Identification of key regulatory factors for m6A in myasthenia gravis and characteristics of the immune characteristics

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The modification of N6-methyladenosine (m6A) regulates diverse immune metabolic and immunopathological processes; however, its role in MG remains unclear. We downloaded dataset GSE85452 from the GEO database to identify differentially expressed genes regulated by m6A. The Random Forest (RF) method was utilized to identify pivotal regulatory genes associated with m6A modification. Subsequently, a prognostic model was crafted and confirmed using this gene set. Patients with MG were stratified according to the expression levels of these key regulatory genes. Additionally, MG-specific immune signatures were delineated by examining immune cell infiltration patterns and their correlations. Further functional annotation, protein-protein interaction mapping, and molecular docking analyses were performed on these immune biomarkers, leading to the discovery of three genes that exhibited significant differential expression within the dataset: RBM15, CBLL1, and YTHDF1.The random forest algorithm confirmed these as key regulatory genes of m6A in MG, validated by constructing a clinical prediction model. Based on key regulatory gene expression, we divided MG patients into two groups, revealing two distinct m6A modification patterns with varying immune cell abundances. We also discovered 61 genes associated with the m6A phenotype and conducted an in-depth exploration of their biological roles. RBM15, CBLL1, and YTHDF1 were found positively correlated with CD56dim natural killer cells, natural killer T cells, and type 1 helper T cells. These genes were stable diagnostic m6A-related markers in both discovery and validation cohorts. Our findings suggest RBM15, CBLL1, and YTHDF1 as immune markers for MG. Further analysis of these genes may elucidate their roles in the immune microenvironment of MG. myasthenia gravis N6-methyladenosine regulation prediction model immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Myasthenia gravis (MG), an autoimmune neuromuscular disorder, is characterized by the deterioration of synaptic transmission at the neuromuscular junction, manifesting as progressive muscle weakness and profound fatigue 1 . The incidence is 15-25 cases per 100,000 people and is most common in women under 40 and men over 60 2-3 . The disease often recurs and follows a prolonged course. While 80% of patients experience generalized muscle weakness, only 20% are limited to ocular muscles. It can become life-threatening if the respiratory muscles and medulla are involved 3-4 . MG not only causes physical dysfunction and reduces quality of life but also leads to psychological issues like anxiety and depression, placing a significant burden on families and society 5-6 . Diagnosis is based on typical clinical features and the presence of antibodies against neuromuscular junction proteins, yet up to 46% of patients remain undiagnosed within the first year 7 . Standard treatments include acetylcholinesterase inhibitors, corticosteroids, and immunosuppressants, which effectively improve symptoms in most patients, but may cause significant long-term adverse effects. Additionally, up to 15% of patients respond poorly or not at all to these therapies 8-9 . The occurrence of MG is thought to be linked to antibody-mediated activity against acetylcholine receptors (AChR), cellular immune responses, and complement activities, though the exact mechanisms remain unclear 10 . The consensus among medical professionals is that aberrant immune responses significantly contribute to the etiological mechanisms and disease progression in MG 11-12 . Recent studies 13-14 underscore the pivotal role of both innate and adaptive immune responses in the pathogenesis of MG, providing critical insights that advance our comprehension of the disease's complex immunological underpinnings. N6-methyladenosine (m6A) RNA methylation, akin to DNA methylation, is a dynamic and reversible modification process 15-16 . This modification is facilitated by m6A methyltransferases, or "writers" (such as METTL3/14/16, CBLL1,VIRMA, RBM15/15B, ZC3H3,WTAP, and KIAA1429), and removed by demethylases or "erasers" (including ALKBH5 and FTO). It is identified by m6A-binding proteins like YTHDC1/2, IGF2BP1/2/3, YTHDF1/2/3, and HNRNPA2B1, known as "readers" 17 .Functionally, m6A methylation regulates mRNA splicing, expression, decay, and translation, playing a pivotal role in cellular pathways and processes such as cell differentiation, development, and metabolism 18 . This modification is closely linked to various diseases, particularly having a significant impact on tumor progression 19-21 . Previous studies 22 have indicated that dysregulation of m6A methylation in neurodegenerative diseases can disrupt RNA metabolism, resulting in abnormal protein synthesis and potentially contributing to the emergence of conditions like Alzheimer's disease (AD) 23-24 , amyotrophic lateral sclerosis (ALS) 25 , and Parkinson's disease (PD) 26 . However, research on the regulatory factors of m6A methylation in MG remains limited. Therefore, we aim to explore the potential association between m6A methylation and myasthenia gravis. In our comprehensive analysis, we employed a systematic approach to scrutinize the expression profiles of m6A methylation regulatory elements across the sample cohort. Utilizing a random forest algorithm, we successfully pinpointed three genes that exhibit a significant correlation with the risk of developing the disease. We thoroughly investigated the dynamic interactions between immune cells and immune responses across various samples. The aim of this research is to elucidate the potential connection between MG and the regulatory mechanisms of m6A methylation. We hope the results will offer valuable insights and references for future diagnostic and treatment options for MG. Materials and methods Data Processing and Download This study downloaded the RNA expression profiles and clinical information of the myasthenia gravis dataset GSE85452 27 from the GEO database. This dataset encompasses RNA detection data from 13 myasthenia gravis samples and 12 control group samples. These samples were sequenced using the GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip) platform. The "Normalize Between Arrays" function from the "limma" package (bioconductor.org/packages/release/bioc/html/limma.html) in R was used to normalize the expressionmatrix. Gene probes were annotated using official symbols. This study included 26 m6A-related genes, including coding genes (METTL3,WTAP, VIRMA,RBM15, ZC3H13,METTL14,METTL16,CBLL1,RBM15B), reading genes (YTHDC1, YTHDC2, HNRNPC,YTHDF3,YTHDF2,YTHDF1,LRPPRC, FMR1, RBMX,HNRNPA2B1, IGFBP3,IGFBP2,IGFBP1,ELAVL1,IGF2BP1) and demethylation genes (FTO, ALKBH5) 28 – 30 . We employed Perl scripting to ascertain the chromosomal locales of m6A-associated genes,subsequently leveraging the 'RCircos' package in R for their graphical representation. Screening and Expression Analysis of m6A Regulatory Factors The expression of m6A-related genes in each sample was extracted using the R package "limma." The Wilcoxon test was then employed to detect differences in the expression of the above m6A-related genes between myasthenia gravis patients and the control group, with P < 0.05 considered statistically significant. Heatmaps and bar charts were generated to visualize the differences using "pheatmap" ( https://CRAN.R-project.org/package=pheatmap ), "reshape2" 31 , and "ggpubr" R packages ( https://CRAN.R-project.org/package=ggpubr ). Spearmancorrelation analysis was conducted, and relationships between differentially expressed m6A regulatory factors were assessed and visualized using "limma," "ggplot2" 32 , "ggExtra" ( https://CRAN.R-project.org/package=ggExtra ), and "ggpubr" R packages. A scatter plot was created for the two genes with the highest correlation to display the results. Model Selection The R software was used to compare the Support Vector Machine (SVM) model and Random Forest (RF) model in machine learning, utilizing boxplots of residuals and reverse cumulative distribution plots to determine the methods used for subsequent disease feature gene selection, with validation performed using Receiver Operating Characteristic (ROC) curves. The SVM and RF models were employed to predict the diagnosis of MG. The "caret" 33 , "kernlab" ( https://CRAN.R-project.org/package=kernlab ), and "randomForest" (The R Journal: Classification and regression by randomForest (R-project.org)) R packages were used for SVM and RF. Residuals were calculated to compare the discrimination performance of both models using the "DALEX" 34 package. Upon identifying the optimal model, the 'randomForest' package also assessed the importance scores of m6A regulatory factors. Finally, the 'ggplot2' and 'pROC' packages were employed for visualizing the results. Random Forest Trees Based on m6A-related differential genes, important genes with a score > 2 were screened using the R package "randomForest" 36 , and a nomogram was constructed for these feature genes using the R package "rms" 37 . Disease incidence was predicted by aggregating individual gene scores within the nomogram to derive a total score. Construction and Validation of the Plot Utilizing the expression levels of three pivotal m6A modulatory factors within the gene set, a prognostic nomogram was developed employing the "rms" library in R. Subsequently, calibration plots were generated to appraise the accuracy of the nomogram's predictive capacity. The clinical utility of the nomogram was evaluated through decision curve analysis. Ultimately, a Receiver Operating Characteristic (ROC) curve analysis was implemented to determine the nomogram's diagnostic efficacy in differentiating patients with MG from healthy individuals. Consistent Clustering and Immune Cell Infiltration Analysis Based on the m6A-related differential genes, consistent clustering analysis of patients in the dataset was performed using the R package "ConsensusClusterPlus" 38 . Stratification was carried out using the optimal k-value determined from the cumulative distribution function (CDF) curves.Principal Component Analysis (PCA) corroborated the distinct m6A modification profiles delineated by the trio of key m6A regulatory factors.Single-sample gene set enrichment analysis (ssGSEA) was applied to quantify immune cell infiltration for subsequent relevance studies, with graphical representations to illustrate the characteristics. The expression profiles of m6A regulatory elements and the abundance scores of immune cell infiltrates were juxtaposed across two distinct modification patterns, and their distributions were depicted through boxplots and heatmaps utilizing R packages including "limma," "pheatmap," and "ggpubr." Correlation Between Immune Cells and Feature Genes in Different m6A Clusters Using R software, Spearman correlation analysis was conducted to calculate the correlationcoefficients of m6A-related genes with immune cells, exploring the relationship between immune cells and feature genes. Statistical Analysis Statistical computations were performed utilizing R (version 4.4.1). Linear regression analysis along with Pearson correlation coefficients (r) were deployed to ascertain the relationships within gene expression data. For assessing differences across multiple groups, a nonparametric one-way ANOVA was implemented. When comparing pairs of groups, T-tests were applied. A threshold of P < 0.05 was set to define statistical significance. Results Acquisition and Differential Analysis of m6A Related Genes First, we studied the 26 m6A regulatory factors in the GSE85452 dataset, but only 16 m6A regulatory factors were extracted from the dataset, including METTL3, RBM15, WTAP, RBM15B, CBLL1,YTHDF1,YTHDF2,YTHDF3,HNRNPC,YTHDC1,YTHDC2,LRPPRC,RBMX,HNRNPA2B1, FTO, and ALKBH5. We partitioned the samples into experimental and control groups, and examined the differential expression of 26 m6A-related genes between the two groups.Compared to the healthy control group (HC), the expression of 3 m6A related genes significantly increased in MG, including YTHDF1, CBLL1, and RBM15 (Fig. 1A, B). Figure 1C shows the positions of the 3 differentially expressed m6A