Identification of important genes related to ferroptosis in early missed abortion based on WGCNA

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This is a commonly observed and significant pathological state that has an impact on the overall well-being of human reproductive health. The aim of this study was to identify key genes related to ferroptosis that could serve as novel biomarkers for early missed abortion. Relevant findings from gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicate a correlation between iron DEFRGS in key modules and the p53 signaling, mitophagy-animal, as well as protein digestion and absorption pathways. An analysis of the protein-protein interaction (PPI) network was conducted on DEFRGS, resulting in the identification of five central genes (TP53, EZH2, TIMP1, SLC3A2, and GABARAPL2) through the utilization of STRING and Cytohubba ROC curves.The expression of pivotal genes in the missed-abortion and control groups was verified by RT-qPCR. CIBERSORT analysis revealed a notable increase in the infiltration levels of CD8 T lymphocytes and M2 macrophages among individuals in the early missed abortion group. Ultimately, a ceRNA network was established in order to anticipate the connections between mRNA-miRNA-lncRNA of the central genes. However, the interacting miRNAs predicted by SLC3A2 in the miRanda, miRDB, and TargetScan databases were hsa-miR-661, hsa-miR-4311. There were no interacting lncRNAs in the spongeScan database. This research has discovered novel genes that can be targeted for the early detection and management of miscarriages. Biological sciences/Biochemistry Biological sciences/Genetics Biological sciences/Immunology Health sciences/Biomarkers Early missed abortion ferroptosis WGCNA immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Early missed abortion refers to a form of miscarriage in which the development of the fetus stops and there is no outflow of embryonic tissue. Most induced abortions occur during the early stages of pregnancy[ 1 ]. Early missed abortion is defined as a pregnancy of ≤ 12 weeks in which the embryo or fetus has died and is retained in the uterine cavity without timely spontaneous expulsion[ 2 , 3 ]. Studies have shown that missed abortions are mainly associated with chromosomal abnormalities of the embryo and that infections, imbalances in immune regulation, malformations of the maternal reproductive organs, and abnormalities of the intrauterine environment are also involved in missed abortions. However, the exact mechanism of development has not yet been clarified [ 4 , 5 ]. Ferroptosis is a recently discovered cell death pathway, whose main mechanisms are iron overload, lipid peroxidation, and glutathione/GPX4 non-dependent axis imbalance[ 6 , 7 ]. Previous studies have shown that ferroptosis is associated with pregnancy-related conditions, such as spontaneous abortion, preeclampsia, gestational diabetes mellitus, intrahepatic cholestasis during pregnancy, and spontaneous preterm labor[ 6 ], which directly inhibits angiogenesis and ultimately induces abortion[ 8 ]. An alysis of co-expression networks using weighted gene correlation (WGCNA) is an algorithm that links modular signature genes to clinical traits and can be used to identify biomarkers and therapeutic targets for diseases[ 9 ]. Immune cell infiltration plays an increasingly prominent role in various diseases and has been used to assess immune cell infiltration in recurrent miscarriages[ 10 ]. An imbalance of immune cells at the maternal-fetal interface, such as the imbalance of natural killer (NK) cells, macrophages, T-cells, B-cells, and dendritic cells (DCs), is one of the main causes of miscarriage[ 11 ]. Chorionic villi are crucial in fetal development and are closely related to perinatal relationships[ 12 ]; however, there have been no definitive studies on immune cell infiltration of chorionic villus tissue in early obstructed abortion. Hence, the examination of immune infiltration could potentially offer novel insights into the immunodiagnosis and management of early undetected miscarriages. Ferroptosis plays a crucial role in pregnancy-related diseases, and could serve as a novel biomarker or a potential therapeutic target for early missed abortions. Thus, this study aimed to identify key genes associated with early missed abortions. However, no studies have explored the potential mechanisms of ferroptosis in the development of early missed abortions. A comprehensive investigation into the phenomenon of ferroptosis could help accurately regulate the development of early missed abortions. We used WGCNA to identify key modules and genes associated with early missed abortion and ferroptosis and to highlight the significance of these components and genes in early missed abortions. Additionally, we confirmed the atypical mRNA expression level of the pivotal gene in early missed abortion using RT-qPCR and evaluated the infiltration of immune cells in early missed abortion using CIBERSORT to explore the correlation between the hub genes and immune cells. Finally, a ceRNA network was constructed to predict the miRNA and lncRNA interaction relationships of the hub genes. Finally, a ceRNA network was constructed to predict miRNA and lncRNA interactions of the hub genes. Therefore, this research study establishes a foundation for investigating the potential regulatory objectives and potential mechanisms of early miscarriages, offering innovative perspectives for treatment approaches. 2. Materials and methods This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (No. 2024-K186-01 ), and all patients signed an informed consent form. 2.1 Data sources and patient selection In this study, whole transcriptome sequencing was performed on chorionic tissue collected from women who were treated at the First Affiliated Hospital of Guangxi Medical University. The sample included five cases of embryonic arrest in females who experience early spontaneous termination of pregnancyand five cases of women with early missed abortions, which were collected in the operating room of the birth control clinic in June 2023. Twenty cases of early pregnant women who had an induced abortion in early pregnancy from January 2023 to February 2023 were collected as the ly missed-abortion group, and 20 cases of early pregnant women with normal pregnancy who voluntarily underwent induced abortion in the same period were selected as the induced abortion group. The inclusion criteria for the missed abortion group were: samples collected at 6 to 12 weeks gestation that met the diagnostic criteria for induced abortion in early pregnancy[ 13 , 14 ]. Exclusion criteria were: (1) a history of spontaneous abortion; (2) a difference of more than 2 weeks between the gestational week of menopause and the ultrasound gestational week; (3) missed abortion caused by heredity, mycoplasma or chlamydia infections, etc.; (4) obvious hepatic or renal insufficiency, or cardiac or cerebrovascular diseases; (5) malignant tumors; (6) hematologic or immune diseases; or (7) psychiatric diseases. The induced abortion group was selected from pregnant women with normal fertility who chose abortion for non-medical reasons, without signs and symptoms of abortion, without a history of spontaneous abortion, with fetal heartbeat seen on ultrasound within 3–4 d before the procedure, and gestational week determined by ultrasound. Data on the participants’ age, gestational week, body mass index (BMI), number of previous pregnancies, number of deliveries, and number of abortions were collected. Chorionic tissue specimens were collected aseptically under negative pressure aspiration, the clots were removed using sterile forceps, and then rinsed repeatedly in physiological saline until they were free of blood color. The specimens were stored in liquid nitrogen for 24 h and then refrigerated at -80°C until they were used for real-time fluorescence quantitative PCR (RT-qPCR) analysis. 