collectively suggest a complex interplay between metabolism and fibrosis, providing insights into potential therapeutic avenues and a deeper molecular understanding. There is evidence suggesting an association between changes in intrauterine microbiota and its metabolic components in the context of intrauterine adhesions (IUAs) [ 16 ] . Metabolomics and pharmacodynamic analyses have revealed that IUA detrimentally affects the reproductive system and fertility, establishing it as a significant public health concern [ 17 ] . Furthermore, the exploration of endometrial stem cells underscores their role in treating IUAs, emphasizing their capacity to restore endometrial function [ 18 ] . Metabolic changes within the uterine environment, encompassing alterations in microbiota and the presence of scar tissues, contribute to the development and impact of IUAs. Ongoing research seeks to enhance our understanding of the intricate relationship between metabolism and the formation of adhesions in the uterus. In summary, employing a metabolomics approach, this research scrutinizes the composition of metabolites and metabolic pathways in IUAs. The ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) was analyzed, aiming to explore and identify effective biomarkers for uterine adhesion and provide new targets and strategies for the prevention and treatment of IUAs 2. Materials and Methods 2.1 Sample Collection and Pathological Diagnosis Endometrial samples were procured from patients with intrauterine adhesions (IUA) undergoing Gynecology surgery, following approved procedures before 2023.2.24. by the Ethics Committee Board of The Affiliated People’s Hospital of Ningbo University, Ningbo (NO.0402017). Statement: (i) identifying the Ethics Committee Board approving the experiments(The ethical approval letter was confirmed before the experiment); (ii) confirming that all experiments were performed in accordance with relevant guidelines and regulations.The study encompassed five patients with IUA lasting more than 1 year (Pathological biopsy confirmed the endometrial samples as IUA.). For comparison, six corresponding endometrial samples were obtained from healthy female donors undergoing routine gynecological examinations. The specimens were promptly snap-frozen and stored in liquid nitrogen. 2.2 Tissue Metabolomics Analysis 2.21 Sample Preparation A precisely weighed mg sample was transferred into a 1.5 mL Eppendorf tube. Two small steel balls were then added to the tube. Subsequently, µL of L-2-chlorophenylalanine (0.3 mg/mL), dissolved in methanol and serving as the internal standard, along with mL of a methanol and water mixture (4:1, vol/vol), were added to each sample. After storing the samples at -20 ℃ for 2 minutes, they were ground at 60 Hz for 2 minutes. The entire samples underwent extraction by ultrasonication for 10 minutes in an ice-water bath and were then stored at -20 ℃ for an additional 30 minutes. The resulting extracts were centrifuged at 4°C (13,000 rpm) for 10 minutes. µL of the supernatant was transferred to a glass vial and dried in a freeze concentration centrifugal dryer. Subsequently, 5 µL of a methanol and water mixture (1:4, vol/vol) was added to each sample. The samples were vortexed for 30 seconds, subjected to ultrasonication for 3 minutes in an ice-water bath, and then stored at -20°C for 2 hours. Following this, the samples were centrifuged at 4°C (13,000 rpm) for 10 minutes. The resulting supernatants (µL) from each tube were collected using crystal syringes, filtered through 0.22 µm microfilters, and transferred to LC vials. These vials were stored at -80°C until LC-MS analysis. Quality control (QC) samples were prepared by mixing aliquots of all the samples to create a pooled sample. 2.22 Tissue Metabolomics Analysis For tissue metabolomics analysis, we utilized liquid chromatography-mass spectrometry (LC-MS) employing the ACQUITY I-Class UPLC system, which was equipped with an ACQUITY UPLC HSS T3 column (Waters, Milford, USA), and the QE Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). 2.23 Data Preprocessing and Statistical Analysis The original LC-MS data underwent processing using Progenesis QI V2.3 software (Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. The key parameters employed were a 5 ppm precursor tolerance, 10 ppm product tolerance, and a 5% product ion threshold. Compound identification relied on the precise mass-to-charge ratio (m/z), secondary fragments, and isotopic distribution with reference to databases, including The Human Metabolome Database (HMDB), Lipidmaps (V2.3), Metlin, EMDB, PMDB, and self-established databases for qualitative analysis. Subsequently, the data underwent additional processing, involving the removal of peaks with missing values (ion intensity = 0) in more than 50% of groups, substitution of zero values with half of the minimum value, and screening based on the qualitative results of the compounds. Compounds with scores below 36 (out of 60) were considered inaccurate and excluded. A data matrix amalgamated from positive and negative ion data was introduced into R for Principle Component Analysis (PCA) to assess overall sample distribution and analysis process stability. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were employed for distinguishing metabolites differing between groups. To mitigate overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) iterations assessed model quality. Variable Importance of Projection (VIP) values derived from the OPLS-DA model ranked each variable's overall contribution to group discrimination. A two-tailed Student’s T-test verified the significance of group differences for selected differential metabolites, determined by VIP values greater than 1.0 and p-values less than 0.05. 2.24 Identification of differentially expressed metabolites (DEMs) By employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. The criteria for selection were VIP values of the first principal component in the OPLS-DA model > 1 and p-value of T-test < 0.05. Variable Importance in Projection (VIP) values from OPLS-DA analysis were used to measure the impact strength and explanatory power of the expression patterns of various metabolites on the classification and discrimination of samples between groups. This approach helped to identify differentially expressed metabolites with biological significance. Further validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was 0.5 were categorized as up-regulated, while those with log2FC<-0.5 were considered down-regulated. To visually represent the relationships between samples and the expression differences of metabolites among different samples, we performed hierarchical clustering on all significantly different metabolites. The color gradient from blue to red represents the expression abundance of metabolites from low to high, where a more intense red indicates a higher expression abundance of differentially expressed metabolites. 2.25 Metabolic Pathway KEGG Enrichment Analysis To elucidate the role of differentially expressed metabolites (DEMs) in intrauterine adhesions(IUA) patients and individuals in healthy female donors, the construction of the KEGG ( https://www.kegg.jp/ ) database aims to understand the functions and interactions of metabolites in biological systems. Information related to metabolic pathways can be queried. Conducting pathway enrichment analysis on differential metabolites helps to comprehend the mechanism of metabolic pathway alterations in differential samples. This is based on the KEGG database for the enrichment analysis of differential metabolites. pathway enrichment analysis is performed using the KEGG IDs of differential metabolites to obtain results of metabolic pathway enrichment. The hypergeometric test is applied to identify pathway entries significantly enriched in differentially expressed metabolites compared to the entire background. The formula for calculation is as follows: P ≤ 0.05, p − value ≤ 0.05 is set as the threshold, and pathways meeting this criterion are considered significantly enriched in differential metabolites. A smaller p-value indicates greater significance of the metabolic pathway difference. 2.3 Analysis of Ferroptosis-related metabolic regulatory network MetaboAnalyst currently supports metabolic pathway analysis (integrating pathway enrichment analysis and pathway topology analysis) and visual exploration for > 120 species. In addition, users can also perform joint pathway analysis by uploading both gene list together with the metabolite/peak list for ~ 25 common model organisms. Among DEMs and Ferroptosis-related genes ( http://www.zhounan.org/ferrdb/current/ ), Ferroptosis-related metabolic regulatory network was generated using an online tool ( https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml )). The result from the integration of Ferroptosis-related genes (official gene symbol) and targeted metabolomics data (HMDB ID) were visualized using joint-pathway analysis. 3. Results 3.1 Comparison of general characteristics between two groups There were no statistically significant differences in age, BMI, Full-term birth, Premature birth, Miscarriage, and Survival between the two groups (P > 0.05)(Table 1 ). Six endometrial samples from normal uterine cavities and five endometrial samples from patients with intrauterine adhesions were pathologically confirmed(Figure 1 A-K) Table 1 Comparison of general characteristics between two groups There were no statistically significant differences in age, BMI, Full-term birth, Premature birth, Miscarriage, and Survival between the two groups (P > 0.05). Healthy female donors IUA Patients T value P age 40.33 ± 9.50 38.60 ± 5.50 0.359 0.728 BMI 23.33 ± 4.18 23.35 ± 4.65 0.008 0.994 Full-term birth 0.83 ± 0.753 0.60 ± 0.548 0.576 0.579 Premature birth 0.00 ± 0.00 0.00 ± 0.00 0.631 0.544 Miscarriage 3.17 ± 2.137 2.20 ± 2.950 0.631 0.544 Survival 0.83 ± 0.753 0.60 ± 0.548 0.576 0.579 3.2 Overview of This Workflow(Fig. 2 ) The metabolomics workflow includes sample pre-treatment, metabolite extraction, LC-MS full-scan detection, data pre-processing, and statistical analysis. Utilizing non-targeted metabolomics with ultra-high-performance liquid chromatography-tandem mass spectrometry, in conjunction with the metabolomics data processing software Progenesis QI v2.3, qualitative and relative quantitative analyses are conducted on the raw data. Standardized pre-processing is applied to the original data. 3.3 QC Sample Quality Control and Qualitative and Quantitative Results of Data Preprocessing Through the quality control of QC samples, the experimental pre-processing, sample on-machine, and mass spectrometry system stability of this project are analyzed and evaluated. By screening the relative standard deviation (RSD) of QC samples and removing ion peaks with an RSD > 0.3 in the QC group. The PCA model、A Boxplot of the metabolite intensities and hierarchical clustering shows that the QC samples are closely clustered, indicating good instrument detection stability during the experiment (Fig. 3 A- 3 C). For the extracted data, ion peaks with more than 50% missing values within each group were removed, and zero values were replaced with half of the minimum value. Compounds were qualitatively screened based on the Score obtained from the qualitative results, with a screening criterion of 36 points (out of 60). Compounds scoring below 36 were considered qualitatively inaccurate and were removed. Finally, positive and negative ion data were merged into a single data matrix , which includes all analyzable information extracted from the original data and are presented in Table SI. Subsequent analyses were conducted based on this integrated matrix. 