Investigating the Association Between Rheumatoid Arthritis and Membranous Nephropathy: Mendelian Randomization and Bioinformatic Analysis

Investigating the Association Between Rheumatoid Arthritis and Membranous Nephropathy: Mendelian Randomization and Bioinformatic Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigating the Association Between Rheumatoid Arthritis and Membranous Nephropathy: Mendelian Randomization and Bioinformatic Analysis Chuan He, PhD，Mingxin An, Yuxuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3434459/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Rheumatoid arthritis (RA) and membranous nephropathy (MN) are two autoimmune diseases that may coexist in some patients. Investigating the relationship between these diseases and elucidating potential shared pathogenic mechanisms is critical to understanding their co-occurrence. Methods: MR analysis was performed using two separate samples. Genetic variants were used as instrumental variables to estimate causality between diseases. Bioinformatic analysis was performed on publicly available gene expression datasets from GEO databases to identify common genes and molecular pathways in immune cells associated with RA and MN. Results: MR analysis did not reveal a causal relationship between RA and MN. [IVW:(MN on RA and RA on MN) OR0.05)]. However, the bioinformatic analysis identified correlations between several genes involved in immune regulation, suggesting potential common molecular pathways underlying the co-occurrence of RA and MN. Conclusions: Our findings suggest that the coexistence of RA and MN may not be directly causally related. The identified shared genes and immune pathways provide valuable insights into the pathogenesis of the co-occurrence, which may guide future investigations and therapeutic strategies for patients with these coexisting autoimmune diseases. Rheumatoid Arthritis Membranous Nephropathy Mendelian Randomization Bioinformatic Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The autoimmune illness rheumatoid arthritis (RA), which affects a number of organ systems, is linked to autoantibodies such as rheumatoid factor (RF) and anticyclic citrullinated peptide antibody (anti-CCP Ab)[ 1 , 2 ]. RA is one of the most common and debilitating autoimmune inflammatory chronic diseases, typically affecting the synovial membrane of joints and characterized by symmetrical destructive polyarthritis as well as extra-articular tissue. RF and anti-CCP Ab in RA patients may predispose them to greater erosive joint disease and increase their risk of developing extra-articular manifestations, including Felty syndrome, vasculitis, rheumatoid nodulosis, and serositis[ 3 , 4 ]. Renal involvement in RA patients is fairly common. The main causes are secondary amyloidosis and nephrotoxic side effects from RA medications like D-penicillamine, bucillamine, cyclosporine, gold salt, nonsteroidal anti-inflammatory drugs, and anti-tumor necrosis factor- drugs[ 5 , 6 ]. Additionally, a previous analysis of renal biopsy results in RA patients revealed that mesangial proliferative glomerulonephritis is the most common histopathologic finding, followed by amyloidosis, membranous nephropathy (MN), focal proliferative GN, minimal-change nephropathy, and acute interstitial nephritis[ 7 , 8 ]. Together, AA amyloidosis and drug-induced MN are very frequent and clinically significant in RA patients. MN that is specifically caused by RA, like membranous lupus nephritis in systemic lupus erythematosus, is uncommon. MN is a glomerular illness characterized by extensive subepithelial deposition of immune complexes in the glomerular basement membrane, which is followed by basement membrane thickening and is one of the main causes of adult nephrotic syndrome[ 9 ]. Approximately 80% of cases with no identifiable etiology are classified as "primary MN". The remaining 20% are classed as "secondary MN”, which occurs in people who also have immune disorders including RA, systemic lupus erythematosus, or infections, cancer and drug intoxication[ 10 ]. Over the last few decades, research on the immunopathogenesis of MN has made substantial progress. MN pathogenesis involves a number of antigen-antibody systems, as well as genes and cytokines involved in immune response, progression, and recovery. All autoimmune illnesses are characterized by a loss of immunological tolerance to autoantigens, and a variety of autoimmune diseases cause kidney injury as a result of autoantibodies that target antigens intrinsic to the glomeruli[ 11 ]. A paradigm shift in the diagnosis and monitoring of the disease resulted in the discovery of the phospholipase A2 receptor (PLA2R) as the primary antigen in adults in 2009. Since then, a number of other antigens have been described[ 12 ]. The chronic inflammatory disease RA, on the other hand, primarily affects the joints but can also cause systemic inflammation. Previous clinical investigations have revealed important distinctions between people with RA and MN, raising the possibility that both conditions are related. As dysregulated immune responses have been associated with both disorders, research has been conducted to determine whether there is a causative connection or common genetic elements that contribute to their contemporaneous development. Two different autoimmune illnesses, RA and MN, can coexist in some people, raising issues about their potential interaction and common pathogenic processes. It is essential to comprehend the connections between various ailments in order to enhance patient care in general as well as diagnostic and therapeutic methods. Mendelian randomization (MR)analysis, which uses genetic variants as instrumental variables, provides a potent method for assessing causality in observational investigations[ 13 , 14 ]. Using RA or MN as the exposure and the other disease as the outcome, we sought to ascertain whether one condition had a direct impact on the emergence of the other. To further explore potential shared molecular pathways and genetic associations between RA and MN, we turned to bioinformatics analysis. Using publicly available gene expression datasets from the Gene Expression Omnibus (GEO) databases, we sought to identify common genes and molecular pathways in immune cells associated with both RA and MN. Interestingly, our bioinformatics analysis revealed correlations between several genes involved in immune regulation, suggesting the existence of overlapping molecular mechanisms underlying the pathogenesis of these two diseases, although a direct causal relationship is lacking. Further investigation of these shared genetic factors and immune pathways promises deeper insights into the co-occurrence and potential therapeutic targets for RA and MN. This study aims to shed light on the relationship between RA and MN using a multifaceted approach combining MR analysis and bioinformatics techniques. While the MR analysis did not reveal a causal relationship between the diseases, the bioinformatics analysis uncovered shared genes and immune pathways, providing valuable insights into the pathogenesis of the co-occurrence of MN and RA. These findings have the potential to guide future research directions and improve clinical management strategies for patients with these coexisting autoimmune diseases. 2. Materials and methods 2.1. Study design RA and MN GWAS data specific to the Asian population from the publicly available database ( https://gwas.mrcieu.ac.uk/ ) were selected. Two-sample MR analysis was conducted to investigate whether a causal relationship exists between RA and MN[ 15 ]. Molecular mechanisms of their co-occurrence were investigated using the GEO database ( http://www.ncbi.nlm.nih.gov/geo ). Figure 1 shows the framework. 2.2 Clinical data for RA and MN 2.2.1 Patient Inclusion Criteria The clinical laboratory tests from patients diagnosed with both RA and MN at the First Hospital of China Medical University from 2008 to 2023 were collected. The order of RA and MN diagnoses was recorded, and laboratory tests were gathered longitudinally. 2.2.2 Inclusion Criteria for laboratory tests The laboratory tests obtained were analyzed when the patient suffered from both two diseases concurrently. The laboratory tests that available in ≤ 20% of patients were excluded from the analysis. Missing values were imputed using the mean value. The data were standardized to 0–1 range to ensure consistency. 2.3 Genome-Wide Association Study (GWAS)Dataset Sources The large-scale GWAS summary datasets from open project( https://gwas.mrcieu.ac.uk/ ) on RA and MN diseases were obtained. All participants were of East Asian ancestry. MN GWAS (ebi-a-GCST010004462, 2150 cases and 5829 controls) was selected. RA GWAS (bbj-a-72,3636 cases and 15554 controls), (bbj-a-151,4199 cases and 208254 controls), (ieu-a-831,4873 cases and 17642 controls) were selected. 2.4 Quality Control and Instrumental variables (IVs) selection for MR analysis To enhance the statistical power of genetic variants, several filtering criteria were implemented. Single-nucleotide polymorphisms (SNPs) associated with RA or MN and possessing a minor allele frequency (MAF) below 1% were excluded[ 14 , 16 ]. In addition, we removed variants located within a physical distance of ≤ 10 4 kb and with an R2 value below 0.001 to mitigate the effects of linkage disequilibrium (LD)[ 17 ]. For the pre-processed exposure data (RA or MN), genetic variants that exceeded the GWAS threshold (p < 5×10 − 8 ) were specifically selected to serve as IVs.[ 17 ]. 2.5 Two-Sample MR Analysis Statistical analysis used R (v4.1.2) with the "Two Sample MR" package (v0.5.6). Methods like inverse variance weighted (IVW), simple mode, weighted mode, MR Egger, and weighted median were deployed to ensure thorough evaluation [ 18 , 19 ]. 2.6 Sensitivity Analysis A comprehensive set of sensitivity analyses was conducted to ensure the robustness of our findings, including heterogeneity tests, leave-one-out analyses, and the utilization of funnel plots and MR-Egger regression [ 20 ]. Statistical significance was defined as a p-value ≤ 0.05[ 21 ]. These rigorous sensitivity tests were implemented to enhance the reliability and validity of our results. 2.7 Access to Gene Expression Omnibus (GEO) database Genes were screened using the GEO ( http://www.ncbi.nlm.nih.gov/geo ) database. GSE55235, GSE55457, GSE1919, GSE15573 were selected for RA analysis, and GSE104948, GSE108109 were selected for MN analysis. The basic information of the datasets selected is showed in Table 1 . We applied sva, limma packages to batch different datasets and screen the differentially expressed genes (DEGs). The values for statistical significance were set as adjusted p value ≤ 0.05 and |Fold change|≥1. Table 1 The information selected using the GEO database. Disease Tissue Series GEO accession Series type Number of samples Group Organism Series platform ID MN Glomerular GSE104948 Expression profiling by array 24 MN(n = 21) HC(n = 3) Homo sapiens GPL24120 MN Glomerular GSE108109 Expression profiling by array 50 MN(n = 44) HC(n = 4) Homo sapiens GPL19983 RA Synovial tissue GSE55235 Expression profiling by array 20 MN(n = 10) HC(n = 10) Homo sapiens GPL96 RA Synovial tissue GSE55457 Expression profiling by array 23 MN(n = 13) HC(n = 10) Homo sapiens GPL96 RA Synovial tissue GSE1919 Expression profiling by array 23 MN(n = 5) HC(n = 5) Homo sapiens GPL91 RA Synovial tissue GSE15573 Expression profiling by array 33 MN(n = 18) HC(n = 15) Homo sapiens GPL6102 2.8 Analyses of DEGs We utilized ggplot2 and VennDiagram packages to visualize DEGs and used GENEMANIA ( http://genemania.org/search/ ) for gene-gene interaction evaluation and visualization. 