2.2 Differential Expression Analysis Differential expression analysis of the entire transcriptome sequencing data from induced abortion was conducted in our research group using the limma package, employing screening criteria of |log2 FC| > 0.585 and p-value < 0.05. Heatmaps and volcano diagrams were visualized using the "heatmap" and "ggplot2" packages. Heatmaps and volcano maps were visualized using different packages, namely the "heatmap" package for heatmaps and the "ggplot2" package for volcano maps. 2.3. Co-expression network analysis and identification of differentially expressed ferroptosis-related genes (DEFRGS) A co-expression network analysis was performed using the "WGCNA" software package. Initially, a soft threshold was established to create a network with scale-free characteristics, and subsequently the matrix underwent conversion into neighboring and TOM matrices.The TOM matrix was clustered into genes and grouped into modules by dynamic tree clipping, and each module contained at least 60 genes. The modules were clustered to obtain similar modules and merged. Clustering was performed to obtain and merge similar modules. The clinical data was combined with the modules, and a correlation analysis using Pearson's method was conducted to determine which modules were most strongly linked to induced abortion. We intersected the most closely related module genes with the missed abortion DEGs in a Wayne plot, and further intersected them with the ferroptosis-related genes, which were obtained from the ferroptosis database ( http://www.zhounan.org/ferrdb/current/ ). We downloaded the three datasets (Marker, Suppressor, and Driver), and detailed information of all the ferroptosis-related genes (Supplementary Table 2). 2.4 Functional enrichment analysis GO/KEGG pathway analysis of DEFRGS was performed using the "clusterProfiler" software package[ 15 , 16 ], and p-values of below 0.05 were set as criteria for significant enrichment. 2.5. PPI network analysis and hub gene identification Iron death-associated DEGs were analyzed using the STRING database with the PPI network. Ten hub genes were screened using cytoHubba (a Cytoscape plug-in). As a part of the research, we utilized ROC curves to identify the most significant genes for diagnosis, resulting in the selection of the top five hub genes. 2.6 Quantitative real time polymerase chain reaction (RT-qPCR) We used TRIzol (Takara, Japan) to extract RNA from the molt tissue of the induced abortion group and the molt tissue of the control group was subjected to reverse transcription into cDNA using the Prime Script RT kit manufactured by Takara in Japan. Subsequently, SYBR Green Master Mix kit from Qiagen in Germany was utilized for conducting RT-PCR. The internal reference was β-Atin, the analytical method was 2 −ΔΔCt , and the primers used are listed in Table 1 . Table 1 The primers of hub genes and β-Actin. Gene name Forward Reverse TP53 TCTGACTGTACCACCATCCACTA TGTTCCGTCCCAGTAGATTACCA EZH2 TGGTGAATGCCCTTGGTCAATAT AGTTCTTCTGCTGTGCCCTTATC SLC3A2 CCAGGTTCGGGACATAGAGAATC CCCAGTAGAACCAGAATCAGACA TIMP1 CAGACCACCTTATACCAGCGTTAT GTTGTGGGACCTGTGGAAGTATC GABARAPL2 TCTCAGGCTCTCAGATTGTTGAC TCTCGTAAAGCTGTCCCATAGTT β-Atin TGCCACCCAGCACAATGAA CTAAGTCATAGTCCGCCTAGAAGCA 2.7 Analysis of immune cell infiltration CIBERSORT serves as an analytical instrument capable of determining the proportional expression levels of various immune cell types within tissue gene expression profiles. We analyzed the relative content of immune cell infiltration in induced abortion using the CIBERSORT software package and the LM22 file containing 22 immune cells[ 17 ], and visualized immune cell heat maps and correlation maps using the "pheatmap" and "corrplot" software packages, respectively. We conducted an analysis on the outcomes of immune cell infiltration by employing the "vioplot" software package to examine any disparities in the findings related to immune cell infiltration. Finally, we conducted an examination of the association between crucial genes and immune cells. 2.8 Construction of the ceRNA network We employed the miRDB, TargetScan, and miRanda databases for the prediction of miRNA pairs linked to the with the five hub genes, the SpongeScan database to predict lncRNAs, and finally constructed ceRNA networks using Cytoscap3. 9.1 software. 2.9 Statistical analysis The data was statistically analyzed using SPSS software (version 23.0), and the measurement information was represented as the average ± variability (xˉ± s). Group comparisons were conducted using the independent samples t-test, and differences were considered significant at P < 0.05. 3. Results 3.1 Patient demographics There were no significant differences in age, gestational age, body mass index (BMI), number of previous pregnancies, number of deliveries, or number of abortions between the two groups (all P > 0.05; Table 2 ). Table 2 Control(n = 20) Missed abortion group (n = 20) P Age(years) 29.15 ± 5.28 31.40 ± 4.30 0.14 Gestational 1.30 ± 1.42 1.00 ± 1.17 0.47 Weeks(weeks) 7.73 ± 0.74 7.99 ± 0.80 0.31 Number of births (times) 0.75 ± 0.91 0.45 ± 0.51 0.21 Number of induced abortions (number) 0.35 ± 0.67 0.40 ± 0.88 0.84 BMI(kg/m2) 21.66 ± 2.16 22.25 ± 1.92 0.37 3.2 Differential gene analysis We screened 805 upregulated DEGs and 576 downregulated DEGs in induced abortion tissues compared to normal tissues using the limma package, applying the criteria of |log2(FC)| > 0.585 and p-value < 0.05 as thresholds for the dataset (Supplementary table 1 ). Volcano and heat maps were used to visualize DEGs (Fig. 1 A, B). The heat maps show only the top 50 differentially expressed genes. 3.3 WGCNA results and DEFRGS identification We performed a WGCNA on the dataset to plot a sample clustering dendrogram (Fig. 2 A). The construction of a scale-free network involved setting the soft threshold at 15 (R 2 = 0.972)(Fig. 2 B). The matrices underwent a conversion process to generate an adjacency matrix and a TOM matrix, followed by TOM matrix gene clustering, dynamic tree clipping, clustering modules, and merging of similar modules to obtain 10 modules (Fig. 2 D). Among the 10 modules examined, it was observed that the green module exhibited a significantly strong association with missed abortions (correlation coefficient = 0.96, P-value = 9e-06)(Fig. 2 C). Hence, the green module was chosen for further analysis due to its clinical significance. We performed MM and gene significance (GS) correlation analyses and found a significant positive correlation between them (correlation coefficient = 0.91, p < 1e-200) (Fig. 2 E). These results suggest that the genes within the module associated with green color were most closely associated with missed abortions. The screening process identified 896 key genes by applying criteria such as geneTraitSignificance > 0.5 and geneModuleMembership > 0.8. To exclude DEFRGS, 896 key genes were Wayne plotted against the missed-abortion differential genes to obtain 786 intersecting DEGs (Fig. 3 A). A further 29 DEFRGS were obtained by taking the intersections using a Wayne plot with the ferroptosis-related genes (Fig. 3 B, C). 3.4 Functional enrichment analysis The "clusterProfiler" software package was utilized to conduct functional enrichment analysis of DEFRGS. GO analysis was performed, and ferroptosis-related DEGs were concentrated in bioengineering, and ferroptosis-related DEGs were mainly involved in cellular response to abiotic and environmental stimulus, chemical stress, and peptides; in terms of cellular components, DEGs associated with ferroptosis were predominantly enriched in the uppermost region of the cell, the membrane facing outward from the top surface, and a specialized membrane involved in cellular transport.; in terms of molecular functions, iron-death-related DEGs were concentrated in bioengineering. In terms of molecular function, DEGs associated with ferroptosis were mainly enriched in proteinase binding, ATP hydrolysis activity, organic anion transmembrane transporter activity, and tein ligase binding (Fig. 4 A). Ferroptosis-related DEGs were centrally enriched in GO:0040015, 0071236, 0015562, and 0070064 (Fig. 4 B). Furthermore, the KEGG enrichment analysis revealed that differentially expressed genes (DEGs) were linked to ferroptosis were involved in the p53 signaling, animal mitophagy, protein digestion and absorption, and ferroptosis pathways (Fig. 4 C). 3.5 PPI network analysis and hub gene identification A PPI network of DEFRGS was constructed using STRING to identify interactions between ferroptosis-related DEGs (Fig. 5 A). The top 10 hub genes, NEDD4, GJA1, SLC3A2, TIMP1, RRM2, EZH2, KDM6B, YAP1, GABARAP2, and TP53, were selected using Cytohubba Cytoscope plug-ins according to their degree (v. 3.9.1; Fig. 5 B). To assess the reliability of the pivotal genes, we screened the top 5 pivotal genes, TP53 (AUC = 1.000), EZH2 (AUC = 1.000), TIMP1 (AUC = 1.000), SLC3A2 (AUC = 1.000), and GABARAPL2 (AUC = 1.000), vie ROC curve analysis(Fig. 5 C). And heat maps was used to visualize the top 5 pivotal genes (Fig. 5 D). Additionally, the correlations between the five hub genes were analyzed (Fig. 5 E). 