Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues

Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues | 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 Article Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues Hui Ding, Yajie Gao, Yuan Gao, Yulu Chen, Ruimin Liu, Caili Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4441689/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background Recurrent pregnancy loss (RPL) is a common reproductive complication, and the specific pathogenesis is still unclear. This study aimed to investigate RPL-related biomarkers and molecular mechanisms from the transcriptome of RPL decidua tissue using modern bioinformatics techniques, providing new perspectives for the etiology and clinical diagnosis and treatment of RPL. Methods Three gene expression profiles of RPL decidua tissue were retrieved and downloaded from the GEO database. Differential analysis, WGCNA analysis, and functional enrichment analysis were performed on the merged data. Subsequently, three machine learning methods (LASSO, SVM-RFE, and RF) were used to select the optimal feature genes for RPL, which were experimentally validated by RT-qPCR. The immune cell infiltration in RPL was evaluated using the ssGSEA algorithm, and the biological functions of the optimal feature genes were explored. Lastly, a heatmap was constructed to assist clinical physicians. Results 10 key differentially expressed genes were identified: CFHR1, GPR155, TIMP4, WAKMAR2, COL15A1, LNCOG, C1QL1, KLK3, XG, and XGY2. Enrichment analysis showed associations with complement and coagulation cascade pathways. The three machine learning algorithms identified CFHR1 as the optimal feature gene for RPL, and RT-qPCR confirmed its high expression in RPL. ROC curve and nomogram demonstrated its diagnostic efficacy for RPL. Immune infiltration analysis revealed increased macrophages and γδT cells in RPL decidua tissue, with a significant positive correlation between CFHR1 and macrophages. Conclusion Transcriptomic abnormalities exist in RPL decidua tissue, with key genes closely related to complement and coagulation cascade pathways; CFHR1 is identified as the optimal feature gene for RPL. Abnormal immune infiltration and correlation with CFHR1 are observed in RPL decidua tissue. Health sciences/Medical research/Biomarkers Biological sciences/Immunology/Immunological disorders Biological sciences/Molecular biology Biological sciences/Molecular biology/Transcriptomics Recurrent pregnancy loss WGCNA machine learning CFHR1 nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Recurrent pregnancy loss (RPL) is defined as two or more clinically recognized pregnancy failures, including embryonic and fetal loss, occurring before 20–24 weeks of gestation 1 . It is a pregnancy-related disorder that significantly affects female reproductive health, with a prevalence of approximately 2.5% among women attempting to conceive 2 ༎Established etiologies of RPL include genetic factors, endocrine factors, anatomical abnormalities, coagulation disorders, immunological abnormalities, and infections 3 . However, the etiology remains unexplained in over 50% of RPL cases 2 . The complex pathophysiology, diverse clinical presentations, and heterogeneous treatment approaches pose significant challenges for early diagnosis and precise treatment of RPL. Therefore, further research is warranted to elucidate the pathogenesis of RPL, identify potential diagnostic biomarkers, and develop clinical prediction models. Endometrial decidualization is a crucial process in early pregnancy, essential for embryo development and pregnancy maintenance.Decidualization of the endometrium not only facilitates improved nutrient supply to the embryo but also allows decidual cells to migrate towards and enwrap the embryo, thereby achieving a dynamic equilibrium in response to invasive trophoblast cells 4 . In addition, decidualization also plays a role in regulating maternal immune responses and promoting vascular remodeling 5 . Aberrant decidualization has been implicated in a range of reproductive complications, such as repeated implantation failure and pregnancy loss, potentially serving as a key underlying mechanism for RPL 6 . With the rapid advancement of modern bioinformatics, the development of weighted gene co-expression network analysis (WGCNA) and machine learning (ML)algorithms has provided novel strategies for investigating the complex molecular mechanisms underlying diseases 7 . Currently, researchers have analyzed transcriptomic datasets of endometrial tissue from RPL patients and identified several key biomarkers through machine learning approaches 8 . However, these findings alone may not provide a comprehensive solution for early diagnosis and clinical prognosis assessment in RPL patients. Therefore, there is an urgent need to explore additional biomarkers that can effectively assess the occurrence, progression, and prognosis of RPL. Furthermore, it is worth mentioning that many studies have only analyzed a single dataset, and some experimental validation designs may lack rigor. This limitation hinders the representation of the heterogeneous nature of RPL patients and consequently reduces the reliability of the conclusions drawn from these studies. To the best of our knowledge, there is a limited amount of bioinformatics analysis focusing on RPL endometrial transcriptomic data. In this study, we have taken a pioneering approach by integrating three endometrial datasets and utilizing the combination of WGCNA and various ML algorithms to identify key genes. Subsequently, we have collected RPL endometrial tissue samples to validate the expression profiles of these key genes. Additionally, we have evaluated the infiltration of immune cells in RPL endometrial tissue, and constructed column charts with the aim of providing novel insights for the clinical diagnosis and treatment of RPL, as well as future research directions. 2. Materials and Methods 2.1Acquisition and Normalization of Datasets We retrieved and downloaded transcriptomic sequencing datasets of uterine endometrial tissue from individuals with RPL from the GEO database ( https://www.ncbi.nlm.nih.gov/geo ) on December 21, 2023. The specific datasets included GSE113790 (control = 3, RPL = 3) 9 , GSE161969 (control = 3, RPL = 4) 10 , and GSE178535 (control = 3, RPL = 3) 11 , encompassing a total of 19 samples. These datasets were merged and subjected to normalization using R software version 4.3.1 to prepare them for subsequent analyses. Table 1 provides detailed information for each dataset. Table 1 Basic Characteristics of Datasets Used in this Study GSE series Platform Sample tissue type GSE113790 GPL11154 6(3RPL,3control) decidua GSE161969 GPL20795 7(4RPL,3control) decidua GSE178535 GPL11154 6(3RPL,3control) decidua 2.2 Subject Selection and Sample Collection We collected endometrial tissue samples from 10 patients (5 controls, 5 RPL) who received treatment at the Reproductive Center of Zhoukou Central Hospital from January 2024 to March 2024 for experimental validation of key differentially expressed genes. This study was conducted in accordance with the principles outlined in the Helsinki Declaration and obtained ethical approval from the Ethics Committee of Zhoukou Central Hospital (Ethics Approval No: 20231227005). The inclusion criteria were as follows: Experimental group: 1. History of two or more pregnancy losses before 20–24 weeks of gestation. 2. Current pregnancy conceived naturally, with the absence of fetal heartbeats confirmed by ultrasound. Control group: 1. Current pregnancy conceived naturally, with fetal heartbeats detected at 6–8 weeks of gestation but terminated due to unplanned pregnancy. 2. No history of adverse pregnancy outcomes and no predisposition to threatened miscarriage in the current pregnancy. The exclusion criteria were as follows: 1. Abnormal embryonic chromosomal karyotype analysis. 2. Age > 40 years. 3. Presence of autoimmune diseases, hypertension, endocrine disorders, or other diseases. 4. Current pregnancy conceived through non-natural methods. The endometrial tissue samples were collected immediately during surgery. After thorough rinsing with physiological saline, approximately 3–5 grams of tissue were taken and placed into cryovials, which were then properly labeled. The samples were rapidly frozen using liquid nitrogen and stored at -80°C for subsequent experiments.All patients involved in this study were provided with detailed information about the purpose of the research and signed informed consent forms. They were fully informed about the study procedures, potential risks, and benefits, and their rights as participants were respected throughout the study. 2.3 Differential Analysis We performed differential analysis using the normalized data with the "limma" package. Genes were considered differentially expressed if they had |logFC| > 0.585 and a p-value < 0.05. The differential expression genes were visualized using the "heatmap" and "ggplot2" packages. 2.4 Weighted Gene Co-expression Network Analysis WGCNA is a systems biology approach used to describe gene correlation patterns across different samples. It can identify gene modules that exhibit highly coordinated changes and is commonly used to select disease-associated gene modules 12 . The "WGCNA" R package was used for this analysis, with the "Hclust" function to remove outliers from the dataset, and the "pickSoftThreshold" function to select the optimal soft-thresholding value. Subsequently, the adjacency matrix was modified based on the chosen soft-thresholding value to obtain the topological overlap matrix (TOM) and the corresponding dissimilarity (1-TOM). Finally, gene modules most correlated with RPL were identified, and further gene selection was performed within these modules to obtain the gene set most strongly associated with RPL. 2.5 Key Differential Gene Selection and Enrichment Analysis We obtained the key differential genes by taking the intersection of the gene set obtained from the WGCNA analysis and the differentially expressed genes identified from the differential analysis. Subsequently, we performed GO and KEGG enrichment analysis on the key differential genes using R packages such as "org.Hs.eg.db", "clusterProfiler", "enrichplot", and "stringr". The enrichment results were visualized using R packages such as "ggplot2", "GOplot", "topGO", and "circlize". 