2.9 CIBERSORT immune cell scores Estimating the relative proportions of cell subpopulations in complex tissue expression profiles and quantifying cell abundance in mixed tissue samples were performed using the CIBERSORT method by using CIBERSORT R package, available at https://cibersort.stanford.edu/ . Absolute immune cell scores were calculated based on the gene expression datasets using the absolute model with 1000 permutations and disabling quantile normalization for RNA-seq data, as recommended. The differences in immune infiltration levels of each immune cell type between the two groups (RA-HC and MN-HC) were analyzed using the vioplot package. 2.10 Identification of immune-related key genes The immune cell association is defined as the absolute value of Pearson's correlation between each gene and the immune cells by using Corrplot package. Genes associated with immune cells with P value ≤ 0.05 were considered candidate genes. 3. Results 3.1 Association between RA and MN in clinical laboratory tests The research methodology roadmap is shown in Fig. 1 A. By querying the historical data of our hospital from 2008 to 2023, we preliminarily screened out 27 patients who suffered from RA and MN simultaneously. However, the chronological order of the patients suffering from the two conditions is different. To gain insights into the possible association between these two conditions, our initial analysis focuses on the clinical laboratory tests level. The association between RA and MN was studied in three different groups: Group 1 (MN secondary to RA), Group 2 (RA secondary to MN), and Group 3 (concurrent diagnosis of MN and RA). When comparing Group 1 with Group 3, several tests showed significant results. WBC-Urine showed a statistically significant association between MN secondary to RA and Group 3 (p = 0.042). Similarly, thyroid-stimulating hormone (TSH) (p = 0.014), cystatin C (Cys-C) (p = 0.005), hematocrit (HCT) (p = 0.008), and red blood cells (RBC) (p = 0.003) all showed significant associations between MN secondary to RA and Group 3. In addition, platelet distribution width (PDW) (p = 0.048), hemoglobin (Hb) (p = 0.019), cast-urine (p = 0.033), epithelial cells-urine(p = 0.011), urea (p = 0.034), total protein (TP)-Urine (p = 0.018), and IgM (p = 0.008) showed statistically significant associations between Group 1 and Group 3. When comparing group 2 with group 3, RBC (p = 0.034) and activated partial thromboplastin time (APTT) (p = 0.046) showed significant associations between Group 2 and Group 3. In addition, microalbuminuria (mAlb)(p = 0.028), epithelial cells-urine (p = 0.033), and rheumatoid factor (RF) (p = 0.021) also showed statistically significant associations between two groups. When comparing Group 1 with Group 2, APTT (p = 0.001), Total bile acids (TBA) (p = 0.012), RF (p = 0.047), and antistreptolysin O antibodies (ASO) (p = 0.038) showed statistically significant associations between MN secondary to RA and RA secondary to MN (Fig. 2 ). Based on the abnormal findings of the above laboratory tests, we obtained a comprehensive overview of the 27 patients who were diagnosed with RA and MN consecutively. These evaluations have provided insights into possible abnormalities in various aspects of the following conditions: 1. complete blood count (CBC), 2. renal function, 3. coagulation profile, 4. liver function, 5. thyroid function, 6. urinalysis, 7. autoimmune markers, 8. other markers. These findings suggest a significant association between MN and RA, particularly in the context of MN secondary to RA and its relationship to both group 2 (RA secondary to MN) and group 3 (concurrent diagnosis of MN and RA). Further analysis and investigation are recommended to better understand this association of underlying mechanisms and clinical implications. 3.2 Causal effects of MN on RA and RA on MN 3.2.1 Selection of IVs The research methodology roadmap is shown in Fig. 1 B. We included SNPs that were both significantly (p < 5×10 − 8 ) and independently (r 2 10,000) associated with MN. Finally, 5 SNPs were identified as IVs in MN (ebi-a-GCST010004) vs. RA (bbj-a-72), and 7 SNPs were identified as IVs in MN vs. RA (bbj-a-151) and 5 SNPs were identified as IVs in MN vs. RA (ieu-a-831) ( Supplementary Table 1 ). 3.2.2 Two-sample MR analysis For MR analysis of MN on RA (bbj-a-72 dataset), weighted median analysis revealed a significant inverse association of MN on RA (p = 0.0285), with an estimated odds ratio (OR) of 0.86 (95% CI: 0.75–0.98). However, other MR methods did not show statistically significant associations, with OR estimates ranging from 0.54 to 0.93. Similarly, in the bbj-a-151 dataset, the weighted median and weighted mode analysis showed a significant inverse association of MN with RA (p = 0.0085 and 0.0121), with an OR of 0.85 (95% CI: 0.75–0.96) and 0.83 (95% CI: 0.75–0.92), respectively. However, the other MR methods did not show statistically significant associations, with OR estimates ranging from 0.70 to 0.93. In the ieu-a-831 dataset, weighted median analysis showed a significant inverse association of MN with RA (p = 0.0163), with an OR of 0.87 (95% CI: 0.77–0.97). Similar to the previous datasets, the MR Egger, IVW, simple mode and weighted mode analyses did not yield significant associations, with OR estimates ranging from 0.53 to 0.92 (Fig. 3 A). Overall, while the weighted median analysis indicated a significant inverse association of MN with RA in all three datasets, the other MR methods did not fully support a causal relationship. For the MR analysis of RA on MN, bbj-a-72 dataset, the MR Egger analysis revealed a significant inverse association of RA on MN (p = 2.928E-05), with an estimated odds ratio (OR) of 0.53 (95% CI: 0.28–1.03). The weighted median, IVW, simple mode and weighted mode analyses also showed no significant causal effect between the two diseases, with OR estimates ranging from 0.87 to 0.92. Similarly, in the bbj-a-151 dataset, the MR Egger and weighted median analyses showed a significant inverse association of RA with MN (p = 0.0375 and 0.0286, respectively), with ORs of 0.57 (95% CI: 0.34–0.94) and 0.81 (95% CI: 0.68–0.98). However, the other MR methods did not show statistically significant associations, with OR estimates ranging from 0.81 to 0.84. In the ieu-a-831 dataset, MR Egger and weighted mode analysis showed a significant inverse association of RA with MN (p = 6.261E-05 and p = 4.500E-09), with an OR of 0.47 (95% CI: 0.34–0.63) and 0.52 (95% CI: 0.45–0.60), respectively. Similar to the previous data sets, the weighted median, inverse variance-weighted, simple mode, and simple mode analyses did not yield significant associations, with OR estimates ranging from 0.84 to 1.01 (Fig. 3 B). The results of OR values were all less than 1 after transformation of the relative risk ratios (Fig. 4 ). Overall, while the MR Egger analysis indicated a significant inverse association of RA on MN in all three datasets, the other MR methods did not fully support a causal relationship using the selected IVs and methods. Further investigation with larger sample sizes and additional IVs may be needed to determine the potential causal relationship between these conditions. The combined results suggest that there may be other factors or common mechanisms of immune dysregulation that contribute to the co-occurrence of MN and RA and warrant further investigation. 3.2.3 Sensitivity analysis and visualization MR-Egger regression and IVW analysis were used to detect heterogeneity. For MN-RA and RA-MN analysis, both MR-Egger regression and IVW analysis indicated that there was heterogeneity in the study, with a Cochran's Q of MR-Egger from 64.3117 to 182.8472(p < 0.05) and IVW from 115.54326 to 201.0359(p 0.05). However, the MR-Egger intercept showed horizontal pleiotropy RA (bbj-a-151 and ieu-a-831)-MN (p = 0.0001 and p = 0.0003) in ( Supplementary Table 3 ). We used the leave-one-out method to eliminate SNPs one by one to determine whether the causal association was due to a single IV, and the final results showed that the TSMR analysis results were not robust (Fig. 5 ). Forest plots for MR analyses of the association between MN and RA ( Supplementary Fig. 2 ). Our MR analysis results do not support the causal relationship between MN and RA from a genetic perspective. Large-scale randomized controlled trials are needed to gain a deeper understanding of this association in the future. 3.3 Determination of specific antigens MR analysis found no causal relationship between RA and MN. Therefore, we further screened for MN-specific antigens by comparing logFC and their genes were analyzed again as exposure and outcome using GWAS and/or GEO database. We compared these genes after normalization, in which NELL1, EXT1, PCDH7 had adj.P. Val < 0.05 and logFC were greater than RA group. SEMA3B, FAT1, NDNF, PCSK6 were not screened in RA group ( Supplementary Fig. 3 ). Therefore, from the data, we suggest that NELL1, EXT1, PCDH7, SEMA3B, FAT1, NDNF, PCSK6 may be the specific antigens for MN. Although PLA2R was not a significantly expressed gene in this dataset, the importance of PLA2R has been confirmed in previous study. The basic information of the GWAS databases that were obtained by searching with the key words PLA2R, THSD7A, and NELL-1, respectively ( Supplementary Table 4 ). Because we first confirmed that the products of expression of these three genes could be MN-specific antigens, we performed MR analysis of these three genes as MN-specific exposures with RA ( Supplementary Table 5 ). 3.4 Integrated Bioinformatics Analysis Although this MR study revealed a non-genetic association between MN and RA using the largest GWAS data to date, the onset and development of RA and MN may share similar underlying mechanisms. Therefore, the link between RA and MN may be through other common pathways, as opposed to the diseases themselves. 3.4.1 Identification and Analysis of DEGs Our research methodology roadmap is shown in Fig. 1 C. We drew volcano plots and heat maps of MN and RA datasets to display gene expression characteristics. In summary, gene expression profiles of MN and RA were obtained from GEO database, including GSE104948, GSE108109, GSE55235, GSE155457, GSE1919, GSE15573 (Table 1 ). In MN and RA samples compared to living donors, the MN datasets identified 168 up-regulated and 224 down-regulated DEGs, the RA datasets identified 23 up-regulated and 107 down-regulated DEGs. The two disease datasets shared 1 up-regulated DEG and 7 down-regulated DEGs, including CD52(up-regulated), GLUL, MT1X, SLC19A2, RND3, NFIL3, ZBTB16, C7(down-regulated) ( Supplementary Fig. 4 ) 3.4.2 Gene Interaction Analysis The gene-gene interaction network for DEGs was constructed to analyze the function of these genes using the GeneMANIA database. The 8 hub nodes representing DEGs were surrounded by 20 nodes representing genes that were significantly correlated with DEGs ( Supplementary Fig. 5 ). In short, all these genes were co-expressed and 8 genes were involved in genetic interactions. 