regulatory factors on the chromosomes. This information can reveal the interactions between genes and help us find potential modification sites regulated by m6A regulatory factors. We also investigated the correlated expression of different regulatory factors across the entire sample and found that FTO had a significant negative correlation with CBLL1 (R = -0.65) and RBM15 (R = -0.64), while FTO had a significant positive correlation with RBM15B (R = 0.57) (Fig. 1D, E, F). Model Selection To explore the contribution of m6A regulatory factors to the pathogenesis of MG, we established RF and SVM models to screen feature genes from m6A related genes for predicting the occurrence of MG. The application of R software for external validation of the GSE85452 dataset showed that the residual boxplot, residual reverse cumulative distribution, and ROC curve results (Fig. 2A-C) suggested that the Random Forest (RF) model demonstrated superior predictive accuracy, suggesting that this model outperforms the SVM model. In contrast to the SVM model, the RF model showed lower residuals and a larger area under the ROC curve. The RF model's proficiency in handling complex interactions and non-linear relationships among variables, coupled with its capacity to mitigate overfitting and bias through the aggregation of outcomes from numerous decision trees, made it the preferred choice. Consequently, we opted for the RF model (Fig. 2D).As shown in Fig. 2E, the importance scores of the 3 core m6A regulators were greater than 2. Establishment of Nomogram Model Given that their importance scores exceeded the threshold of 2, these variables were deemed suitable for the construction of the nomogram model(Fig. 3A). Within the nomogram model, each gene is independently scored. The scores are summed to calculate the total score, predicting the incidence of myasthenia gravis. External validation was again conducted using data from the GSE85452 dataset.In the decision curve analysis, the red line, signifying the m6A genes, distinctly diverged from the gray and black lines (Fig. 3B).The solid and dashed lines of the calibration curve were very close (Fig. 3C).The above charts and clinical impact curves (Fig. 3D) consistently suggest that our model holds significant promise for accurately predicting the prognosis of myasthenia gravis patients. m6A Subtyping Drawing on the variance in m6A expression profiles across various samples, we conducted gene subtype analysis. At k = 2, the CDF achieved its peak, prompting the stratification of all samples into two distinct subtypes(Fig. 4A-C).Subsequent PCA revealed that samples could be clearly differentiated based on their m6A expression levels, underscoring the robustness of our subtyping approach (Fig. 4D).From the box plot and gene heat map, the expression levels of Class A MG feature genes were relatively high, while those of Class B MG feature genes were relatively low. The expression of YTHDF1, CBLL1, and RBM15 showed significant differences between the two clusters (Fig. 4E, F). Biological Characteristics of m6A Subtyping Stratified by m6A subtyping, we performed an assessment of immune cell populations to explore the link between m6A-associated gene markers and immune system functionality. Cluster A showed elevated levels of CD56bright NK cells, eosinophils, and T follicular helper cells compared to Cluster B, which had increased levels of CD56dim NK cells, NKT cells, and Type 1 T helper cells (Fig. 5A). We further examined the correlation between m6A signature genes and immune cell subsets. Notably, RBM15 exhibited the strongest correlation with immune cells and was selected for further investigation (Fig. 5B). The low expression group of RBM15 had a higher proportion of CD56bright NK cells and NKT cells, whereas the high expression group had a greater proportion of Type 1 T helper cells (Fig. 5C). We identified 61 DEGs between clusters A and B and depicted them in a Venn diagram (Fig. 5D). Subsequently, we conducted GO and KEGG enrichment analyses for these DEGs (Fig. 5E, F). Enriched processes included regulation of N-acetylneuraminic acid metabolism, ATPase V1 domain, and unstable activity of protein-containing complexes. The outcomes of the KEGG enrichment analysis are presented in the figure. Identification of Two m6A Gene Subtypes Categorization of DEGs Based on Expression Levels Subsequently, we stratified the samples according to the levels of DEGs. At k = 2, the cumulative distribution function (CDF) peaked, prompting us to segregate the samples into two distinct groups (Fig. 6A-C). The heatmap revealed that the majority of genes in cluster B exhibit higher expression levels compared to those in cluster A (Fig. 6D). In the differential analysis of m6A gene signatures, the m6A-associated genes in cluster A demonstrated elevated expression relative to cluster B (Fig. 6E). Ultimately, we conducted an analysis to compare the immune cell proportions between these two clusters.In cluster A, the abundance of CD56dim natural killer cells, natural killer T cells, and type 1 T helper cells is higher than that in cluster B, while the proportion of CD56bright natural killer cells and T follicular helper cells is lower than that in cluster B (Fig. 6F). Characteristics and Inflammatory Factors Based on m6A Scores The scores in cluster A are higher compared to those in cluster B (Fig. 7A, B). In the Sankey diagram, the results of m6a typing and gene typing show a certain similarity (Fig. 7C). Inflammatory mediators are intricately linked to the immune response. To conclude, we explored the correlations between various subtypes and inflammatory mediators. The results highlight disparities in the expression levels of inflammatory mediators across distinct subtypes (Fig. 7D, E). Discussion Myasthenia gravis is an autoimmune disease caused by specific antibodies targeting different postsynaptic components at the neuromuscular junction, characterized clinically by fatigable muscle weakness 43 . The pathogenesis of MG has not been completely elucidated, suspected to be related to genetic, environmental, infectious, and immune factors 44 . N6-methyladenosine (m6A) modification is an important modification in the transcriptome, associated with various RNA biological processes, including RNA processing, translation, stability, splicing, and degradation 45 – 46 . m6A RNA modification plays various key roles in numerous biological processes, such as neurogenesis, embryonic development, stress response, circadian rhythms, and tumorigenesis 47 – 48 .Previous studies have indicated that m6A methylation regulates cancer malignancy by controlling the expression of cancer-associated genes, and abnormal levels of m6A methylation contribute to tumor pathogenesis and progression 49 . m6A modification maintains muscle health and promotes regeneration by regulating muscle stem cell function and differentiation 50 . In recent years, researchers have found that m6A RNA modification plays an important role in the occurrence and progression of autoimmune diseases 46 . However, few studies have focused on the role of m6A modification in the pathogenesis of MG. Therefore, we systematically studied the m6A modification patterns in the immune microenvironment of MG and explored the immune characteristics related to m6A modification. In this study, we constructed a modality map using an RF model to estimate the incidence rate of MG; we hypothesized that m6A methylation might be associated with MG. Utilizing the Random Forest (RF) model, we identified three signature m6A genes: RBM15, YTHDC1, and CBLL1. RBM15, a protein-coding gene belonging to the split-end protein family, is a pivotal member of the methyltransferase complex responsible for m6A methylation. This gene is crucial for preserving hematopoietic cell homeostasis, modulating mRNA alternative splicing, and enhancing transcriptional repression activity 51 – 52 . Notably, RBM15 has been observed to be aberrantly expressed across a spectrum of cancers, where it contributes to tumorigenesis and progression, including esophageal squamous cell carcinoma (ESCC), cervical cancer (CRC), and bladder cancer 53 – 55 . Quan's research suggests that m6A methyltransferase RBM15 can influence tumor cell proliferation, metastasis, and stemness by stabilizing HEIH expression 56 . Recent studies indicate that RBM15 protein expression is related to cell apoptosis 57 – 58 . In diabetic nephropathy, RBM15 may regulate cell proliferation, inflammation, and oxidative stress through activating the AGE-RAGE pathway, thus accelerating disease progression 59 .RBM15 mitigates NAFLD inflammation and oxidative stress by upregulating RNF5 expression through m6A methylation 60 . Based on these previous findings, RBM15 plays an essential role in apoptosis, inflammation, and oxidative stress, which may be related to MG. In recent years, YTHDC1-mediated m6A modification has played a key role in various biological functions and the occurrence and development of various diseases, especially cancer 61 – 63 . YTHDC1 enhances FOXM1 expression through m6A modification, promoting migration, invasion, and glycolysis in triple-negative breast cancer 64 . YTHDC1 promotes CDK6 methylation and affects the invasion, migration, and tube formation capability of endothelial cells, exacerbating diabetic retinopathy 65 . YTHDC1 may regulate Beclin1 by enhancing mRNA stability through m6A modification and affecting autophagy-dependent NF-κB signaling, thereby modulating IBD macrophage-mediated inflammation 66 . CBLL1 is an E3 ubiquitin ligase with a ring-type structure domain, regarded as one of the m6A-related genes 67 – 68 . CBLL1 is upregulated in non-small cell lung cancer (NSCLC) tissues and promotes NSCLC cell proliferation 67 . It has been found that CBLL1 expression is increased in NSCLC tissues and cells, protecting NSCLC cells from cisplatin (DDP)-induced damage as a downstream gene of circ_0072083/miR-545-3p 69 . Therefore, the three feature genes RBM15, YTHDC1, and CBLL1 related to m6A in this study may be associated with MG. The constructed modality map demonstrates a negative correlation between these genes and the incidence of MG. Immune-related analysis shows that these three genes are associated with immunity, particularly the correlation of RBM15, which may underpin their significance in MG. Existing studies indicate that dysfunction of T, B lymphocytes, and natural killer cells may play an important role in triggering immune responses in the pathogenesis of MG 70 – 72 . NKT cells can rapidly release large amounts of pro-inflammatory and anti-inflammatory cytokines, such as IL-10, IL-17, and IFN-γ, after stimulation, primarily performing immune regulatory functions 44 . Recent studies have discovered anomalies in the phenotype and function of natural killer (NK) cells in patients with MG 73 . Follicular helper T cells (Tfh) are a distinct subgroup of CD4 + T cells that promote excessive proliferation, somatic hypermutation, and class switching of B cells 74 . Tfh may promote the development of MG by acting on antibody-secreting B cells through relevant cytokines 75 . A recent study found that MuSK-MG patients exhibited a higher Tfh:Tfr ratio, indicating inadequate regulation of Tfh cells 74 . T helper 1 (Th1) is a functional subgroup of natural CD4 + T cells 76 . Numerous data indicate that Th1, Th17, and Treg cells are involved in the occurrence of MG, and the interactions between cells and their cytokines have complex correlations 77 . Th1 cells producing IFN-γprimarily activate antigen-presenting cells and promote cellular immune responses 76 . Studies show that the levels of Th1 and Th17 cells in the peripheral blood of MG patients are higher than those of healthy individuals, while Treg cell levels are lower than those of healthy individuals 78 – 80 . Our study identified variations in the infiltration of immune cells, such as CD56bright NK cells, eosinophils, follicular helper T cells, CD56dim NK cells, NKT cells, and Th1 cells, between m6A subtypes and MG samples stratified by CBLL1 expression levels. These observations imply that m6A might be involved in the pathogenesis of MG through the modulation of immune cell infiltration. In this study, we identified two different m6A patterns (cluster A and cluster B) based on three significant m6A regulatory factors, as well as two distinct m6A