3.4 Multivariate Statistical Analysis and Univariate Statistical Analysis Multivariate statistical analysis will first employ unsupervised Principal Component Analysis (PCA) to observe the overall distribution among samples and the stability of the entire analysis process (Fig. 4 A). Subsequently, supervised Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) will be used to differentiate the overall differences in metabolic profiles among groups and identify differentially regulated metabolites between groups. The PLS-DA model provides better interpretation and prediction of differences between the two sample groups, representing improved predictive ability (Fig. 4 B). Significant differences between the two sample groups are evident on the OPLS-DA score plot (Fig. 4 C). Permutation plots are employed to assess overfitting (Fig. 4 D). Loading plots are utilized to identify the impact strength of metabolites on the comparative groups (Fig. 4 E). The S-plot, shows all points distributed in the first and third quadrants(Fig. 4 F). Further validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was < 0.05, and the gene expression fold change (FC) value was ≥ 1 or ≤ 1(|log2 FC| ≥0.5). Further validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was < 0.05, and the gene expression fold change (FC) value was ≥ 1 or ≤ 1(|log2 FC|≥0.5). 3.5 Identification of differentially expressed metabolites (DEMs) By employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. Among the identified 6250 metabolites, there are 102 differential metabolites (VIP value > 1, P < 0.05), including 29 upregulated and 73 downregulated, which are present in Table SII, compared to healthy female endometrial samples. Visualization was carried out using Volcano plots(Fig. 5 A) and heat map plots(Fig. 5 B). To visually represent the relationships between samples and the expression differences of metabolites among different samples, we performed hierarchical clustering on all significantly different metabolites(Fig. 5 C). 3.6 Metabolic Pathway Enrichment Analysis KEGG analysis indicates that the expression of differential metabolites is mainly associated with pathways such as unsaturated fatty acid biosynthesis, AMPK signaling pathway, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism(Fig. 6 A-B). From the perspective of metabolomics, it can be observed that after the occurrence of intrauterine adhesions, significant changes occur in the metabolites and metabolic pathways of the endometrium in patients with intrauterine adhesions. 3.7 Analysis of Ferroptosis-related metabolic regulatory network Applying the online tool ( https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml )), we identified some joint-pathways between Ferroptosis-related genes and DEMs from IUAs. These Ferroptosis-related genes and DEMs from IUAs were then used to contruct a Ferroptosis-related metabolic regulatory network to pinpoint the hub joint-pathways. Through the analysis of joint-pathway, we identified a Ferroptosis-related metabolic regulatory pathway. Six metabolites metabolism (Glutathione metabolism, Citrate cycle (TCA cycle), Glycerolipid metabolism, Lysine degradation, Arachidonic acid metabolism, Cysteine and methionine metabolism) among the DEMs were ultimately selected (P < 0.05) (Fig. 7 A-F). These hub joint-pathways related to six metabolites metabolism may play a crucial role in the occurrence and progression of USAs. 4. Discussion By selecting differential metabolites between the normal uterine cavity and the uterine cavity with adhesions, and exploring their patterns of change, we elucidate the relationship between differential metabolites and intrauterine adhesions. The results revealed 102 differential metabolites among the identified 6250 metabolites, including 29 upregulated and 73 downregulated. From the perspective of metabolomics, it can be observed that significant changes occur in the metabolites and metabolic pathways of the endometrium in patients with intrauterine adhesions. KEGG analysis suggested that the expression of differential metabolites is notably associated with pathways such as AMPK signaling pathway, unsaturated fatty acid biosynthesis, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism. We applied online tools ChemSpider and HMDB to analyze the basic characteristics of differential metabolites and found that DEMs’s functions involve cell cytoplasm development, cell movement and contraction, mucosal mucus secretion, metabolism, and signal transduction, among others. Notably, several metabolites and metabolic pathways related to ferroptosis attracted our attention. Ferroptosis is an oxidative stress-induced cell death mechanism caused by the accumulation of iron-dependent lipid peroxides [19] . Key factors triggering ferroptosis include the accumulation of intracellular iron and reactive oxygen species (ROS), depletion of glutathione, and disruption of lipid peroxide metabolism [20] . Iron overload and lipid peroxide accumulation are recognized as central events in ferroptosis [21] .The mechanisms related to ferroptosis play a crucial regulatory role in the progression of fibrotic diseases, which develop from the complex pathological process of continuous chronic injury to cells [22, 23] . After endometrial injury in patients with intrauterine adhesions, the normal repair mechanism is disrupted, replaced by excessive proliferation of fibrotic tissue, essentially a fibrotic process [24] , suggesting a potential association with ferroptosis. Increased lipid peroxidation, depletion of intracellular glutathione (GSH), accumulation of iron mediated by transferrin, and accumulation of free fatty acids can induce ferroptosis. Hyemin Lee et al. demonstrated the regulatory relationship between ferroptosis and AMP-activated protein kinase (AMPK) using inhibitors/inducers related to the ferroptosis pathway and establishing AMPK knockout cell lines [25] . Through metabolomics of the endometrial tissue in patients with intrauterine adhesions, KEGG analysis suggests the presence of the AMPK signaling pathway among differential metabolites, further confirming the correlation between IUA and ferroptosis from the perspective of signaling pathways. Ferroptosis-related metabolic regulatory networks were identified using an online tool, revealing six metabolite metabolism pathways—Glutathione metabolism, Citrate cycle (TCA cycle), Glycerolipid metabolism, Lysine degradation, Arachidonic acid metabolism, and Cysteine and methionine metabolism—potentially crucial in the occurrence and progression of USAs. Glutathione metabolism and ferroptosis are closely interconnected concepts in cellular biology and biochemistry [26] . Glutathione, composed of cysteine, glutamic acid, and glycine, plays pivotal roles in cellular processes, including antioxidant defense, detoxification, and redox balance maintenance [27] .Conversely, ferroptosis, characterized by iron-dependent lipid peroxidation, is distinct from other cell death modes, implicated in various physiological and pathological processes [26] .The nexus between glutathione metabolism and ferroptosis lies in regulating cellular redox balance, where glutathione counters oxidative stress, and its depletion triggers ferroptosis [28] .Understanding this interplay is crucial for unraveling cellular homeostasis mechanisms [19] . The Citric Acid Cycle, also known as the Tricarboxylic Acid (TCA) cycle or Krebs cycle, and ferroptosis, while distinct cellular processes, exhibit potential connections [29] .The TCA cycle, occurring within eukaryotic cell mitochondria, is vital for ATP generation through acetyl-CoA oxidation from various metabolic sources.Through enzymatic reactions, it releases carbon dioxide and produces reducing equivalents (NADH and FADH2), crucial for the electron transport chain and oxidative phosphorylation.The potential link between the TCA cycle and ferroptosis arises from reactive oxygen species (ROS) production during mitochondrial respiration [30] .While not directly implicated in ferroptosis, heightened mitochondrial activity associated with the TCA cycle can elevate ROS levels [26] .Uncontrolled ROS production, without efficient antioxidant systems, may contribute to lipid peroxidation and initiate ferroptosis [28] . Further research is essential to grasp comprehensively the interplay between these processes and their implications in diverse cellular contexts and disease states [19] Sensitivity to ferroptosis links to various biological processes, including iron metabolism, unsaturated fatty acids, glutathione, and phospholipid biosynthesis [31] .Polyunsaturated fatty acids (PUFAs), prone to oxidation, promote ferroptosis by impairing membrane function (ALOXs) [32] .Metabolomics analysis suggests a potential link between intrauterine adhesions and ferroptosis through unsaturated fatty acid biosynthesis (KEGG pathway analysis).Glycerolipid metabolism and ferroptosis are closely linked cellular processes, with lipid metabolism regulation, particularly polyunsaturated fatty acids (PUFAs) within glycerolipids, crucial in initiating and advancing ferroptosis. Glycerolipids, including triglycerides, phospholipids, and glycolipids, are vital for cellular membranes and fatty acid storage.The metabolism of glycerolipids involves synthesis and breakdown, with membrane lipid remodeling, particularly phospholipids, essential for membrane integrity and functionality [33] .The connection between glycerolipid metabolism and ferroptosis centers on lipid peroxidation's role in initiating and promoting ferroptotic cell death [34] .Polyunsaturated fatty acids (PUFAs), like arachidonic acid, are susceptible to oxidation, generating lipid peroxides that accumulate in cellular membranes, triggering ferroptosis [19] .Several key points highlight this connection: PUFA-Containing Phospholipids are primary substrates for lipid peroxidation reactions driving ferroptosis [26] .GPX4 Inhibition increases susceptibility to ferroptosis when inhibited, directly or indirectly [28] .Ferroptosis