3.4.3 Immunological Infiltration in the MN and RA datasets The differences in immune infiltration of RA and MN versus living donors in 22 subpopulations of immune cells were analyzed by the CIBERSORT algorithm, and the results are shown in a bar graph ( Supplementary Fig. 6A ). A correlation heat map of the 22 immune cell types in MN showed that resting master cells and M0 macrophages had the highest positive correlation, while memory B cells and naive B cells had the highest negative correlation. And in RA, eosinophil cells and resting dendritic cells had the highest positive correlation, and CD8 + T cells and neutrophil cells had the highest negative correlation ( Supplementary Fig. 6B ). The MN violin plot of immune cell infiltration differences shows that monocytes had higher infiltration than in the control samples, and plasma cells and CD4 memory resting T cells had lower infiltration. While the RA violin plot of immune cell infiltration differences shows that B cells memory, plasma cells CD4 + memory activated, macrophages M2 had higher infiltration and that naive B cells, naive T cells CD4, regulatory T cells (Tregs), NK cells resting had lower infiltration ( Supplementary Fig. 6C ). 3.4.4 Expression level of DEGs correlates with immune features in MN and RA CIBERSORT analysis showed a correlation between DEGs expression and immune cell infiltration in RA and MN ( Supplementary Fig. 7A, B ). We found that CD52 and ZBTB16 expression were both correlated with immune infiltration. A correlation heat map of immune cells and DEGs in MN showed that NFIL3 and MT1X had the highest positive correlation, ZBTB16 and NFIL3 had the highest negative correlation, while in RA, NFIL3 and C7 had the highest positive correlation, B-cell memory and RND3 had the highest negative correlation ( Supplementary Fig. 7C ). Discussion To our knowledge, this is the first bi-directional MR design to analyze the causal association between RA and MN. However, our data provided evidence supporting no causal association between RA and MN using the MR approach. RA is characterized by inflammation and synovitis as typical features, which can eventually lead to cartilage destruction, the destruction of bone tissue in the proximal joints. Although great progress has been made in the treatment of RA despite tremendous advances in the treatment of RA, complete remission of the disease remains a challenge. During the progression of RA, a series of extra-articular manifestations may occur, such as involvement of the eyes, lungs, skin, and heart. Patients with RA typically have renal dysfunction, and MN is a common pathologic finding. Cases directly linked to RA have been reported, albeit being rare[ 22 ]. The glomerular disease known as MN can affect people of any age. It is the most typical cause of adult nephrotic syndrome. The remaining cases of MN are linked to drugs or other illnesses including systemic lupus erythematosus, hepatitis virus infection, or cancer. In about 80% of individuals, MN has no underlying etiology (primary MN). To date, several case reports have shown the possibility of MN caused by RA itself[ 5 , 6 , 23 ]. However, its incidence and pathogenetic mechanisms are still uncovered. Neural epidermal growth factor-like 1 (NELL1), a recently identified target antigen in MN, has been examined in observational research[ 24 ]. Semaphorin 3B-associated MN is usually seen in children and accounts for approximately 16% of all non-lupus MN in childhood[ 10 ]. These findings are already leading to a rethinking of diagnostic and therapeutic algorithms in the direction of more personalized medicine. The secondary renal damage in RA is mostly caused by the side effects of treatment drugs, which is a pervasive environmental factor that significantly affects susceptibility to renal disorders in RA. Drug-associated nephropathy, which is most frequently brought on by disease-modifying antirheumatic (DMARDs) medications such as D-penicillamine, gold salts and bucillamine, and infrequently by tumor necrosis factor-α (TNF-α) inhibitor, is the most prevalent type of MN linked with RA [ 8 , 9 ]. Some studies suggest that biologic DMARD may adversely affect renal function in addition to treating RA, but this remains controversial and the overall incidence is low. Although renal disorders caused by biologic DMARD are uncommon, the occurrence of autoimmune renal parenchymal disease has been increasingly reported with the use of biologic DMARDs such as tumor necrosis factor inhibitors, cytotoxic T lymphocyte-associated antigen 4-Ig, and IL-6 receptor antagonists[ 25 – 28 ]. Estimated glomerular filtration rate declines over time in RA patients treated with biologic DMARDs. A retrospective study evaluating risk factors for changes in renal function in patients with RA and ankylosing spondylitis treated with different biologic DMARDs found that male sex may be responsible for the decrease in estimated glomerular filtration rate in these patients, possibly through the involvement of sex hormones in sex-specific differentiation and homeostasis of various organs, including the kidney, resulting in differences in renal function[ 29 ]. This MR study explored a possible causal relationship between RA and MN. We found that MR Egger analysis showed a significant inverse association between MN and RA in all three datasets, the other MR methods also did not support a causal relationship. These combined results suggest that there may be other factors or common mechanisms of immune dysregulation that contribute to the co-occurrence of MN and RA and warrant further investigation. Nevertheless, our MR study indicated that genetically predisposed RA was not associated with an increased risk of MN. Though the mechanism for how RA altered the risk of MN remains unclear, there are several possible explanations. Additionally, utilizing the selected IVs and techniques, TSMR and the Reverse TSMR analyses did not consistently show a causal link between RA and MN. Further investigations with larger sample sizes and additional IVs may be necessary to ascertain the potential causal link between these conditions. In addition, we also performed the Leave-one-out sensitivity test for MR. This test mainly calculates the MR results of the remaining IVs after eliminating them one by one. Our results are less than 0. Therefore, the results may be unstable, which suggests that we may need more SNPs for the experiment. Next, we selected 2 GEO databases from MN and 4 databases from RA, and subsequently removed the batch effects separately. After difference analysis as well as cluster analysis, 1 co-up-regulated gene was screened, including CD52 and 7 co-down-regulated genes, including GLUL, MT1X, SLC19A2, RND3, NFIL3, ZBTB16 and C7. In order to examine the roles of these 8 DEGs using the GeneMANIA database, we built a gene-gene interaction network for them. The eight genes share co-expression with other genes for their primary biological roles, and a few other genes interact with these eight genes in different ways. Furthermore, we performed immune infiltration analysis on the collated datasets of the two diseases separately and found significant differences between the following concentrations of immune cells in MN: Plasma cells, T cells CD4, memory resting and monocytes, plasma cells, CD4 naïve T cells, CD4 memory activated T cells, regulatory T cells, NK cells resting and type 2 Macrophages. We further analyzed the correlation of the expression of 8 DEGs in immune cells with significant differences in MN and RA, respectively. The results revealed that the genes that may be associated with the simultaneous occurrence of both diseases are CD52, ZBTB16. The immune cells that may be associated with the simultaneous occurrence of both diseases may be B cells memory cells naïve, Macrophages M2, Monocytes cells resting, Plasma cells CD4 memory resting cells CD4, naïve cells regulatory (Tregs). T cells are a critical component of regulatory and control immune responses that can promote B cell-related responses while also activating inflammation and cytotoxicity that causes kidney tissue damage[ 30 ]. Furthermore, earlier research has demonstrated that anti-CD20 antibodies are potent immunosuppressants capable of suppressing B cell proliferation and the generation of pathogenic antibodies in MN. It is worth noting that MN patients have higher levels of lipopolysaccharide than normal people, which can activate resting B lymphocytes to perform antigen presentation[ 31 ]. In the meantime, B cells are mostly exposed to soluble antigens, and soluble PLA2R can be detected in the blood of MN patients [ 32 ]. Our study has several limitations. Firstly, this is an East Asian-based study. Our findings cannot be generalized to other populations. Second, some of our MR analyses were underpowered to detect small effects because of the limited variance of exposures explained by the SNPs instruments. In this direction, the exclusion of palindromic or ambiguous SNPs from our MR instruments may have further reduced the power of our MR investigation. Third, clinical laboratory data from only twenty-seven individuals, which is especially true for the three-group analysis. This is because the prevalence of RA in China is approximately 1% and that of MN is approximately 1.2/100,000 people. As a result, real-world data suggest that even fewer people suffer from both diseases, and there are comparable difficulties with the accessibility of laboratory data. This is an unavoidable limitation of our research. Conclusions Our results do not support that susceptibility to RA affects MN risk in East Asian population and the common genetic effects or environmental confounders could explain the observed associations. Declarations Declarations of interest None Author statement Chuan He: Conceptualization, Methodology, Software Mingxin An, Yuxuan Li: Data curation, Writing- Original draft preparation. Chuan He, Mingxin An: Visualization, Investigation. Chuan He: Supervision. Chuan He, Yuxuan Li: Software, Validation. Chuan He, Yuxuan Li: Writing- Reviewing and Editing. Funding This study was funded by 345 Talent Project of Shengjing Hospital of China Medical University, China Postdoctoral Science Foundation (2020M670099ZX), National Natural Science Foundation of China (32000811) and the Natural Science Foundation Project of Liaoning Province (2022JH2/20200024). Authors’ contributions Chuan He conceived and designed the study. Yuan Li and Mingxin An collected the data. Chuan He analyzed the data and results. Yuan Li and Mingxin An completed the writing of the manuscript. Yuxuan Li, Mingxin An and Chuan He reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version. Funding This study was funded by 345 Talent Project of Shengjing Hospital of China Medical University, China Postdoctoral Science Foundation (2020M670099ZX), National Natural Science Foundation of China (32000811) and the Natural Science Foundation Project of Liaoning Province (2022JH2/20200024). Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate Data were collected from public databases. And there is no ethical approval necessary. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Scott DL, Wolfe F, Huizinga TW. Rheumatoid arthritis. Lancet. 