3.6 Validation of pivotal genes RT-qPCR analysis was conducted on chorionic villus tissues obtained from both themissed abortion and control groups. The results revealed that the mRNA expression levels of TP53, EZH2, and SLC3A2 were elevated in the missed abortion group compared to the control group. Conversely, TIMP1 and GABARAPL2 exhibited lower mRNA expression levels in the missed abortion group as compared to the control group.(Fig. 6 ). 3.7 Immune cell infiltration results CIBERSORT found that the infiltration levels of CD8 T lymphocytes and M2 macrophages were significantly higher in the missed-abortion group than in the control group (Fig. 7 B, C). A positive correlation was observed between CD8 T lymphocytes and M2 macrophages (Fig. 7 A). The associations between the five hub genes and immune cells were additionally explored through Spearman's correlation analysis.The results showed that TIMP1 was negatively correlated with B cell ntive (Fig. 8 A, E) and exhibits a strong positive correlation with B cell memory and RMSE(Fig. 8 B-C, E). The expression of SLC3A2 showed an inverse correlation with the presence of CD8 T lymphocytes(Fig. 8 D, F). 3.8 Construction of the ceRNA network We constructed ceRNA networks and predicted the mRNA-miRNA-lncRNA relationships for the hub genes TP53, EZH2, TIMP1, and GABARAPL2 (Fig. 9 A, B, C, D). However, the interacting miRNAs predicted by SLC3A2 in the miRanda, miRDB, and TargetScan daabases were hsa-miR-661, hsa-miR-4311. There were no interacting lncRNAs in the spongteScan database. 4. Discussion Early missed abortions refer to a form of pregnancy termination where the embryo or fetus has ceased development but remains retained within the uterus[ 18 ]. Ferroptosis is a unique form of cellular demise that can be distinguished from autophagy and necrosis[ 19 , 20 ]. GPX4, the fatty acid-activating enzyme ACSL4, glutathione (GSH), and cysteine-glutamate reverse transporter proteins mediate iron-mediated death by regulating lipid peroxidation in cells [ 21 ]. In the case of induced abortion, the mechanism underlying the involvement of iron-mediated death in abortion has yet to be investigated. The recent development of bioinformatics has resulted in bioinformatic methods have increasingly being used for the diagnosis and treatment of miscarriage [ 22 – 24 ]. This study used bioinformatics to explore potential target genes and pathways of ferroptosis in early missed abortions. In this research, we conducted differential expression analysis on the complete transcriptome data of aborted tissues and identified a total of 805 differentially upregulated genes and 576 differentially downregulated genes. A green gene module containing 896 key genes was screened using WGCNA and intersected with ferroptosis-related genes to identify 29 DEFRGS. Finally, a series of bioinformatic analyses were performed on these ferroptosis-related DEGs. KEGG results showed that missed abortion-associated DEGs were mainly involved in the p53 signaling pathway and the mitophagy-animal, protein digestion, and absorption pathways. In the p53 signaling pathway, p53 protein accumulates in cells after activation by various stress responses, regulates the expression of other target genes, and further regulates biological processes such as iron death, apoptosis, and metabolism[ 21 ]. Interestingly, it has been found that P53 both induces and inhibits the onset of iron-mediated cell death, with the latter achieved by promoting the expression of the downstream target CDKN1A[ 25 , 26 ]. Increasing evidence suggests that the p53 signaling pathway plays an important role in abortive disorders, with deletion of PARP-1 and PARP-2 promoting increased p53 signaling, resulting in metaphase arrest[ 27 ]. Additionally, genetic variability in the p53 signaling pathway plays a role in endometrial tolerance and pregnancy maintenance during in vitro fertilization [ 28 ]. PPI analysis of 29 ferroptosis-related DEGs was performed, and five hub genes (TP53, EZH2, TIMP1, SLC3A2, and GABARAPL2) were screened using Cytoscape 3.9.1 software and analyzed using ROC curves. Additionally, we verified the manifestation of the central genes using RT-qPCR. mRNA expression of TP53, EZH2, and SLC3A2 and TIMP1 and GABARAPL2 was increased and decreased, respectively, in the missed-abortion group. TP53 encodes the p53 oncoprotein. In the context of transcription, p53 has been suggested to have an impact on autophagy, apoptosis, senescence, DNA repair, and ferroptosis pathways [ 29 ]. During embryo implantation, p53 regulates apoptosis, angiogenesis, and genome stability [ 30 ]. Reduced EZH2 expression, which leads to attenuated trophoblast invasion and induces the polarization of meconium M1 macrophages, has been associated with recurrent abortion [ 31 , 32 ]. TIMP1 belongs to a group of metalloproteinase tissue inhibitors, and genetic variations in TIMP1 are known causes of miscarriages [ 33 ]. Previous studies have identified SLC3A2 as a potential focus for the treatment of various medical conditions, including iron death-associated osteoarthritis [ 34 ]. Furthermore, SLC3A2 downregulation leads to vascular endothelial iron death and promotes atherosclerosis progression [ 35 ]. GABARAPL2 may be involved in aortic coarctation, sepsis, and iron death in adrenocortical carcinoma [ 36 – 38 ]. Additionally, Our analysis of immune infiltration revealed increased levels of CD8 T lymphocytes and M2 macrophages in the group that underwent induced abortion. Cornish et al. [ 39 ] reported that maternal CD8 T lymphocytes invaded the chorionic villi and underwent destructive infiltration with the activation of fetal chorionic macrophages, which in turn led to recurrent adverse pregnancy outcomes. Although numerous studies have found that M1 and M2 macrophage imbalances promote the progression of recurrent miscarriage, the specific underlying mechanism still requires further investigation [ 40 ]. We found increased M2 macrophage infiltration, but we were unable to determine its effect on miscarriage. Finally, we explored the mRNA-miRNA-lncRNA relationships of hub genes using the ceRNA network. This study confirmed the key modules and genes associated with ferroptosis in missed abortions, studied Immune cell infiltration during missed abortions, and constructed a ceRNA network. Our research offers novel perspectives on the mitigation, identification, and management of undetected pregnancy loss. However, this study is subject to certain limitations: the sample size was limited, which increases the likelihood of false positives resulting from the constraints of the enrichment method. Additionally, the functional impacts of the hub genes that have been screened were not explored, limiting the interpretation of our results. 5. Conclusion Five iron ferroptosis-related DEGs associated with missed abortions were identified by WGCNA. These DEGs are likely key targets for the prevention and treatment of missed abortions. Immune cell infiltration of missed abortions was also investigated and a ceRNA network was constructed. This research offers a fresh perspective for investigating the potential targets of regulation and the potential mechanisms involved in cases of missed abortions, and provides new targets that can be explored and eventually targeted for the diagnosis and treatment of missed abortions. Declarations Author Contributions Yulu Zeng extracted the data, performed statistical analysis, and drafted a paper. Jayi Gan conducted a survey of literature and data validation. Jinlian Cheng contributed to the language revision. Changqiang Wei and Xiangyun Zhu supplemented the literature and participated in the revision of the article. Shisi Wei was involved in the literature survey and statistical analysis, Lihong Pang reviewed the manuscript. Funding This research was funded by grants from the National Natural Science Foundation of China (Nos.82260306)，Special Fund of Clinical Research Climbing Program Innovation Team of the First Afliated Hospital of Guangxi Medical University (YYZS2022006) and Guangxi key R & D program (2023AB22091) Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethical Review Committee First Affiliated Hospital of Guangxi Medical University (protocol code 2024-E136-01 and February 28, 2024 of approval) Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement Data is provided within the manuscript or supplementary information files.Sequence data that support the findings of this study is available from the corresponding author on reasonable request. Acknowledgments We acknowledge the support from the National Natural Science Foundation of China. 