gene patterns (gene cluster A and gene cluster B) based on 61 m6A-related DEG. We calculated the m6A score for each sample between the two different m6A patterns or m6A gene patterns using PCA algorithms to quantify the m6A patterns. We found that cluster A or gene cluster A exhibited higher m6A scores than cluster B or gene cluster B. We analyzed the eight MG-related genes between m6A subtypes and m6A gene subtypes, finding significant differences in IL16 and CD1A between the two gene typing methods. Inflammation is a key influencing factor in the pathology of diseases related to skeletal muscle dysfunction 81 . Inflammation has long been considered an important factor influencing the pathogenesis of systemic and ocular MG, occurring in approximately 80% and 50% of MG patients, respectively 82 – 83 . High serum IL-16 levels are associated with the occurrence and progression of malignant tumors and poor survival in patients with gastric cancer and sarcopenia 84 . The different expressions of the confirmed myasthenia gravis-related genes in our classification subtypes indicate that our results are consistent with previous studies, and the classification is meaningful. Furthermore, the immune-related pathways influenced by different m6A modification clusters vary significantly. The significant differences in the immune microenvironment between these two clusters may lead to different responses to treatment by m6A and produce different outcomes. By identifying differing expression patterns of m6A regulatory factors, it may be possible to develop more effective and targeted interventions to improve the prognosis of MG patients. Our work, through the integration of GEO datasets containing a relatively small sample size, investigates the role of m6A in the immune microenvironment of MG, but some limitations should be considered. Further research is still needed to more thoroughly characterize the infiltrating immune cells in MG patients and their exact mechanisms. Secondly, since our results are primarily based on bioinformatics analysis of datasets, additional validation may be required from experimental studies. Conclusion In conclusion, we have investigated the link between m6A RNA methylation and MG, pinpointing key m6A-associated genes and exploring their interplay with the immune system. The discoveries made could offer new therapeutic insights for developing future treatment strategies for MG. Declarations Data availability The dataset for this study is GSE85452 from the GEO database.For more information, visit https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85452.This dataset is publicly available. Acknowledgements We would like to thank all teammates for contributing this work. Author contributions Y.F.S Designed the study with Y.Q.W; Y.Q.W, X.Q.C and Y.Y.J participated in the analysisof the data and wrote the manuscript; L.N.Z, Q.L.J and T.K.C critically revised the manuscript. P.D.Y, T.J.H and J.Ya suggested revisions to the article. All of the authors read andapproved the final manuscript. Funding The present study was supported by the National Natural Science Foundation of China(Grant No.82374391), the Project in Key Fields of Universities in Guangdong Province (Grant No.2021ZDZX2032) and the Natural Science Foundation of Guangdong Province (Grant No.2023A1515011127). Competing interests Te authors declare no competing interests. References Cavalcante, P., Mantegazza, R., & Antozzi, C.Targeting autoimmune mechanisms by precision medicine in Myasthenia Gravis. Frontiers in immunology , 15 , 1404191. https://doi.org/10.3389/fimmu.2024.1404191 (2024). Marcus R.What Is Myasthenia Gravis?. JAMA , 331 (5), 452. https://doi.org/10.1001/jama.2023.16872 (2024). Ma, C., Liu, D., Wang, B., Yang, Y., & Zhu, R.Advancements and prospects of novel biologicals for myasthenia gravis: toward personalized treatment based on autoantibody specificities. Frontiers in pharmacology , 15 , 1370411. https://doi.org/10.3389/fphar.2024.1370411 (2024). Gilhus N. E.Myasthenia gravis, respiratory function, and respiratory tract disease. Journal of neurology , 270 (7), 3329–3340. https://doi.org/10.1007/s00415-023-11733-y (2023). Mahic, M.et al.Physician- and patient-reported perspectives on myasthenia gravis in Europe: a real-world survey. Orphanet journal of rare diseases , 18 (1), 169. https://doi.org/10.1186/s13023-023-02727-0 (2023). Dewilde, S.et al.People Diagnosed with Myasthenia Gravis have Lower health-related quality of life and Need More Medical and Caregiver Help in Comparison to the General Population: Analysis of Two Observational Studies. Advances in therapy , 40 (10), 4377–4394. https://doi.org/10.1007/s12325-023-02604-z (2023). Ruiter, A. M.et al.Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning. Annals of clinical and translational neurology , 10 (8), 1314–1325. https://doi.org/10.1002/acn3.51823 (2023). Huang, J., Yan, Z., Song, Y., & Chen, T.Nanodrug Delivery Systems for Myasthenia Gravis: Advances and Perspectives. Pharmaceutics , 16 (5), 651. https://doi.org/10.3390/pharmaceutics16050651 (2024). Iorio R. Myasthenia gravis: the changing treatment landscape in the era of molecular therapies. Nature reviews. Neurology , 20 (2), 84–98. https://doi.org/10.1038/s41582-023-00916-w (2024). Rahman, M. M., Islam, M. R., & Dhar, P. S. Myasthenia gravis in current status: epidemiology, types, etiology, pathophysiology, symptoms, diagnostic tests, prevention, treatment, and complications - correspondence. International journal of surgery (London, England) , 109 (2), 178–180. https://doi.org/10.1097/JS9.0000000000000164 (2023). Berrih-Aknin, S., & Le Panse, R.Myasthenia gravis: a comprehensive review of immune dysregulation and etiological mechanisms. Journal of autoimmunity , 52 , 90–100. https://doi.org/10.1016/j.jaut.2013.12.011 (2014). Le Panse, R., & Berrih-Aknin, S.Autoimmune myasthenia gravis: autoantibody mechanisms and new developments on immune regulation. Current opinion in neurology , 26 (5), 569–576. https://doi.org/10.1097/WCO.0b013e328364d6cd (2013). Zhang, P.et al.Diabetes mellitus exacerbates experimental autoimmune myasthenia gravis via modulating both adaptive and innate immunity. Journal of neuroinflammation , 18 (1), 244. https://doi.org/10.1186/s12974-021-02298-6 (2021). Al-Kuraishy, H. M.et al.Defective autophagy and autophagy activators in myasthenia gravis: a rare entity and unusual scenario. Autophagy , 20 (7), 1473–1482. https://doi.org/10.1080/15548627.2024.2315893 (2024). Xia, M.et al..Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy. Frontiers in immunology , 13 , 995645. https://doi.org/10.3389/fimmu.2022.995645 (2022). Zhuo, R.et al.The regulatory role of N6 -methyladenosine modification in the interaction between host and microbes. Wiley interdisciplinary reviews. RNA , 13 (6), e1725. https://doi.org/10.1002/wrna.1725 (2022). Jiang, X.et al.The role of m6A modification in the biological functions and diseases. Signal transduction and targeted therapy , 6 (1), 74. https://doi.org/10.1038/s41392-020-00450-x (2021). Zhang, H.et al.Dynamic landscape and evolution of m6A methylation in human. Nucleic acids research , 48 (11), 6251–6264. https://doi.org/10.1093/nar/gkaa347 (2020). He, L.et al.Functions of N6-methyladenosine and its role in cancer. Molecular cancer , 18 (1), 176. https://doi.org/10.1186/s12943-019-1109-9 (2019). An, Y., & Duan, H.The role of m6A RNA methylation in cancer metabolism. Molecular cancer , 21 (1), 14. https://doi.org/10.1186/s12943-022-01500-4 (2022). Wang, T., Kong, S., Tao, M., & Ju, S.The potential role of RNA N6-methyladenosine in Cancer progression. Molecular cancer , 19 (1), 88. https://doi.org/10.1186/s12943-020-01204-7 (2020). Khan, F. A.et al.Regulatory roles of N6-methyladenosine (m6A) methylation in RNA processing and non-communicable diseases. Cancer gene therapy , 10.1038/s41417-024-00789-1. Advance online publication. https://doi.org/10.1038/s41417-024-00789-1 (2024). Liu, Z.et al.The Landscape of m6A Regulators in Multiple Brain Regions of Alzheimer's Disease. Molecular neurobiology , 60 (9), 5184–5198. https://doi.org/10.1007/s12035-023-03409-5 (2023). Yin, H.et al.Loss of the m6A methyltransferase METTL3 in monocyte-derived macrophages ameliorates Alzheimer's disease pathology in mice. PLoS biology , 21 (3), e3002017. https://doi.org/10.1371/journal.pbio.3002017 (2023). McMillan, M.et al.RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia. Molecular cell , 83 (2), 219–236.e7. https://doi.org/10.1016/j.molcel.2022.12.019 (2023). Feng, J.et al.Soot nanoparticles promote ferroptosis in dopaminergic neurons via alteration of m6A RNA methylation in Parkinson's disease. Journal of hazardous materials , 473 , 134691. https://doi.org/10.1016/j.jhazmat.2024.134691 (2024). Mamrut, S.et al.Methylome and transcriptome profiling in Myasthenia Gravis monozygotic twins. Journal of autoimmunity , 82 , 62–73. https://doi.org/10.1016/j.jaut.2017.05.005 (2017). Hu, X.et al.The Emerging Role of RNA N6-Methyladenosine Modification in Pancreatic Cancer. Frontiers in oncology , 12 , 927640. https://doi.org/10.3389/fonc.2022.927640 (2022). Shang, Q. X.et al.Identification of m6a-related signature genes in esophageal squamouscell carcinoma by machine learning method. Frontiers in genetics , 14 , 1079795. https://doi.org/10.3389/fgene.2023.1079795 (2023). Zhao, L.et al.Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke. Scientific reports , 14 (1), 3456. https://doi.org/10.1038/s41598-024-53788-5 (2024). Wickham, H. Reshaping data with the reshape package. J. Statist. Softw. 21, 1–20. https://doi.org/10.18637/jss.v021.i12 (2007). Wickham, H. Elegant graphics for data analysis. Media 35(211), 10–1007 (2009). Kuhn, M. Building predictive models in R using the caret package. J. Statist. Softw. 28, 1–26. https://doi.org/10.18637/jss.v028.i05(2008). Biecek, P. DALEX: Explainers for complex predictive models in R. J. Mach. Learn. Res. 19, 1–5 (2018). Robin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77. https://doi.org/10.1186/1471-2105-12-77 (2011). Liaw A, Wiener MC. Classification and regression by randomforest. R News . 2002; 2 (3):18–22. Harrell JFE. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis . New York: Springer; 2001. Wilkerson, M. D., & Hayes, D. N.ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics (Oxford, England) , 26 (12), 1572–1573. https://doi.org/10.1093/bioinformatics/btq170 (2010). Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592. https://doi.org/10.1093/nar/gkac963(2023). Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. NucleicAcids Res. 28, 27–30 (2000). Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951. https://doi.org/10.1002/pro.3715 (2019). Zhang, P.et al.Bioinformatics identification and experimental validation of m6A-relateddiagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients. Frontiers in endocrinology , 14 , 990078. https://doi.org/10.3389/fendo.2023.990078 (2023). García Estévez, D. A., & Pardo Fernández, J.Myasthenia gravis. Update on diagnosis and therapy. Miastenia gravis. Actualización diagnóstica y terapéutica. Medicina clinica , 161 (3), 119–127. https://doi.org/10.1016/j.medcli.2023.04.006 (2023). Zarobkiewicz, M. K., Morawska, I., Michalski, A., Roliński, J., & Bojarska-Junak, A. NKT and NKT-like Cells in Autoimmune Neuroinflammatory Diseases-Multiple Sclerosis, Myasthenia Gravis and Guillain-Barre Syndrome. International journal of molecularsciences , 22 (17), 9520. https://doi.org/10.3390/ijms22179520 (2021). Li, Y.et al.The role of RNA methylation in tumor immunity and its potential in immunotherapy. Molecular cancer , 23 (1), 130. https://doi.org/10.1186/s12943-024-02041-8 (2024). Liu, T.et al.The m6A reader YTHDF1 promotes ovarian cancer progression via augmenting EIF3C translation. Nucleic acids research , 48 (7), 3816–3831. https://doi.org/10.1093/nar/gkaa048 (2020). Shan, Y., Chen, W., & Li, Y.The role of m6A RNA methylation in autoimmune diseases: Novel therapeutic opportunities. Genes & diseases , 11 (1), 252–267. https://doi.org/10.1016/j.gendis.2023.02.013 (2023). Yang, W., Zhao, Y., & Yang, Y.Dynamic RNA methylation modifications and their regulatory role in mammalian development and diseases. Science China. Life sciences , 10.1007/s11427-023-2526-2. Advance online publication. https://doi.org/10.1007/s11427-023-2526-2 (2024). He, L.et al.Functions of N6-methyladenosine and its role in cancer. Molecular cancer , 18 (1), 176. https://doi.org/10.1186/s12943-019-1109-9 (2019). Ogbe, S. E.et al.Insights into the epitranscriptomic role of N6-methyladenosine on aging skeletal muscle. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie , 177 , 117041. https://doi.org/10.1016/j.biopha.2024.117041 (2024). Ma, M.et al.RBM15 facilities lung adenocarcinoma cell progression by regulating RASSF8 stability through N6 Methyladenosine modification. Translational oncology , 46 , 102018.https://doi.org/10.1016/j.tranon.2024.102018 (2024). Jiang, J.et al. Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15. Scientific reports , 13 (1), 5255.https://doi.org/10.1038/s41598-023-28739-1 (2023). Wang H. The RNA m6A writer RBM15 contributes to the progression of esophageal squamous cell carcinoma by regulating miR-3605-5p/KRT4 pathway. Heliyon , 10 (2), e24459. https://doi.org/10.1016/j.heliyon.2024.e24459 (2024). Zhang, C., Gu, L., Xiao, J., & Jin, F.Knockdown of RBM15 inhibits tumor progression and the JAK-STAT signaling pathway in cervical cancer. BMC cancer , 23 (1), 684. https://doi.org/10.1186/s12885-023-11163-z (2023). Huang, Y.et al.Enhancing m6A modification of lncRNA through METTL3 and RBM15 to promote malignant progression inbladder cancer. Heliyon , 10 (7), e28165. https://doi.org/10.1016/j.heliyon.2024.e28165 (2024). Quan, Y.et al.The m6A methyltransferase RBM15 affects tumor cell stemness and progression of cervical cancer by regulating the stability of lncRNA HEIH. Experimental cell research , 436 (2), 113924. https://doi.org/10.1016/j.yexcr.2024.113924 (2024). Cheng, H.et al.RBM15 Protects From Myocardial Infarction by Stabilizing NAE1. JACC. Basic to translational science , 9 (5), 631–648. https://doi.org/10.1016/j.jacbts.2024.01.017 (2024). Morel O.Potential of Epigenetic Therapy in Alleviating Cardiac Death and Fibrotic Remodeling in Myocardial Infarction. JACC. Basic to translational science, 9(5), 649–651.https://doi.org/10.1016/j.jacbts.2024.03.005 (2024). Qin, Y.et al.N6-methyladenosine methylation regulator RBM15 promotes the progression of diabetic nephropathy by regulating cell proliferation, inflammation, oxidative stress, and pyroptosis through activating the AGE-RAGE pathway. Environmental toxicology , 38 (11), 2772–2782. https://doi.org/10.1002/tox.23917 (2023). Li, S., Lian, S., Cheng, W., Zhang, T., & Gong, X.THE ROLE OF N6-METHYLADENOSINE METHYLTRANSFERASE RBM15 IN NONALCOHOLIC FATTY LIVER DISEASE. Shock (Augusta, Ga.) , 61 (2), 311–321. https://doi.org/10.1097/SHK.0000000000002294 (2024). Xie, W.et al.SILENCING M 6 A READER YTHDC1 REDUCES INFLAMMATORYRESPONSE IN SEPSIS-INDUCED CARDIOMYOPATHY BY INHIBITING SERPINA3N EXPRESSION. Shock (Augusta, Ga.) , 59 (5), 791–802. https://doi.org/10.1097/SHK.0000000000002106 (2023). Li, X., Yang, Y., & Chen, Z. Downregulation of the m6A reader protein YTHDC1 leads to islet β-cell failure and diabetes. Metabolism: clinical and experimental , 138 , 155339. https://doi.org/10.1016/j.metabol.2022.155339 (2023). Yan, H., Zhang, L., Cui, X., Zheng, S., & Li, R.Roles and mechanisms of the m6A reader YTHDC1 in biological processes and diseases. Cell death discovery , 8 (1), 237. https://doi.org/10.1038/s41420-022-01040-2 (2022). Shen, X.et al.YTHDC1-dependent m6A modification modulated FOXM1 promotes glycolysis and tumor progression through CENPA in triple-negative breast cancer. Cancer science , 115 (6), 1881–1895. https://doi.org/10.1111/cas.16137 (2024). Zhou, Q.et al.YTHDC1 aggravates high glucose-induced retinal vascular endothelial cell injury via m6A modification of CDK6. Biology direct , 19 (1), 54. https://doi.org/10.1186/s13062-024-00498-7 (2024). Zhou, L.et al.YTHDC1 inhibits autophagy-dependent NF-κB signaling by stabilizing Beclin1 mRNA in macrophages. Journal of inflammation (London, England) , 21 (1), 22. https://doi.org/10.1186/s12950-024-00393-y (2024). Hui, L.et al.CBLL1 is highly expressed in non-small cell lung cancer and promotes cell proliferation and invasion. Thoracic cancer , 10 (6), 1479–1488. https://doi.org/10.1111/1759-7714.13097 (2019). Liu, X.et al.CBLL1 promotes endometrial stromal cell senescence via inhibiting PTEN in recurrent spontaneous abortion. FASEB journal : official publication of the Federation of American Societies for Experimental Biology , 38 (14), e23833. https://doi.org/10.1096/fj.202400972R (2024). Li, H., Liu, F., & Qin, W.Circ_0072083 interference enhances growth-inhibiting effectsof cisplatin in non-small-cell lung cancer cells via miR-545-3p/CBLL1 axis. Cancer cell international , 20 , 78. https://doi.org/10.1186/s12935-020-1162-x (2020). Uzawa, A.et al.Roles of cytokines and T cells in the pathogenesis of myasthenia gravis. Clinical and experimental immunology , 203 (3), 366–374. https://doi.org/10.1111/cei.13546 (2021). Jing, S.et al.Effect of low-dose rituximab treatment on T- and B-cell lymphocyte imbalance in refractory myasthenia gravis. Journal of neuroimmunology , 332 , 216–223. https://doi.org/10.1016/j.jneuroim.2019.05.004 (2019). Zhang, Q.et al.Therapeutic potential of natural killer cells in neuroimmunological diseases. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie , 173 , 116371. https://doi.org/10.1016/j.biopha.2024.116371 (2024). Liu, R. T.et al. Naturalkiller cells promote the differentiation of follicular helper T cells instead of inducing apoptosis in myasthenia gravis. International immunopharmacology , 98 , 107880. https://doi.org/10.1016/j.intimp.2021.107880 (2021). Li, Y.et al.Imbalance in T follicular helper cells producing IL-17 promotes pro-inflammatory responses in MuSK antibody positive myasthenia gravis. Journal of neuroimmunology , 345 , 577279. https://doi.org/10.1016/j.jneuroim.2020.577279 (2020). Wu, N.et al.Central Role of T Follicular Helper Cells in Myasthenia Gravis. Noro psikiyatri arsivi , 58 (1), 68–72. https://doi.org/10.29399/npa.27193 (2021). Weng, S.et al.Astragaloside IV ameliorates experimental autoimmune myasthenia gravis by regulating CD4 + T cells and altering gut microbiota. Chinese medicine , 18 (1), 97. https://doi.org/10.1186/s13020-023-00798-6 (2023). Song, J.et al.Inhibition of ROCK activity regulates the balance of Th1, Th17 and Treg cells in myasthenia gravis. Clinical immunology (Orlando,Fla.) , 203 , 142–153. https://doi.org/10.1016/j.clim.2019.05.006 (2019). Link, J.et al.Organ-specific autoantigens induce interferon-gamma and interleukin-4 mRNA expression in mononuclear cells in multiple sclerosis and myasthenia gravis. Neurology , 44 (4), 728–734. https://doi.org/10.1212/wnl.44.4.728 (1994). Wang, Z., Wang, W., Chen, Y., & Wei, D.T helper type 17 cells expand in patients with myasthenia-associated thymoma. Scandinavian journal of immunology , 76 (1), 54–61. https://doi.org/10.1111/j.1365-3083.2012.02703.x (2012). Xu, W. H.et al.Changes of Treg-associated moleculeson CD4+CD25 +Treg cells in myasthenia gravis and effects of immunosuppressants. Journal of clinical immunology , 32 (5), 975–983. https://doi.org/10.1007/s10875-012-9685-0 (2012). Dalle, S., Rossmeislova, L., & Koppo, K.The Role of Inflammation in Age-Related Sarcopenia. Frontiers in physiology , 8 , 1045. https://doi.org/10.3389/fphys.2017.01045 (2017). Molin, C. J., Westerberg, E., & Punga, A. R.Profile of upregulated inflammatory proteins in sera of Myasthenia Gravis patients. Scientific reports , 7 , 39716. https://doi.org/10.1038/srep39716 (2017). Huda R.New Approaches to Targeting B Cells for Myasthenia Gravis Therapy. Frontiers in immunology , 11 , 240. https://doi.org/10.3389/fimmu.2020.00240 (2020). Xiong, J.et al.Association of Sarcopenia and Expression of Interleukin-16 in Gastric Cancer Survival. Nutrients , 14 (4), 838. https://doi.org/10.3390/nu14040838 (2022). Additional Declarations No competing interests reported. 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(A) Box plot of differential expression of 26 m6A regulatory genes between the two groups. (B) Expression heatmap of three important m6A regulatory genes in MG patients. (C) Chromosomal locations of m6A-related genes. (D) The relationship between FTO and CBLL1. (E) The relationship between FTO and RBM15. (F) The relationship between FTO and RBM15B. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/7aab847d352326dcbc51267a.jpg"},{"id":69442549,"identity":"7e6f3ed3-5545-45ce-b96c-b77dae18007d","added_by":"auto","created_at":"2024-11-20 11:25:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":776811,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest (RF) model and Support Vector Machine (SVM) model. (A) The residual box plot of the RF model shows lower residual values. (B) The reverse cumulative distribution of residuals demonstrates the residual distribution of RF and SVM models. (C) The ROC curve shows that the predicted value of the RF model is 1, and the predicted value of the SVM model is 0.878. (D) The red curve represents the error level of the AF group, the green curve represents the SR group, and the black curve represents the overall sample. (E) Importance scores of three significantly regulated m6A genes.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/1b745526d83541178815a0a6.jpg"},{"id":69442551,"identity":"bded5bc1-f6ab-4ffb-ab24-451f17dc01ef","added_by":"auto","created_at":"2024-11-20 11:25:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":764570,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of the modal graph model. (A) Nomogram model established based on five features of m6A regulatory genes. The total score predicts incidence. A score of 56 indicates an incidence of 10%, and a score of 108 indicates an incidence of 90%. (B) The red line in the decision curve that deviates from the gray-black line represents m6A genes, also demonstrating the feasibility of the model. (C) The solid and dashed lines of the calibration curve are very close, indicating that the nomogram model has strong predictive ability. (D) The red line is high-risk myasthenia gravis patients, and the blue line is myasthenia gravis patients.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/c6418700cced12024789a328.jpg"},{"id":69443203,"identity":"b0179f95-7b43-435b-abb3-acc9747e4ae8","added_by":"auto","created_at":"2024-11-20 11:33:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1118702,"visible":true,"origin":"","legend":"\u003cp\u003em6A methylation modification patterns mediated by three regulators related to MG. (A) Consensus clustering analysis. (B) CDF curve of consistent clustering. (C) The CDF reaches its maximum at k=2. (D) PCA shows a significant difference between class A and class B. (E, F) Expression heatmap and box plot show expression differences of three important m6A regulatory genes between cluster A and cluster B. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/dab7620c8b8ad459539db68f.jpg"},{"id":69443205,"identity":"41f5e298-c8ee-4718-ad06-23793488a63b","added_by":"auto","created_at":"2024-11-20 11:33:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2219703,"visible":true,"origin":"","legend":"\u003cp\u003eBiological characteristics of m6A subtyping. (A) Differential analysis of immune cells between the two gene subgroups. (B) Correlation analysis of immune cells related to three m6A regulatory factors; RBM15 shows the highest correlation with immune cells. (C) Differential analysis of immune cells in high-expression and low-expression RBM15 groups. (D) Venn diagram shows 61 m6A phenotype-related DEGs. (E, F) GO function and KEGG pathway enrichment analysis of m6A subtypes revealing biological characteristics of m6A phenotype-related genes. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/3108365c43193b04d9194146.jpg"},{"id":69442547,"identity":"8497f649-fb54-41a4-8b23-9f8f0a26d13d","added_by":"auto","created_at":"2024-11-20 11:25:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2006387,"visible":true,"origin":"","legend":"\u003cp\u003eSubtyping based on DEG levels. (A) Consensus clustering analysis. (B) CDF curve for consistent clustering analysis. (C) The CDF shows the maximum value at k=2. (D) Heatmap of DEGs between clusters A and B. (E) Box plot shows the differences in expression of m6A characteristic genes across different types. (F) Differences in immune cells between different types. *p \u0026lt; 0.05, **p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/699d67c112b846762d4e1380.jpg"},{"id":69444566,"identity":"759d7664-2568-43f1-a5fc-f2d0f16727ca","added_by":"auto","created_at":"2024-11-20 11:41:51","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":774791,"visible":true,"origin":"","legend":"\u003cp\u003em6A scores of different types. (A) Differences in genotype m6A scores. (B) Differences in m6A scores by m6A type. (C) Sankey diagram for different types and m6A scores. (D) Expression differences of inflammatory factors based on m6A subtyping. (E) Expression differences of inflammatory factors based on gene typing.