depends on iron, contributing to lipid peroxidation initiation, and glycerolipid metabolism can influence cellular iron availability and utilization [26] . Sensitivity to ferroptosis links to various biological processes, including iron metabolism, unsaturated fatty acids, glutathione, and phospholipid biosynthesis [31] .Polyunsaturated fatty acids (PUFAs), prone to oxidation, promote ferroptosis by impairing membrane function (ALOXs) [32] .Metabolomics analysis suggests a potential link between intrauterine adhesions and ferroptosis through unsaturated fatty acid biosynthesis (KEGG pathway analysis).Glycerolipid metabolism and ferroptosis are closely linked cellular processes, with lipid metabolism regulation, particularly polyunsaturated fatty acids (PUFAs) within glycerolipids, crucial in initiating and advancing ferroptosis. Glycerolipids, including triglycerides, phospholipids, and glycolipids, are vital for cellular membranes and fatty acid storage.The metabolism of glycerolipids involves synthesis and breakdown, with membrane lipid remodeling, particularly phospholipids, essential for membrane integrity and functionality [33] .The connection between glycerolipid metabolism and ferroptosis centers on lipid peroxidation's role in initiating and promoting ferroptotic cell death [34] .Polyunsaturated fatty acids (PUFAs), like arachidonic acid, are susceptible to oxidation, generating lipid peroxides that accumulate in cellular membranes, triggering ferroptosis [19] .Several key points highlight this connection: PUFA-Containing Phospholipids are primary substrates for lipid peroxidation reactions driving ferroptosis [26] .GPX4 Inhibition increases susceptibility to ferroptosis when inhibited, directly or indirectly [28] .Ferroptosis depends on iron, contributing to lipid peroxidation initiation, and glycerolipid metabolism can influence cellular iron availability and utilization [26] . Lysine degradation and ferroptosis, although distinct, show emerging evidence of a potential link, particularly in regulating cellular redox balance [26] .Lysine, essential for protein synthesis, undergoes degradation, potentially influencing ferroptosis by affecting cellular redox balance [35] .Research indicates that lysine degradation affects glutathione availability, impacting the cell's ability to counteract oxidative stress and lipid peroxidation—central processes in ferroptosis [28] ...Further research is crucial to fully unravel the molecular mechanisms underlying this connection [36] . Arachidonic acid metabolism, crucial in inflammation and signaling, influences cellular redox balance and ferroptosis susceptibility [37] .Recent studies highlight arachidonic acid metabolism's modulation of cellular environment susceptibility to ferroptosis, suggesting novel therapeutic strategies [28] .Ongoing research aims to uncover molecular mechanisms and crosstalk between lipid metabolism and ferroptosis, promising targeted interventions in pathogenic conditions [38] . Cysteine and methionine metabolism intricately influence ferroptosis, determining cellular susceptibility to ferroptotic cell death [26] .Cysteine, crucial for glutathione synthesis, is essential for cellular antioxidant defense against oxidative stress and lipid peroxidation [27] .Glutathione peroxidase 4 (GPX4) utilizes glutathione to prevent lipid peroxidation initiation, crucial in ferroptosis [28] .Methionine, a precursor for S-adenosylmethionine (SAM) in cellular methylation reactions, influences gene expression and redox status, impacting ferroptosis susceptibility [39] .The interplay between cysteine, methionine metabolism, and ferroptosis involves glutathione depletion, SAM's role in methylation reactions, disruption of the transsulfuration pathway affecting glutathione synthesis, and GPX4 regulation [32] . In summary, Starting from the perspective of differential metabolites associated with ferroptosis, this study elucidates the role of metabolites in the occurrence and development of intrauterine adhesions (IUA). It has preliminarily established an Ferroptosis-related metabolic regulatory network based on differential metabolites associated with ferroptosis. Similar studies have not been reported, and the research approach demonstrates originality at its inception. This will undoubtedly offer significant research value and societal significance, providing new insights and clues for the prevention and treatment of IUA. Precise prevention and treatment of IUA, shifting the focus of IUA prevention and reducing the incidence of IUA, undoubtedly hold great significance. Declarations We have identified the committee that approved the research, confirm that all research was performed in accordance with relevant guidelines/regulations, and confirmed that informed consent was obtained from all participants and/or their legal guardians(For the informed consent of human participants, please refer to the related files). The research have been performed in accordance with the Declaration of Helsinki. Conflict of interest: The authors declare that they have no conflict of interest. Funding: our work was supported by grants from Ningbo Health Science and Technology Program (NO.2022y39). Author Contribution FZ performed the bioinformation analysis, designed and performed the experiments, and wrote the manuscript. AJY analyzed data and performed some in vitro experiments. JB and MJL participated in the experiments. FZ, CHY and JH designed, supervised the study, and performed manuscript editing. Acknowledgments Not applicable. Data availability The data presented in this study are real and valid.The datasets used and analyzed during the current study are available from the corresponding author on request.the original data is provided in Table SI and Table SII. References Di Sardo S. Prevention of intrauterine post-surgical adhesions in hysteroscopy. A systematic review. Eur J Obstet Gynecol Reprod Biol. 2016;203:182–92. Santamaria X, Isaacson K, Simón C. Asherman's Syndrome: it may not be all our fault. Hum Reprod. 2018;33(8):1374–80. van Wessel S, et al. Anti-adhesion Gel versus No gel following Operative Hysteroscopy prior to Subsequent fertility Treatment or timed InterCourse (AGNOHSTIC), a randomised controlled trial: protocol. Hum Reprod Open. 2021;2021(1):hoab001. Zhao G, et al. Transplantation of collagen scaffold with autologous bone marrow mononuclear cells promotes functional endometrium reconstruction via downregulating ∆Np63 expression in Asherman's syndrome. 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Endometrial Stem Cells and Their Applications in Intrauterine Adhesion. Cell Transpl. 2023;32:9636897231159561. Stockwell BR, et al. Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell. 2017;171(2):273–85. Bertrand RL. Iron accumulation, glutathione depletion, and lipid peroxidation must occur simultaneously during ferroptosis and are mutually amplifying events. Med Hypotheses. 2017;101:69–74. Dixon SJ, Stockwell BR. The role of iron and reactive oxygen species in cell death. Nat Chem Biol. 2014;10(1):9–17. Cheng H, et al. Iron deposition-induced ferroptosis in alveolar type II cells promotes the development of pulmonary fibrosis. Biochim Biophys Acta Mol Basis Dis. 2021;1867(12):166204. Yu Y, et al. Hepatic transferrin plays a role in systemic iron homeostasis and liver ferroptosis. Blood. 2020;136(6):726–39. Evans-Hoeker EA, Young SL. Endometrial receptivity and intrauterine adhesive disease. Semin Reprod Med. 2014;32(5):392–401. Lee H, et al. Energy-stress-mediated AMPK activation inhibits ferroptosis. Nat Cell Biol. 2020;22(2):225–34. Dixon SJ, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. Lu SC. Glutathione synthesis. Biochim Biophys Acta. 2013;1830(5):3143–53. Yang WS, et al. Regulation of ferroptotic cancer cell death by GPX4. Cell. 2014;156(1–2):317–31. Tang D, et al. Ferroptosis: molecular mechanisms and health implications. Cell Res. 2021;31(2):107–25. Gao M, et al. Glutaminolysis and Transferrin Regulate Ferroptosis. Mol Cell. 2015;59(2):298–308. Feng H, et al. Transferrin Receptor Is a Specific Ferroptosis Marker. Cell Rep. 2020;30(10):3411–e34237. Hassannia B, Vandenabeele P, Vanden Berghe T. Targeting Ferroptosis to Iron Out Cancer. Cancer Cell. 2019;35(6):830–49. Fujimoto T, Parton RG. Not just fat: the structure and function of the lipid droplet. Cold Spring Harb Perspect Biol, 2011. 3(3). Chen X, et al. Ferroptosis: machinery and regulation. Autophagy. 2021;17(9):2054–81. Badawy AA. Kynurenine Pathway of Tryptophan Metabolism: Regulatory and Functional Aspects. Int J Tryptophan Res. 2017;10:1178646917691938. Zhou B, et al. Ferroptosis is a type of autophagy-dependent cell death. Semin Cancer Biol. 2020;66:89–100. Kuhn H, Banthiya S, van Leyen K. Mammalian lipoxygenases and their biological relevance. Biochim Biophys Acta. 2015;1851(4):308–30. Zou Y, et al. Plasticity of ether lipids promotes ferroptosis susceptibility and evasion. Nature. 2020;585(7826):603–8. Dai E, et al. Autophagy-dependent ferroptosis drives tumor-associated macrophage polarization via release and uptake of oncogenic KRAS protein. Autophagy. 2020;16(11):2069–83. Additional Declarations No competing interests reported. Supplementary Files TableSIdatamatrix.xlsx Table SI(data matrix.xlsx): For the extracted data, ion peaks with more than 50% missing values within each group were removed, and zero values were replaced with half of the minimum value. Compounds were qualitatively screened based on the Score obtained from the qualitative results, with a screening criterion of 36 points (out of 60). Compounds scoring below 36 were considered qualitatively inaccurate and were removed. Finally, positive and negative ion data were merged into a single data matrix, which includes all analyzable information extracted from the original data. TableSIIDEMs.xlsx Table SII(DEMs.xlsx): Identification of differentially expressed metabolites (DEMs). By employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. Among the identified 6250 metabolites, there are 102 differential metabolites (VIP value > 1, P < 0.05), including 29 upregulated and 73 downregulated. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5817199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401852814,"identity":"b582b2d4-fbd8-4fde-8408-317c1c63b47d","order_by":0,"name":"Fang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoElEQVRIiWNgGAWjYDAC5sMHmEnUwpaWQLKWHAMStci38Xx7XNhWl7jhAPPDRzeI0cLYxrvdeGYbG1ALm7FxDjFamOV7t0nztvEAtfCwSROlhY2N5xlQiwQJWnjYgCp52wxI0CLBxmYmPeNcgvHMw8T6Rb6N+Zl0QVmdbN/x5oePidICBoxsDI4LDhOtHAz+MNjLN5CmZRSMglEwCkYQAACgziw0ZL3RFAAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Zhang","suffix":""},{"id":401852817,"identity":"35c4e5ed-ed73-435f-ac09-bc9462055eec","order_by":1,"name":"Aijuan Yuan","email":"","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Aijuan","middleName":"","lastName":"Yuan","suffix":""},{"id":401852819,"identity":"7308d6c7-ab70-415d-9377-7c2f1b847449","order_by":2,"name":"Jia Bian","email":"","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Bian","suffix":""},{"id":401852822,"identity":"03a29f50-33aa-4334-855a-c137c7fcd3af","order_by":3,"name":"Minjie Liu","email":"","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Minjie","middleName":"","lastName":"Liu","suffix":""},{"id":401852823,"identity":"52e097da-6f9f-4259-8b37-1dec7e4b3a70","order_by":4,"name":"Chunhong Yan","email":"","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Chunhong","middleName":"","lastName":"Yan","suffix":""},{"id":401852825,"identity":"4a45b8bb-77a2-4127-b40d-f127f46cf9ae","order_by":5,"name":"Jie Hu","email":"","orcid":"","institution":"The Affiliated People’s Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-01-13 06:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5817199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5817199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73872737,"identity":"8edea5ad-6163-40ca-a5d7-4cb400e6dfd3","added_by":"auto","created_at":"2025-01-15 12:29:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8720127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of general characteristics between two groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix endometrial samples from normal uterine cavities and five endometrial samples from patients with intrauterine adhesions were pathologically confirmed（Figure 1A-B）.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/f830e07879072db2e13cc601.png"},{"id":73872727,"identity":"c4d246cc-b052-4b4d-9e52-27c545749608","added_by":"auto","created_at":"2025-01-15 12:29:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":364060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of This Workflow\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metabolomics workflow includes sample pre-treatment, metabolite extraction, LC-MS full-scan detection, data pre-processing, and statistical analysis. Utilizing non-targeted metabolomics with ultra-high-performance liquid chromatography-tandem mass spectrometry, in conjunction with the metabolomics data processing software Progenesis QI v2.3, qualitative and relative quantitative analyses are conducted on the raw data. Standardized pre-processing is applied to the original data.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/b2f5b200e052a2469e68e965.png"},{"id":73872742,"identity":"4d8026bd-a568-4bd4-b666-5282cf082ab5","added_by":"auto","created_at":"2025-01-15 12:29:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1267412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQC Sample Quality Control and Qualitative and Quantitative Results of Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough the quality control of QC samples, the experimental pre-processing, sample on-machine, and mass spectrometry system stability of this project are analyzed and evaluated. By screening the relative standard deviation (RSD) of QC samples and removing ion peaks with an RSD\u0026gt;0.3 in the QC group. The PCA model obtained through 7-fold cross-validation shows that the QC samples are closely clustered, indicating good instrument detection stability during the experiment (Figure 3A). A Boxplot of the metabolite intensities of QC samples is created to assess the distribution of QC metabolite intensities. The Y-coordinate represents the log10 value of mass spectrometry intensity, and to ensure visualization, the Boxplot is drawn for up to 60 samples (Figure 3B). For a more intuitive display of the relationship between QC samples and other samples, as well as the stability among QC samples, hierarchical clustering is performed on the expression levels of all metabolites, as shown in the following figure (Figure 3C). For the extracted data, ion peaks with more than 50% missing values within each group were removed, and zero values were replaced with half of the minimum value. Compounds were qualitatively screened based on the Score obtained from the qualitative results, with a screening criterion of 36 points (out of 60). Compounds scoring below 36 were considered qualitatively inaccurate and were removed. Finally, positive and negative ion data were merged into a single data matrix, which includes all analyzable information extracted from the original data. Subsequent analyses were conducted based on this integrated matrix.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/03a6e1391dbfdf2018a65495.png"},{"id":73873289,"identity":"44b29faf-c466-4c0d-a5bb-9ae7acdbd5d1","added_by":"auto","created_at":"2025-01-15 12:37:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1285959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariate Statistical Analysis and Univariate Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate statistical analysis will first employ unsupervised Principal Component Analysis (PCA) to observe the overall distribution among samples and the stability of the entire analysis process (Figure 4A). Subsequently, supervised Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) will be used to differentiate the overall differences in metabolic profiles among groups and identify differentially regulated metabolites between groups. The PLS-DA model provides better interpretation and prediction of differences between the two sample groups, representing improved predictive ability (Figure 4B). Significant differences between the two sample groups are evident on the OPLS-DA score plot (Figure 4C). Potential biomarkers are identified based on Variable Importance of Projection (VIP) values from the OPLS-DA model, where VIP\u0026gt;1 indicates significance. Permutation plots are employed to assess overfitting; generally, a Q2 value below zero is desired when using Response Permutation Testing (RPT) (Figure 4D).Loading plots are utilized to identify the impact strength of metabolites on the comparative groups, where the range of loadings can be from -1 to 1. Loadings close to -1 or 1 indicate a strong influence of the variable on the component, while loadings close to 0 suggest a weak influence. Loading plots help characterize each component based on variables (Figure 4E). The S-plot, where the x-axis represents the characteristic values of metabolites influencing the comparative groups, and the y-axis represents the correlation between sample scores and metabolites, shows all points distributed in the first and third quadrants, forming an S-shape. Metabolites closer to the upper right and lower left corners of the plot indicate more significant differences (Figure 4F).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/559618d80a46a77cec549195.png"},{"id":73873283,"identity":"27cf0c65-f843-4f86-a688-6be05792485b","added_by":"auto","created_at":"2025-01-15 12:37:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4738368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed metabolites (DEMs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. Among the identified 6250 metabolites, there are 102 differential metabolites (VIP value \u0026gt;1, P\u0026lt;0.05), including 29 upregulated and 73 downregulated(Table SII), compared to healthy female endometrial samples. Visualization was carried out using Volcano plots(Figure 5A) and heat map plots(Figure 5B). To visually represent the relationships between samples and the expression differences of metabolites among different samples, we performed hierarchical clustering on all significantly different metabolites(Figure 5C).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/c9b8860e3d7a3a907778a86e.png"},{"id":73872747,"identity":"ba3b0e59-a4ba-434c-898a-405190098846","added_by":"auto","created_at":"2025-01-15 12:29:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1395189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic Pathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG analysis indicates that the expression of differential metabolites is mainly associated with pathways such as unsaturated fatty acid biosynthesis, AMPK signaling pathway, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism. From the perspective of metabolomics, it can be observed that after the occurrence of intrauterine adhesions, significant changes occur in the metabolites and metabolic pathways of the endometrium in patients with intrauterine adhesions.\u003c/p\u003e\n\u003cp\u003eFigure6A: KEGG enrichment analysis of DEGs；\u003c/p\u003e\n\u003cp\u003eFigure6B: Bubble chart of metabolic pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/a3cfdf717f8790d0040b3701.png"},{"id":73872744,"identity":"a6e36383-273a-4501-b705-f307d72dc3ab","added_by":"auto","created_at":"2025-01-15 12:29:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1405094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Ferroptosis-related metabolic regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplying the online tool (https://www.metaboanalyst.ca/MetaboAnalyst /ModuleView.xhtml)),\u003c/p\u003e\n\u003cp\u003ewe identified some joint-pathways between Ferroptosis-related genes and DEMs from IUAs. These Ferroptosis-related genes and DEMs from IUAs were then used to contruct a Ferroptosis-related metabolic regulatory network to pinpoint the hub joint-pathways. Through the analysis of joint-pathway, We identified a Ferroptosis-related metabolic regulatory pathway. Six metabolites metabolism (Glutathione metabolism, Arachidonic acid metabolism, Citrate cycle (TCA cycle), Lysine degradation, Glycerolipid metabolism, Cysteine and methionine metabolism) among the DEMs were ultimately selected (P\u0026lt;0.05). These hub joint-pathways related to six metabolites metabolism may play a crucial role in the occurrence and progression of USAs.\u003c/p\u003e\n\u003cp\u003eThe result from the integration of transcriptomics (gene list) and targeted metabolomics data (compoundlist). The pathways are displayed as a scatter plot (left). The x axis shows pathway impact scores, which summarize normalized topology measures of those perturbed genes/metabolites in each pathway. The y axis shows −log10(P) values of the enrichment analysis results. The sizes of the data points are correlated with their x values, and the color gradients correspond to their y values. By clicking a node in the left panel, one can view the corresponding pathway in the right panel. Genes are shown as rectangles, and compounds as circles. The matched nodes are colored on the basis of their logFC, with green for negative values and red for positive values. b, The result from the integration of transcriptomics (gene list) and untargeted metabolomics data (peak list). Both axes show −log10(P) values of the pathway enrichment analysis based on transcriptomics (x axis) and metabolomics (y axis), respectively. The size and color of the data points are based on their merged −log10(P) values. Clicking a node in the left panel will show the corresponding pathway in the right panel. Clicking a compound (blue circle node) in the pathway will show all the peaks assigned to this compound.