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Reproductive sciences (Thousand Oaks, Calif) 2022, 29(10):2820–2828. Pereza N, Volk M, Zrakić N, Kapović M, Peterlin B, Ostojić S: Genetic variation in tissue inhibitors of metalloproteinases as a risk factor for idiopathic recurrent spontaneous abortion. Fertility and sterility 2013, 99(7):1923–1929. Liu H, Deng Z, Yu B, Liu H, Yang Z, Zeng A, Fu M: Identification of SLC3A2 as a Potential Therapeutic Target of Osteoarthritis Involved in Ferroptosis by Integrating Bioinformatics, Clinical Factors and Experiments. Cells 2022, 11(21). Xiang P, Chen Q, Chen L, Lei J, Yuan Z, Hu H, Lu Y, Wang X, Wang T, Yu R et al : Metabolite Neu5Ac triggers SLC3A2 degradation promoting vascular endothelial ferroptosis and aggravates atherosclerosis progression in ApoE(-/-)mice. Theranostics 2023, 13(14):4993–5016. Pan H, Lu W, Liu Z, Wang Y: Identification of ferroptosis-associated biomarkers in Stanford type A aortic dissection based on machine learning. American journal of translational research 2023, 15(5):3092–3114. Zhu S, Huang Y, Ye C: Identification of a Ferroptosis-Related Prognostic Signature in Sepsis via Bioinformatics Analyses and Experiment Validation. BioMed research international 2022, 2022:8178782. Lin C, Hu R, Sun F, Liang W: Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression. Journal of clinical laboratory analysis 2022, 36(6):e24465. Cornish EF, McDonnell T, Williams DJ: Chronic Inflammatory Placental Disorders Associated With Recurrent Adverse Pregnancy Outcome. Frontiers in immunology 2022, 13:825075. Zhao QY, Li QH, Fu YY, Ren CE, Jiang AF, Meng YH: Decidual macrophages in recurrent spontaneous abortion. Frontiers in immunology 2022, 13:994888. Supplementary Tables Supplementary Table 2 is not available with this version. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.pdf Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers invited by journal 13 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Editor invited by journal 05 Aug, 2024 Submission checks completed at journal 31 Jul, 2024 First submitted to journal 19 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4766662","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":344559719,"identity":"e60119d2-ae13-4129-8fc2-d54d6a1efd7d","order_by":0,"name":"Yulu Zeng","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Zeng","suffix":""},{"id":344559720,"identity":"5649b37e-2e18-47f7-b007-b8e120dd1988","order_by":1,"name":"Jayi Gan","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jayi","middleName":"","lastName":"Gan","suffix":""},{"id":344559721,"identity":"024149e5-0622-4d8a-abc8-32af3ad07641","order_by":2,"name":"Jinlian Cheng","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinlian","middleName":"","lastName":"Cheng","suffix":""},{"id":344559722,"identity":"0e157abd-4441-4e06-b0c3-56cf13cba527","order_by":3,"name":"Changqiang Wei","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changqiang","middleName":"","lastName":"Wei","suffix":""},{"id":344559723,"identity":"052922b9-e969-4cdb-807d-a3c742a0be50","order_by":4,"name":"Xiangyun Zhu","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangyun","middleName":"","lastName":"Zhu","suffix":""},{"id":344559724,"identity":"f972e882-54f7-4647-8e19-614fd79dd1cb","order_by":5,"name":"Shisi Wei","email":"","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shisi","middleName":"","lastName":"Wei","suffix":""},{"id":344559725,"identity":"0f423740-fd52-4a47-bef2-6037636400dd","order_by":6,"name":"Lihong Pang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYLCCBwYHgCTzAYQIDyEtCWAtbAmkaGEAaeExIE6LOfvZwy8SCu7ImfOv+fy5gqE2ce2MBMYHb9sY5M1xaLHsyUuzSDB4Zmw54+02yTMMx43NbiQwG85tYzDc2YBdi8GBHDODBIPDiRtunN3G2MBwTA6ohU2atw3qQWxazr+BaTnz+CNQCw9QC/tvvFpu5Bg/AGs538Mg2cBQA7aFGZ8WyxlvzICyh40NbrCZSTYYHDA2O/OwWXLOOQnDDTi0mPPnGH/48OewnMH5w0CHVdQlbjuefPDDmzIbeZwOA0ahBJglkQDiHgYSwFAAcrGrh2hh/gBm8YMNrcOpchSMglEwCkYuAAAe52PqI63M9AAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Prenatal Diagnosis, The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Pang","suffix":""}],"badges":[],"createdAt":"2024-07-19 07:10:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4766662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4766662/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84135-3","type":"published","date":"2025-01-03T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63376846,"identity":"77fe4981-b272-499e-9697-ee10c4003681","added_by":"auto","created_at":"2024-08-27 12:56:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":327682,"visible":true,"origin":"","legend":"\u003cp\u003eDEG analysis of patients with missed abortion and healthy controls. (A) Heatmap displaying the top 50 DEGs. (B) DEGs are depicted in a volcano plot, with up-regulated genes represented by red dots, down-regulated genes represented by green dots, and non-significant genes represented by black dots.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/c1b33bf17554f6bd82394242.png"},{"id":63377343,"identity":"2ca14eda-5513-4186-a35a-1b4f2d43ec62","added_by":"auto","created_at":"2024-08-27 13:04:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":728991,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of modules with co-expression.(A) Clustering dendrogram depicting the relationships among a set of 10 samples;(B) scale-free exponential analysis of various soft-threshold efficacies (β) and average connectivity analytical properties of various soft-threshold powers; (C) heatmap of the correlation between genes and clinical traits; (D) clustering dendrogram of genes with different colors representing different modules; and (E) scatter plot depicting the relationship between MM and GS in the green module.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/ee385aa224a8dc5d54f77bc3.png"},{"id":63377342,"identity":"e5cc7cd1-73b0-43b8-8ce3-3b84b2873a42","added_by":"auto","created_at":"2024-08-27 13:04:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211046,"visible":true,"origin":"","legend":"\u003cp\u003eDEFRGS identification. (A) A Venn diagram of green module genes and DEGs; (B) a Venn diagram of DEFRGS; (C) the heat map of DEFRGS\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/214c1d2cc021cc6b0ac86f6c.png"},{"id":63377344,"identity":"f557e690-743e-4734-a6bf-29bffd31fff7","added_by":"auto","created_at":"2024-08-27 13:04:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":647661,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment pathway analysis of DEG associated with ferrous (A) GO enrichment analysis histogram (BP, biological process; CC, cellular component; MF, molecular function),(B) GO enrichment analysis circle diagram, and (C) KEGG pathway enrichment analysis histogram.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/f51a1cecaba40567ded1c162.png"},{"id":63376853,"identity":"3bde95d2-6a35-440e-8b93-22c603fcba31","added_by":"auto","created_at":"2024-08-27 12:56:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":427103,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network and hub genes. (A) PPI network composed of iron death-associated DEGs; (B) top 10 hub genes. Redder colors indicate more likely network core genes; (C) ROC curves of the top 5 hub genes; (D) heatmap of the top 5 hub genes; (E) correlation of the top 5 hub genes\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/c93152c5767088b36e2eeaad.png"},{"id":63377345,"identity":"c795b8e5-e214-4a5d-a1db-ef06b973138b","added_by":"auto","created_at":"2024-08-27 13:04:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":260277,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene mRNA expression levels. (A) EZH2; (B)GABARAPL2 ; (C) TIMP1 ; (D) SLC3A2 ; (E)TP53.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/ae7aa129ffff70504f6a78a8.png"},{"id":63376854,"identity":"3cda4d65-ff84-4412-af04-cdf78adab9ef","added_by":"auto","created_at":"2024-08-27 12:56:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":592481,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape depicting the presence of 22 distinct immune cell infiltrations associated with early missed abortions. (A) Heat map illustrating the associations among different immune cell populations, where shades of red and blue represent positive and negative correlations correspondingly.(B) Heat map depicting the distribution of immune cell populations. (C) Schematic illustration depicting the disparity in immune cell infiltration proportions observed between the early missed abortion group and the control group.