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/8d04cb0d7c04178d079fc42a.jpg"},{"id":74912711,"identity":"5763357b-6f42-4bf6-ad9f-cec23c261632","added_by":"auto","created_at":"2025-01-28 09:24:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9993290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5264805/v1/d5d82da4-d8e1-4187-8f18-a979497a415b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of key regulatory factors for m6A in myasthenia gravis and characteristics of the immune characteristics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyasthenia gravis (MG), an autoimmune neuromuscular disorder, is characterized by the deterioration of synaptic transmission at the neuromuscular junction, manifesting as progressive muscle weakness and profound fatigue\u003csup\u003e1\u003c/sup\u003e. The incidence is 15-25 cases per 100,000 people and is most common in women under 40 and men over 60\u003csup\u003e2-3\u003c/sup\u003e. The disease often recurs and follows a prolonged course. While 80% of patients experience generalized muscle weakness, only 20% are limited to ocular muscles. It can become life-threatening if the respiratory muscles and medulla are involved\u003csup\u003e3-4\u003c/sup\u003e. MG not only causes physical dysfunction and reduces quality of life but also leads to psychological issues like anxiety and depression, placing a significant burden on families and society\u003csup\u003e5-6\u003c/sup\u003e. Diagnosis is based on typical clinical features and the presence of antibodies against neuromuscular junction proteins, yet up to 46% of patients remain undiagnosed within the first year\u003csup\u003e7\u003c/sup\u003e. Standard treatments include acetylcholinesterase inhibitors, corticosteroids, and immunosuppressants, which effectively improve symptoms in most patients, but may cause significant long-term adverse effects. Additionally, up to 15% of patients respond poorly or not at all to these therapies\u003csup\u003e8-9\u003c/sup\u003e. The occurrence of MG is thought to be linked to antibody-mediated activity against acetylcholine receptors (AChR), cellular immune responses, and complement activities, though the exact mechanisms remain unclear\u003csup\u003e10\u003c/sup\u003e. The consensus among medical professionals is that aberrant immune responses significantly contribute to the etiological mechanisms and disease progression in MG\u003csup\u003e11-12\u003c/sup\u003e. Recent studies\u003csup\u003e13-14\u003c/sup\u003e underscore the pivotal role of both innate and adaptive immune responses in the pathogenesis of MG, providing critical insights that advance our comprehension of the disease\u0026apos;s complex immunological underpinnings.\u003c/p\u003e\n\u003cp\u003eN6-methyladenosine (m6A) RNA methylation, akin to DNA methylation, is a dynamic and reversible modification process\u003csup\u003e15-16\u003c/sup\u003e. This modification is facilitated by m6A methyltransferases, or \u0026quot;writers\u0026quot; (such as METTL3/14/16, CBLL1,VIRMA, RBM15/15B, ZC3H3,WTAP, and KIAA1429), and removed by demethylases or \u0026quot;erasers\u0026quot; (including ALKBH5 and FTO). It is identified by m6A-binding proteins like YTHDC1/2, IGF2BP1/2/3, YTHDF1/2/3, and HNRNPA2B1, known as \u0026quot;readers\u0026quot;\u003csup\u003e17\u003c/sup\u003e.Functionally, m6A methylation regulates mRNA splicing, expression, decay, and translation, playing a pivotal role in cellular pathways and processes such as cell differentiation, development, and metabolism\u003csup\u003e18\u003c/sup\u003e. This modification is closely linked to various diseases, particularly having a significant impact on tumor progression\u003csup\u003e19-21\u003c/sup\u003e. Previous studies\u003csup\u003e22\u003c/sup\u003e have indicated that dysregulation of m6A methylation in neurodegenerative diseases can disrupt RNA metabolism, resulting in abnormal protein synthesis and potentially contributing to the emergence of conditions like Alzheimer\u0026apos;s disease (AD)\u003csup\u003e23-24\u003c/sup\u003e, amyotrophic lateral sclerosis (ALS)\u003csup\u003e25\u003c/sup\u003e, and Parkinson\u0026apos;s disease (PD)\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, research on the regulatory factors of m6A methylation in MG remains limited. Therefore, we aim to explore the potential association between m6A methylation and myasthenia gravis.\u003c/p\u003e\n\u003cp\u003eIn our comprehensive analysis, we employed a systematic approach to scrutinize the expression profiles of m6A methylation regulatory elements across the sample cohort. Utilizing a random forest algorithm, we successfully pinpointed three genes that exhibit a significant correlation with the risk of developing the disease. We thoroughly investigated the dynamic interactions between immune cells and immune responses across various samples. The aim of this research is to elucidate the potential connection between MG and the regulatory mechanisms of m6A methylation. We hope the results will offer valuable insights and references for future diagnostic and treatment options for MG.\u003c/p\u003e"},{"header":"Materials and methods","content":"\n\u003ch3\u003eData Processing and Download\u003c/h3\u003e\n\u003cp\u003eThis study downloaded the RNA expression profiles and clinical information of the myasthenia gravis dataset GSE85452\u003csup\u003e27\u003c/sup\u003e from the GEO database. This dataset encompasses RNA detection data from 13 myasthenia gravis samples and 12 control group samples. These samples were sequenced using the GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip) platform. The \"Normalize Between Arrays\" function from the \"limma\" package (bioconductor.org/packages/release/bioc/html/limma.html) in R was used to normalize the expressionmatrix. Gene probes were annotated using official symbols.\u003c/p\u003e \u003cp\u003eThis study included 26 m6A-related genes, including coding genes (METTL3,WTAP, VIRMA,RBM15, ZC3H13,METTL14,METTL16,CBLL1,RBM15B), reading genes (YTHDC1, YTHDC2, HNRNPC,YTHDF3,YTHDF2,YTHDF1,LRPPRC, FMR1, RBMX,HNRNPA2B1, IGFBP3,IGFBP2,IGFBP1,ELAVL1,IGF2BP1) and demethylation genes (FTO, ALKBH5)\u003csup\u003e\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. We employed Perl scripting to ascertain the chromosomal locales of m6A-associated genes,subsequently leveraging the 'RCircos' package in R for their graphical representation.\u003c/p\u003e\n\u003ch3\u003eScreening and Expression Analysis of m6A Regulatory Factors\u003c/h3\u003e\n\u003cp\u003eThe expression of m6A-related genes in each sample was extracted using the R package \"limma.\" The Wilcoxon test was then employed to detect differences in the expression of the above m6A-related genes between myasthenia gravis patients and the control group, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Heatmaps and bar charts were generated to visualize the differences using \"pheatmap\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=pheatmap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), \"reshape2\"\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and \"ggpubr\" R packages (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=ggpubr\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=ggpubr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Spearmancorrelation analysis was conducted, and relationships between differentially expressed m6A regulatory factors were assessed and visualized using \"limma,\" \"ggplot2\"\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, \"ggExtra\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=ggExtra\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=ggExtra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and \"ggpubr\" R packages. A scatter plot was created for the two genes with the highest correlation to display the results.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModel Selection\u003c/h2\u003e \u003cp\u003eThe R software was used to compare the Support Vector Machine (SVM) model and Random Forest (RF) model in machine learning, utilizing boxplots of residuals and reverse cumulative distribution plots to determine the methods used for subsequent disease feature gene selection, with validation performed using Receiver Operating Characteristic (ROC) curves. The SVM and RF models were employed to predict the diagnosis of MG. The \"caret\"\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, \"kernlab\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=kernlab\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=kernlab\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and \"randomForest\" (The R Journal: Classification and regression by randomForest (R-project.org)) R packages were used for SVM and RF. Residuals were calculated to compare the discrimination performance of both models using the \"DALEX\"\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e package. Upon identifying the optimal model, the 'randomForest' package also assessed the importance scores of m6A regulatory factors. Finally, the 'ggplot2' and 'pROC' packages were employed for visualizing the results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRandom Forest Trees\u003c/h3\u003e\n\u003cp\u003eBased on m6A-related differential genes, important genes with a score\u0026thinsp;\u0026gt;\u0026thinsp;2 were screened using the R package \"randomForest\"\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and a nomogram was constructed for these feature genes using the R package \"rms\"\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Disease incidence was predicted by aggregating individual gene scores within the nomogram to derive a total score.\u003c/p\u003e\n\u003ch3\u003eConstruction and Validation of the Plot\u003c/h3\u003e\n\u003cp\u003eUtilizing the expression levels of three pivotal m6A modulatory factors within the gene set, a prognostic nomogram was developed employing the \"rms\" library in R. Subsequently, calibration plots were generated to appraise the accuracy of the nomogram's predictive capacity. The clinical utility of the nomogram was evaluated through decision curve analysis. Ultimately, a Receiver Operating Characteristic (ROC) curve analysis was implemented to determine the nomogram's diagnostic efficacy in differentiating patients with MG from healthy individuals.\u003c/p\u003e\n\u003ch3\u003eConsistent Clustering and Immune Cell Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eBased on the m6A-related differential genes, consistent clustering analysis of patients in the dataset was performed using the R package \"ConsensusClusterPlus\"\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Stratification was carried out using the optimal k-value determined from the cumulative distribution function (CDF) curves.Principal Component Analysis (PCA) corroborated the distinct m6A modification profiles delineated by the trio of key m6A regulatory factors.Single-sample gene set enrichment analysis (ssGSEA) was applied to quantify immune cell infiltration for subsequent relevance studies, with graphical representations to illustrate the characteristics. The expression profiles of m6A regulatory elements and the abundance scores of immune cell infiltrates were juxtaposed across two distinct modification patterns, and their distributions were depicted through boxplots and heatmaps utilizing R packages including \"limma,\" \"pheatmap,\" and \"ggpubr.\"\u003c/p\u003e\n\u003ch3\u003eCorrelation Between Immune Cells and Feature Genes in Different m6A Clusters\u003c/h3\u003e\n\u003cp\u003eUsing R software, Spearman correlation analysis was conducted to calculate the correlationcoefficients of m6A-related genes with immune cells, exploring the relationship between immune cells and feature genes.