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/abf93af131bf414d783a0616.png"},{"id":73878458,"identity":"bca3e4f6-4b6a-4f8c-af93-3e295b65b001","added_by":"auto","created_at":"2025-01-15 13:32:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27275459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/a6680599-8ca1-4b6d-93dd-b498b8aeff02.pdf"},{"id":73872733,"identity":"8a3a57fd-47ce-4f76-98c7-01ab4fe05c57","added_by":"auto","created_at":"2025-01-15 12:29:23","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5242414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable SI(data matrix.xlsx): \u003c/strong\u003eFor the extracted data, ion peaks with more than 50% missing values within each group were removed, and zero values were replaced with half of the minimum value. Compounds were qualitatively screened based on the Score obtained from the qualitative results, with a screening criterion of 36 points (out of 60). Compounds scoring below 36 were considered qualitatively inaccurate and were removed. Finally, positive and negative ion data were merged into a single \u003cstrong\u003edata matrix\u003c/strong\u003e, which includes all analyzable information extracted from the original data.\u003c/p\u003e","description":"","filename":"TableSIdatamatrix.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/1cfccd2e57c679b7f5ef5269.xlsx"},{"id":73872728,"identity":"15297276-4b98-4016-b0f1-682d95e1cd86","added_by":"auto","created_at":"2025-01-15 12:29:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59050,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable SII(DEMs.xlsx): \u003c/strong\u003eIdentification of differentially expressed metabolites (DEMs). By employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. Among the identified 6250 metabolites, there are 102 differential metabolites (VIP value \u0026gt; 1, P \u0026lt; 0.05), including 29 upregulated and 73 downregulated.\u003c/p\u003e","description":"","filename":"TableSIIDEMs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5817199/v1/0dc329d44ce9f7398b315266.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe analysis of the ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) using a metabolomics approach\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntrauterine adhesion(IUA)is a condition where the uterine cavity becomes mutually adherent due to damage to the basal layer of the endometrium, resulting from uterine surgery or factors such as infection and radiation. It is a common uterine cavity disorder that significantly impacts the fertility \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The primary pathological change in IUA is the replacement of normal endometrial tissue by newly formed fibrous scar tissue after damage to the basal layer of the endometrium \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the increasing prevalence of invasive uterine surgeries like induced abortion, fertility problems caused by uterine adhesions have become a focus of clinical research. For patients with moderate to severe IUA, the high recurrence rate after surgery, scarring of the uterine cavity, slow and asynchronous growth of the endometrium, and undesirable reproductive outcomes make uterine adhesion a global challenge, posing a serious threat to female reproductive health and function, thereby affecting family stability and societal harmony \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe formation of uterine adhesions has diverse causes, including uterine surgical procedures, infections, and genetic factors, with individual variations. Although various theories, such as neural reflex, trauma, and infection, exist regarding the mechanisms of IUA \u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, there is still no consensus on the exact pathogenesis. Currently, common methods for examining uterine adhesions include hysteroscopy, transvaginal ultrasound examination, hysterosalpingography, and uterotubal angiography. However, early prediction of the occurrence of uterine adhesions remains challenging. Given the limitations in the early prediction of uterine adhesions, researchers have been progressively exploring new methods for early diagnosis at the molecular level. The pathogenesis of IUA involves the imbalance of various molecular proteins related to adhesion formation9 \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, such as transforming growth factor β1 (TGFβ1), hypoxia-inducible factor α (HIF1α), basic fibroblast growth factor (β-FGF), and platelet-derived growth factor (PDGF). Although these studies contribute to the diagnosis of uterine adhesions, they are insufficient for early prediction of the risk of uterine adhesion occurrence.\u003c/p\u003e \u003cp\u003eIn recent years, the concept of active metabolomics has broadened our previous understanding \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e: metabolomic analysis is no longer a simple biomarker identification tool but is considered a novel technology capable of exploring active driving factors in physiological and pathological processes. Metabolites not only serve as markers of the organism's phenotype but also influence the physiological function of cells by regulating other omics (genomics, epigenomics, transcriptomics, and proteomics). Metabolites are essential regulators of life activities, influencing protein function through various mechanisms such as epigenetic modifications, conformational regulation, and signal transduction, forming complex interaction networks \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent investigations have illuminated the intricate link between metabolism and fibrosis. Studies suggest that the metabolic reprogramming or dysregulation of cells plays a pivotal role in their profibrotic function, impacting conditions such as pulmonary fibrosis and other fibrotic diseasess \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The identification of metabolism-related subtypes has been associated with immune-metabolic interactions in idiopathic pulmonary fibrosis, unveiling a significant interplay between metabolism and immunity \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Recent studies have delved into immunometabolism changes in fibrosis, accentuating the cellular metabolic reprogramming experienced by immune cells during the development of various profibrotic diseases \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The evolving role of metabolism in fibrosis has become a rapidly expanding area of interest, implicating metabolism in multiple profibrotic conditions \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. These findings collectively suggest a complex interplay between metabolism and fibrosis, providing insights into potential therapeutic avenues and a deeper molecular understanding. There is evidence suggesting an association between changes in intrauterine microbiota and its metabolic components in the context of intrauterine adhesions (IUAs) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Metabolomics and pharmacodynamic analyses have revealed that IUA detrimentally affects the reproductive system and fertility, establishing it as a significant public health concern \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the exploration of endometrial stem cells underscores their role in treating IUAs, emphasizing their capacity to restore endometrial function \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Metabolic changes within the uterine environment, encompassing alterations in microbiota and the presence of scar tissues, contribute to the development and impact of IUAs. Ongoing research seeks to enhance our understanding of the intricate relationship between metabolism and the formation of adhesions in the uterus.\u003c/p\u003e \u003cp\u003eIn summary, employing a metabolomics approach, this research scrutinizes the composition of metabolites and metabolic pathways in IUAs. The ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) was analyzed, aiming to explore and identify effective biomarkers for uterine adhesion and provide new targets and strategies for the prevention and treatment of IUAs\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection and Pathological Diagnosis\u003c/h2\u003e \u003cp\u003e Endometrial samples were procured from patients with intrauterine adhesions (IUA) undergoing Gynecology surgery, following approved procedures before 2023.2.24. by the Ethics Committee Board of The Affiliated People\u0026rsquo;s Hospital of Ningbo University, Ningbo (NO.0402017). Statement: (i) identifying the Ethics Committee Board approving the experiments(The ethical approval letter was confirmed before the experiment); (ii) confirming that all experiments were performed in accordance with relevant guidelines and regulations.The study encompassed five patients with IUA lasting more than 1 year (Pathological biopsy confirmed the endometrial samples as IUA.). For comparison, six corresponding endometrial samples were obtained from healthy female donors undergoing routine gynecological examinations. The specimens were promptly snap-frozen and stored in liquid nitrogen.