2.6 Optimal Feature Genes Selection Three machine learning methods, namely least absolute shrinkage and selection operator (LASSO), support vector machine - recursive feature elimination (SVM-RFE), and Random forest (RF), were employed for the selection of optimal feature genes (OFGs). LASSO is a method used for feature selection and regularization 13 . The "glmnet" R package was utilized to implement it. SVM-RFE evaluates the importance of features by using Support Vector Machine (SVM) as the estimator and iteratively eliminates features contributing less to the model performance, thereby enhancing the model's generalization ability and prediction accuracy 14 . The R packages used for SVM-RFE include "e1071", "kernlab", and "caret". RF is an ensemble learning method that improves prediction accuracy by constructing multiple decision trees and integrating their predictions 15 . It has been widely applied for feature importance assessment. R packages such as "randomForest", "Boruta", "ImageGP", and "ggrepel" were utilized for the RF algorithm. The intersection of the computational results from these three machine learning methods was visualized using a Venn diagram, ultimately identifying the OFGs for RPL. 2.7 Validation of OFGs The experimental validation of the OFGs was executed through real-time polymerase chain reaction (RT-qPCR) Initially, RNA extraction from the tissues and subsequent reverse transcription into cDNA were performed. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the endogenous reference gene. We utilized the SYBR Green Master Mix kit, provided by Qiagen (Germany), for conducting RT-qPCR using cDNA as a template on the Applied Biosystems 7500 Real-Time PCR System. The primer design and synthesis were executed by Sango Biotech (Shanghai) Co., with their sequences outlined in the accompanying Table 2 . Gene expression levels were quantified using the 2^-ΔΔCT method. Each experimental sample was subjected to triplicate technical repeats. A CT value difference within 0.5 among replicate wells was considered indicative of validity. Table 2 Primer Sequences for Internal Control Genes and Target Genes Gene Name Primer Location Sequence GAPDH Forward CACCCACTCCTCCACCTTTGAC Reverse GTCCACCACCCTGTTGCTGTAG CFHR1 Forward GCATTAAGGTGGACAGCCAAACAG Reverse TTCGCAATGTGTGAGAACGTGATG 2.8 Construction of Nomogram We utilized the "pROC" package to plot ROC curves, evaluating the efficacy of the optimal feature genes 16 . Subsequently, employing the "survival" and "rms" R packages, we constructed nomogram models, followed by the utilization of the "PredictABEL" and "rmda" packages to draw calibration curves and clinical impact curves, respectively, to assess the effectiveness of the nomogram charts. This endeavor aims to furnish clinicians with a simplified diagnostic tool. 2.9 Immune Infiltration Assessment Single-sample gene set enrichment analysis (ssGSEA) was employed to evaluate the immune cell infiltration landscape within the RPL decidua. The correlation between the optimal characteristic genes and immune cells was also analyzed. The R packages utilized for these analyses included "GSVA," "Hmisc," "tinyarray," "pheatmap," "rio," and "ggplot2". 2.10 ssGSEA to Investigate the Impact of OFGs on RPL ssGSEA is a method based on gene set enrichment analysis, and this method can help researchers to understand the activity of different gene sets in a single sample, and thus infer the activity of the relevant biological processes to support personalised medicine and precision medicine. Taking the best characterised gene as an entry point, we classified RPL into two groups of high and low expression according to the median expression of this gene, and explored its biological impact on RPL using the ssGSEA algorithm. 3. Methods of statistical analyses Statistical analyses were performed using R version 4.3.1 and GraphPad Prism 9 software. The Shapiro-Wilk test was used to assess the normality of the data. For normally distributed continuous variables, the Student's t-test was employed. For non-normally distributed variables, the Wilcoxon test was used. The "pROC" package was utilized to evaluate the performance of the optimal signature genes. A p-value<0.05 was considered statistically significant. 4. Result 4.1 Comparisons of baseline information between groups The flowchart of the study is presented in Fig. 1 . We collected the basic clinical information and decidual tissues from 10 patients. The comparative information between the two groups is summarized in Table 3 . There were significant differences in gestational age at termination and number of miscarriages between the two groups (P 0.05). Table 3 Basic characteristics of the study population and the results of intergroup comparisons Control RPL F value P value 95%CI age 27.80 ± 7.26 30.40 ± 4.83 3.58 0.52 -11.59 ~ 6.39 BMI 22.47 ± 0.77 23.18 ± 0.42 3.05 0.11 -1.61 ~ 0.19 Gestational Age 7.18 ± 0.28 7.72 ± 0.30 0.04 0.02 -0.96~-0.11 Number of Live Births 1.60 ± 1.14 0.80 ± 0.84 0.55 0.24 -0.66 ~ 2.26 Number of Miscarriages 0 2.4 ± 0.89 7.11 0.004 --3.51~-1.29 4.2 Analysis of Differential Expression Results We performed a merging and standardization procedure on the datasets GSE117390, GSE161969, and GSE178535 for subsequent analysis ( Fig. 2 A-B ) . Following the differential expression analysis, a total of 151 differentially expressed genes were identified, including 90 upregulated and 61 downregulated genes. The volcano plot and heatmap provided visual representation of the differential gene expression patterns ( Fig. 2 C-D ) . 4.3 WGCNA Results A total of 27,018 genes were used for WGCNA analysis. The sample dendrogram and trait heatmap are shown in Fig. 3 A. Based on the analysis results, we selected a soft threshold of β = 14 (R2 = 0.9) as the optimal value for constructing the scale-free network (Fig. 3 B). The modules were generated through hierarchical clustering of the TOM matrix, with similar modules being merged together (Fig. 3 C). The correlation between modules is depicted in Fig. 3 D. The analysis revealed a total of 51 co-expression modules, with the mediumpurple3 module showing the closest relationship with RPL (Fig. 3 E). By applying a filter of abs(geneModuleMembership) > 0.8 and abs(geneTraitSignificance) > 0.2, we identified 66 genes that are most strongly associated with RPL. 4.4 Screening and enrichment analysis of key differential gene We identified 10 key differentially expressed genes by taking the intersection of the 151 differentially expressed genes from the differential expression analysis and the 66 genes from the WGCNA analysis. These key genes include CFHR1, GPR155, TIMP4, WAKMAR2, COL15A1, LNCOG, C1QL1, KLK3, XG, and XGY2 (Fig. 4 A). The results of GO enrichment analysis showed that these genes are primarily involved in biological processes such as humoral immune response,antimicrobial peptide production,and regulation of antimicrobial humoral response.In terms of cellular components, they are enriched in collagen trimer. In molecular function, the enrichment is observed in activities such as complement component C3b binding, and metalloendopeptidase inhibitor activity (Fig. 4 B). KEGG enrichment analysis revealed enrichment in three pathways: Complement and coagulation cascades, Prostate cancer,Protein digestion and absorption (Fig. 4 C). Furthermore, we explored the correlation between the key genes and the enriched terms, as shown in Fig. 4 D-E. 4.5 Selection of the OFGs for RPL Using the SVM-RFE algorithm, we identified 9 genes as potential feature genes (Fig. 5 A-B). In LASSO regression, with the optimal value of λ = 0.08772176, 3 genes were selected (Fig. 5 C-D). Additionally, employing the RF algorithm with 500 random trees, we determined 7 important genes (Fig. 5 E-G). By taking the intersection of these three selection methods, we obtained 1 optimal feature gene, CFHR1 (Fig. 5 H). The intergroup analysis revealed that the expression level of CFHR1 in RPL is significantly higher than in the control group (P < 0.001) ( Supplementary Fig. 1 ). 4.6 Experimental Validation of CFHR1 and Construction of Nomogram We included 5 normal samples and 5 RPL patient samples of decidua tissues for experimental validation of CFHR1 using RT-qPCR technology. After RNA extraction, gel electrophoresis results revealed significant degradation of RNA in sample 4 of the control group, leading to its exclusion from further analysis. Finally, we performed target gene detection on 9 tissue samples. Figure 6 A displays the expression levels of CFHR1 in each sample. Comparative analysis between groups demonstrated higher expression of CFHR1 in RPL samples (Fig. 6 B). To further evaluate the discriminatory ability of CFHR1 for RPL, we plotted the ROC curve. The area under the curve (AUC) in the public dataset was 0.878 (Fig. 6 C), while in our dataset, the AUC was 0.950 ( Supplementary Fig. 2 ). Additionally, we constructed a nomogram using CFHR1 (Fig. 6 D), which, based on the calibration curve and clinical impact curve, demonstrated its clinical predictive value for RPL (Fig. 6 E-F). 4.7 Results of Immune Infiltration Assessment The immune infiltration analysis revealed significant differences in macrophages, γδT cells, and memory B cells between RPL patients and the control group in decidua tissues (Fig. 7 A). Specifically, there was a notable increase in macrophages and γδT cells, while memory B cells were significantly decreased in RPL patients. No significant differences were observed in other immune cells between the two groups. Figure 7 B illustrates the expression of immune cells in each sample. Subsequently, we analyzed the correlation between the three immune cell types mentioned above and CFHR1. The results demonstrated a significant correlation between macrophages and γδT cells (r = 0.69, p < 0.001), as well as a correlation between CFHR1 and macrophages (r = 0.64, p < 0.01) ( Fig. 7 C). 4.8 Mechanisms of CFHR1 Impact on RPL We divided RPL cases into two groups based on the median expression level of CFHR1. Using the ssGSEA algorithm, we explored the biological effects of CFHR1 on RPL. The results revealed upregulation of pathways such as Epstein-Barr virus infection, neutrophil extracellular trap formation, and NOD-like receptor signaling pathway in the CFHR1 high expression group. Conversely, metabolic pathways such as alpha-Linolenic acid metabolism were downregulated (Fig. 8 A-B). This indicates that the aberrant expression of CFHR1 can influence RPL through multiple pathways. 5. Discussion RPL is a prevalent reproductive disorder, accounting for approximately 15% of clinically confirmed pregnancies and affecting around 5% of women of reproductive age globally 17 . Furthermore, RPL serves as a precursor risk indicator for future obstetric complications, including placental abruption, fetal growth restriction, preterm birth, and stillbirth. It is also a predictive factor for long-term health issues in women, such as venous thromboembolism and cardiovascular disease 18 .Therefore, further research in this field is urgently needed. Adequate decidualization of the endometrium is crucial for establishing pregnancy, regulating trophoblast invasion, and ensuring proper placental perfusion 19 . As research on RPL has advanced, scholars have discovered that decidual stromal cells not only act as biosensors for embryonic-derived signals but also have the ability to "select" embryos for implantation based on their quality. Abnormal decidualization may be an important pathogenic factor in RPL 20 . 