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/9da68799e0175b31e45d9b1f.png"},{"id":63376848,"identity":"e9f9668d-8cfb-40c3-806f-8c881263f559","added_by":"auto","created_at":"2024-08-27 12:56:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":527076,"visible":true,"origin":"","legend":"\u003cp\u003eLollipop diagrams showing the relationship between missed abortion-associated hub genes and immune cell infiltration. (A-B, E) TIMP1; (D, F) SLC3A2.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/ea2806130f5c8e2f28b91e74.png"},{"id":63376850,"identity":"4cfeae0a-bb60-4e46-83cf-e574ab9bedd8","added_by":"auto","created_at":"2024-08-27 12:56:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":913356,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of ceRNA networks. (A) TP53 ceRNA network; (B) EZH2 ceRNA network; (C) TIMP1 ceRNA network; (D) GABARAPL2 ceRNA network. Red diamonds, mRNAs; green triangles, miRNAs; blue circles, lncRNAs; lines between nodes, regulatory relationships.\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/cb0ea5a14f09ce79e960852b.png"},{"id":73093222,"identity":"3f92e46f-0e4f-40fa-9aac-d4f666cb5821","added_by":"auto","created_at":"2025-01-06 16:11:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1865743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/37ab2fe3-32c7-4029-ba61-f4c5a1307d9c.pdf"},{"id":63376851,"identity":"f9d8afea-a541-4899-ad81-01bbf641ee56","added_by":"auto","created_at":"2024-08-27 12:56:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":290293,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4766662/v1/36f96bd9d819760581c841bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of important genes related to ferroptosis in early missed abortion based on WGCNA","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEarly missed abortion refers to a form of miscarriage in which the development of the fetus stops and there is no outflow of embryonic tissue. Most induced abortions occur during the early stages of pregnancy[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early missed abortion is defined as a pregnancy of \u0026le;\u0026thinsp;12 weeks in which the embryo or fetus has died and is retained in the uterine cavity without timely spontaneous expulsion[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Studies have shown that missed abortions are mainly associated with chromosomal abnormalities of the embryo and that infections, imbalances in immune regulation, malformations of the maternal reproductive organs, and abnormalities of the intrauterine environment are also involved in missed abortions. However, the exact mechanism of development has not yet been clarified [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFerroptosis is a recently discovered cell death pathway, whose main mechanisms are iron overload, lipid peroxidation, and glutathione/GPX4 non-dependent axis imbalance[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have shown that ferroptosis is associated with pregnancy-related conditions, such as spontaneous abortion, preeclampsia, gestational diabetes mellitus, intrahepatic cholestasis during pregnancy, and spontaneous preterm labor[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which directly inhibits angiogenesis and ultimately induces abortion[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn alysis of co-expression networks using weighted gene correlation (WGCNA) is an algorithm that links modular signature genes to clinical traits and can be used to identify biomarkers and therapeutic targets for diseases[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImmune cell infiltration plays an increasingly prominent role in various diseases and has been used to assess immune cell infiltration in recurrent miscarriages[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. An imbalance of immune cells at the maternal-fetal interface, such as the imbalance of natural killer (NK) cells, macrophages, T-cells, B-cells, and dendritic cells (DCs), is one of the main causes of miscarriage[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Chorionic villi are crucial in fetal development and are closely related to perinatal relationships[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; however, there have been no definitive studies on immune cell infiltration of chorionic villus tissue in early obstructed abortion. Hence, the examination of immune infiltration could potentially offer novel insights into the immunodiagnosis and management of early undetected miscarriages.\u003c/p\u003e \u003cp\u003eFerroptosis plays a crucial role in pregnancy-related diseases, and could serve as a novel biomarker or a potential therapeutic target for early missed abortions. Thus, this study aimed to identify key genes associated with early missed abortions. However, no studies have explored the potential mechanisms of ferroptosis in the development of early missed abortions. A comprehensive investigation into the phenomenon of ferroptosis could help accurately regulate the development of early missed abortions.\u003c/p\u003e \u003cp\u003eWe used WGCNA to identify key modules and genes associated with early missed abortion and ferroptosis and to highlight the significance of these components and genes in early missed abortions. Additionally, we confirmed the atypical mRNA expression level of the pivotal gene in early missed abortion using RT-qPCR and evaluated the infiltration of immune cells in early missed abortion using CIBERSORT to explore the correlation between the hub genes and immune cells. Finally, a ceRNA network was constructed to predict the miRNA and lncRNA interaction relationships of the hub genes. Finally, a ceRNA network was constructed to predict miRNA and lncRNA interactions of the hub genes. Therefore, this research study establishes a foundation for investigating the potential regulatory objectives and potential mechanisms of early miscarriages, offering innovative perspectives for treatment approaches.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (No. 2024-K186-01 ), and all patients signed an informed consent form.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources and patient selection\u003c/h2\u003e \u003cp\u003eIn this study, whole transcriptome sequencing was performed on chorionic tissue collected from women who were treated at the First Affiliated Hospital of Guangxi Medical University. The sample included five cases of embryonic arrest in females who experience early spontaneous termination of pregnancyand five cases of women with early missed abortions, which were collected in the operating room of the birth control clinic in June 2023. Twenty cases of early pregnant women who had an induced abortion in early pregnancy from January 2023 to February 2023 were collected as the ly missed-abortion group, and 20 cases of early pregnant women with normal pregnancy who voluntarily underwent induced abortion in the same period were selected as the induced abortion group. The inclusion criteria for the missed abortion group were: samples collected at 6 to 12 weeks gestation that met the diagnostic criteria for induced abortion in early pregnancy[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Exclusion criteria were: (1) a history of spontaneous abortion; (2) a difference of more than 2 weeks between the gestational week of menopause and the ultrasound gestational week; (3) missed abortion caused by heredity, mycoplasma or chlamydia infections, etc.; (4) obvious hepatic or renal insufficiency, or cardiac or cerebrovascular diseases; (5) malignant tumors; (6) hematologic or immune diseases; or (7) psychiatric diseases. The induced abortion group was selected from pregnant women with normal fertility who chose abortion for non-medical reasons, without signs and symptoms of abortion, without a history of spontaneous abortion, with fetal heartbeat seen on ultrasound within 3\u0026ndash;4 d before the procedure, and gestational week determined by ultrasound. Data on the participants\u0026rsquo; age, gestational week, body mass index (BMI), number of previous pregnancies, number of deliveries, and number of abortions were collected. Chorionic tissue specimens were collected aseptically under negative pressure aspiration, the clots were removed using sterile forceps, and then rinsed repeatedly in physiological saline until they were free of blood color. The specimens were stored in liquid nitrogen for 24 h and then refrigerated at -80\u0026deg;C until they were used for real-time fluorescence quantitative PCR (RT-qPCR) analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eDifferential expression analysis of the entire transcriptome sequencing data from induced abortion was conducted in our research group using the limma package, employing screening criteria of |log2 FC| \u0026gt; 0.585 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Heatmaps and volcano diagrams were visualized using the \"heatmap\" and \"ggplot2\" packages. Heatmaps and volcano maps were visualized using different packages, namely the \"heatmap\" package for heatmaps and the \"ggplot2\" package for volcano maps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Co-expression network analysis and identification of differentially expressed ferroptosis-related genes (DEFRGS)\u003c/h2\u003e \u003cp\u003eA co-expression network analysis was performed using the \"WGCNA\" software package. Initially, a soft threshold was established to create a network with scale-free characteristics, and subsequently the matrix underwent conversion into neighboring and TOM matrices.The TOM matrix was clustered into genes and grouped into modules by dynamic tree clipping, and each module contained at least 60 genes. The modules were clustered to obtain similar modules and merged. Clustering was performed to obtain and merge similar modules. The clinical data was combined with the modules, and a correlation analysis using Pearson's method was conducted to determine which modules were most strongly linked to induced abortion. We intersected the most closely related module genes with the missed abortion DEGs in a Wayne plot, and further intersected them with the ferroptosis-related genes, which were obtained from the ferroptosis database (\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). We downloaded the three datasets (Marker, Suppressor, and Driver), and detailed information of all the ferroptosis-related genes (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eGO/KEGG pathway analysis of DEFRGS was performed using the \"clusterProfiler\" software package[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and p-values of below 0.05 were set as criteria for significant enrichment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. PPI network analysis and hub gene identification\u003c/h2\u003e \u003cp\u003eIron death-associated DEGs were analyzed using the STRING database with the PPI network. Ten hub genes were screened using cytoHubba (a Cytoscape plug-in). As a part of the research, we utilized ROC curves to identify the most significant genes for diagnosis, resulting in the selection of the top five hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantitative real time polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eWe used TRIzol (Takara, Japan) to extract RNA from the molt tissue of the induced abortion group and the molt tissue of the control group was subjected to reverse transcription into cDNA using the Prime Script RT kit manufactured by Takara in Japan. Subsequently, SYBR Green Master Mix kit from Qiagen in Germany was utilized for conducting RT-PCR. The internal reference was β-Atin, the analytical method was 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e, and the primers used are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primers of hub genes and β-Actin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCTGACTGTACCACCATCCACTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGTTCCGTCCCAGTAGATTACCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEZH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGTGAATGCCCTTGGTCAATAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGTTCTTCTGCTGTGCCCTTATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLC3A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCAGGTTCGGGACATAGAGAATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCAGTAGAACCAGAATCAGACA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIMP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAGACCACCTTATACCAGCGTTAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTGTGGGACCTGTGGAAGTATC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGABARAPL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCTCAGGCTCTCAGATTGTTGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCTCGTAAAGCTGTCCCATAGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Atin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCCACCCAGCACAATGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTAAGTCATAGTCCGCCTAGAAGCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Analysis of immune cell infiltration\u003c/h2\u003e \u003cp\u003eCIBERSORT serves as an analytical instrument capable of determining the proportional expression levels of various immune cell types within tissue gene expression profiles. We analyzed the relative content of immune cell infiltration in induced abortion using the CIBERSORT software package and the LM22 file containing 22 immune cells[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and visualized immune cell heat maps and correlation maps using the \"pheatmap\" and \"corrplot\" software packages, respectively. We conducted an analysis on the outcomes of immune cell infiltration by employing the \"vioplot\" software package to examine any disparities in the findings related to immune cell infiltration. Finally, we conducted an examination of the association between crucial genes and immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Construction of the ceRNA network\u003c/h2\u003e \u003cp\u003eWe employed the miRDB, TargetScan, and miRanda databases for the prediction of miRNA pairs linked to the with the five hub genes, the SpongeScan database to predict lncRNAs, and finally constructed ceRNA networks using Cytoscap3. 9.1 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data was statistically analyzed using SPSS software (version 23.0), and the measurement information was represented as the average\u0026thinsp;\u0026plusmn;\u0026thinsp;variability (xˉ\u0026plusmn; s). Group comparisons were conducted using the independent samples t-test, and differences were considered significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient demographics\u003c/h2\u003e \u003cp\u003eThere were no significant differences in age, gestational age, body mass index (BMI), number of previous pregnancies, number of deliveries, or number of abortions between the two groups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissed abortion group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeeks(weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of births (times)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of induced abortions (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Differential gene analysis\u003c/h2\u003e \u003cp\u003eWe screened 805 upregulated DEGs and 576 downregulated DEGs in induced abortion tissues compared to normal tissues using the limma package, applying the criteria of |log2(FC)| \u0026gt; 0.585 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as thresholds for the dataset (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Volcano and heat maps were used to visualize DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The heat maps show only the top 50 differentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 WGCNA results and DEFRGS identification\u003c/h2\u003e \u003cp\u003eWe performed a WGCNA on the dataset to plot a sample clustering dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The construction of a scale-free network involved setting the soft threshold at 15 (R 2\u0026thinsp;=\u0026thinsp;0.972)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The matrices underwent a conversion process to generate an adjacency matrix and a TOM matrix, followed by TOM matrix gene clustering, dynamic tree clipping, clustering modules, and merging of similar modules to obtain 10 modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Among the 10 modules examined, it was observed that the green module exhibited a significantly strong association with missed abortions (correlation coefficient\u0026thinsp;=\u0026thinsp;0.96, P-value\u0026thinsp;=\u0026thinsp;9e-06)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Hence, the green module was chosen for further analysis due to its clinical significance. We performed MM and gene significance (GS) correlation analyses and found a significant positive correlation between them (correlation coefficient\u0026thinsp;=\u0026thinsp;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;1e-200) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). These results suggest that the genes within the module associated with green color were most closely associated with missed abortions.