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical computations were performed utilizing R (version 4.4.1). Linear regression analysis along with Pearson correlation coefficients (r) were deployed to ascertain the relationships within gene expression data. For assessing differences across multiple groups, a nonparametric one-way ANOVA was implemented. When comparing pairs of groups, T-tests were applied. A threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set to define statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition and Differential Analysis of m6A Related Genes\u003c/h2\u003e \u003cp\u003eFirst, we studied the 26 m6A regulatory factors in the GSE85452 dataset, but only 16 m6A regulatory factors were extracted from the dataset, including METTL3, RBM15, WTAP, RBM15B, CBLL1,YTHDF1,YTHDF2,YTHDF3,HNRNPC,YTHDC1,YTHDC2,LRPPRC,RBMX,HNRNPA2B1, FTO, and ALKBH5.\u003c/p\u003e \u003cp\u003eWe partitioned the samples into experimental and control groups, and examined the differential expression of 26 m6A-related genes between the two groups.Compared to the healthy control group (HC), the expression of 3 m6A related genes significantly increased in MG, including YTHDF1, CBLL1, and RBM15 (Fig.\u0026nbsp;1A, B). Figure\u0026nbsp;1C shows the positions of the 3 differentially expressed m6A regulatory factors on the chromosomes. This information can reveal the interactions between genes and help us find potential modification sites regulated by m6A regulatory factors. We also investigated the correlated expression of different regulatory factors across the entire sample and found that FTO had a significant negative correlation with CBLL1 (R = -0.65) and RBM15 (R = -0.64), while FTO had a significant positive correlation with RBM15B (R\u0026thinsp;=\u0026thinsp;0.57) (Fig.\u0026nbsp;1D, E, F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eModel Selection\u003c/h2\u003e \u003cp\u003eTo explore the contribution of m6A regulatory factors to the pathogenesis of MG, we established RF and SVM models to screen feature genes from m6A related genes for predicting the occurrence of MG. The application of R software for external validation of the GSE85452 dataset showed that the residual boxplot, residual reverse cumulative distribution, and ROC curve results (Fig.\u0026nbsp;2A-C) suggested that the Random Forest (RF) model demonstrated superior predictive accuracy, suggesting that this model outperforms the SVM model. In contrast to the SVM model, the RF model showed lower residuals and a larger area under the ROC curve. The RF model's proficiency in handling complex interactions and non-linear relationships among variables, coupled with its capacity to mitigate overfitting and bias through the aggregation of outcomes from numerous decision trees, made it the preferred choice. Consequently, we opted for the RF model (Fig.\u0026nbsp;2D).As shown in Fig.\u0026nbsp;2E, the importance scores of the 3 core m6A regulators were greater than 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of Nomogram Model\u003c/h2\u003e \u003cp\u003eGiven that their importance scores exceeded the threshold of 2, these variables were deemed suitable for the construction of the nomogram model(Fig.\u0026nbsp;3A). Within the nomogram model, each gene is independently scored. The scores are summed to calculate the total score, predicting the incidence of myasthenia gravis. External validation was again conducted using data from the GSE85452 dataset.In the decision curve analysis, the red line, signifying the m6A genes, distinctly diverged from the gray and black lines (Fig.\u0026nbsp;3B).The solid and dashed lines of the calibration curve were very close (Fig.\u0026nbsp;3C).The above charts and clinical impact curves (Fig.\u0026nbsp;3D) consistently suggest that our model holds significant promise for accurately predicting the prognosis of myasthenia gravis patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003em6A Subtyping\u003c/h2\u003e \u003cp\u003eDrawing on the variance in m6A expression profiles across various samples, we conducted gene subtype analysis. At k\u0026thinsp;=\u0026thinsp;2, the CDF achieved its peak, prompting the stratification of all samples into two distinct subtypes(Fig.\u0026nbsp;4A-C).Subsequent PCA revealed that samples could be clearly differentiated based on their m6A expression levels, underscoring the robustness of our subtyping approach (Fig.\u0026nbsp;4D).From the box plot and gene heat map, the expression levels of Class A MG feature genes were relatively high, while those of Class B MG feature genes were relatively low. The expression of YTHDF1, CBLL1, and RBM15 showed significant differences between the two clusters (Fig.\u0026nbsp;4E, F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBiological Characteristics of m6A Subtyping\u003c/h2\u003e \u003cp\u003eStratified by m6A subtyping, we performed an assessment of immune cell populations to explore the link between m6A-associated gene markers and immune system functionality. Cluster A showed elevated levels of CD56bright NK cells, eosinophils, and T follicular helper cells compared to Cluster B, which had increased levels of CD56dim NK cells, NKT cells, and Type 1 T helper cells (Fig.\u0026nbsp;5A). We further examined the correlation between m6A signature genes and immune cell subsets. Notably, RBM15 exhibited the strongest correlation with immune cells and was selected for further investigation (Fig.\u0026nbsp;5B). The low expression group of RBM15 had a higher proportion of CD56bright NK cells and NKT cells, whereas the high expression group had a greater proportion of Type 1 T helper cells (Fig.\u0026nbsp;5C). We identified 61 DEGs between clusters A and B and depicted them in a Venn diagram (Fig.\u0026nbsp;5D). Subsequently, we conducted GO and KEGG enrichment analyses for these DEGs (Fig.\u0026nbsp;5E, F). Enriched processes included regulation of N-acetylneuraminic acid metabolism, ATPase V1 domain, and unstable activity of protein-containing complexes. The outcomes of the KEGG enrichment analysis are presented in the figure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Two m6A Gene Subtypes\u003c/h2\u003e \u003cp\u003eCategorization of DEGs Based on Expression Levels Subsequently, we stratified the samples according to the levels of DEGs. At k\u0026thinsp;=\u0026thinsp;2, the cumulative distribution function (CDF) peaked, prompting us to segregate the samples into two distinct groups (Fig.\u0026nbsp;6A-C). The heatmap revealed that the majority of genes in cluster B exhibit higher expression levels compared to those in cluster A (Fig.\u0026nbsp;6D). In the differential analysis of m6A gene signatures, the m6A-associated genes in cluster A demonstrated elevated expression relative to cluster B (Fig.\u0026nbsp;6E). Ultimately, we conducted an analysis to compare the immune cell proportions between these two clusters.In cluster A, the abundance of CD56dim natural killer cells, natural killer T cells, and type 1 T helper cells is higher than that in cluster B, while the proportion of CD56bright natural killer cells and T follicular helper cells is lower than that in cluster B (Fig.\u0026nbsp;6F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics and Inflammatory Factors Based on m6A Scores\u003c/h2\u003e \u003cp\u003eThe scores in cluster A are higher compared to those in cluster B (Fig.\u0026nbsp;7A, B). In the Sankey diagram, the results of m6a typing and gene typing show a certain similarity (Fig.\u0026nbsp;7C). Inflammatory mediators are intricately linked to the immune response. To conclude, we explored the correlations between various subtypes and inflammatory mediators. The results highlight disparities in the expression levels of inflammatory mediators across distinct subtypes (Fig.\u0026nbsp;7D, E).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMyasthenia gravis is an autoimmune disease caused by specific antibodies targeting different postsynaptic components at the neuromuscular junction, characterized clinically by fatigable muscle weakness\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The pathogenesis of MG has not been completely elucidated, suspected to be related to genetic, environmental, infectious, and immune factors\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. N6-methyladenosine (m6A) modification is an important modification in the transcriptome, associated with various RNA biological processes, including RNA processing, translation, stability, splicing, and degradation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. m6A RNA modification plays various key roles in numerous biological processes, such as neurogenesis, embryonic development, stress response, circadian rhythms, and tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.Previous studies have indicated that m6A methylation regulates cancer malignancy by controlling the expression of cancer-associated genes, and abnormal levels of m6A methylation contribute to tumor pathogenesis and progression\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. m6A modification maintains muscle health and promotes regeneration by regulating muscle stem cell function and differentiation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In recent years, researchers have found that m6A RNA modification plays an important role in the occurrence and progression of autoimmune diseases\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. However, few studies have focused on the role of m6A modification in the pathogenesis of MG. Therefore, we systematically studied the m6A modification patterns in the immune microenvironment of MG and explored the immune characteristics related to m6A modification. In this study, we constructed a modality map using an RF model to estimate the incidence rate of MG; we hypothesized that m6A methylation might be associated with MG.\u003c/p\u003e \u003cp\u003eUtilizing the Random Forest (RF) model, we identified three signature m6A genes: RBM15, YTHDC1, and CBLL1. RBM15, a protein-coding gene belonging to the split-end protein family, is a pivotal member of the methyltransferase complex responsible for m6A methylation. This gene is crucial for preserving hematopoietic cell homeostasis, modulating mRNA alternative splicing, and enhancing transcriptional repression activity\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Notably, RBM15 has been observed to be aberrantly expressed across a spectrum of cancers, where it contributes to tumorigenesis and progression, including esophageal squamous cell carcinoma (ESCC), cervical cancer (CRC), and bladder cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Quan's research suggests that m6A methyltransferase RBM15 can influence tumor cell proliferation, metastasis, and stemness by stabilizing HEIH expression\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Recent studies indicate that RBM15 protein expression is related to cell apoptosis\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In diabetic nephropathy, RBM15 may regulate cell proliferation, inflammation, and oxidative stress through activating the AGE-RAGE pathway, thus accelerating disease progression\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.RBM15 mitigates NAFLD inflammation and oxidative stress by upregulating RNF5 expression through m6A methylation\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Based on these previous findings, RBM15 plays an essential role in apoptosis, inflammation, and oxidative stress, which may be related to MG. In recent years, YTHDC1-mediated m6A modification has played a key role in various biological functions and the occurrence and development of various diseases, especially cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. YTHDC1 enhances FOXM1 expression through m6A modification, promoting migration, invasion, and glycolysis in triple-negative breast cancer\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. YTHDC1 promotes CDK6 methylation and affects the invasion, migration, and tube formation capability of endothelial cells, exacerbating diabetic retinopathy\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. YTHDC1 may regulate Beclin1 by enhancing mRNA stability through m6A modification and affecting autophagy-dependent NF-κB signaling, thereby modulating IBD macrophage-mediated inflammation\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. CBLL1 is an E3 ubiquitin ligase with a ring-type structure domain, regarded as one of the m6A-related genes\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. CBLL1 is upregulated in non-small cell lung cancer (NSCLC) tissues and promotes NSCLC cell proliferation\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. It has been found that CBLL1 expression is increased in NSCLC tissues and cells, protecting NSCLC cells from cisplatin (DDP)-induced damage as a downstream gene of circ_0072083/miR-545-3p\u003csup\u003e69\u003c/sup\u003e. Therefore, the three feature genes RBM15, YTHDC1, and CBLL1 related to m6A in this study may be associated with MG. The constructed modality map demonstrates a negative correlation between these genes and the incidence of MG. Immune-related analysis shows that these three genes are associated with immunity, particularly the correlation of RBM15, which may underpin their significance in MG.