5.1 The Association of Complement and Coagulation Pathways with RPL This study conducted a comprehensive analysis and exploration of the transcriptome of decidual tissue in RPL using three different datasets, marking the first of its kind. In addition, a combination of differential expression analysis and WGCNA was employed to identify key differentially expressed genes, setting it apart from previous similar studies. GO enrichment analysis revealed that these key differentially expressed genes were enriched in various aspects, including humoral immune response and C3b binding, a component of the complement system. The humoral immune response, an essential part of the immune system, regulates the immune balance between the mother and fetus through the production of antibodies and activation of the complement system. This helps protect the fetus from infections and other threats, supporting healthy fetal development 21 . The study revealed that the maternal-fetal interface is rich in complement inhibitors, which can prevent adverse pregnancy outcomes resulting from excessive complement activation, such as preeclampsia, recurrent miscarriage, intrauterine growth restriction, and antiphospholipid syndrome 22 . Reichhardt et al., in their study, found that pregnancy failure may be attributed to excessive complement activation during the pre-implantation stage of the embryo 23 . The conversion of C3 into C3b or iC3b has been shown to promote embryo growth and prevent autophagy 24 . Furthermore, researchers studying patients with a history of two previous miscarriages observed significantly higher levels of C3 and C4 in the serum of women who experienced a third miscarriage compared to those who achieved subsequent successful pregnancies and live births 25 . This suggests that an excessive complement response is indeed harmful. In a recent study, it was discovered that in the extracellular vesicles of decidual stromal cells (ESCs) with impaired decidualization, from day 2 to day 14 of decidual stimulation, 89 differentially expressed proteins were identified. It was found that these proteins were mainly involved in the complement and coagulation cascades 19 .Our KEGG enrichment analysis also revealed that key differentially expressed genes were enriched in the complement and coagulation cascades pathway. The complement and coagulation cascades, as part of the innate immune system, play crucial roles in embryo implantation, maintenance of pregnancy, and endometrial regeneration 26 , 27 . Research has revealed that localized activation of this pathway during endometrial stromal decidualization is involved in physiological remodeling of spiral artery vasculature 28 . However, when the downstream pathways of C3 are excessively activated and not properly regulated by complement inhibitors, it can lead to detrimental effects on embryo development and result in pregnancy loss 23 . Additionally, dysregulation and/or excessive activation of the complement and coagulation pathways have been observed in various reproductive complications, such as early-onset preeclampsia and intrauterine growth restriction 29 , 30 . The coagulation process plays a crucial role in stabilizing the attachment site and providing a physical barrier to protect the embryo from the influence of the maternal immune system. Increased coagulation in the microvasculature at the site of embryo implantation and the formation of blood clots around the implanted embryo contribute to this protective mechanism 31 . This coordinated coagulation activity ensures proper nutrient supply to the embryo. However, excessive activation of the coagulation system can lead to decreased nutrient supply, placental infarction, and fetal growth restriction, ultimately resulting in RPL 31 , 32 . In the study conducted by Gurung et al., it was found that the expression of procoagulant proteins, such as FGG, HRG, and ORM1, was significantly increased in endometrial stromal cells with impaired decidualization, while other coagulation regulatory factors showed no significant differences 19 . This finding suggests that excessive coagulation can further impair embryo implantation and development by affecting decidualization. Based on these findings, it is proposed that abnormalities in the complement and coagulation pathways may be important contributing factors to RPL. In the future, new treatment strategies for RPL could potentially be developed by targeting complement inhibition and coagulation regulation. 5.2 CFHR1 and RPL In this study, CFHR1 was identified as the optimal characteristic gene for RPL through the utilization of three machine learning algorithms: LASSO, SVM-REF, and RF. The subsequent histological validation results were consistent with the earlier data analysis, confirming a significant upregulation of CFHR1 expression in the decidual tissue of RPL. CFHR1 is a protein associated with complement factor H (Factor H) and is a member of the complement system family, playing a crucial role in regulating immune response and inflammation processes. It achieves this by competing with factor H for binding to various ligands on different surfaces or by directly interacting with C3b and native C3 to facilitate complement activation 33 . We conducted a literature search, and there is very limited research on CFHR1 in the context of reproduction. However, it is worth mentioning that some studies have shown that women carrying complement gene defects or autoantibodies against complement regulatory proteins are at a higher risk of developing atypical haemolytic uraemic syndrome (aHUS). This is associated with uncontrolled activation of the complement alternative pathway and complement-mediated endothelial injury in microvasculature, which is caused by genetic defects in patients 34 . In another study, researchers found high expression of CFHR1 in peripheral blood of pregnant women affected by Down's syndrome (DS), suggesting its potential as a predictive biomarker for pregnancies affected by DS 35 . There are also studies indicating the association of CFHR1 with various clinical disorders, including anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis 36 , HELLP syndrome 37 , age-related macular degeneration 38 , IgA nephropathy 39 , and thrombotic microangiopathy 40 . Importantly, in this study, we utilized the ssGSEA algorithm and observed an upregulation of pathways related to NOD-like receptor signaling in the CFHR1 high-expression group. Notably, existing research indicates a potential association between the occurrence of RPL and abnormal expression of NOD1 in DSCs 41 . Based on these findings, we speculate that CFHR1 may be closely linked to human reproduction and merits further investigation. 5.3 Macrophages and RPL Maternal immune tolerance to the fetus is a prerequisite for successful pregnancy 42 , 43 . The paternal antigens can be expressed by the fetus, and the maternal immune system's response to fetal antigens may contribute to the pathogenesis of RPL 44 . In this study, we evaluated the infiltration of immune cells in the decidua of RPL patients and found abnormal infiltration of macrophages. This finding is consistent with previous research results 45 . As the second-largest population of immune cells in the human body, macrophages constitute approximately 20% of the total leukocytes at the maternal-fetal interface 46 . They play a role in all physiological events of the female reproductive system, including menstruation, implantation, and parturition 47 . After conception, as extravillous trophoblasts (EVTs) invade the decidua, decidual macrophages undergo a transformation into a mixed M1/M2 phenotype. To establish immune tolerance at the maternal-fetal interface, macrophages shift towards an M2 phenotype 48 . M2 macrophages contribute to the unique vascular development and immune suppression in the placental microenvironment by releasing angiogenic factors such as interleukin-8(IL-8) ,vascular endothelial growth factor A(VEGF-A), and VEGF-C 49 . Abnormal recruitment and differentiation of macrophages have been closely associated with RPL 50 . Fang Yu Tsao et al. discovered an imbalance of M1/M2 macrophages at the maternal-fetal interface in RPL 51 In RPL patients, macrophages predominantly exhibit an M1 subtype, which can directly suppress the expression of TRAF6 through the transport of miR-146b-5p. This suppression inhibits the epithelial-mesenchymal transition (EMT), migration, and invasion of trophoblast cells, thereby contributing to the pathogenesis of RPL 52 . The study by Wei Chunyan et al. revealed that JAK2 inhibitors regulate the ratio of M1/M2 macrophages through the CCL2/CCR2/JAK2 pathway, thereby impacting the outcome of early pregnancy 53 . Through further analysis of the correlation between CFHR1 and immune cells, we discovered a positive association between CFHR1 and macrophages. This suggests a potential link between the abnormal expression of key genes in RPL and impaired immune cell infiltration. In future studies, it will be necessary to explore the regulatory relationship between CFHR1 and macrophages in the process of RPL. 5.4 γδ T Cells and RPL γδ T cells play a crucial role in immune surveillance and defense. Unlike conventional αβ T cells, γδ T cells are considered a bridge between innate and adaptive immunity 54 . During normal human pregnancy, there is an increase in the number of γδ T cells, and the Vδ1-T cell subset within the γδ T cell population secretes cytokines that support pregnancy maintenance. However, in adverse pregnancies, there is an abnormal expression of receptors on γδ T cells, as well as an imbalance in the proportion of Vγ9Vδ2 T cells and Vδ1 T cells 55 . The study by Dongli Cai et al. found that an increased percentage of Vδ2 subset γδ T cells in the early pregnancy decidua is associated with unexplained spontaneous miscarriage 56 . Our research also identified abnormal expression of γδ T cells in the decidua tissue of RPL, further highlighting the correlation between γδ T cells and adverse pregnancy outcomes. Unfortunately, we did not further investigate the subsets of γδ T cells in our study. In future research, we will focus on exploring the changes in γδ T cell subsets in decidua tissue and their correlation with RPL, as well as their underlying mechanisms. 