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Tissue Metabolomics Analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.21 Sample Preparation\u003c/h2\u003e \u003cp\u003eA precisely weighed mg sample was transferred into a 1.5 mL Eppendorf tube. Two small steel balls were then added to the tube. Subsequently, \u0026micro;L of L-2-chlorophenylalanine (0.3 mg/mL), dissolved in methanol and serving as the internal standard, along with mL of a methanol and water mixture (4:1, vol/vol), were added to each sample. After storing the samples at -20 ℃ for 2 minutes, they were ground at 60 Hz for 2 minutes. The entire samples underwent extraction by ultrasonication for 10 minutes in an ice-water bath and were then stored at -20 ℃ for an additional 30 minutes. The resulting extracts were centrifuged at 4\u0026deg;C (13,000 rpm) for 10 minutes. \u0026micro;L of the supernatant was transferred to a glass vial and dried in a freeze concentration centrifugal dryer. Subsequently, \u003cb\u003e5\u003c/b\u003e\u0026micro;L of a methanol and water mixture (1:4, vol/vol) was added to each sample. The samples were vortexed for 30 seconds, subjected to ultrasonication for 3 minutes in an ice-water bath, and then stored at -20\u0026deg;C for 2 hours. Following this, the samples were centrifuged at 4\u0026deg;C (13,000 rpm) for 10 minutes. The resulting supernatants (\u0026micro;L) from each tube were collected using crystal syringes, filtered through 0.22 \u0026micro;m microfilters, and transferred to LC vials. These vials were stored at -80\u0026deg;C until LC-MS analysis. Quality control (QC) samples were prepared by mixing aliquots of all the samples to create a pooled sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.22 Tissue Metabolomics Analysis\u003c/h2\u003e \u003cp\u003eFor tissue metabolomics analysis, we utilized liquid chromatography-mass spectrometry (LC-MS) employing the ACQUITY I-Class UPLC system, which was equipped with an ACQUITY UPLC HSS T3 column (Waters, Milford, USA), and the QE Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.23 Data Preprocessing and Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe original LC-MS data underwent processing using Progenesis QI V2.3 software (Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. The key parameters employed were a 5 ppm precursor tolerance, 10 ppm product tolerance, and a 5% product ion threshold. Compound identification relied on the precise mass-to-charge ratio (m/z), secondary fragments, and isotopic distribution with reference to databases, including The Human Metabolome Database (HMDB), Lipidmaps (V2.3), Metlin, EMDB, PMDB, and self-established databases for qualitative analysis. Subsequently, the data underwent additional processing, involving the removal of peaks with missing values (ion intensity\u0026thinsp;=\u0026thinsp;0) in more than 50% of groups, substitution of zero values with half of the minimum value, and screening based on the qualitative results of the compounds. Compounds with scores below 36 (out of 60) were considered inaccurate and excluded. A data matrix amalgamated from positive and negative ion data was introduced into R for Principle Component Analysis (PCA) to assess overall sample distribution and analysis process stability. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) and Partial Least-Squares-Discriminant Analysis (PLS-DA) were employed for distinguishing metabolites differing between groups. To mitigate overfitting, 7-fold cross-validation and 200 Response Permutation Testing (RPT) iterations assessed model quality. Variable Importance of Projection (VIP) values derived from the OPLS-DA model ranked each variable's overall contribution to group discrimination. A two-tailed Student\u0026rsquo;s T-test verified the significance of group differences for selected differential metabolites, determined by VIP values greater than 1.0 and p-values less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.24 Identification of differentially expressed metabolites (DEMs)\u003c/h2\u003e \u003cp\u003eBy employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. The criteria for selection were VIP values of the first principal component in the OPLS-DA model\u0026thinsp;\u0026gt;\u0026thinsp;1 and p-value of T-test\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Variable Importance in Projection (VIP) values from OPLS-DA analysis were used to measure the impact strength and explanatory power of the expression patterns of various metabolites on the classification and discrimination of samples between groups. This approach helped to identify differentially expressed metabolites with biological significance. Further validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was \u0026lt;\u0026thinsp;0.05, and the gene expression fold change (FC) value was \u0026ge;\u0026thinsp;1 or \u0026le;\u0026thinsp;1(|log2 FC|\u0026ge;0.5). Visualization was carried out using Volcano plots and heat map plots. Genes with log2FC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 were categorized as up-regulated, while those with log2FC\u0026lt;-0.5 were considered down-regulated. To visually represent the relationships between samples and the expression differences of metabolites among different samples, we performed hierarchical clustering on all significantly different metabolites. The color gradient from blue to red represents the expression abundance of metabolites from low to high, where a more intense red indicates a higher expression abundance of differentially expressed metabolites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.25 Metabolic Pathway KEGG Enrichment Analysis\u003c/h2\u003e \u003cp\u003eTo elucidate the role of differentially expressed metabolites (DEMs) in intrauterine adhesions(IUA) patients and individuals in healthy female donors, the construction of the KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database aims to understand the functions and interactions of metabolites in biological systems. Information related to metabolic pathways can be queried. Conducting pathway enrichment analysis on differential metabolites helps to comprehend the mechanism of metabolic pathway alterations in differential samples. This is based on the KEGG database for the enrichment analysis of differential metabolites. pathway enrichment analysis is performed using the KEGG IDs of differential metabolites to obtain results of metabolic pathway enrichment. The hypergeometric test is applied to identify pathway entries significantly enriched in differentially expressed metabolites compared to the entire background. The formula for calculation is as follows: P\u0026thinsp;\u0026le;\u0026thinsp;0.05, p\u0026thinsp;\u0026minus;\u0026thinsp;value\u0026thinsp;\u0026le;\u0026thinsp;0.05 is set as the threshold, and pathways meeting this criterion are considered significantly enriched in differential metabolites. A smaller p-value indicates greater significance of the metabolic pathway difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of Ferroptosis-related metabolic regulatory network\u003c/h2\u003e \u003cp\u003eMetaboAnalyst currently supports metabolic pathway analysis (integrating pathway enrichment analysis and pathway topology analysis) and visual exploration for \u0026gt;\u0026thinsp;120 species. In addition, users can also perform joint pathway analysis by uploading both gene list together with the metabolite/peak list for ~\u0026thinsp;25 common model organisms. Among DEMs and Ferroptosis-related genes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.zhounan.org/ferrdb/current/\u003c/span\u003e\u003cspan address=\"http://www.zhounan.org/ferrdb/current/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Ferroptosis-related metabolic regulatory network was generated using an online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)). The result from the integration of Ferroptosis-related genes (official gene symbol) and targeted metabolomics data (HMDB ID) were visualized using joint-pathway analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Comparison of general characteristics between two groups\u003c/h2\u003e\n \u003cp\u003eThere were no statistically significant differences in age, BMI, Full-term birth, Premature birth, Miscarriage, and Survival between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Six endometrial samples from normal uterine cavities and five endometrial samples from patients with intrauterine adhesions were pathologically confirmed(Figure\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA-K)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of general characteristics between two groups There were no statistically significant differences in age, BMI, Full-term birth, Premature birth, Miscarriage, and Survival between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHealthy female donors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIUA Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.33\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull-term birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremature birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiscarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Overview of This Workflow(Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e\n \u003cp\u003eThe metabolomics workflow includes sample pre-treatment, metabolite extraction, LC-MS full-scan detection, data pre-processing, and statistical analysis. Utilizing non-targeted metabolomics with ultra-high-performance liquid chromatography-tandem mass spectrometry, in conjunction with the metabolomics data processing software Progenesis QI v2.3, qualitative and relative quantitative analyses are conducted on the raw data. Standardized pre-processing is applied to the original data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 QC Sample Quality Control and Qualitative and Quantitative Results of Data Preprocessing\u003c/h2\u003e\n \u003cp\u003eThrough the quality control of QC samples, the experimental pre-processing, sample on-machine, and mass spectrometry system stability of this project are analyzed and evaluated. By screening the relative standard deviation (RSD) of QC samples and removing ion peaks with an RSD\u0026thinsp;\u0026gt;\u0026thinsp;0.3 in the QC group. The PCA model、A Boxplot of the metabolite intensities and hierarchical clustering shows that the QC samples are closely clustered, indicating good instrument detection stability during the experiment (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). For the extracted data, ion peaks with more than 50% missing values within each group were removed, and zero values were replaced with half of the minimum value. Compounds were qualitatively screened based on the Score obtained from the qualitative results, with a screening criterion of 36 points (out of 60). Compounds scoring below 36 were considered qualitatively inaccurate and were removed. Finally, positive and negative ion data were merged into a single \u003cstrong\u003edata matrix\u003c/strong\u003e, which includes all analyzable information extracted from the original data and are presented in Table SI. Subsequent analyses were conducted based on this integrated matrix.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Multivariate Statistical Analysis and Univariate Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eMultivariate statistical analysis will first employ unsupervised Principal Component Analysis (PCA) to observe the overall distribution among samples and the stability of the entire analysis process (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, supervised Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) will be used to differentiate the overall differences in metabolic profiles among groups and identify differentially regulated metabolites between groups. The PLS-DA model provides better interpretation and prediction of differences between the two sample groups, representing improved predictive ability (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Significant differences between the two sample groups are evident on the OPLS-DA score plot (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Permutation plots are employed to assess overfitting (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Loading plots are utilized to identify the impact strength of metabolites on the comparative groups (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). The S-plot, shows all points distributed in the first and third quadrants(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eFurther validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was \u0026lt;\u0026thinsp;0.05, and the gene expression fold change (FC) value was \u0026ge;\u0026thinsp;1 or \u0026le;\u0026thinsp;1(|log2 FC| \u0026ge;0.5). Further validation of inter-group differential metabolites was conducted using T-test. A gene was considered differentially expressed between intrauterine adhesion (IUA) endometrial samples and normal endometrial samples when the P-value was \u0026lt;\u0026thinsp;0.05, and the gene expression fold change (FC) value was \u0026ge;\u0026thinsp;1 or \u0026le;\u0026thinsp;1(|log2 FC|\u0026ge;0.