\u003c/p\u003e \u003cp\u003eExisting studies indicate that dysfunction of T, B lymphocytes, and natural killer cells may play an important role in triggering immune responses in the pathogenesis of MG\u003csup\u003e\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. NKT cells can rapidly release large amounts of pro-inflammatory and anti-inflammatory cytokines, such as IL-10, IL-17, and IFN-γ, after stimulation, primarily performing immune regulatory functions\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Recent studies have discovered anomalies in the phenotype and function of natural killer (NK) cells in patients with MG\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Follicular helper T cells (Tfh) are a distinct subgroup of CD4\u0026thinsp;+\u0026thinsp;T cells that promote excessive proliferation, somatic hypermutation, and class switching of B cells\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Tfh may promote the development of MG by acting on antibody-secreting B cells through relevant cytokines\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. A recent study found that MuSK-MG patients exhibited a higher Tfh:Tfr ratio, indicating inadequate regulation of Tfh cells\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. T helper 1 (Th1) is a functional subgroup of natural CD4\u0026thinsp;+\u0026thinsp;T cells\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Numerous data indicate that Th1, Th17, and Treg cells are involved in the occurrence of MG, and the interactions between cells and their cytokines have complex correlations\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Th1 cells producing IFN-γprimarily activate antigen-presenting cells and promote cellular immune responses\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Studies show that the levels of Th1 and Th17 cells in the peripheral blood of MG patients are higher than those of healthy individuals, while Treg cell levels are lower than those of healthy individuals\u003csup\u003e\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Our study identified variations in the infiltration of immune cells, such as CD56bright NK cells, eosinophils, follicular helper T cells, CD56dim NK cells, NKT cells, and Th1 cells, between m6A subtypes and MG samples stratified by CBLL1 expression levels. These observations imply that m6A might be involved in the pathogenesis of MG through the modulation of immune cell infiltration.\u003c/p\u003e \u003cp\u003eIn this study, we identified two different m6A patterns (cluster A and cluster B) based on three significant m6A regulatory factors, as well as two distinct m6A gene patterns (gene cluster A and gene cluster B) based on 61 m6A-related DEG. We calculated the m6A score for each sample between the two different m6A patterns or m6A gene patterns using PCA algorithms to quantify the m6A patterns. We found that cluster A or gene cluster A exhibited higher m6A scores than cluster B or gene cluster B.\u003c/p\u003e \u003cp\u003eWe analyzed the eight MG-related genes between m6A subtypes and m6A gene subtypes, finding significant differences in IL16 and CD1A between the two gene typing methods. Inflammation is a key influencing factor in the pathology of diseases related to skeletal muscle dysfunction\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Inflammation has long been considered an important factor influencing the pathogenesis of systemic and ocular MG, occurring in approximately 80% and 50% of MG patients, respectively\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. High serum IL-16 levels are associated with the occurrence and progression of malignant tumors and poor survival in patients with gastric cancer and sarcopenia\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. The different expressions of the confirmed myasthenia gravis-related genes in our classification subtypes indicate that our results are consistent with previous studies, and the classification is meaningful.\u003c/p\u003e \u003cp\u003eFurthermore, the immune-related pathways influenced by different m6A modification clusters vary significantly. The significant differences in the immune microenvironment between these two clusters may lead to different responses to treatment by m6A and produce different outcomes. By identifying differing expression patterns of m6A regulatory factors, it may be possible to develop more effective and targeted interventions to improve the prognosis of MG patients.\u003c/p\u003e \u003cp\u003eOur work, through the integration of GEO datasets containing a relatively small sample size, investigates the role of m6A in the immune microenvironment of MG, but some limitations should be considered. Further research is still needed to more thoroughly characterize the infiltrating immune cells in MG patients and their exact mechanisms. Secondly, since our results are primarily based on bioinformatics analysis of datasets, additional validation may be required from experimental studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we have investigated the link between m6A RNA methylation and MG, pinpointing key m6A-associated genes and exploring their interplay with the immune system. The discoveries made could offer new therapeutic insights for developing future treatment strategies for MG.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset for this study is GSE85452 from the GEO database.For more information, visit https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85452.This dataset is publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all teammates for contributing this work. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.F.S Designed the study with Y.Q.W; Y.Q.W, X.Q.C and Y.Y.J participated in the analysisof the data and wrote the manuscript; L.N.Z, Q.L.J and T.K.C critically revised the manuscript. P.D.Y, T.J.H and J.Ya suggested revisions to the article. All of the authors read andapproved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by \u0026nbsp;the National Natural Science Foundation of China(Grant\u0026nbsp;No.82374391), the Project in Key Fields of Universities in Guangdong\u0026nbsp;Province (Grant\u0026nbsp;No.2021ZDZX2032) and\u0026nbsp;the Natural Science Foundation of Guangdong Province (Grant\u0026nbsp;No.2023A1515011127).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCavalcante, P., Mantegazza, R., \u0026amp; Antozzi, C.Targeting autoimmune mechanisms by precision medicine in Myasthenia Gravis. \u003cem\u003eFrontiers in immunology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1404191. https://doi.org/10.3389/fimmu.2024.1404191 (2024). \u003c/li\u003e\n\u003cli\u003eMarcus R.What Is Myasthenia Gravis?. \u003cem\u003eJAMA\u003c/em\u003e, \u003cem\u003e331\u003c/em\u003e(5), 452. https://doi.org/10.1001/jama.2023.16872 (2024). \u003c/li\u003e\n\u003cli\u003eMa, C., Liu, D., Wang, B., Yang, Y., \u0026amp; Zhu, R.Advancements and prospects of novel biologicals for myasthenia gravis: toward personalized treatment based on autoantibody specificities. \u003cem\u003eFrontiers in pharmacology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1370411. https://doi.org/10.3389/fphar.2024.1370411 (2024). \u003c/li\u003e\n\u003cli\u003eGilhus N. E.Myasthenia gravis, respiratory function, and respiratory tract disease. \u003cem\u003eJournal of neurology\u003c/em\u003e, \u003cem\u003e270\u003c/em\u003e(7), 3329\u0026ndash;3340. https://doi.org/10.1007/s00415-023-11733-y (2023). \u003c/li\u003e\n\u003cli\u003eMahic, M.et al.Physician- and patient-reported perspectives on myasthenia gravis in Europe: a real-world survey. \u003cem\u003eOrphanet journal of rare diseases\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 169. https://doi.org/10.1186/s13023-023-02727-0 (2023). \u003c/li\u003e\n\u003cli\u003eDewilde, S.et al.People Diagnosed with Myasthenia Gravis have Lower health-related quality of life and Need More Medical and Caregiver Help in Comparison to the General Population: Analysis of Two Observational Studies. \u003cem\u003eAdvances in therapy\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(10), 4377\u0026ndash;4394. https://doi.org/10.1007/s12325-023-02604-z (2023). \u003c/li\u003e\n\u003cli\u003eRuiter, A. M.et al.Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning. \u003cem\u003eAnnals of clinical and translational neurology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(8), 1314\u0026ndash;1325. https://doi.org/10.1002/acn3.51823 (2023). \u003c/li\u003e\n\u003cli\u003eHuang, J., Yan, Z., Song, Y., \u0026amp; Chen, T.Nanodrug Delivery Systems for Myasthenia Gravis: Advances and Perspectives. \u003cem\u003ePharmaceutics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 651. https://doi.org/10.3390/pharmaceutics16050651 (2024). \u003c/li\u003e\n\u003cli\u003eIorio R. Myasthenia gravis: the changing treatment landscape in the era of molecular therapies. \u003cem\u003eNature reviews. Neurology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(2), 84\u0026ndash;98. https://doi.org/10.1038/s41582-023-00916-w (2024). \u003c/li\u003e\n\u003cli\u003eRahman, M. M., Islam, M. R., \u0026amp; Dhar, P. S. Myasthenia gravis in current status: epidemiology, types, etiology, pathophysiology, symptoms, diagnostic tests, prevention, treatment, and complications - correspondence. \u003cem\u003eInternational journal of surgery (London, England)\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(2), 178\u0026ndash;180. https://doi.org/10.1097/JS9.0000000000000164 (2023). \u003c/li\u003e\n\u003cli\u003eBerrih-Aknin, S., \u0026amp; Le Panse, R.Myasthenia gravis: a comprehensive review of immune dysregulation and etiological mechanisms. \u003cem\u003eJournal of autoimmunity\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e, 90\u0026ndash;100. https://doi.org/10.1016/j.jaut.2013.12.011 (2014). \u003c/li\u003e\n\u003cli\u003eLe Panse, R., \u0026amp; Berrih-Aknin, S.Autoimmune myasthenia gravis: autoantibody mechanisms and new developments on immune regulation. \u003cem\u003eCurrent opinion in neurology\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(5), 569\u0026ndash;576. https://doi.org/10.1097/WCO.0b013e328364d6cd (2013). \u003c/li\u003e\n\u003cli\u003eZhang, P.et al.Diabetes mellitus exacerbates experimental autoimmune myasthenia gravis via modulating both adaptive and innate immunity. \u003cem\u003eJournal of neuroinflammation\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 244. https://doi.org/10.1186/s12974-021-02298-6 (2021). \u003c/li\u003e\n\u003cli\u003eAl-Kuraishy, H. M.et al.Defective autophagy and autophagy activators in myasthenia gravis: a rare entity and unusual scenario. \u003cem\u003eAutophagy\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(7), 1473\u0026ndash;1482. https://doi.org/10.1080/15548627.2024.2315893 (2024). \u003c/li\u003e\n\u003cli\u003eXia, M.et al..Effect of the m6ARNA gene on the prognosis of thyroid cancer, immune infiltration, and promising immunotherapy. \u003cem\u003eFrontiers in immunology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 995645. https://doi.org/10.3389/fimmu.2022.995645 (2022). \u003c/li\u003e\n\u003cli\u003eZhuo, R.et al.The regulatory role of N6 -methyladenosine modification in the interaction between host and microbes. \u003cem\u003eWiley interdisciplinary reviews. RNA\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), e1725. https://doi.org/10.1002/wrna.1725 (2022). \u003c/li\u003e\n\u003cli\u003eJiang, X.et al.The role of m6A modification in the biological functions and diseases. \u003cem\u003eSignal transduction and targeted therapy\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 74. https://doi.org/10.1038/s41392-020-00450-x (2021). \u003c/li\u003e\n\u003cli\u003eZhang, H.et al.Dynamic landscape and evolution of m6A methylation in human. \u003cem\u003eNucleic acids research\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(11), 6251\u0026ndash;6264. https://doi.org/10.1093/nar/gkaa347 (2020). \u003c/li\u003e\n\u003cli\u003eHe, L.et al.Functions of N6-methyladenosine and its role in cancer. \u003cem\u003eMolecular cancer\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 176. https://doi.org/10.1186/s12943-019-1109-9 (2019).