6. Conclusion Through the utilization of multiple datasets and various machine learning techniques, we have identified CFHR1 as the optimal feature gene for RPL. This gene is closely associated with the complement system and macrophages. Additionally, we have discovered an imbalance in immune cell infiltration in RPL. Our study provides new insights into the pathogenesis of RPL and potential therapeutic targets. Declarations Author Contribution H.D conceived the project, designed the research framework, and conducted data organization and analysis, as well as the initial drafting of the manuscript. Y J. G and Y. G performed the experimental procedures. Y.C conducted literature review and assisted in data organization . R. L was responsible for the inclusion and management of research subjects, while C. W ensured the quality control of the experimental procedures. Y Q.G made revisions to the manuscript. Acknowledgement We would like to express our gratitude to Professor Ping Zhang from the Family Planning Clinic for her assistance in the collection of tissue specimens. We extend our thanks to the laboratory of the Prenatal Diagnosis Center for providing us with the necessary experimental equipment. We are also grateful to Nurse Lili Lu for her assistance in specimen storage and management. Special thanks go to technicians Zhuangzhuang Kang and Shumin Zhu for their guidance during the experiments and their quality control of the data. Data Availability The datasets supporting the conclusions of this article are available in the GEO database, including GSE113790 , GSE161969, and GSE178535.(https://www.ncbi.nlm.nih.gov/geo) References Bender Atik, R. et al. ESHRE guideline: recurrent pregnancy loss. Human reproduction open 2018, hoy004, doi: 10.1093/hropen/hoy004 (2018). Dimitriadis, E., Menkhorst, E., Saito, S., Kutteh, W. H. & Brosens, J. J. Recurrent pregnancy loss. Nature reviews. Disease primers 6, 98, doi: 10.1038/s41572-020-00228-z (2020). Quenby, S. et al. Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss. Lancet (London, England) 397, 1658–1667, doi: 10.1016/s0140-6736(21)00682-6 (2021). Davidson, L. M. & Coward, K. Molecular mechanisms of membrane interaction at implantation. Birth defects research. 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The Role of Decidual Macrophages During Normal and Pathological Pregnancy. American journal of reproductive immunology (New York, N.Y. : 1989) 75, 298–309, doi: 10.1111/aji.12477 (2016). Zhao, Q. Y. et al. Decidual macrophages in recurrent spontaneous abortion. Frontiers in immunology 13, 994888, doi: 10.3389/fimmu.2022.994888 (2022). Zhao, Y., Sun, J. & Jin, L. The N6-Methyladenosine Regulator ALKBH5 Mediated Stromal Cell-Macrophage Interaction via VEGF Signaling to Promote Recurrent Spontaneous Abortion: A Bioinformatic and In Vitro Study. International journal of molecular sciences 23, doi: 10.3390/ijms232415819 (2022). Tsao, F. Y., Wu, M. Y., Chang, Y. L., Wu, C. T. & Ho, H. N. M1 macrophages decrease in the deciduae from normal pregnancies but not from spontaneous abortions or unexplained recurrent spontaneous abortions. Journal of the Formosan Medical Association = Taiwan yi zhi 117, 204–211, doi: 10.1016/j.jfma.2017.03.011 (2018). Ding, J. et al. Extracellular vesicles derived from M1 macrophages deliver miR-146a-5p and miR-146b-5p to suppress trophoblast migration and invasion by targeting TRAF6 in recurrent spontaneous abortion. Theranostics 11, 5813–5830, doi: 10.7150/thno.58731 (2021). Wei, C. Y., Li, M. Q., Zhu, X. Y. & Li, D. J. Immune status of decidual macrophages is dependent on the CCL2/CCR2/JAK2 pathway during early pregnancy. American journal of reproductive immunology (New York, N.Y.: 1989) 86, e13480, doi: 10.1111/aji.13480 (2021). Xu, Q. H. et al. Roles of γδT cells in pregnancy and pregnancy-related complications. American journal of reproductive immunology (New York, N.Y.: 1989) 86, e13487, doi: 10.1111/aji.13487 (2021). Huang, C., Zeng, Y. & Tu, W. The role of γδ-T cells during human pregnancy. American journal of reproductive immunology (New York, N.Y. : 1989) 78, doi: 10.1111/aji.12713 (2017). Cai, D., Tang, Y. & Yao, X. Changes of γδT cell subtypes during pregnancy and their influences in spontaneous abortion. Journal of reproductive immunology 131, 57–62, doi: 10.1016/j.jri.2019.01.003 (2019). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.pdf Cite Share Download PDF Status: Published Journal Publication published 15 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Feb, 2025 Reviews received at journal 14 Feb, 2025 Reviewers agreed at journal 04 Feb, 2025 Reviews received at journal 14 Jul, 2024 Reviewers agreed at journal 07 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Editor invited by journal 22 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 18 May, 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. 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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-4441689","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309820481,"identity":"443436f5-e413-40c8-91fb-9bc4ba2cad4e","order_by":0,"name":"Hui Ding","email":"","orcid":"","institution":"Zhoukou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Ding","suffix":""},{"id":309820484,"identity":"bf66c48a-a1d0-45a3-8e56-6a4cc3736cf4","order_by":1,"name":"Yajie Gao","email":"","orcid":"","institution":"Zhoukou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yajie","middleName":"","lastName":"Gao","suffix":""},{"id":309820486,"identity":"e2055295-da69-449f-a737-2b8c03240509","order_by":2,"name":"Yuan Gao","email":"","orcid":"","institution":"Zhoukou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Gao","suffix":""},{"id":309820487,"identity":"eee3a900-3014-4641-9ff0-a8a126328815","order_by":3,"name":"Yulu Chen","email":"","orcid":"","institution":"Zhoukou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Chen","suffix":""},{"id":309820489,"identity":"b20d5aec-8b4b-4b01-b504-17ab994c798d","order_by":4,"name":"Ruimin Liu","email":"","orcid":"","institution":"Zhoukou Central 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study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/b954329ccea9a3f88d07b8e2.png"},{"id":57691323,"identity":"74578470-38e4-4bfd-a1b9-10854b12f220","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":921782,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Differential Expression Results. \u003cstrong\u003eA\u003c/strong\u003e：Boxplots of the raw data before standardization ；\u003cstrong\u003eB：\u003c/strong\u003eBoxplots of the standardized data；\u003cstrong\u003eC：\u003c/strong\u003eVolcano plot illustrating differential gene expression；\u003cstrong\u003eD：\u003c/strong\u003eHeatmap visualizing the expression patterns of differentially expressed genes\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/ebe8672ca91f9573615b052b.png"},{"id":57691322,"identity":"2ee1a714-c1d6-4f5d-ae58-aa15bdd8de8d","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":994996,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA Results. \u003cstrong\u003eA: \u003c/strong\u003eDendrogram of sample clustering;\u003cstrong\u003e B: \u003c/strong\u003eScale-free fit index and average connectivity plot for different soft-thresholding powers;\u003cstrong\u003eC: \u003c/strong\u003eGene dendrogram based on the dissimilarity measure clustering;\u003cstrong\u003eD\u003c/strong\u003e:Heatmap depicting the correlation between different gene modules ;\u003cstrong\u003eE:\u003c/strong\u003e Heatmap illustrating the correlation between genes and clinical features.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/a425787a84db8a613ea98789.png"},{"id":57691324,"identity":"9d2ec111-6f77-4039-ae6f-af067b86f1db","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":709424,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment Analysis Results\u003cstrong\u003e. A: \u003c/strong\u003eVenn diagram showing the selection of key differentially expressed genes; \u003cstrong\u003eB: \u003c/strong\u003eBar plot illustrating the results of GO enrichment analysis; \u003cstrong\u003eC: \u003c/strong\u003eBubble plot representing the results of KEGG enrichment analysis;\u003cstrong\u003eD: \u003c/strong\u003eChord diagram depicting the relationships between genes and GO enrichment terms; \u003cstrong\u003eE: \u003c/strong\u003eHeatmap illustrating the relationships between genes and pathways.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/6f4f43cee2d1337911e94db2.png"},{"id":57691328,"identity":"5bfce7d4-cbbc-48bb-9489-e2bf52c0bd2a","added_by":"auto","created_at":"2024-06-04 11:25:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1022525,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of the Optimal Feature Gene\u003cstrong\u003e. A-B: \u003c/strong\u003eResults of feature gene selection using SVM-RFE;\u003cstrong\u003e C-D: \u003c/strong\u003eResults of feature gene selection using LASSO regression; \u003cstrong\u003eE-G: \u003c/strong\u003eResults of identifying the optimal feature gene using the random forest algorithm;\u003cstrong\u003e H: \u003c/strong\u003eVenn diagram illustrating the intersection of the OFGs determined by the three machine learning algorithms\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/57cf897643c4d0b1b5d97795.png"},{"id":57691326,"identity":"a93a0a4f-c66e-4b42-89a1-9005e00e88bd","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":703857,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental Validation of CFHR1 and Construction of nomogram. \u003cstrong\u003eA:\u003c/strong\u003e Expression levels of CFHR1 in each sample; \u003cstrong\u003eB:\u003c/strong\u003e Comparison of CFHR1 expression levels between RPL and normal control decidua tissues; \u003cstrong\u003eC:\u003c/strong\u003e AUC of the ROC curve for CFHR1 in the public dataset; \u003cstrong\u003eD:\u003c/strong\u003e Column chart constructed using CFHR1; \u003cstrong\u003eE:\u003c/strong\u003e Calibration curve; \u003cstrong\u003eF:\u003c/strong\u003e Clinical impact curve.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/855f0f4b2df8cb2e74dd5dfe.png"},{"id":57691327,"identity":"bccbe01d-347f-483e-b80a-13927c9ead4e","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":975889,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Immune Infiltration Assessment. \u003cstrong\u003eA:\u003c/strong\u003e Comparison of immune cell infiltration in RPL and control groups; \u003cstrong\u003eB:\u003c/strong\u003eHeatmap of immune cell infiltration in each sample; \u003cstrong\u003eC: \u003c/strong\u003eHeatmap showing the relationship between the three different immune cell types and CFHR1.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/54b829f43d12f79b1377837b.png"},{"id":57691329,"identity":"50f9d0b9-12ce-4944-8d97-7bae59a4c18b","added_by":"auto","created_at":"2024-06-04 11:25:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":666318,"visible":true,"origin":"","legend":"\u003cp\u003essGSEA Analysis of CFHR1. \u003cstrong\u003eA:\u003c/strong\u003e Top 5 upregulated pathways in the RPL group; \u003cstrong\u003eB:\u003c/strong\u003e Top 5 downregulated pathways in the RPL group.