\u003c/p\u003e \u003cp\u003eThe screening process identified 896 key genes by applying criteria such as geneTraitSignificance\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and geneModuleMembership\u0026thinsp;\u0026gt;\u0026thinsp;0.8. To exclude DEFRGS, 896 key genes were Wayne plotted against the missed-abortion differential genes to obtain 786 intersecting DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A further 29 DEFRGS were obtained by taking the intersections using a Wayne plot with the ferroptosis-related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe \"clusterProfiler\" software package was utilized to conduct functional enrichment analysis of DEFRGS. GO analysis was performed, and ferroptosis-related DEGs were concentrated in bioengineering, and ferroptosis-related DEGs were mainly involved in cellular response to abiotic and environmental stimulus, chemical stress, and peptides; in terms of cellular components, DEGs associated with ferroptosis were predominantly enriched in the uppermost region of the cell, the membrane facing outward from the top surface, and a specialized membrane involved in cellular transport.; in terms of molecular functions, iron-death-related DEGs were concentrated in bioengineering. In terms of molecular function, DEGs associated with ferroptosis were mainly enriched in proteinase binding, ATP hydrolysis activity, organic anion transmembrane transporter activity, and tein ligase binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Ferroptosis-related DEGs were centrally enriched in GO:0040015, 0071236, 0015562, and 0070064 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, the KEGG enrichment analysis revealed that differentially expressed genes (DEGs) were linked to ferroptosis were involved in the p53 signaling, animal mitophagy, protein digestion and absorption, and ferroptosis pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 PPI network analysis and hub gene identification\u003c/h2\u003e \u003cp\u003eA PPI network of DEFRGS was constructed using STRING to identify interactions between ferroptosis-related DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The top 10 hub genes, NEDD4, GJA1, SLC3A2, TIMP1, RRM2, EZH2, KDM6B, YAP1, GABARAP2, and TP53, were selected using Cytohubba Cytoscope plug-ins according to their degree (v. 3.9.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). To assess the reliability of the pivotal genes, we screened the top 5 pivotal genes, TP53 (AUC\u0026thinsp;=\u0026thinsp;1.000), EZH2 (AUC\u0026thinsp;=\u0026thinsp;1.000), TIMP1 (AUC\u0026thinsp;=\u0026thinsp;1.000), SLC3A2 (AUC\u0026thinsp;=\u0026thinsp;1.000), and GABARAPL2 (AUC\u0026thinsp;=\u0026thinsp;1.000), vie ROC curve analysis(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). And heat maps was used to visualize the top 5 pivotal genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Additionally, the correlations between the five hub genes were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Validation of pivotal genes\u003c/h2\u003e \u003cp\u003eRT-qPCR analysis was conducted on chorionic villus tissues obtained from both themissed abortion and control groups. The results revealed that the mRNA expression levels of TP53, EZH2, and SLC3A2 were elevated in the missed abortion group compared to the control group. Conversely, TIMP1 and GABARAPL2 exhibited lower mRNA expression levels in the missed abortion group as compared to the control group.(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Immune cell infiltration results\u003c/h2\u003e \u003cp\u003eCIBERSORT found that the infiltration levels of CD8 T lymphocytes and M2 macrophages were significantly higher in the missed-abortion group than in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C). A positive correlation was observed between CD8 T lymphocytes and M2 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe associations between the five hub genes and immune cells were additionally explored through Spearman's correlation analysis.The results showed that TIMP1 was negatively correlated with B cell ntive (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, E) and exhibits a strong positive correlation with B cell memory and RMSE(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-C, E). The expression of SLC3A2 showed an inverse correlation with the presence of CD8 T lymphocytes(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Construction of the ceRNA network\u003c/h2\u003e \u003cp\u003eWe constructed ceRNA networks and predicted the mRNA-miRNA-lncRNA relationships for the hub genes TP53, EZH2, TIMP1, and GABARAPL2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, C, D). However, the interacting miRNAs predicted by SLC3A2 in the miRanda, miRDB, and TargetScan daabases were hsa-miR-661, hsa-miR-4311. There were no interacting lncRNAs in the spongteScan database.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEarly missed abortions refer to a form of pregnancy termination where the embryo or fetus has ceased development but remains retained within the uterus[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ferroptosis is a unique form of cellular demise that can be distinguished from autophagy and necrosis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. GPX4, the fatty acid-activating enzyme ACSL4, glutathione (GSH), and cysteine-glutamate reverse transporter proteins mediate iron-mediated death by regulating lipid peroxidation in cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the case of induced abortion, the mechanism underlying the involvement of iron-mediated death in abortion has yet to be investigated. The recent development of bioinformatics has resulted in bioinformatic methods have increasingly being used for the diagnosis and treatment of miscarriage [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This study used bioinformatics to explore potential target genes and pathways of ferroptosis in early missed abortions.\u003c/p\u003e \u003cp\u003eIn this research, we conducted differential expression analysis on the complete transcriptome data of aborted tissues and identified a total of 805 differentially upregulated genes and 576 differentially downregulated genes. A green gene module containing 896 key genes was screened using WGCNA and intersected with ferroptosis-related genes to identify 29 DEFRGS. Finally, a series of bioinformatic analyses were performed on these ferroptosis-related DEGs.\u003c/p\u003e \u003cp\u003eKEGG results showed that missed abortion-associated DEGs were mainly involved in the p53 signaling pathway and the mitophagy-animal, protein digestion, and absorption pathways. In the p53 signaling pathway, p53 protein accumulates in cells after activation by various stress responses, regulates the expression of other target genes, and further regulates biological processes such as iron death, apoptosis, and metabolism[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Interestingly, it has been found that P53 both induces and inhibits the onset of iron-mediated cell death, with the latter achieved by promoting the expression of the downstream target CDKN1A[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Increasing evidence suggests that the p53 signaling pathway plays an important role in abortive disorders, with deletion of PARP-1 and PARP-2 promoting increased p53 signaling, resulting in metaphase arrest[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, genetic variability in the p53 signaling pathway plays a role in endometrial tolerance and pregnancy maintenance during in vitro fertilization [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePPI analysis of 29 ferroptosis-related DEGs was performed, and five hub genes (TP53, EZH2, TIMP1, SLC3A2, and GABARAPL2) were screened using Cytoscape 3.9.1 software and analyzed using ROC curves. Additionally, we verified the manifestation of the central genes using RT-qPCR. mRNA expression of TP53, EZH2, and SLC3A2 and TIMP1 and GABARAPL2 was increased and decreased, respectively, in the missed-abortion group. TP53 encodes the p53 oncoprotein. In the context of transcription, p53 has been suggested to have an impact on autophagy, apoptosis, senescence, DNA repair, and ferroptosis pathways [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. During embryo