2010;376(9746):1094-108. Epub 2010/09/28. doi: 10.1016/s0140-6736(10)60826-4. PubMed PMID: 20870100. van Delft MAM, Huizinga TWJ. 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Neural Epidermal Growth Factor-Like 1-Positive Membranous Nephropathy With Rheumatoid Arthritis. Kidney Int Rep. 2023;8(4):921-4. Epub 2023/04/19. doi: 10.1016/j.ekir.2022.12.026. PubMed PMID: 37069980; PubMed Central PMCID: PMCPMC10105036. Piga M, Chessa E, Ibba V, Mura V, Floris A, Cauli A, et al. Biologics-induced autoimmune renal disorders in chronic inflammatory rheumatic diseases: systematic literature review and analysis of a monocentric cohort. Autoimmun Rev. 2014;13(8):873-9. Epub 2014/05/21. doi: 10.1016/j.autrev.2014.05.005. PubMed PMID: 24840285. Stokes MB, Foster K, Markowitz GS, Ebrahimi F, Hines W, Kaufman D, et al. Development of glomerulonephritis during anti-TNF-alpha therapy for rheumatoid arthritis. Nephrol Dial Transplant. 2005;20(7):1400-6. Epub 2005/04/21. doi: 10.1093/ndt/gfh832. PubMed PMID: 15840673. Michel M, Henri P, Vincent FB, Leon N, Marcelli C. Mesangial immunoglobulin (Ig)A glomerulonephritis in a patient with rheumatoid arthritis treated with abatacept. Joint Bone Spine. 2013;80(6):660-3. Epub 2013/07/03. doi: 10.1016/j.jbspin.2013.05.003. PubMed PMID: 23816503. Matsuo Y, Mizoguchi F, Kohsaka H, Ito E, Eishi Y, Miyasaka N. Tocilizumab-induced immune complex glomerulonephritis in a patient with rheumatoid arthritis. Rheumatology (Oxford). 2013;52(7):1341-3. Epub 2013/01/18. doi: 10.1093/rheumatology/kes403. PubMed PMID: 23325036. Kim SK, Choe JY. Gender Is a Risk Factor for Annual Decline in Estimated Glomerular Filtration Rate in Patients Treated with Biological DMARDs in Rheumatoid Arthritis and Ankylosing Spondylitis: a Retrospective Observational Study. J Korean Med Sci. 2018;33(30):e188. Epub 2018/07/24. doi: 10.3346/jkms.2018.33.e188. PubMed PMID: 30034303; PubMed Central PMCID: PMCPMC6052330. Zhao Q, Dai H, Liu X, Jiang H, Liu W, Feng Z, et al. Helper T Cells in Idiopathic Membranous Nephropathy. Front Immunol. 2021;12:665629. Epub 2021/06/08. doi: 10.3389/fimmu.2021.665629. PubMed PMID: 34093559; PubMed Central PMCID: PMCPMC8173183. Wang GH, Lu J, Ma KL, Zhang Y, Hu ZB, Chen PP, et al. The Release of Monocyte-Derived Tissue Factor-Positive Microparticles Contributes to a Hypercoagulable State in Idiopathic Membranous Nephropathy. J Atheroscler Thromb. 2019;26(6):538-46. Epub 2018/11/16. doi: 10.5551/jat.46284. PubMed PMID: 30429407; PubMed Central PMCID: PMCPMC6545459. van de Logt AE, Fresquet M, Wetzels JF, Brenchley P. The anti-PLA2R antibody in membranous nephropathy: what we know and what remains a decade after its discovery. Kidney Int. 2019;96(6):1292-302. Epub 2019/10/16. doi: 10.1016/j.kint.2019.07.014. PubMed PMID: 31611068. Additional Declarations No competing interests reported. Supplementary Files supplementaryfigure1.tif Supplementary Fig. 1: Funnel plot of SNPs. (A, B, C) The funnel plot of the analysis of MN-RA, which are bbj-a-72, bbj-a-151, ieu-a-831, separately. (D, E, F) The funnel plot of the analysis of RA-MN, which are bbj-a-72, bbj-a-151, ieu-a-831 separately. supplementaryfigure2.tif Supplementary Fig. 2 Forest plot of the bidirectional causal effect of MN-RA and RA-MN and associated SNPs. The red and black dots/lines indicate the causal estimates of the bidirectional risk between MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, SNPs, single-nucleotide polymorphisms. supplementaryfigure3.tif Supplementary Fig. 3 Bioinformatics analysis of MN specific antigen genes. Based on literature mining, EXT1/2, FAT1, NDNF, NELL-1, PCDH7, PCSK6, PLA2R, and Sema3B were identified as genes specific for MN. The antigen genes of RA and MN using GEO datasets were also compared (Table 1) and some genes were found that to be unique to MN. The genes are represented by circles of different colors, and the logFC values are indicated by the circle sizes. supplementaryfigure4.tif Supplementary Fig. 4 Identification and Analysis of DEGs. A. The volcano plot illustrates DEGs in MN and RA datasets. The above plot represents MN dataset, and the following plot represents RA dataset. Blue dots represent down-regulated genes and red dots represent up-regulated genes. B. heatmap of top 50 DEGs. C. Shared DEGs of MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, DEGs, differentially expressed genes. supplementaryfigure5.tif Supplementary Fig. 5 Interaction analysis of 8 characteristic DEGs. A: The interaction network between DEGs was analyzed using the GeneMANIA database. The top 20 most changed neighboring genes are shown. Each node in the network represents a single gene. Abbreviation: DEGs, differentially expressed genes. supplementaryfigure6.tif Supplementary Fig. 6 Landscape of immune cell infiltration in MN and RA versus normal controls. The above plot represents MN dataset, and the following plot represents RA dataset. A: Relative percentages of 22 immune cell subpopulations in the samples. B: Correlation heat map of 22 immune cell types. C: Difference in immune infiltration of MN and RA versus control samples. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy. supplementaryfigure7.tif Supplementary Fig.7 Expression of DEGs was associated with immune cell infiltration. A: Correlation between DEGs expression and the content of 3 immune cells in MN. B: Correlation between DEGs expression and the contents of 7 immune cells in RA. C: Correlation heat map of immune cells and DEGs in MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, DEGs, differentially expressed genes. supplementarytable1.xlsx supplementarytable2.xlsx supplementarytable3.xlsx supplementarytable4.docx supplementarytable5.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3434459","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":239677860,"identity":"cefe0499-bcb6-49db-abe3-f04519e62b88","order_by":0,"name":"Chuan He","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"He","suffix":""},{"id":239677861,"identity":"3c9dcbc9-b77d-45a2-8d16-4f8618abc207","order_by":1,"name":"PhD，Mingxin An","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"PhD，Mingxin","middleName":"","lastName":"An","suffix":""},{"id":239677863,"identity":"85ea23bf-731e-41c8-bdf9-cd6c71bd4b5d","order_by":2,"name":"Yuxuan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYFAC5oYDHyAsA2K1MDYcnEGyFmYekrQY3EhsPGxTsy2xgb15mwRDzR0itJw52HA459jtxAaeY2USDMeeEaHleGPD4dwGoBaJHDMJxobDRGg5DFRmCdIi/4ZYLSBbGMG28BCpRRLol4M9x24bt/GkFVskHCNCC9+N5MMfftTclu1nP7zxxocaIrQoHIAy2EBEAmENDAzyDcSoGgWjYBSMgpENADtyQHaE0BuOAAAAAElFTkSuQmCC","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-10-11 23:59:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3434459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3434459/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44742025,"identity":"1e4bab60-7c61-48ab-a0af-54c136e4b833","added_by":"auto","created_at":"2023-10-16 23:29:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89503,"visible":true,"origin":"","legend":"\u003cp\u003eAn overview of this MR study and bioinformatic analysis design. Abbreviations: RA, rheumatoid arthritis; MN, membranous nephropathy; IVs, instrumental variables; MR, mendelian randomization; DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/c5da89708a8a3ef2a1a0393a.jpg"},{"id":44740232,"identity":"a2337ca6-3567-4eb1-b5e2-54335fff4b7c","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143170,"visible":true,"origin":"","legend":"\u003cp\u003eStatistically significant associations between MN and RA. Abbreviations: RA, rheumatoid arthritis; MN, membranous nephropathy; TSH, thyroid-stimulating hormone; Cys-C, cystatin C, HCT, hematocrit; RBC, red blood cells; PDW, platelet distribution width; Hb, hemoglobin; TP, total protein; APTT, activated partial thromboplastin time; mAlb, microalbuminuria; RF, rheumatoid factor; TBA, total bile acids; GGT, gamma-glutamyltransferase; ALB, albumin; ALT, alanine aminotransferase; ALP, alkaline phosphatase; TBIL, total bilirubin; ASO, antistreptolysin O antibodies.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/b44ca000f8753fac6e326e0d.jpg"},{"id":44740234,"identity":"c3d25050-0a48-4897-a62d-cca0b3ae548d","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103722,"visible":true,"origin":"","legend":"\u003cp\u003eEstimation of the causal relationship between MN and RA using TSMR methods. Forest plots were employed to depict results in a MR investigation, utilizing genetically predicted MN and RA by using MR-Egger, weighted median, IVW, simple mode, and weighted mode. An OR value greater than 1 indicates that the exposure indicator functions as a risk factor, whereas the opposite suggests a protective factor. (A) The exposure was MN and the outcome is RA. (B) The exposure was RA and the outcome is MN. Abbreviation: MR, mendelian randomization; IVW, inverse-variance-weighted; OR, odds ratio.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/182ff51eb2bb691b8a6821c0.jpg"},{"id":44740857,"identity":"62e16325-783c-471a-b1a9-9d1fe2223f3a","added_by":"auto","created_at":"2023-10-16 23:21:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101991,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of genetic correlation between RA and MN using different MR analysis methods. (A-F). The slope value equals the b-value calculated using the five methods and represents the causal effect. Positive slope indicates that exposure is a risk factor, whereas a negative slope is the opposite. Abbreviations: RA, rheumatoid arthritis; MN, membranous nephropathy; MR, Mendelian randomization.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/b608b55bb64789e40837acc6.jpg"},{"id":44740236,"identity":"e7095ed9-a534-4550-972c-5a2d93a8953d","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":133202,"visible":true,"origin":"","legend":"\u003cp\u003eBidirectional leave-one-out sensitivity analysis between MN and RA. Red lines represent estimates from IVW tests. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy; MR, Mendelian randomization; IVW, inverse variance weighted.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/cdbf56361774a073d6a25f82.jpg"},{"id":46931483,"identity":"6439c88f-7cec-44c5-b691-dabdb11e92fd","added_by":"auto","created_at":"2023-11-22 16:37:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1004689,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/23b34000-4e41-49ac-ac94-35218ff340f1.pdf"},{"id":44740859,"identity":"fa01790e-c445-42c8-a0de-05c51b07bbc4","added_by":"auto","created_at":"2023-10-16 23:21:07","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":303520,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 1: Funnel plot of SNPs. (A, B, C) The funnel plot of the analysis of MN-RA, which are bbj-a-72, bbj-a-151, ieu-a-831, separately. (D, E, F) The funnel plot of the analysis of RA-MN, which are bbj-a-72, bbj-a-151, ieu-a-831 separately.