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/ba041027bcce5316e729f7e0.png"},{"id":87219345,"identity":"52a2046f-1725-475d-a376-03515f8e509f","added_by":"auto","created_at":"2025-07-21 16:04:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5667901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/b769b1e7-d6b6-4cd5-80da-255b78002e3f.pdf"},{"id":57691321,"identity":"fac78cb0-e66f-4e90-8fef-85498968a034","added_by":"auto","created_at":"2024-06-04 11:25:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":171690,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4441689/v1/fbb3755df7f85a1ad041098d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecurrent pregnancy loss (RPL) is defined as two or more clinically recognized pregnancy failures, including embryonic and fetal loss, occurring before 20\u0026ndash;24 weeks of gestation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is a pregnancy-related disorder that significantly affects female reproductive health, with a prevalence of approximately 2.5% among women attempting to conceive\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e༎Established etiologies of RPL include genetic factors, endocrine factors, anatomical abnormalities, coagulation disorders, immunological abnormalities, and infections\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, the etiology remains unexplained in over 50% of RPL cases\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The complex pathophysiology, diverse clinical presentations, and heterogeneous treatment approaches pose significant challenges for early diagnosis and precise treatment of RPL. Therefore, further research is warranted to elucidate the pathogenesis of RPL, identify potential diagnostic biomarkers, and develop clinical prediction models.\u003c/p\u003e \u003cp\u003eEndometrial decidualization is a crucial process in early pregnancy, essential for embryo development and pregnancy maintenance.Decidualization of the endometrium not only facilitates improved nutrient supply to the embryo but also allows decidual cells to migrate towards and enwrap the embryo, thereby achieving a dynamic equilibrium in response to invasive trophoblast cells\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition, decidualization also plays a role in regulating maternal immune responses and promoting vascular remodeling\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Aberrant decidualization has been implicated in a range of reproductive complications, such as repeated implantation failure and pregnancy loss, potentially serving as a key underlying mechanism for RPL \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the rapid advancement of modern bioinformatics, the development of weighted gene co-expression network analysis (WGCNA) and machine learning (ML)algorithms has provided novel strategies for investigating the complex molecular mechanisms underlying diseases \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Currently, researchers have analyzed transcriptomic datasets of endometrial tissue from RPL patients and identified several key biomarkers through machine learning approaches\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, these findings alone may not provide a comprehensive solution for early diagnosis and clinical prognosis assessment in RPL patients. Therefore, there is an urgent need to explore additional biomarkers that can effectively assess the occurrence, progression, and prognosis of RPL. Furthermore, it is worth mentioning that many studies have only analyzed a single dataset, and some experimental validation designs may lack rigor. This limitation hinders the representation of the heterogeneous nature of RPL patients and consequently reduces the reliability of the conclusions drawn from these studies.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, there is a limited amount of bioinformatics analysis focusing on RPL endometrial transcriptomic data. In this study, we have taken a pioneering approach by integrating three endometrial datasets and utilizing the combination of WGCNA and various ML algorithms to identify key genes. Subsequently, we have collected RPL endometrial tissue samples to validate the expression profiles of these key genes. Additionally, we have evaluated the infiltration of immune cells in RPL endometrial tissue, and constructed column charts with the aim of providing novel insights for the clinical diagnosis and treatment of RPL, as well as future research directions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Acquisition and Normalization of Datasets\u003c/h2\u003e \u003cp\u003eWe retrieved and downloaded transcriptomic sequencing datasets of uterine endometrial tissue from individuals with RPL from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on December 21, 2023. The specific datasets included GSE113790 (control\u0026thinsp;=\u0026thinsp;3, RPL\u0026thinsp;=\u0026thinsp;3) \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, GSE161969 (control\u0026thinsp;=\u0026thinsp;3, RPL\u0026thinsp;=\u0026thinsp;4) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and GSE178535 (control\u0026thinsp;=\u0026thinsp;3, RPL\u0026thinsp;=\u0026thinsp;3) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, encompassing a total of 19 samples. These datasets were merged and subjected to normalization using R software version 4.3.1 to prepare them for subsequent analyses. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed information for each dataset.\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\u003eBasic Characteristics of Datasets Used in this Study\u003c/p\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE series\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etissue type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE113790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL11154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3RPL,3control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edecidua\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE161969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL20795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(4RPL,3control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edecidua\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE178535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL11154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3RPL,3control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edecidua\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=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Subject Selection and Sample Collection\u003c/h2\u003e \u003cp\u003eWe collected endometrial tissue samples from 10 patients (5 controls, 5 RPL) who received treatment at the Reproductive Center of Zhoukou Central Hospital from January 2024 to March 2024 for experimental validation of key differentially expressed genes. This study was conducted in accordance with the principles outlined in the Helsinki Declaration and obtained ethical approval from the Ethics Committee of Zhoukou Central Hospital (Ethics Approval No: 20231227005). The inclusion criteria were as follows: Experimental group: 1. History of two or more pregnancy losses before 20\u0026ndash;24 weeks of gestation. 2. Current pregnancy conceived naturally, with the absence of fetal heartbeats confirmed by ultrasound. Control group: 1. Current pregnancy conceived naturally, with fetal heartbeats detected at 6\u0026ndash;8 weeks of gestation but terminated due to unplanned pregnancy. 2. No history of adverse pregnancy outcomes and no predisposition to threatened miscarriage in the current pregnancy. The exclusion criteria were as follows: 1. Abnormal embryonic chromosomal karyotype analysis. 2. Age\u0026thinsp;\u0026gt;\u0026thinsp;40 years. 3. Presence of autoimmune diseases, hypertension, endocrine disorders, or other diseases. 4. Current pregnancy conceived through non-natural methods.\u003c/p\u003e \u003cp\u003eThe endometrial tissue samples were collected immediately during surgery. After thorough rinsing with physiological saline, approximately 3\u0026ndash;5 grams of tissue were taken and placed into cryovials, which were then properly labeled. The samples were rapidly frozen using liquid nitrogen and stored at -80\u0026deg;C for subsequent experiments.All patients involved in this study were provided with detailed information about the purpose of the research and signed informed consent forms. They were fully informed about the study procedures, potential risks, and benefits, and their rights as participants were respected throughout the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Differential Analysis\u003c/h2\u003e \u003cp\u003eWe performed differential analysis using the normalized data with the \"limma\" package. Genes were considered differentially expressed if they had |logFC| \u0026gt; 0.585 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The differential expression genes were visualized using the \"heatmap\" and \"ggplot2\" packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Weighted Gene Co-expression Network Analysis\u003c/h2\u003e \u003cp\u003eWGCNA is a systems biology approach used to describe gene correlation patterns across different samples. It can identify gene modules that exhibit highly coordinated changes and is commonly used to select disease-associated gene modules\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The \"WGCNA\" R package was used for this analysis, with the \"Hclust\" function to remove outliers from the dataset, and the \"pickSoftThreshold\" function to select the optimal soft-thresholding value. Subsequently, the adjacency matrix was modified based on the chosen soft-thresholding value to obtain the topological overlap matrix (TOM) and the corresponding dissimilarity (1-TOM). Finally, gene modules most correlated with RPL were identified, and further gene selection was performed within these modules to obtain the gene set most strongly associated with RPL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Key Differential Gene Selection and Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe obtained the key differential genes by taking the intersection of the gene set obtained from the WGCNA analysis and the differentially expressed genes identified from the differential analysis. Subsequently, we performed GO and KEGG enrichment analysis on the key differential genes using R packages such as \"org.Hs.eg.db\", \"clusterProfiler\", \"enrichplot\", and \"stringr\". The enrichment results were visualized using R packages such as \"ggplot2\", \"GOplot\", \"topGO\", and \"circlize\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Optimal Feature Genes Selection\u003c/h2\u003e \u003cp\u003eThree machine learning methods, namely least absolute shrinkage and selection operator (LASSO), support vector machine - recursive feature elimination (SVM-RFE), and Random forest (RF), were employed for the selection of optimal feature genes (OFGs). LASSO is a method used for feature selection and regularization \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The \"glmnet\" R package was utilized to implement it. SVM-RFE evaluates the importance of features by using Support Vector Machine (SVM) as the estimator and iteratively eliminates features contributing less to the model performance, thereby enhancing the model's generalization ability and prediction accuracy\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The R packages used for SVM-RFE include \"e1071\", \"kernlab\", and \"caret\". RF is an ensemble learning method that improves prediction accuracy by constructing multiple decision trees and integrating their predictions\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It has been widely applied for feature importance assessment. R packages such as \"randomForest\", \"Boruta\", \"ImageGP\", and \"ggrepel\" were utilized for the RF algorithm. The intersection of the computational results from these three machine learning methods was visualized using a Venn diagram, ultimately identifying the OFGs for RPL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Validation of OFGs\u003c/h2\u003e \u003cp\u003eThe experimental validation of the OFGs was executed through real-time polymerase chain reaction (RT-qPCR) Initially, RNA extraction from the tissues and subsequent reverse transcription into cDNA were performed. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the endogenous reference gene. We utilized the SYBR Green Master Mix kit, provided by Qiagen (Germany), for conducting RT-qPCR using cDNA as a template on the Applied Biosystems 7500 Real-Time PCR System. The primer design and synthesis were executed by Sango Biotech (Shanghai) Co., with their sequences outlined in the accompanying Table\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Gene expression levels were quantified using the 2^-ΔΔCT method. Each experimental sample was subjected to triplicate technical repeats. A CT value difference within 0.5 among replicate wells was considered indicative of validity.\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 \u003cp\u003ePrimer Sequences for Internal Control Genes and Target Genes\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\u003ePrimer Location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCACCCACTCCTCCACCTTTGAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTCCACCACCCTGTTGCTGTAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCFHR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCATTAAGGTGGACAGCCAAACAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCGCAATGTGTGAGAACGTGATG\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Construction of Nomogram\u003c/h2\u003e \u003cp\u003eWe utilized the \"pROC\" package to plot ROC curves, evaluating the efficacy of the optimal feature genes\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Subsequently, employing the \"survival\" and \"rms\" R packages, we constructed nomogram models, followed by the utilization of the \"PredictABEL\" and \"rmda\" packages to draw calibration curves and clinical impact curves, respectively, to assess the effectiveness of the nomogram charts. This endeavor aims to furnish clinicians with a simplified diagnostic tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Immune Infiltration Assessment\u003c/h2\u003e \u003cp\u003eSingle-sample gene set enrichment analysis (ssGSEA) was employed to evaluate the immune cell infiltration landscape within the RPL decidua. The correlation between the optimal characteristic genes and immune cells was also analyzed. The R packages utilized for these analyses included \"GSVA,\" \"Hmisc,\" \"tinyarray,\" \"pheatmap,\" \"rio,\" and \"ggplot2\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 ssGSEA to Investigate the Impact of OFGs on RPL\u003c/h2\u003e \u003cp\u003essGSEA is a method based on gene set enrichment analysis, and this method can help researchers to understand the activity of different gene sets in a single sample, and thus infer the activity of the relevant biological processes to support personalised medicine and precision medicine. Taking the best characterised gene as an entry point, we classified RPL into two groups of high and low expression according to the median expression of this gene, and explored its biological impact on RPL using the ssGSEA algorithm.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods of statistical analyses","content":"\u003cp\u003eStatistical analyses were performed using R version 4.3.1 and GraphPad Prism 9 software. The Shapiro-Wilk test was used to assess the normality of the data. For normally distributed continuous variables, the Student's t-test was employed. For non-normally distributed variables, the Wilcoxon test was used. The \"pROC\" package was utilized to evaluate the performance of the optimal signature genes. A p-value\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"4. Result","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.1 Comparisons of baseline information between groups\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe flowchart of the study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We collected the basic clinical information and decidual tissues from 10 patients. The comparative information between the two groups is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There were significant differences in gestational age at termination and number of miscarriages between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while age, body mass index, and number of live births did not differ significantly (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of the study population and the results of intergroup comparisons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRPL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.80\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.59\u0026thinsp;~\u0026thinsp;6.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e23.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.61\u0026thinsp;~\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.96~-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Live Births\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.66\u0026thinsp;~\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Miscarriages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e--3.51~-1.29\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Differential Expression Results\u003c/h2\u003e \u003cp\u003eWe performed a merging and standardization procedure on the datasets GSE117390, GSE161969, and GSE178535 for subsequent analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B\u003cb\u003e)\u003c/b\u003e. Following the differential expression analysis, a total of 151 differentially expressed genes were identified, including 90 upregulated and 61 downregulated genes. The volcano plot and heatmap provided visual representation of the differential gene expression patterns \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 WGCNA Results\u003c/h2\u003e \u003cp\u003eA total of 27,018 genes were used for WGCNA analysis. The sample dendrogram and trait heatmap are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Based on the analysis results, we selected a soft threshold of β\u0026thinsp;=\u0026thinsp;14 (R2\u0026thinsp;=\u0026thinsp;0.9) as the optimal value for constructing the scale-free network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The modules were generated through hierarchical clustering of the TOM matrix, with similar modules being merged together (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The correlation between modules is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. The analysis revealed a total of 51 co-expression modules, with the mediumpurple3 module showing the closest relationship with RPL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). By applying a filter of abs(geneModuleMembership)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and abs(geneTraitSignificance)\u0026thinsp;\u0026gt;\u0026thinsp;0.2, we identified 66 genes that are most strongly associated with RPL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Screening and enrichment analysis of key differential gene\u003c/h2\u003e \u003cp\u003eWe identified 10 key differentially expressed genes by taking the intersection of the 151 differentially expressed genes from the differential expression analysis and the 66 genes from the WGCNA analysis. These key genes include CFHR1, GPR155, TIMP4, WAKMAR2, COL15A1, LNCOG, C1QL1, KLK3, XG, and XGY2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The results of GO enrichment analysis showed that these genes are primarily involved in biological processes such as humoral immune response,antimicrobial peptide production,and regulation of antimicrobial humoral response.In terms of cellular components, they are enriched in collagen trimer. In molecular function, the enrichment is observed in activities such as complement component C3b binding, and metalloendopeptidase inhibitor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). KEGG enrichment analysis revealed enrichment in three pathways: Complement and coagulation cascades, Prostate cancer,Protein digestion and absorption (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Furthermore, we explored the correlation between the key genes and the enriched terms, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Selection of the OFGs for RPL\u003c/h2\u003e \u003cp\u003eUsing the SVM-RFE algorithm, we identified 9 genes as potential feature genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). In LASSO regression, with the optimal value of λ\u0026thinsp;=\u0026thinsp;0.08772176, 3 genes were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). Additionally, employing the RF algorithm with 500 random trees, we determined 7 important genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-G). By taking the intersection of these three selection methods, we obtained 1 optimal feature gene, CFHR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). The intergroup analysis revealed that the expression level of CFHR1 in RPL is significantly higher than in the control group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Experimental Validation of CFHR1 and Construction of Nomogram\u003c/h2\u003e \u003cp\u003eWe included 5 normal samples and 5 RPL patient samples of decidua tissues for experimental validation of CFHR1 using RT-qPCR technology. After RNA extraction, gel electrophoresis results revealed significant degradation of RNA in sample 4 of the control group, leading to its exclusion from further analysis. Finally, we performed target gene detection on 9 tissue samples. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA displays the expression levels of CFHR1 in each sample. Comparative analysis between groups demonstrated higher expression of CFHR1 in RPL samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). To further evaluate the discriminatory ability of CFHR1 for RPL, we plotted the ROC curve. The area under the curve (AUC) in the public dataset was 0.878 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), while in our dataset, the AUC was 0.950 (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Additionally, we constructed a nomogram using CFHR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), which, based on the calibration curve and clinical impact curve, demonstrated its clinical predictive value for RPL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Results of Immune Infiltration Assessment\u003c/h2\u003e \u003cp\u003eThe immune infiltration analysis revealed significant differences in macrophages, γδT cells, and memory B cells between RPL patients and the control group in decidua tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Specifically, there was a notable increase in macrophages and γδT cells, while memory B cells were significantly decreased in RPL patients. No significant differences were observed in other immune cells between the two groups. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB illustrates the expression of immune cells in each sample. Subsequently, we analyzed the correlation between the three immune cell types mentioned above and CFHR1. The results demonstrated a significant correlation between macrophages and γδT cells (r\u0026thinsp;=\u0026thinsp;0.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as a correlation between CFHR1 and macrophages (r\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Mechanisms of CFHR1 Impact on RPL\u003c/h2\u003e \u003cp\u003eWe divided RPL cases into two groups based on the median expression level of CFHR1. Using the ssGSEA algorithm, we explored the biological effects of CFHR1 on RPL. The results revealed upregulation of pathways such as Epstein-Barr virus infection, neutrophil extracellular trap formation, and NOD-like receptor signaling pathway in the CFHR1 high expression group. Conversely, metabolic pathways such as alpha-Linolenic acid metabolism were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). This indicates that the aberrant expression of CFHR1 can influence RPL through multiple pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eRPL is a prevalent reproductive disorder, accounting for approximately 15% of clinically confirmed pregnancies and affecting around 5% of women of reproductive age globally\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Furthermore, RPL serves as a precursor risk indicator for future obstetric complications, including placental abruption, fetal growth restriction, preterm birth, and stillbirth. It is also a predictive factor for long-term health issues in women, such as venous thromboembolism and cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.Therefore, further research in this field is urgently needed. Adequate decidualization of the endometrium is crucial for establishing pregnancy, regulating trophoblast invasion, and ensuring proper placental perfusion\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As research on RPL has advanced, scholars have discovered that decidual stromal cells not only act as biosensors for embryonic-derived signals but also have the ability to \"select\" embryos for implantation based on their quality. Abnormal decidualization may be an important pathogenic factor in RPL \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.1 The Association of Complement and Coagulation Pathways with RPL\u003c/h2\u003e \u003cp\u003eThis study conducted a comprehensive analysis and exploration of the transcriptome of decidual tissue in RPL using three different datasets, marking the first of its kind. In addition, a combination of differential expression analysis and WGCNA was employed to identify key differentially expressed genes, setting it apart from previous similar studies. GO enrichment analysis revealed that these key differentially expressed genes were enriched in various aspects, including humoral immune response and C3b binding, a component of the complement system. The humoral immune response, an essential part of the immune system, regulates the immune balance between the mother and fetus through the production of antibodies and activation of the complement system. This helps protect the fetus from infections and other threats, supporting healthy fetal development\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The study revealed that the maternal-fetal interface is rich in complement inhibitors, which can prevent adverse pregnancy outcomes resulting from excessive complement activation, such as preeclampsia, recurrent miscarriage, intrauterine growth restriction, and antiphospholipid syndrome \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Reichhardt et al., in their study, found that pregnancy failure may be attributed to excessive complement activation during the pre-implantation stage of the embryo \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The conversion of C3 into C3b or iC3b has been shown to promote embryo growth and prevent autophagy\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Furthermore, researchers studying patients with a history of two previous miscarriages observed significantly higher levels of C3 and C4 in the serum of women who experienced a third miscarriage compared to those who achieved subsequent successful pregnancies and live births\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This suggests that an excessive complement response is indeed harmful.\u003c/p\u003e \u003cp\u003eIn a recent study, it was discovered that in the extracellular vesicles of decidual stromal cells (ESCs) with impaired decidualization, from day 2 to day 14 of decidual stimulation, 89 differentially expressed proteins were identified. It was found that these proteins were mainly involved in the complement and coagulation cascades\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.Our KEGG enrichment analysis also revealed that key differentially expressed genes were enriched in the complement and coagulation cascades pathway. The complement and coagulation cascades, as part of the innate immune system, play crucial roles in embryo implantation, maintenance of pregnancy, and endometrial regeneration\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Research has revealed that localized activation of this pathway during endometrial stromal decidualization is involved in physiological remodeling of spiral artery vasculature\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, when the downstream pathways of C3 are excessively activated and not properly regulated by complement inhibitors, it can lead to detrimental effects on embryo development and result in pregnancy loss\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, dysregulation and/or excessive activation of the complement and coagulation pathways have been observed in various reproductive complications, such as early-onset preeclampsia and intrauterine growth restriction\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe coagulation process plays a crucial role in stabilizing the attachment site and providing a physical barrier to protect the embryo from the influence of the maternal immune system. Increased coagulation in the microvasculature at the site of embryo implantation and the formation of blood clots around the implanted embryo contribute to this protective mechanism\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This coordinated coagulation activity ensures proper nutrient supply to the embryo. However, excessive activation of the coagulation system can lead to decreased nutrient supply, placental infarction, and fetal growth restriction, ultimately resulting in RPL\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In the study conducted by Gurung et al., it was found that the expression of procoagulant proteins, such as FGG, HRG, and ORM1, was significantly increased in endometrial stromal cells with impaired decidualization, while other coagulation regulatory factors showed no significant differences\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This finding suggests that excessive coagulation can further impair embryo implantation and development by affecting decidualization. Based on these findings, it is proposed that abnormalities in the complement and coagulation pathways may be important contributing factors to RPL. In the future, new treatment strategies for RPL could potentially be developed by targeting complement inhibition and coagulation regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.2 CFHR1 and RPL\u003c/h2\u003e \u003cp\u003eIn this study, CFHR1 was identified as the optimal characteristic gene for RPL through the utilization of three machine learning algorithms: LASSO, SVM-REF, and RF. The subsequent histological validation results were consistent with the earlier data analysis, confirming a significant upregulation of CFHR1 expression in the decidual tissue of RPL. CFHR1 is a protein associated with complement factor H (Factor H) and is a member of the complement system family, playing a crucial role in regulating immune response and inflammation processes. It achieves this by competing with factor H for binding to various ligands on different surfaces or by directly interacting with C3b and native C3 to facilitate complement activation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We conducted a literature search, and there is very limited research on CFHR1 in the context of reproduction. However, it is worth mentioning that some studies have shown that women carrying complement gene defects or autoantibodies against complement regulatory proteins are at a higher risk of developing atypical haemolytic uraemic syndrome (aHUS). This is associated with uncontrolled activation of the complement alternative pathway and complement-mediated endothelial injury in microvasculature, which is caused by genetic defects in patients\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In another study, researchers found high expression of CFHR1 in peripheral blood of pregnant women affected by Down's syndrome (DS), suggesting its potential as a predictive biomarker for pregnancies affected by DS\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. There are also studies indicating the association of CFHR1 with various clinical disorders, including anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, HELLP syndrome\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, age-related macular degeneration \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, IgA nephropathy\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and thrombotic microangiopathy\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Importantly, in this study, we utilized the ssGSEA algorithm and observed an upregulation of pathways related to NOD-like receptor signaling in the CFHR1 high-expression group. Notably, existing research indicates a potential association between the occurrence of RPL and abnormal expression of NOD1 in DSCs\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Based on these findings, we speculate that CFHR1 may be closely linked to human reproduction and merits further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Macrophages and RPL\u003c/h2\u003e \u003cp\u003eMaternal immune tolerance to the