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Identification of differentially expressed metabolites (DEMs)\u003c/h2\u003e\n \u003cp\u003eBy employing a combined approach of multidimensional and unidimensional analysis, we screened for inter-group differential metabolites. Among the identified 6250 metabolites, there are 102 differential metabolites (VIP value\u0026thinsp;\u0026gt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 29 upregulated and 73 downregulated, which are present in Table SII, compared to healthy female endometrial samples. Visualization was carried out using Volcano plots(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA) and heat map plots(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). To visually represent the relationships between samples and the expression differences of metabolites among different samples, we performed hierarchical clustering on all significantly different metabolites(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Metabolic Pathway Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eKEGG analysis indicates that the expression of differential metabolites is mainly associated with pathways such as unsaturated fatty acid biosynthesis, AMPK signaling pathway, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). From the perspective of metabolomics, it can be observed that after the occurrence of intrauterine adhesions, significant changes occur in the metabolites and metabolic pathways of the endometrium in patients with intrauterine adhesions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Analysis of Ferroptosis-related metabolic regulatory network\u003c/h2\u003e\n \u003cp\u003eApplying the online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml\u003c/span\u003e\u003c/span\u003e)), we identified some joint-pathways between Ferroptosis-related genes and DEMs from IUAs. These Ferroptosis-related genes and DEMs from IUAs were then used to contruct a Ferroptosis-related metabolic regulatory network to pinpoint the hub joint-pathways. Through the analysis of joint-pathway, we identified a Ferroptosis-related metabolic regulatory pathway. Six metabolites metabolism (Glutathione metabolism, Citrate cycle (TCA cycle), Glycerolipid metabolism, Lysine degradation, Arachidonic acid metabolism, Cysteine and methionine metabolism) among the DEMs were ultimately selected (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-F). These hub joint-pathways related to six metabolites metabolism may play a crucial role in the occurrence and progression of USAs.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBy selecting differential metabolites between the normal uterine cavity and the uterine cavity with adhesions, and exploring their patterns of change, we elucidate the relationship between differential metabolites and intrauterine adhesions. The results revealed 102 differential metabolites among the identified 6250 metabolites, including 29 upregulated and 73 downregulated. From the perspective of metabolomics, it can be observed that significant changes occur in the metabolites and metabolic pathways of the endometrium in patients with intrauterine adhesions. KEGG analysis suggested that the expression of differential metabolites is notably associated with pathways such as AMPK signaling pathway, unsaturated fatty acid biosynthesis, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism.\u003c/p\u003e\n\u003cp\u003eWe applied online tools ChemSpider and HMDB to analyze the basic characteristics of differential metabolites and found that DEMs\u0026rsquo;s functions involve cell cytoplasm development, cell movement and contraction, mucosal mucus secretion, metabolism, and signal transduction, among others. Notably, several metabolites and metabolic pathways related to ferroptosis attracted our attention. Ferroptosis is an oxidative stress-induced cell death mechanism caused by the accumulation of iron-dependent lipid peroxides \u003csup\u003e[19]\u003c/sup\u003e. Key factors triggering ferroptosis include the accumulation of intracellular iron and reactive oxygen species (ROS), depletion of glutathione, and disruption of lipid peroxide metabolism \u003csup\u003e[20]\u003c/sup\u003e. Iron overload and lipid peroxide accumulation are recognized as central events in ferroptosis \u003csup\u003e[21]\u003c/sup\u003e.The mechanisms related to ferroptosis play a crucial regulatory role in the progression of fibrotic diseases, which develop from the complex pathological process of continuous chronic injury to cells \u003csup\u003e[22, 23]\u003c/sup\u003e. After endometrial injury in patients with intrauterine adhesions, the normal repair mechanism is disrupted, replaced by excessive proliferation of fibrotic tissue, essentially a fibrotic process \u003csup\u003e[24]\u003c/sup\u003e, suggesting a potential association with ferroptosis. Increased lipid peroxidation, depletion of intracellular glutathione (GSH), accumulation of iron mediated by transferrin, and accumulation of free fatty acids can induce ferroptosis. Hyemin Lee et al. demonstrated the regulatory relationship between ferroptosis and AMP-activated protein kinase (AMPK) using inhibitors/inducers related to the ferroptosis pathway and establishing AMPK knockout cell lines \u003csup\u003e[25]\u003c/sup\u003e. Through metabolomics of the endometrial tissue in patients with intrauterine adhesions, KEGG analysis suggests the presence of the AMPK signaling pathway among differential metabolites, further confirming the correlation between IUA and ferroptosis from the perspective of signaling pathways.\u003c/p\u003e\n\u003cp\u003eFerroptosis-related metabolic regulatory networks were identified using an online tool, revealing six metabolite metabolism pathways\u0026mdash;Glutathione metabolism, Citrate cycle (TCA cycle), Glycerolipid metabolism, Lysine degradation, Arachidonic acid metabolism, and Cysteine and methionine metabolism\u0026mdash;potentially crucial in the occurrence and progression of USAs.\u003c/p\u003e\n\u003cp\u003eGlutathione metabolism and ferroptosis are closely interconnected concepts in cellular biology and biochemistry \u003csup\u003e[26]\u003c/sup\u003e. Glutathione, composed of cysteine, glutamic acid, and glycine, plays pivotal roles in cellular processes, including antioxidant defense, detoxification, and redox balance maintenance \u003csup\u003e[27]\u003c/sup\u003e.Conversely, ferroptosis, characterized by iron-dependent lipid peroxidation, is distinct from other cell death modes, implicated in various physiological and pathological processes \u003csup\u003e[26]\u003c/sup\u003e.The nexus between glutathione metabolism and ferroptosis lies in regulating cellular redox balance, where glutathione counters oxidative stress, and its depletion triggers ferroptosis \u003csup\u003e[28]\u003c/sup\u003e.Understanding this interplay is crucial for unraveling cellular homeostasis mechanisms \u003csup\u003e[19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe Citric Acid Cycle, also known as the Tricarboxylic Acid (TCA) cycle or Krebs cycle, and ferroptosis, while distinct cellular processes, exhibit potential connections \u003csup\u003e[29]\u003c/sup\u003e.The TCA cycle, occurring within eukaryotic cell mitochondria, is vital for ATP generation through acetyl-CoA oxidation from various metabolic sources.Through enzymatic reactions, it releases carbon dioxide and produces reducing equivalents (NADH and FADH2), crucial for the electron transport chain and oxidative phosphorylation.The potential link between the TCA cycle and ferroptosis arises from reactive oxygen species (ROS) production during mitochondrial respiration \u003csup\u003e[30]\u003c/sup\u003e.While not directly implicated in ferroptosis, heightened mitochondrial activity associated with the TCA cycle can elevate ROS levels \u003csup\u003e[26]\u003c/sup\u003e.Uncontrolled ROS production, without efficient antioxidant systems, may contribute to lipid peroxidation and initiate ferroptosis \u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurther research is essential to grasp comprehensively the interplay between these processes and their implications in diverse cellular contexts and disease states \u003csup\u003e[19]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity to ferroptosis links to various biological processes, including iron metabolism, unsaturated fatty acids, glutathione, and phospholipid biosynthesis \u003csup\u003e[31]\u003c/sup\u003e.Polyunsaturated fatty acids (PUFAs), prone to oxidation, promote ferroptosis by impairing membrane function (ALOXs) \u003csup\u003e[32]\u003c/sup\u003e.Metabolomics analysis suggests a potential link between intrauterine adhesions and ferroptosis through unsaturated fatty acid biosynthesis (KEGG pathway analysis).Glycerolipid metabolism and ferroptosis are closely linked cellular processes, with lipid metabolism regulation, particularly polyunsaturated fatty acids (PUFAs) within glycerolipids, crucial in initiating and advancing ferroptosis. Glycerolipids, including triglycerides, phospholipids, and glycolipids, are vital for cellular membranes and fatty acid storage.The metabolism of glycerolipids involves synthesis and breakdown, with membrane lipid remodeling, particularly phospholipids, essential for membrane integrity and functionality \u003csup\u003e[33]\u003c/sup\u003e.The connection between glycerolipid metabolism and ferroptosis centers on lipid peroxidation\u0026apos;s role in initiating and promoting ferroptotic cell death \u003csup\u003e[34]\u003c/sup\u003e.Polyunsaturated fatty acids (PUFAs), like arachidonic acid, are susceptible to oxidation, generating lipid peroxides that accumulate in cellular membranes, triggering ferroptosis \u003csup\u003e[19]\u003c/sup\u003e.Several key points highlight this connection: PUFA-Containing Phospholipids are primary substrates for lipid peroxidation reactions driving ferroptosis \u003csup\u003e[26]\u003c/sup\u003e.GPX4 Inhibition increases susceptibility to ferroptosis when inhibited, directly or indirectly \u003csup\u003e[28]\u003c/sup\u003e.Ferroptosis depends on iron, contributing