\u003c/p\u003e","description":"","filename":"supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/f08885b6c5651e069699d12d.tif"},{"id":44740241,"identity":"ffb61bb2-0872-4a32-8ffb-017db9a2954d","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":428298,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 2 Forest plot of the bidirectional causal effect of MN-RA and RA-MN and associated SNPs. The red and black dots/lines indicate the causal estimates of the bidirectional risk between MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, SNPs, single-nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"supplementaryfigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/7d4b7c646c66cbadcc64f6eb.tif"},{"id":44742026,"identity":"aee5a0b0-e636-4e04-bbbc-ce34fd513454","added_by":"auto","created_at":"2023-10-16 23:29:07","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17354,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 3 Bioinformatics analysis of MN specific antigen genes. Based on literature mining, EXT1/2, FAT1, NDNF, NELL-1, PCDH7, PCSK6, PLA2R, and Sema3B were identified as genes specific for MN. The antigen genes of RA and MN using GEO datasets were also compared (Table 1) and some genes were found that to be unique to MN. The genes are represented by circles of different colors, and the logFC values are indicated by the circle sizes.\u003c/p\u003e","description":"","filename":"supplementaryfigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/23f765ffdfcb9bb53f7055ee.tif"},{"id":44740237,"identity":"b617a54e-b6c8-41a4-851c-5ffb8e5483a2","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1316072,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 4 Identification and Analysis of DEGs. A. The volcano plot illustrates DEGs in MN and RA datasets. The above plot represents MN dataset, and the following plot represents RA dataset. Blue dots represent down-regulated genes and red dots represent up-regulated genes. B. heatmap of top 50 DEGs. C. Shared DEGs of MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"supplementaryfigure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/82c7821aeccf06416cb1f625.tif"},{"id":44740242,"identity":"db405447-5d53-4afd-a084-0c9645deb04d","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":917594,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 5 Interaction analysis of 8 characteristic DEGs. A: The interaction network between DEGs was analyzed using the GeneMANIA database. The top 20 most changed neighboring genes are shown. Each node in the network represents a single gene. Abbreviation: DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"supplementaryfigure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/27410b5f5e004b80c7901f87.tif"},{"id":44742029,"identity":"1ca558d6-508f-4072-888a-7724e9eebd2e","added_by":"auto","created_at":"2023-10-16 23:29:07","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":953852,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 6 Landscape of immune cell infiltration in MN and RA versus normal controls. The above plot represents MN dataset, and the following plot represents RA dataset. A: Relative percentages of 22 immune cell subpopulations in the samples. B: Correlation heat map of 22 immune cell types. C: Difference in immune infiltration of MN and RA versus control samples. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy.\u003c/p\u003e","description":"","filename":"supplementaryfigure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/864a792bb5e68b98f62a3de5.tif"},{"id":44740864,"identity":"c459e81a-b9be-4571-9306-2fa628c913ae","added_by":"auto","created_at":"2023-10-16 23:21:07","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":482220,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig.7 Expression of DEGs was associated with immune cell infiltration. A: Correlation between DEGs expression and the content of 3 immune cells in MN. B: Correlation between DEGs expression and the contents of 7 immune cells in RA. C: Correlation heat map of immune cells and DEGs in MN and RA. Abbreviation: RA, rheumatoid arthritis; MN, membranous nephropathy, DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"supplementaryfigure7.tif","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/986824897ddee44b328d9e8b.tif"},{"id":44740862,"identity":"d4d03461-2f77-4104-8fec-06a916431b4f","added_by":"auto","created_at":"2023-10-16 23:21:07","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16597,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/19f40a1cae16f753540d0604.xlsx"},{"id":44742027,"identity":"a10b80f2-d80d-4617-b91d-2aa5ae4c5f92","added_by":"auto","created_at":"2023-10-16 23:29:07","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":9893,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/8e247a1d066e43633e070ed6.xlsx"},{"id":44740247,"identity":"a05bbf8f-c54f-4ce3-af46-f17e2bcb3cb6","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":9806,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/7a5cf5806a2e373844364300.xlsx"},{"id":44740243,"identity":"27844547-d272-4b26-af7d-26dcb64a15a8","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":15581,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/c6a75b642be9519cb72eba55.docx"},{"id":44740249,"identity":"c2b921a2-803e-4475-a92d-6b502d56468a","added_by":"auto","created_at":"2023-10-16 23:13:07","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":16734,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-3434459/v1/0796a65aca30fe9ff1af2838.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Association Between Rheumatoid Arthritis and Membranous Nephropathy: Mendelian Randomization and Bioinformatic Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe autoimmune illness rheumatoid arthritis (RA), which affects a number of organ systems, is linked to autoantibodies such as rheumatoid factor (RF) and anticyclic citrullinated peptide antibody (anti-CCP Ab)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RA is one of the most common and debilitating autoimmune inflammatory chronic diseases, typically affecting the synovial membrane of joints and characterized by symmetrical destructive polyarthritis as well as extra-articular tissue. RF and anti-CCP Ab in RA patients may predispose them to greater erosive joint disease and increase their risk of developing extra-articular manifestations, including Felty syndrome, vasculitis, rheumatoid nodulosis, and serositis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Renal involvement in RA patients is fairly common. The main causes are secondary amyloidosis and nephrotoxic side effects from RA medications like D-penicillamine, bucillamine, cyclosporine, gold salt, nonsteroidal anti-inflammatory drugs, and anti-tumor necrosis factor- drugs[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, a previous analysis of renal biopsy results in RA patients revealed that mesangial proliferative glomerulonephritis is the most common histopathologic finding, followed by amyloidosis, membranous nephropathy (MN), focal proliferative GN, minimal-change nephropathy, and acute interstitial nephritis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Together, AA amyloidosis and drug-induced MN are very frequent and clinically significant in RA patients. MN that is specifically caused by RA, like membranous lupus nephritis in systemic lupus erythematosus, is uncommon.\u003c/p\u003e \u003cp\u003eMN is a glomerular illness characterized by extensive subepithelial deposition of immune complexes in the glomerular basement membrane, which is followed by basement membrane thickening and is one of the main causes of adult nephrotic syndrome[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Approximately 80% of cases with no identifiable etiology are classified as \"primary MN\". The remaining 20% are classed as \"secondary MN\u0026rdquo;, which occurs in people who also have immune disorders including RA, systemic lupus erythematosus, or infections, cancer and drug intoxication[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Over the last few decades, research on the immunopathogenesis of MN has made substantial progress. MN pathogenesis involves a number of antigen-antibody systems, as well as genes and cytokines involved in immune response, progression, and recovery. All autoimmune illnesses are characterized by a loss of immunological tolerance to autoantigens, and a variety of autoimmune diseases cause kidney injury as a result of autoantibodies that target antigens intrinsic to the glomeruli[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A paradigm shift in the diagnosis and monitoring of the disease resulted in the discovery of the phospholipase A2 receptor (PLA2R) as the primary antigen in adults in 2009. Since then, a number of other antigens have been described[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The chronic inflammatory disease RA, on the other hand, primarily affects the joints but can also cause systemic inflammation. Previous clinical investigations have revealed important distinctions between people with RA and MN, raising the possibility that both conditions are related. As dysregulated immune responses have been associated with both disorders, research has been conducted to determine whether there is a causative connection or common genetic elements that contribute to their contemporaneous development. Two different autoimmune illnesses, RA and MN, can coexist in some people, raising issues about their potential interaction and common pathogenic processes. It is essential to comprehend the connections between various ailments in order to enhance patient care in general as well as diagnostic and therapeutic methods.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR)analysis, which uses genetic variants as instrumental variables, provides a potent method for assessing causality in observational investigations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Using RA or MN as the exposure and the other disease as the outcome, we sought to ascertain whether one condition had a direct impact on the emergence of the other. To further explore potential shared molecular pathways and genetic associations between RA and MN, we turned to bioinformatics analysis. Using publicly available gene expression datasets from the Gene Expression Omnibus (GEO) databases, we sought to identify common genes and molecular pathways in immune cells associated with both RA and MN. Interestingly, our bioinformatics analysis revealed correlations between several genes involved in immune regulation, suggesting the existence of overlapping molecular mechanisms underlying the pathogenesis of these two diseases, although a direct causal relationship is lacking. Further investigation of these shared genetic factors and immune pathways promises deeper insights into the co-occurrence and potential therapeutic targets for RA and MN.\u003c/p\u003e \u003cp\u003eThis study aims to shed light on the relationship between RA and MN using a multifaceted approach combining MR analysis and bioinformatics techniques. While the MR analysis did not reveal a causal relationship between the diseases, the bioinformatics analysis uncovered shared genes and immune pathways, providing valuable insights into the pathogenesis of the co-occurrence of MN and RA. These findings have the potential to guide future research directions and improve clinical management strategies for patients with these coexisting autoimmune diseases.