fetus is a prerequisite for successful pregnancy\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The paternal antigens can be expressed by the fetus, and the maternal immune system's response to fetal antigens may contribute to the pathogenesis of RPL\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In this study, we evaluated the infiltration of immune cells in the decidua of RPL patients and found abnormal infiltration of macrophages. This finding is consistent with previous research results\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs the second-largest population of immune cells in the human body, macrophages constitute approximately 20% of the total leukocytes at the maternal-fetal interface\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. They play a role in all physiological events of the female reproductive system, including menstruation, implantation, and parturition\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. After conception, as extravillous trophoblasts (EVTs) invade the decidua, decidual macrophages undergo a transformation into a mixed M1/M2 phenotype. To establish immune tolerance at the maternal-fetal interface, macrophages shift towards an M2 phenotype\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. M2 macrophages contribute to the unique vascular development and immune suppression in the placental microenvironment by releasing angiogenic factors such as interleukin-8(IL-8) ,vascular endothelial growth factor A(VEGF-A), and VEGF-C\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Abnormal recruitment and differentiation of macrophages have been closely associated with RPL\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Fang Yu Tsao et al. discovered an imbalance of M1/M2 macrophages at the maternal-fetal interface in RPL\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e In RPL patients, macrophages predominantly exhibit an M1 subtype, which can directly suppress the expression of TRAF6 through the transport of miR-146b-5p. This suppression inhibits the epithelial-mesenchymal transition (EMT), migration, and invasion of trophoblast cells, thereby contributing to the pathogenesis of RPL\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The study by Wei Chunyan et al. revealed that JAK2 inhibitors regulate the ratio of M1/M2 macrophages through the CCL2/CCR2/JAK2 pathway, thereby impacting the outcome of early pregnancy \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough further analysis of the correlation between CFHR1 and immune cells, we discovered a positive association between CFHR1 and macrophages. This suggests a potential link between the abnormal expression of key genes in RPL and impaired immune cell infiltration. In future studies, it will be necessary to explore the regulatory relationship between CFHR1 and macrophages in the process of RPL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.4 γδ T Cells and RPL\u003c/h2\u003e \u003cp\u003eγδ T cells play a crucial role in immune surveillance and defense. Unlike conventional αβ T cells, γδ T cells are considered a bridge between innate and adaptive immunity \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. During normal human pregnancy, there is an increase in the number of γδ T cells, and the Vδ1-T cell subset within the γδ T cell population secretes cytokines that support pregnancy maintenance. However, in adverse pregnancies, there is an abnormal expression of receptors on γδ T cells, as well as an imbalance in the proportion of Vγ9Vδ2 T cells and Vδ1 T cells \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The study by Dongli Cai et al. found that an increased percentage of Vδ2 subset γδ T cells in the early pregnancy decidua is associated with unexplained spontaneous miscarriage \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Our research also identified abnormal expression of γδ T cells in the decidua tissue of RPL, further highlighting the correlation between γδ T cells and adverse pregnancy outcomes. Unfortunately, we did not further investigate the subsets of γδ T cells in our study. In future research, we will focus on exploring the changes in γδ T cell subsets in decidua tissue and their correlation with RPL, as well as their underlying mechanisms.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThrough the utilization of multiple datasets and various machine learning techniques, we have identified CFHR1 as the optimal feature gene for RPL. This gene is closely associated with the complement system and macrophages. Additionally, we have discovered an imbalance in immune cell infiltration in RPL. Our study provides new insights into the pathogenesis of RPL and potential therapeutic targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.D conceived the project, designed the research framework, and conducted data organization and analysis, as well as the initial drafting of the manuscript. Y J. G and Y. G performed the experimental procedures. Y.C conducted literature review and assisted in data organization . R. L was responsible for the inclusion and management of research subjects, while C. W ensured the quality control of the experimental procedures. Y Q.G made revisions to the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our gratitude to Professor Ping Zhang from the Family Planning Clinic for her assistance in the collection of tissue specimens. We extend our thanks to the laboratory of the Prenatal Diagnosis Center for providing us with the necessary experimental equipment. We are also grateful to Nurse Lili Lu for her assistance in specimen storage and management. Special thanks go to technicians Zhuangzhuang Kang and Shumin Zhu for their guidance during the experiments and their quality control of the data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO database, including GSE113790 , GSE161969, and GSE178535.(https://www.ncbi.nlm.nih.gov/geo)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBender Atik, R. \u003cem\u003eet al.\u003c/em\u003e ESHRE guideline: recurrent pregnancy loss. \u003cem\u003eHuman reproduction open\u003c/em\u003e 2018, hoy004, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/hropen/hoy004\u003c/span\u003e\u003cspan address=\"10.1093/hropen/hoy004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitriadis, E., Menkhorst, E., Saito, S., Kutteh, W. 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Journal of reproductive immunology 131, 57\u0026ndash;62, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jri.2019.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jri.2019.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Recurrent pregnancy loss, WGCNA, machine learning, CFHR1, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4441689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4441689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecurrent pregnancy loss (RPL) is a common reproductive complication, and the specific pathogenesis is still unclear. This study aimed to investigate RPL-related biomarkers and molecular mechanisms from the transcriptome of RPL decidua tissue using modern bioinformatics techniques, providing new perspectives for the etiology and clinical diagnosis and treatment of RPL.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThree gene expression profiles of RPL decidua tissue were retrieved and downloaded from the GEO database. Differential analysis, WGCNA analysis, and functional enrichment analysis were performed on the merged data. Subsequently, three machine learning methods (LASSO, SVM-RFE, and RF) were used to select the optimal feature genes for RPL, which were experimentally validated by RT-qPCR. The immune cell infiltration in RPL was evaluated using the ssGSEA algorithm, and the biological functions of the optimal feature genes were explored. Lastly, a heatmap was constructed to assist clinical physicians.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e10 key differentially expressed genes were identified: CFHR1, GPR155, TIMP4, WAKMAR2, COL15A1, LNCOG, C1QL1, KLK3, XG, and XGY2. Enrichment analysis showed associations with complement and coagulation cascade pathways. The three machine learning algorithms identified CFHR1 as the optimal feature gene for RPL, and RT-qPCR confirmed its high expression in RPL. ROC curve and nomogram demonstrated its diagnostic efficacy for RPL. Immune infiltration analysis revealed increased macrophages and γδT cells in RPL decidua tissue, with a significant positive correlation between CFHR1 and macrophages.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTranscriptomic abnormalities exist in RPL decidua tissue, with key genes closely related to complement and coagulation cascade pathways; CFHR1 is identified as the optimal feature gene for RPL. Abnormal immune infiltration and correlation with CFHR1 are observed in RPL decidua tissue.\u003c/p\u003e","manuscriptTitle":"Exploring Novel Molecular Mechanisms Underlying Recurrent Pregnancy Loss in Decidual Tissues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 11:25:35","doi":"10.21203/rs.3.rs-4441689/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-21T07:48:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-14T22:41:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261318485565956223979697730755311591664","date":"2025-02-04T22:33:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-14T05:24:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40467869267895478681883295614110503630","date":"2024-07-07T18:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93319847088359920487242981401457786935","date":"2024-07-05T17:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T09:43:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-05T09:41:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-22T15:41:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T15:39:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-18T15:44:23+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":"1ebd5318-78f3-4f1f-a283-1b4c272a5644","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32732720,"name":"Health sciences/Medical research/Biomarkers"},{"id":32732721,"name":"Biological sciences/Immunology/Immunological disorders"},{"id":32732722,"name":"Biological sciences/Molecular biology"},{"id":32732723,"name":"Biological sciences/Molecular biology/Transcriptomics"}],"tags":[],"updatedAt":"2025-07-21T16:00:44+00:00","versionOfRecord":{"articleIdentity":"rs-4441689","link":"https://doi.org/10.1038/s41598-025-10604-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-15 15:57:23","publishedOnDateReadable":"July 15th, 2025"},"versionCreatedAt":"2024-06-04 11:25:35","video":"","vorDoi":"10.1038/s41598-025-10604-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10604-y","workflowStages":[]},"version":"v1","identity":"rs-4441689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4441689","identity":"rs-4441689","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}