implantation, p53 regulates apoptosis, angiogenesis, and genome stability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Reduced EZH2 expression, which leads to attenuated trophoblast invasion and induces the polarization of meconium M1 macrophages, has been associated with recurrent abortion [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. TIMP1 belongs to a group of metalloproteinase tissue inhibitors, and genetic variations in TIMP1 are known causes of miscarriages [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous studies have identified SLC3A2 as a potential focus for the treatment of various medical conditions, including iron death-associated osteoarthritis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, SLC3A2 downregulation leads to vascular endothelial iron death and promotes atherosclerosis progression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. GABARAPL2 may be involved in aortic coarctation, sepsis, and iron death in adrenocortical carcinoma [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, Our analysis of immune infiltration revealed increased levels of CD8 T lymphocytes and M2 macrophages in the group that underwent induced abortion. Cornish et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] reported that maternal CD8 T lymphocytes invaded the chorionic villi and underwent destructive infiltration with the activation of fetal chorionic macrophages, which in turn led to recurrent adverse pregnancy outcomes. Although numerous studies have found that M1 and M2 macrophage imbalances promote the progression of recurrent miscarriage, the specific underlying mechanism still requires further investigation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We found increased M2 macrophage infiltration, but we were unable to determine its effect on miscarriage.\u003c/p\u003e \u003cp\u003eFinally, we explored the mRNA-miRNA-lncRNA relationships of hub genes using the ceRNA network.\u003c/p\u003e \u003cp\u003eThis study confirmed the key modules and genes associated with ferroptosis in missed abortions, studied Immune cell infiltration during missed abortions, and constructed a ceRNA network. Our research offers novel perspectives on the mitigation, identification, and management of undetected pregnancy loss. However, this study is subject to certain limitations: the sample size was limited, which increases the likelihood of false positives resulting from the constraints of the enrichment method. Additionally, the functional impacts of the hub genes that have been screened were not explored, limiting the interpretation of our results.\u003c/p\u003e"},{"header":"5. Conclusion ","content":"\u003cp\u003eFive iron ferroptosis-related DEGs associated with missed abortions were identified by WGCNA. These DEGs are likely key targets for the prevention and treatment of missed abortions. Immune cell infiltration of missed abortions was also investigated and a ceRNA network was constructed. This research offers a fresh perspective for investigating the potential targets of regulation and the potential mechanisms involved in cases of missed abortions, and provides new targets that can be explored and eventually targeted for the diagnosis and treatment of missed abortions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYulu Zeng extracted the data, performed statistical analysis, and drafted a paper. Jayi Gan conducted a survey of literature and data validation. Jinlian Cheng contributed to the language revision. Changqiang Wei and Xiangyun Zhu supplemented the literature and participated in the revision of the article. Shisi Wei was involved in the literature survey and statistical analysis, Lihong Pang reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by grants from the National Natural Science Foundation of China (Nos.82260306)，Special Fund of Clinical Research Climbing Program Innovation Team of the First Afliated Hospital of Guangxi Medical University (YYZS2022006) and Guangxi key R \u0026amp; D program (2023AB22091)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethical Review Committee First Affiliated Hospital of Guangxi Medical University (protocol code 2024-E136-01 and February 28, 2024 of approval)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.Sequence data that support the findings of this study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the support from the National Natural Science Foundation of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQuenby S, Gallos ID, Dhillon-Smith RK, Podesek M, Stephenson MD, Fisher J, Brosens JJ, Brewin J, Ramhorst R, Lucas ES \u003cem\u003eet al\u003c/em\u003e: Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. 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Frontiers in immunology 2022, 13:994888.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Table 2 is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early missed abortion, ferroptosis, WGCNA, immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-4766662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4766662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarly missed abortion is defined as a pregnancy of \u0026le;\u0026thinsp;12 weeks in wherein there is a cessation of life in the developing embryo or fetus, leading to its retention within the uterine cavity\", failing to be expelled spontaneously in a timely manner. This is a commonly observed and significant pathological state that has an impact on the overall well-being of human reproductive health. The aim of this study was to identify key genes related to ferroptosis that could serve as novel biomarkers for early missed abortion. Relevant findings from gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicate a correlation between iron DEFRGS in key modules and the p53 signaling, mitophagy-animal, as well as protein digestion and absorption pathways. An analysis of the protein-protein interaction (PPI) network was conducted on DEFRGS, resulting in the identification of five central genes (TP53, EZH2, TIMP1, SLC3A2, and GABARAPL2) through the utilization of STRING and Cytohubba ROC curves.The expression of pivotal genes in the missed-abortion and control groups was verified by RT-qPCR. CIBERSORT analysis revealed a notable increase in the infiltration levels of CD8 T lymphocytes and M2 macrophages among individuals in the early missed abortion group. Ultimately, a ceRNA network was established in order to anticipate the connections between mRNA-miRNA-lncRNA of the central genes. However, the interacting miRNAs predicted by SLC3A2 in the miRanda, miRDB, and TargetScan databases were hsa-miR-661, hsa-miR-4311. There were no interacting lncRNAs in the spongeScan database. This research has discovered novel genes that can be targeted for the early detection and management of miscarriages.\u003c/p\u003e","manuscriptTitle":"Identification of important genes related to ferroptosis in early missed abortion based on WGCNA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 12:56:32","doi":"10.21203/rs.3.rs-4766662/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"308753105015714069899913389583078820261","date":"2024-08-20T11:32:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291801675526355243620265950797512716411","date":"2024-08-14T13:06:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-13T16:44:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T22:37:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-06T03:05:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-31T04:48:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-19T07:09:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2295506e-a36a-4490-9b02-b2574d9b7de0","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36521834,"name":"Biological sciences/Biochemistry"},{"id":36521835,"name":"Biological sciences/Genetics"},{"id":36521836,"name":"Biological sciences/Immunology"},{"id":36521837,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-01-06T16:00:37+00:00","versionOfRecord":{"articleIdentity":"rs-4766662","link":"https://doi.org/10.1038/s41598-024-84135-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-03 15:57:17","publishedOnDateReadable":"January 3rd, 2025"},"versionCreatedAt":"2024-08-27 12:56:32","video":"","vorDoi":"10.1038/s41598-024-84135-3","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84135-3","workflowStages":[]},"version":"v1","identity":"rs-4766662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4766662","identity":"rs-4766662","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}