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eRA and MN GWAS data specific to the Asian population from the publicly available database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were selected. Two-sample MR analysis was conducted to investigate whether a causal relationship exists between RA and MN[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Molecular mechanisms of their co-occurrence were investigated using the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the framework.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical data for RA and MN\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Patient Inclusion Criteria\u003c/h2\u003e \u003cp\u003eThe clinical laboratory tests from patients diagnosed with both RA and MN at the First Hospital of China Medical University from 2008 to 2023 were collected. The order of RA and MN diagnoses was recorded, and laboratory tests were gathered longitudinally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Inclusion Criteria for laboratory tests\u003c/h2\u003e \u003cp\u003eThe laboratory tests obtained were analyzed when the patient suffered from both two diseases concurrently. The laboratory tests that available in \u0026le;\u0026thinsp;20% of patients were excluded from the analysis. Missing values were imputed using the mean value. The data were standardized to 0\u0026ndash;1 range to ensure consistency.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Genome-Wide Association Study (GWAS)Dataset Sources\u003c/h2\u003e \u003cp\u003eThe large-scale GWAS summary datasets from open project(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on RA and MN diseases were obtained. All participants were of East Asian ancestry. MN GWAS (ebi-a-GCST010004462, 2150 cases and 5829 controls) was selected. RA GWAS (bbj-a-72,3636 cases and 15554 controls), (bbj-a-151,4199 cases and 208254 controls), (ieu-a-831,4873 cases and 17642 controls) were selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality Control and Instrumental variables (IVs) selection for MR analysis\u003c/h2\u003e \u003cp\u003eTo enhance the statistical power of genetic variants, several filtering criteria were implemented. Single-nucleotide polymorphisms (SNPs) associated with RA or MN and possessing a minor allele frequency (MAF) below 1% were excluded[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, we removed variants located within a physical distance of \u0026le;\u0026thinsp;10\u003csup\u003e4\u003c/sup\u003e kb and with an R2 value below 0.001 to mitigate the effects of linkage disequilibrium (LD)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For the pre-processed exposure data (RA or MN), genetic variants that exceeded the GWAS threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were specifically selected to serve as IVs.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Two-Sample MR Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis used R (v4.1.2) with the \"Two Sample MR\" package (v0.5.6). Methods like inverse variance weighted (IVW), simple mode, weighted mode, MR Egger, and weighted median were deployed to ensure thorough evaluation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eA comprehensive set of sensitivity analyses was conducted to ensure the robustness of our findings, including heterogeneity tests, leave-one-out analyses, and the utilization of funnel plots and MR-Egger regression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Statistical significance was defined as a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These rigorous sensitivity tests were implemented to enhance the reliability and validity of our results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Access to Gene Expression Omnibus (GEO) database\u003c/h2\u003e \u003cp\u003eGenes were screened using the GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. GSE55235, GSE55457, GSE1919, GSE15573 were selected for RA analysis, and GSE104948, GSE108109 were selected for MN analysis. The basic information of the datasets selected is showed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We applied sva, limma packages to batch different datasets and screen the differentially expressed genes (DEGs). The values for statistical significance were set as adjusted p value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and |Fold change|\u0026ge;1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe information selected using the GEO database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeries GEO accession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeries type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOrganism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSeries platform ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlomerular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE104948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL24120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlomerular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE108109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL19983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynovial tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE55235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynovial tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE55457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynovial tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE1919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynovial tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE15573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMN(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003cp\u003eHC(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHomo sapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGPL6102\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Analyses of DEGs\u003c/h2\u003e \u003cp\u003eWe utilized ggplot2 and VennDiagram packages to visualize DEGs and used GENEMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/search/\u003c/span\u003e\u003cspan address=\"http://genemania.org/search/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for gene-gene interaction evaluation and visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 CIBERSORT immune cell scores\u003c/h2\u003e \u003cp\u003eEstimating the relative proportions of cell subpopulations in complex tissue expression profiles and quantifying cell abundance in mixed tissue samples were performed using the CIBERSORT method by using CIBERSORT R package, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Absolute immune cell scores were calculated based on the gene expression datasets using the absolute model with 1000 permutations and disabling quantile normalization for RNA-seq data, as recommended. The differences in immune infiltration levels of each immune cell type between the two groups (RA-HC and MN-HC) were analyzed using the vioplot package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Identification of immune-related key genes\u003c/h2\u003e \u003cp\u003eThe immune cell association is defined as the absolute value of Pearson's correlation between each gene and the immune cells by using Corrplot package. Genes associated with immune cells with P value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered candidate genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Association between RA and MN in clinical laboratory tests\u003c/h2\u003e \u003cp\u003eThe research methodology roadmap is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. By querying the historical data of our hospital from 2008 to 2023, we preliminarily screened out 27 patients who suffered from RA and MN simultaneously. However, the chronological order of the patients suffering from the two conditions is different. To gain insights into the possible association between these two conditions, our initial analysis focuses on the clinical laboratory tests level. The association between RA and MN was studied in three different groups: Group 1 (MN secondary to RA), Group 2 (RA secondary to MN), and Group 3 (concurrent diagnosis of MN and RA).\u003c/p\u003e \u003cp\u003eWhen comparing Group 1 with Group 3, several tests showed significant results. WBC-Urine showed a statistically significant association between MN secondary to RA and Group 3 (p = 0.042). Similarly, thyroid-stimulating hormone (TSH) (p = 0.014), cystatin C (Cys-C) (p = 0.005), hematocrit (HCT) (p = 0.008), and red blood cells (RBC) (p = 0.003) all showed significant associations between MN secondary to RA and Group 3. In addition, platelet distribution width (PDW) (p = 0.048), hemoglobin (Hb) (p = 0.019), cast-urine (p = 0.033), epithelial cells-urine(p = 0.011), urea (p = 0.034), total protein (TP)-Urine (p = 0.018), and IgM (p = 0.008) showed statistically significant associations between Group 1 and Group 3.\u003c/p\u003e \u003cp\u003eWhen comparing group 2 with group 3, RBC (p = 0.034) and activated partial thromboplastin time (APTT) (p = 0.046) showed significant associations between Group 2 and Group 3. In addition, microalbuminuria (mAlb)(p = 0.028), epithelial cells-urine (p = 0.033), and rheumatoid factor (RF) (p = 0.021) also showed statistically significant associations between two groups.\u003c/p\u003e \u003cp\u003eWhen comparing Group 1 with Group 2, APTT (p = 0.001), Total bile acids (TBA) (p = 0.012), RF (p = 0.047), and antistreptolysin O antibodies (ASO) (p = 0.038) showed statistically significant associations between MN secondary to RA and RA secondary to MN (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the abnormal findings of the above laboratory tests, we obtained a comprehensive overview of the 27 patients who were diagnosed with RA and MN consecutively. These evaluations have provided insights into possible abnormalities in various aspects of the following conditions: 1. complete blood count (CBC), 2. renal function, 3. coagulation profile, 4. liver function, 5. thyroid function, 6. urinalysis, 7. autoimmune markers, 8. other markers.\u003c/p\u003e \u003cp\u003eThese findings suggest a significant association between MN and RA, particularly in the context of MN secondary to RA and its relationship to both group 2 (RA secondary to MN) and group 3 (concurrent diagnosis of MN and RA). Further analysis and investigation are recommended to better understand this association of underlying mechanisms and clinical implications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Causal effects of MN on RA and RA on MN\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Selection of IVs\u003c/h2\u003e \u003cp\u003eThe research methodology roadmap is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. We included SNPs that were both significantly (p \u0026lt; 5×10\u003csup\u003e− 8\u003c/sup\u003e) and independently (r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001 and kb \u0026gt; 10,000) associated with MN. Finally, 5 SNPs were identified as IVs in MN (ebi-a-GCST010004) vs. RA (bbj-a-72), and 7 SNPs were identified as IVs in MN vs. RA (bbj-a-151) and 5 SNPs were identified as IVs in MN vs. RA (ieu-a-831) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Two-sample MR analysis\u003c/h2\u003e \u003cp\u003eFor MR analysis of MN on RA (bbj-a-72 dataset), weighted median analysis revealed a significant inverse association of MN on RA (p = 0.0285), with an estimated odds ratio (OR) of 0.86 (95% CI: 0.75–0.98). However, other MR methods did not show statistically significant associations, with OR estimates ranging from 0.54 to 0.93. Similarly, in the bbj-a-151 dataset, the weighted median and weighted mode analysis showed a significant inverse association of MN with RA (p = 0.0085 and 0.0121), with an OR of 0.85 (95% CI: 0.75–0.96) and 0.83 (95% CI: 0.75–0.92), respectively. However, the other MR methods did not show statistically significant associations, with OR estimates ranging from 0.70 to 0.93. In the ieu-a-831 dataset, weighted median analysis showed a significant inverse association of MN with RA (p = 0.0163), with an OR of 0.87 (95% CI: 0.77–0.97). Similar to the previous datasets, the MR Egger, IVW, simple mode and weighted mode analyses did not yield significant associations, with OR estimates ranging from 0.53 to 0.92 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eOverall, while the weighted median analysis indicated a significant inverse association of MN with RA in all three datasets, the other MR methods did not fully support a causal relationship.