to lipid peroxidation initiation, and glycerolipid metabolism can influence cellular iron availability and utilization \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSensitivity to ferroptosis links to various biological processes, including iron metabolism, unsaturated fatty acids, glutathione, and phospholipid biosynthesis \u003csup\u003e[31]\u003c/sup\u003e.Polyunsaturated fatty acids (PUFAs), prone to oxidation, promote ferroptosis by impairing membrane function (ALOXs) \u003csup\u003e[32]\u003c/sup\u003e.Metabolomics analysis suggests a potential link between intrauterine adhesions and ferroptosis through unsaturated fatty acid biosynthesis (KEGG pathway analysis).Glycerolipid metabolism and ferroptosis are closely linked cellular processes, with lipid metabolism regulation, particularly polyunsaturated fatty acids (PUFAs) within glycerolipids, crucial in initiating and advancing ferroptosis. Glycerolipids, including triglycerides, phospholipids, and glycolipids, are vital for cellular membranes and fatty acid storage.The metabolism of glycerolipids involves synthesis and breakdown, with membrane lipid remodeling, particularly phospholipids, essential for membrane integrity and functionality \u003csup\u003e[33]\u003c/sup\u003e.The connection between glycerolipid metabolism and ferroptosis centers on lipid peroxidation\u0026apos;s role in initiating and promoting ferroptotic cell death \u003csup\u003e[34]\u003c/sup\u003e.Polyunsaturated fatty acids (PUFAs), like arachidonic acid, are susceptible to oxidation, generating lipid peroxides that accumulate in cellular membranes, triggering ferroptosis \u003csup\u003e[19]\u003c/sup\u003e.Several key points highlight this connection: PUFA-Containing Phospholipids are primary substrates for lipid peroxidation reactions driving ferroptosis \u003csup\u003e[26]\u003c/sup\u003e.GPX4 Inhibition increases susceptibility to ferroptosis when inhibited, directly or indirectly \u003csup\u003e[28]\u003c/sup\u003e.Ferroptosis depends on iron, contributing to lipid peroxidation initiation, and glycerolipid metabolism can influence cellular iron availability and utilization \u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLysine degradation and ferroptosis, although distinct, show emerging evidence of a potential link, particularly in regulating cellular redox balance \u003csup\u003e[26]\u003c/sup\u003e.Lysine, essential for protein synthesis, undergoes degradation, potentially influencing ferroptosis by affecting cellular redox balance \u003csup\u003e[35]\u003c/sup\u003e.Research indicates that lysine degradation affects glutathione availability, impacting the cell\u0026apos;s ability to counteract oxidative stress and lipid peroxidation\u0026mdash;central processes in ferroptosis \u003csup\u003e[28]\u003c/sup\u003e...Further research is crucial to fully unravel the molecular mechanisms underlying this connection \u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eArachidonic acid metabolism, crucial in inflammation and signaling, influences cellular redox balance and ferroptosis susceptibility \u003csup\u003e[37]\u003c/sup\u003e.Recent studies highlight arachidonic acid metabolism\u0026apos;s modulation of cellular environment susceptibility to ferroptosis, suggesting novel therapeutic strategies \u003csup\u003e[28]\u003c/sup\u003e.Ongoing research aims to uncover molecular mechanisms and crosstalk between lipid metabolism and ferroptosis, promising targeted interventions in pathogenic conditions \u003csup\u003e[38]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCysteine and methionine metabolism intricately influence ferroptosis, determining cellular susceptibility to ferroptotic cell death \u003csup\u003e[26]\u003c/sup\u003e.Cysteine, crucial for glutathione synthesis, is essential for cellular antioxidant defense against oxidative stress and lipid peroxidation \u003csup\u003e[27]\u003c/sup\u003e.Glutathione peroxidase 4 (GPX4) utilizes glutathione to prevent lipid peroxidation initiation, crucial in ferroptosis \u003csup\u003e[28]\u003c/sup\u003e.Methionine, a precursor for S-adenosylmethionine (SAM) in cellular methylation reactions, influences gene expression and redox status, impacting ferroptosis susceptibility \u003csup\u003e[39]\u003c/sup\u003e.The interplay between cysteine, methionine metabolism, and ferroptosis involves glutathione depletion, SAM\u0026apos;s role in methylation reactions, disruption of the transsulfuration pathway affecting glutathione synthesis, and GPX4 regulation \u003csup\u003e[32]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, Starting from the perspective of differential metabolites associated with ferroptosis, this study elucidates the role of metabolites in the occurrence and development of intrauterine adhesions (IUA). It has preliminarily established an Ferroptosis-related metabolic regulatory network based on differential metabolites associated with ferroptosis. Similar studies have not been reported, and the research approach demonstrates originality at its inception. This will undoubtedly offer significant research value and societal significance, providing new insights and clues for the prevention and treatment of IUA. Precise prevention and treatment of IUA, shifting the focus of IUA prevention and reducing the incidence of IUA, undoubtedly hold great significance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe have identified the committee that approved the research, confirm that all research was performed in accordance with relevant guidelines/regulations, and confirmed that informed consent was obtained from all participants and/or their legal guardians(For the informed consent of human participants, please refer to the related files). The research have been performed in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eour work was supported by grants from Ningbo Health Science and Technology Program (NO.2022y39).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eFZ performed the bioinformation analysis, designed and performed the experiments, and wrote the manuscript. AJY analyzed data and performed some in vitro experiments. JB and MJL participated in the experiments. FZ, CHY and JH designed, supervised the study, and performed manuscript editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eThe data presented in this study are real and valid.The datasets used and analyzed during the current study are available from the corresponding author on request.the original data is provided in Table SI and Table SII.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDi Sardo S. Prevention of intrauterine post-surgical adhesions in hysteroscopy. A systematic review. Eur J Obstet Gynecol Reprod Biol. 2016;203:182\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantamaria X, Isaacson K, Sim\u0026oacute;n C. Asherman's Syndrome: it may not be all our fault. 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Autophagy. 2020;16(11):2069\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"intrauterine adhesions (IUA), ferroptosis, metabolic regulatory network, metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-5817199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5817199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Recently, there has been a notable rise in the prevalence of intrauterine adhesions (IUA), exerting a substantial impact on female reproductive capacity. The phenomenon of ferroptosis is evident in IUA, yet the regulatory network associated with it remains unclear. Consequently, the objective of this study is to elucidate the metabolic regulatory network of ferroptosis in IUA through metabolomics, offering a fresh perspective for a more profound comprehension of the mechanisms underlying IUA. Concurrently, new active metabolites may emerge as potential targets for the prevention and treatment of IUA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results\u003c/strong\u003e: Uterine endometrial samples were collected from both healthy individuals and patients with IUA, with each endometrium pathologically confirmed. Liquid chromatography-mass spectrometry (LC-MS) was employed for sample analysis. A total of 6250 differential metabolites were identified, of which 102 were screened (VIP\u0026gt;1 and P\u0026lt;0.05). Among these, 29 showed upregulation, while 73 were downregulated. KEGG pathway analysis identified biological processes and metabolic pathways. Differentially regulated metabolic pathways included glucose metabolism, amino acid degradation, fatty acid metabolism, etc. Notably associated were pathways like AMPK signaling pathway, unsaturated fatty acid biosynthesis, oxidative phosphorylation, fatty acid biosynthesis, and sphingolipid metabolism. Joint pathway analysis identified six metabolite pathways (Glutathione metabolism, Arachidonic acid metabolism, Citrate cycle (TCA cycle), Lysine degradation, Glycerolipid metabolism, Cysteine and methionine metabolism) from the differentially expressed metabolites (DEMs). These pathways collectively constitute the Ferroptosis metabolic regulatory network in intrauterine adhesions (IUA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study conducted a non-targeted metabolomics investigation on IUA . Taking the perspective of differential metabolites between normal and IUA, the study utilized metabolomics to reveal the metabolic regulatory network associated with iron death in IUA. ferroptosis in IUA is regulated by multiple metabolic pathways, including lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic pathways, by modulating the activity of key enzymes such as lipid peroxidase and glutathione peroxidase, impact the occurrence and progression of ferroptosis. The metabolic regulatory network of iron death in IUA is closely related to the occurrence of intrauterine adhesions (IUA). ferroptosis plays a crucial role in the pathological process of intrauterine adhesions by regulating the metabolic pathways of adhesive tissues, potentially contributing to the prevention and treatment of IUA. This research not only aids in a deeper understanding of the pathological mechanisms of IUA but also provides new targets and strategies for preventing and treating diseases related to IUA.\u003c/p\u003e","manuscriptTitle":"The analysis of the ferroptosis metabolic regulatory network in patients with intrauterine adhesions (IUA) using a metabolomics approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 12:29:17","doi":"10.21203/rs.3.rs-5817199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dab0d814-77cb-467e-b25e-6b3237d3ff4e","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-15T13:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 12:29:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5817199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5817199","identity":"rs-5817199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}