\u003c/p\u003e \u003cp\u003eFor the MR analysis of RA on MN, bbj-a-72 dataset, the MR Egger analysis revealed a significant inverse association of RA on MN (p = 2.928E-05), with an estimated odds ratio (OR) of 0.53 (95% CI: 0.28–1.03). The weighted median, IVW, simple mode and weighted mode analyses also showed no significant causal effect between the two diseases, with OR estimates ranging from 0.87 to 0.92. Similarly, in the bbj-a-151 dataset, the MR Egger and weighted median analyses showed a significant inverse association of RA with MN (p = 0.0375 and 0.0286, respectively), with ORs of 0.57 (95% CI: 0.34–0.94) and 0.81 (95% CI: 0.68–0.98). However, the other MR methods did not show statistically significant associations, with OR estimates ranging from 0.81 to 0.84. In the ieu-a-831 dataset, MR Egger and weighted mode analysis showed a significant inverse association of RA with MN (p = 6.261E-05 and p = 4.500E-09), with an OR of 0.47 (95% CI: 0.34–0.63) and 0.52 (95% CI: 0.45–0.60), respectively. Similar to the previous data sets, the weighted median, inverse variance-weighted, simple mode, and simple mode analyses did not yield significant associations, with OR estimates ranging from 0.84 to 1.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The results of OR values were all less than 1 after transformation of the relative risk ratios (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, while the MR Egger analysis indicated a significant inverse association of RA on MN in all three datasets, the other MR methods did not fully support a causal relationship using the selected IVs and methods. Further investigation with larger sample sizes and additional IVs may be needed to determine the potential causal relationship between these conditions. The combined results suggest that there may be other factors or common mechanisms of immune dysregulation that contribute to the co-occurrence of MN and RA and warrant further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Sensitivity analysis and visualization\u003c/h2\u003e \u003cp\u003eMR-Egger regression and IVW analysis were used to detect heterogeneity. For MN-RA and RA-MN analysis, both MR-Egger regression and IVW analysis indicated that there was heterogeneity in the study, with a Cochran's Q of MR-Egger from 64.3117 to 182.8472(p \u0026lt; 0.05) and IVW from 115.54326 to 201.0359(p \u0026lt; 0.05) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Funnel plots to visualize heterogeneity are shown in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe MR-Egger intercept showed no horizontal pleiotropy of MN-RA (p \u0026gt; 0.05). However, the MR-Egger intercept showed horizontal pleiotropy RA (bbj-a-151 and ieu-a-831)-MN (p = 0.0001 and p = 0.0003) in (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). We used the leave-one-out method to eliminate SNPs one by one to determine whether the causal association was due to a single IV, and the final results showed that the TSMR analysis results were not robust (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Forest plots for MR analyses of the association between MN and RA (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Our MR analysis results do not support the causal relationship between MN and RA from a genetic perspective. Large-scale randomized controlled trials are needed to gain a deeper understanding of this association in the future.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Determination of specific antigens\u003c/h2\u003e \u003cp\u003eMR analysis found no causal relationship between RA and MN. Therefore, we further screened for MN-specific antigens by comparing logFC and their genes were analyzed again as exposure and outcome using GWAS and/or GEO database. We compared these genes after normalization, in which NELL1, EXT1, PCDH7 had adj.P. Val \u0026lt; 0.05 and logFC were greater than RA group. SEMA3B, FAT1, NDNF, PCSK6 were not screened in RA group (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Therefore, from the data, we suggest that NELL1, EXT1, PCDH7, SEMA3B, FAT1, NDNF, PCSK6 may be the specific antigens for MN. Although PLA2R was not a significantly expressed gene in this dataset, the importance of PLA2R has been confirmed in previous study.\u003c/p\u003e \u003cp\u003eThe basic information of the GWAS databases that were obtained by searching with the key words PLA2R, THSD7A, and NELL-1, respectively (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Because we first confirmed that the products of expression of these three genes could be MN-specific antigens, we performed MR analysis of these three genes as MN-specific exposures with RA (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Integrated Bioinformatics Analysis\u003c/h2\u003e \u003cp\u003eAlthough this MR study revealed a non-genetic association between MN and RA using the largest GWAS data to date, the onset and development of RA and MN may share similar underlying mechanisms. Therefore, the link between RA and MN may be through other common pathways, as opposed to the diseases themselves.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Identification and Analysis of DEGs\u003c/h2\u003e \u003cp\u003eOur research methodology roadmap is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. We drew volcano plots and heat maps of MN and RA datasets to display gene expression characteristics. In summary, gene expression profiles of MN and RA were obtained from GEO database, including GSE104948, GSE108109, GSE55235, GSE155457, GSE1919, GSE15573 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In MN and RA samples compared to living donors, the MN datasets identified 168 up-regulated and 224 down-regulated DEGs, the RA datasets identified 23 up-regulated and 107 down-regulated DEGs. The two disease datasets shared 1 up-regulated DEG and 7 down-regulated DEGs, including CD52(up-regulated), GLUL, MT1X, SLC19A2, RND3, NFIL3, ZBTB16, C7(down-regulated) (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Gene Interaction Analysis\u003c/h2\u003e \u003cp\u003eThe gene-gene interaction network for DEGs was constructed to analyze the function of these genes using the GeneMANIA database. The 8 hub nodes representing DEGs were surrounded by 20 nodes representing genes that were significantly correlated with DEGs (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e). In short, all these genes were co-expressed and 8 genes were involved in genetic interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Immunological Infiltration in the MN and RA datasets\u003c/h2\u003e \u003cp\u003eThe differences in immune infiltration of RA and MN versus living donors in 22 subpopulations of immune cells were analyzed by the CIBERSORT algorithm, and the results are shown in a bar graph (\u003cb\u003eSupplementary Fig.\u0026nbsp;6A\u003c/b\u003e). A correlation heat map of the 22 immune cell types in MN showed that resting master cells and M0 macrophages had the highest positive correlation, while memory B cells and naive B cells had the highest negative correlation. And in RA, eosinophil cells and resting dendritic cells had the highest positive correlation, and CD8\u003csup\u003e+\u003c/sup\u003eT cells and neutrophil cells had the highest negative correlation (\u003cb\u003eSupplementary Fig.\u0026nbsp;6B\u003c/b\u003e). The MN violin plot of immune cell infiltration differences shows that monocytes had higher infiltration than in the control samples, and plasma cells and CD4 memory resting T cells had lower infiltration. While the RA violin plot of immune cell infiltration differences shows that B cells memory, plasma cells CD4 + memory activated, macrophages M2 had higher infiltration and that naive B cells, naive T cells CD4, regulatory T cells (Tregs), NK cells resting had lower infiltration (\u003cb\u003eSupplementary Fig.\u0026nbsp;6C\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4 Expression level of DEGs correlates with immune features in MN and RA\u003c/h2\u003e \u003cp\u003eCIBERSORT analysis showed a correlation between DEGs expression and immune cell infiltration in RA and MN (\u003cb\u003eSupplementary Fig.\u0026nbsp;7A, B\u003c/b\u003e). We found that CD52 and ZBTB16 expression were both correlated with immune infiltration. A correlation heat map of immune cells and DEGs in MN showed that NFIL3 and MT1X had the highest positive correlation, ZBTB16 and NFIL3 had the highest negative correlation, while in RA, NFIL3 and C7 had the highest positive correlation, B-cell memory and RND3 had the highest negative correlation (\u003cb\u003eSupplementary Fig.\u0026nbsp;7C\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first bi-directional MR design to analyze the causal association between RA and MN. However, our data provided evidence supporting no causal association between RA and MN using the MR approach.\u003c/p\u003e\u003cp\u003eRA is characterized by inflammation and synovitis as typical features, which can eventually lead to cartilage destruction, the destruction of bone tissue in the proximal joints. Although great progress has been made in the treatment of RA despite tremendous advances in the treatment of RA, complete remission of the disease remains a challenge. During the progression of RA, a series of extra-articular manifestations may occur, such as involvement of the eyes, lungs, skin, and heart. Patients with RA typically have renal dysfunction, and MN is a common pathologic finding. Cases directly linked to RA have been reported, albeit being rare[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The glomerular disease known as MN can affect people of any age. It is the most typical cause of adult nephrotic syndrome. The remaining cases of MN are linked to drugs or other illnesses including systemic lupus erythematosus, hepatitis virus infection, or cancer. In about 80% of individuals, MN has no underlying etiology (primary MN). To date, several case reports have shown the possibility of MN caused by RA itself[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, its incidence and pathogenetic mechanisms are still uncovered. Neural epidermal growth factor-like 1 (NELL1), a recently identified target antigen in MN, has been examined in observational research[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Semaphorin 3B-associated MN is usually seen in children and accounts for approximately 16% of all non-lupus MN in childhood[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These findings are already leading to a rethinking of diagnostic and therapeutic algorithms in the direction of more personalized medicine.\u003c/p\u003e\u003cp\u003eThe secondary renal damage in RA is mostly caused by the side effects of treatment drugs, which is a pervasive environmental factor that significantly affects susceptibility to renal disorders in RA. Drug-associated nephropathy, which is most frequently brought on by disease-modifying antirheumatic (DMARDs) medications such as D-penicillamine, gold salts and bucillamine, and infrequently by tumor necrosis factor-α (TNF-α) inhibitor, is the most prevalent type of MN linked with RA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Some studies suggest that biologic DMARD may adversely affect renal function in addition to treating RA, but this remains controversial and the overall incidence is low. Although renal disorders caused by biologic DMARD are uncommon, the occurrence of autoimmune renal parenchymal disease has been increasingly reported with the use of biologic DMARDs such as tumor necrosis factor inhibitors, cytotoxic T lymphocyte-associated antigen 4-Ig, and IL-6 receptor antagonists[\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Estimated glomerular filtration rate declines over time in RA patients treated with biologic DMARDs. A retrospective study evaluating risk factors for changes in renal function in patients with RA and ankylosing spondylitis treated with different biologic DMARDs found that male sex may be responsible for the decrease in estimated glomerular filtration rate in these patients, possibly through the involvement of sex hormones in sex-specific differentiation and homeostasis of various organs, including the kidney, resulting in differences in renal function[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis MR study explored a possible causal relationship between RA and MN. We found that MR Egger analysis showed a significant inverse association between MN and RA in all three datasets, the other MR methods also did not support a causal relationship. These combined results suggest that there may be other factors or common mechanisms of immune dysregulation that contribute to the co-occurrence of MN and RA and warrant further investigation. Nevertheless, our MR study indicated that genetically predisposed RA was not associated with an increased risk of MN. Though the mechanism for how RA altered the risk of MN remains unclear, there are several possible explanations. Additionally, utilizing the selected IVs and techniques, TSMR and the Reverse TSMR analyses did not consistently show a causal link between RA and MN. Further investigations with larger sample sizes and additional IVs may be necessary to ascertain the potential causal link between these conditions. In addition, we also performed the Leave-one-out sensitivity test for MR. This test mainly calculates the MR results of the remaining IVs after eliminating them one by one. Our results are less than 0. Therefore, the results may be unstable, which suggests that we may need more SNPs for the experiment. Next, we selected 2 GEO databases from MN and 4 databases from RA, and subsequently removed the batch effects separately. After difference analysis as well as cluster analysis, 1 co-up-regulated gene was screened, including CD52 and 7 co-down-regulated genes, including GLUL, MT1X, SLC19A2, RND3, NFIL3, ZBTB16 and C7. In order to examine the roles of these 8 DEGs using the GeneMANIA database, we built a gene-gene interaction network for them. The eight genes share co-expression with other genes for their primary biological roles, and a few other genes interact with these eight genes in different ways. Furthermore, we performed immune infiltration analysis on the collated datasets of the two diseases separately and found significant differences between the following concentrations of immune cells in MN: Plasma cells, T cells CD4, memory resting and monocytes, plasma cells, CD4 naïve T cells, CD4 memory activated T cells, regulatory T cells, NK cells resting and type 2 Macrophages. We further analyzed the correlation of the expression of 8 DEGs in immune cells with significant differences in MN and RA, respectively. The results revealed that the genes that may be associated with the simultaneous occurrence of both diseases are CD52, ZBTB16. The immune cells that may be associated with the simultaneous occurrence of both diseases may be B cells memory cells naïve, Macrophages M2, Monocytes cells resting, Plasma cells CD4 memory resting cells CD4, naïve cells regulatory (Tregs). T cells are a critical component of regulatory and control immune responses that can promote B cell-related responses while also activating inflammation and cytotoxicity that causes kidney tissue damage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, earlier research has demonstrated that anti-CD20 antibodies are potent immunosuppressants capable of suppressing B cell proliferation and the generation of pathogenic antibodies in MN. It is worth noting that MN patients have higher levels of lipopolysaccharide than normal people, which can activate resting B lymphocytes to perform antigen presentation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the meantime, B cells are mostly exposed to soluble antigens, and soluble PLA2R can be detected in the blood of MN patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study has several limitations. Firstly, this is an East Asian-based study. Our findings cannot be generalized to other populations. Second, some of our MR analyses were underpowered to detect small effects because of the limited variance of exposures explained by the SNPs instruments. In this direction, the exclusion of palindromic or ambiguous SNPs from our MR instruments may have further reduced the power of our MR investigation. Third, clinical laboratory data from only twenty-seven individuals, which is especially true for the three-group analysis. This is because the prevalence of RA in China is approximately 1% and that of MN is approximately 1.2/100,000 people. As a result, real-world data suggest that even fewer people suffer from both diseases, and there are comparable difficulties with the accessibility of laboratory data. This is an unavoidable limitation of our research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results do not support that susceptibility to RA affects MN risk in East Asian population and the common genetic effects or environmental confounders could explain the observed associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclarations of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChuan He: Conceptualization, Methodology, Software\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMingxin An, Yuxuan Li: Data curation, Writing- Original draft preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChuan He, Mingxin An: Visualization, Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChuan He: Supervision.\u003c/p\u003e\n\u003cp\u003eChuan He, Yuxuan Li: Software, Validation.\u003c/p\u003e\n\u003cp\u003eChuan He, Yuxuan Li: Writing- Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by 345 Talent Project of Shengjing Hospital of China Medical University, China Postdoctoral Science Foundation (2020M670099ZX), National Natural Science Foundation of China (32000811) and the Natural Science Foundation Project of Liaoning Province (2022JH2/20200024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChuan He conceived and designed the study. Yuan Li and Mingxin An collected the data. Chuan He analyzed the data and results. Yuan Li and Mingxin An completed the writing of the manuscript. Yuxuan Li, Mingxin An and Chuan He reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by 345 Talent Project of Shengjing Hospital of China Medical University, China Postdoctoral Science Foundation (2020M670099ZX), National Natural Science Foundation of China (32000811) and the Natural Science Foundation Project of Liaoning Province (2022JH2/20200024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from public databases. And there is no ethical approval necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eScott DL, Wolfe F, Huizinga TW. Rheumatoid arthritis. Lancet. 2010;376(9746):1094-108. Epub 2010/09/28. doi: 10.1016/s0140-6736(10)60826-4. PubMed PMID: 20870100.\u003c/li\u003e\n\u003cli\u003evan Delft MAM, Huizinga TWJ. An overview of autoantibodies in rheumatoid arthritis. J Autoimmun. 2020;110:102392. Epub 2020/01/09. doi: 10.1016/j.jaut.2019.102392. PubMed PMID: 31911013.\u003c/li\u003e\n\u003cli\u003eCojocaru M, Cojocaru IM, Silosi I, Vrabie CD, Tanasescu R. Extra-articular Manifestations in Rheumatoid Arthritis. 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PubMed PMID: 31611068.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid Arthritis, Membranous Nephropathy, Mendelian Randomization, Bioinformatic Analysis","lastPublishedDoi":"10.21203/rs.3.rs-3434459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3434459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/em\u003e Rheumatoid arthritis (RA) and membranous nephropathy (MN) are two autoimmune diseases that may coexist in some patients. Investigating the relationship between these diseases and elucidating potential shared pathogenic mechanisms is critical to understanding their co-occurrence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e MR analysis was performed using two separate samples. Genetic variants were used as instrumental variables to estimate causality between diseases. Bioinformatic analysis was performed on publicly available gene expression datasets from GEO databases to identify common genes and molecular pathways in immune cells associated with RA and MN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e MR analysis did not reveal a causal relationship between RA and MN. [IVW:(MN on RA and RA on MN) OR\u0026lt;1, P\u0026gt;0.05)]. However, the bioinformatic analysis identified correlations between several genes involved in immune regulation, suggesting potential common molecular pathways underlying the co-occurrence of RA and MN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our findings suggest that the coexistence of RA and MN may not be directly causally related. The identified shared genes and immune pathways provide valuable insights into the pathogenesis of the co-occurrence, which may guide future investigations and therapeutic strategies for patients with these coexisting autoimmune diseases.\u003c/p\u003e","manuscriptTitle":"Investigating the Association Between Rheumatoid Arthritis and Membranous Nephropathy: Mendelian Randomization and Bioinformatic Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-16 23:13:02","doi":"10.21203/rs.3.rs-3434459/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f959ffc7-3f61-480e-a5f9-deafe2a162bf","owner":[],"postedDate":"October 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-22T16:29:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-16 23:13:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3434459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3434459","identity":"rs-3434459","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}