\u003c/li\u003e\n\u003cli\u003eAn, Y., \u0026amp; Duan, H.The role of m6A RNA methylation in cancer metabolism. \u003cem\u003eMolecular cancer\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 14. https://doi.org/10.1186/s12943-022-01500-4 (2022). \u003c/li\u003e\n\u003cli\u003eWang, T., Kong, S., Tao, M., \u0026amp; Ju, S.The potential role of RNA N6-methyladenosine in Cancer progression. \u003cem\u003eMolecular cancer\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 88. https://doi.org/10.1186/s12943-020-01204-7 (2020). \u003c/li\u003e\n\u003cli\u003eKhan, F. A.et al.Regulatory roles of N6-methyladenosine (m6A) methylation in RNA processing and non-communicable diseases. \u003cem\u003eCancer gene therapy\u003c/em\u003e, 10.1038/s41417-024-00789-1. Advance online publication. https://doi.org/10.1038/s41417-024-00789-1 (2024).\u003c/li\u003e\n\u003cli\u003eLiu, Z.et al.The Landscape of m6A Regulators in Multiple Brain Regions of Alzheimer\u0026apos;s Disease. \u003cem\u003eMolecular neurobiology\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(9), 5184\u0026ndash;5198. https://doi.org/10.1007/s12035-023-03409-5 (2023). \u003c/li\u003e\n\u003cli\u003eYin, H.et al.Loss of the m6A methyltransferase METTL3 in monocyte-derived macrophages ameliorates Alzheimer\u0026apos;s disease pathology in mice. \u003cem\u003ePLoS biology\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), e3002017. https://doi.org/10.1371/journal.pbio.3002017 (2023).\u003c/li\u003e\n\u003cli\u003eMcMillan, M.et al.RNA methylation influences TDP43 binding and disease pathogenesis in models of amyotrophic lateral sclerosis and frontotemporal dementia. \u003cem\u003eMolecular cell\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(2), 219\u0026ndash;236.e7. https://doi.org/10.1016/j.molcel.2022.12.019 (2023). \u003c/li\u003e\n\u003cli\u003eFeng, J.et al.Soot nanoparticles promote ferroptosis in dopaminergic neurons via alteration of m6A RNA methylation in Parkinson\u0026apos;s disease. \u003cem\u003eJournal of hazardous materials\u003c/em\u003e, \u003cem\u003e473\u003c/em\u003e, 134691. https://doi.org/10.1016/j.jhazmat.2024.134691 (2024). \u003c/li\u003e\n\u003cli\u003eMamrut, S.et al.Methylome and transcriptome profiling in Myasthenia Gravis monozygotic twins. \u003cem\u003eJournal of autoimmunity\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e, 62\u0026ndash;73. https://doi.org/10.1016/j.jaut.2017.05.005 (2017). \u003c/li\u003e\n\u003cli\u003eHu, X.et al.The Emerging Role of RNA N6-Methyladenosine Modification in Pancreatic Cancer. \u003cem\u003eFrontiers in oncology\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 927640. https://doi.org/10.3389/fonc.2022.927640 (2022). \u003c/li\u003e\n\u003cli\u003eShang, Q. X.et al.Identification of m6a-related signature genes in esophageal squamouscell carcinoma by machine learning method. \u003cem\u003eFrontiers in genetics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1079795. https://doi.org/10.3389/fgene.2023.1079795 (2023). \u003c/li\u003e\n\u003cli\u003eZhao, L.et al.Identification of significant m6A regulators and immune microenvironment characterization in ischemic stroke. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 3456. https://doi.org/10.1038/s41598-024-53788-5 (2024). \u003c/li\u003e\n\u003cli\u003eWickham, H. Reshaping data with the reshape package. J. Statist. Softw. 21, 1\u0026ndash;20. https://doi.org/10.18637/jss.v021.i12 (2007).\u003c/li\u003e\n\u003cli\u003eWickham, H. Elegant graphics for data analysis. Media 35(211), 10\u0026ndash;1007 (2009).\u003c/li\u003e\n\u003cli\u003eKuhn, M. Building predictive models in R using the caret package. J. Statist. Softw. 28, 1\u0026ndash;26. https://doi.org/10.18637/jss.v028.i05(2008).\u003c/li\u003e\n\u003cli\u003eBiecek, P. DALEX: Explainers for complex predictive models in R. J. Mach. Learn. Res. 19, 1\u0026ndash;5 (2018).\u003c/li\u003e\n\u003cli\u003eRobin, X. et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77. https://doi.org/10.1186/1471-2105-12-77 (2011).\u003c/li\u003e\n\u003cli\u003eLiaw A, Wiener MC. Classification and regression by randomforest. \u003cem\u003eR News\u003c/em\u003e. 2002;\u003cstrong\u003e2\u003c/strong\u003e(3):18\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eHarrell JFE. \u003cem\u003eRegression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis\u003c/em\u003e. New York: Springer; 2001.\u003c/li\u003e\n\u003cli\u003eWilkerson, M. D., \u0026amp; Hayes, D. N.ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(12), 1572\u0026ndash;1573. https://doi.org/10.1093/bioinformatics/btq170 (2010). \u003c/li\u003e\n\u003cli\u003eKanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. \u0026amp; Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587\u0026ndash;D592. https://doi.org/10.1093/nar/gkac963(2023).\u003c/li\u003e\n\u003cli\u003eKanehisa, M. \u0026amp; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. NucleicAcids Res. 28, 27\u0026ndash;30 (2000).\u003c/li\u003e\n\u003cli\u003eKanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947\u0026ndash;1951. https://doi.org/10.1002/pro.3715 (2019).\u003c/li\u003e\n\u003cli\u003eZhang, P.et al.Bioinformatics identification and experimental validation of m6A-relateddiagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients. \u003cem\u003eFrontiers in endocrinology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 990078. https://doi.org/10.3389/fendo.2023.990078 (2023).\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a Est\u0026eacute;vez, D. A., \u0026amp; Pardo Fern\u0026aacute;ndez, J.Myasthenia gravis. Update on diagnosis and therapy. Miastenia gravis. Actualizaci\u0026oacute;n diagn\u0026oacute;stica y terap\u0026eacute;utica. \u003cem\u003eMedicina clinica\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e(3), 119\u0026ndash;127. https://doi.org/10.1016/j.medcli.2023.04.006 (2023). \u003c/li\u003e\n\u003cli\u003eZarobkiewicz, M. K., Morawska, I., Michalski, A., Roliński, J., \u0026amp; Bojarska-Junak, A. NKT and NKT-like Cells in Autoimmune Neuroinflammatory Diseases-Multiple Sclerosis, Myasthenia Gravis and Guillain-Barre Syndrome. \u003cem\u003eInternational journal of molecularsciences\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(17), 9520. https://doi.org/10.3390/ijms22179520 (2021). \u003c/li\u003e\n\u003cli\u003eLi, Y.et al.The role of RNA methylation in tumor immunity and its potential in immunotherapy. \u003cem\u003eMolecular cancer\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 130. https://doi.org/10.1186/s12943-024-02041-8 (2024). \u003c/li\u003e\n\u003cli\u003eLiu, T.et al.The m6A reader YTHDF1 promotes ovarian cancer progression via augmenting EIF3C translation. \u003cem\u003eNucleic acids research\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(7), 3816\u0026ndash;3831. https://doi.org/10.1093/nar/gkaa048 (2020). \u003c/li\u003e\n\u003cli\u003eShan, Y., Chen, W., \u0026amp; Li, Y.The role of m6A RNA methylation in autoimmune diseases: Novel therapeutic opportunities. \u003cem\u003eGenes \u0026amp; diseases\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 252\u0026ndash;267. https://doi.org/10.1016/j.gendis.2023.02.013 (2023). \u003c/li\u003e\n\u003cli\u003eYang, W., Zhao, Y., \u0026amp; Yang, Y.Dynamic RNA methylation modifications and their regulatory role in mammalian development and diseases. \u003cem\u003eScience China. Life sciences\u003c/em\u003e, 10.1007/s11427-023-2526-2. Advance online publication. https://doi.org/10.1007/s11427-023-2526-2 (2024). \u003c/li\u003e\n\u003cli\u003eHe, L.et al.Functions of N6-methyladenosine and its role in cancer. \u003cem\u003eMolecular cancer\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 176. https://doi.org/10.1186/s12943-019-1109-9 (2019). \u003c/li\u003e\n\u003cli\u003eOgbe, S. E.et al.Insights into the epitranscriptomic role of N6-methyladenosine on aging skeletal muscle. \u003cem\u003eBiomedicine \u0026amp; pharmacotherapy = Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e, \u003cem\u003e177\u003c/em\u003e, 117041. https://doi.org/10.1016/j.biopha.2024.117041 (2024). \u003c/li\u003e\n\u003cli\u003eMa, M.et al.RBM15 facilities lung adenocarcinoma cell progression by regulating RASSF8 stability through N6 Methyladenosine modification. \u003cem\u003eTranslational oncology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 102018.https://doi.org/10.1016/j.tranon.2024.102018 (2024).\u003c/li\u003e\n\u003cli\u003eJiang, J.et al. Identification of osteosarcoma m6A-related prognostic biomarkers using artificial intelligence: RBM15. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 5255.https://doi.org/10.1038/s41598-023-28739-1 (2023).\u003c/li\u003e\n\u003cli\u003eWang H. The RNA m6A writer RBM15 contributes to the progression of esophageal squamous cell carcinoma by regulating miR-3605-5p/KRT4 pathway. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), e24459. https://doi.org/10.1016/j.heliyon.2024.e24459 (2024). \u003c/li\u003e\n\u003cli\u003eZhang, C., Gu, L., Xiao, J., \u0026amp; Jin, F.Knockdown of RBM15 inhibits tumor progression and the JAK-STAT signaling pathway in cervical cancer. \u003cem\u003eBMC cancer\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 684. https://doi.org/10.1186/s12885-023-11163-z (2023). \u003c/li\u003e\n\u003cli\u003eHuang, Y.et al.Enhancing m6A modification of lncRNA through METTL3 and RBM15 to promote malignant progression inbladder cancer. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(7), e28165. https://doi.org/10.1016/j.heliyon.2024.e28165 (2024). \u003c/li\u003e\n\u003cli\u003eQuan, Y.et al.The m6A methyltransferase RBM15 affects tumor cell stemness and progression of cervical cancer by regulating the stability of lncRNA HEIH. \u003cem\u003eExperimental cell research\u003c/em\u003e, \u003cem\u003e436\u003c/em\u003e(2), 113924. https://doi.org/10.1016/j.yexcr.2024.113924 (2024). \u003c/li\u003e\n\u003cli\u003eCheng, H.et al.RBM15 Protects From Myocardial Infarction by Stabilizing NAE1. \u003cem\u003eJACC. Basic to translational science\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 631\u0026ndash;648. https://doi.org/10.1016/j.jacbts.2024.01.017 (2024). \u003c/li\u003e\n\u003cli\u003eMorel O.Potential of Epigenetic Therapy in Alleviating Cardiac Death and Fibrotic Remodeling in Myocardial Infarction. JACC. Basic to translational science, 9(5), 649\u0026ndash;651.https://doi.org/10.1016/j.jacbts.2024.03.005 (2024). \u003c/li\u003e\n\u003cli\u003eQin, Y.et al.N6-methyladenosine methylation regulator RBM15 promotes the progression of diabetic nephropathy by regulating cell proliferation, inflammation, oxidative stress, and pyroptosis through activating the AGE-RAGE pathway. \u003cem\u003eEnvironmental toxicology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(11), 2772\u0026ndash;2782. https://doi.org/10.1002/tox.23917 (2023). \u003c/li\u003e\n\u003cli\u003eLi, S., Lian, S., Cheng, W., Zhang, T., \u0026amp; Gong, X.THE ROLE OF N6-METHYLADENOSINE METHYLTRANSFERASE RBM15 IN NONALCOHOLIC FATTY LIVER DISEASE. \u003cem\u003eShock (Augusta, Ga.)\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(2), 311\u0026ndash;321. https://doi.org/10.1097/SHK.0000000000002294 (2024). \u003c/li\u003e\n\u003cli\u003eXie, W.et al.SILENCING M 6 A READER YTHDC1 REDUCES INFLAMMATORYRESPONSE IN SEPSIS-INDUCED CARDIOMYOPATHY BY INHIBITING SERPINA3N EXPRESSION. \u003cem\u003eShock (Augusta, Ga.)\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(5), 791\u0026ndash;802. https://doi.org/10.1097/SHK.0000000000002106 (2023). \u003c/li\u003e\n\u003cli\u003eLi, X., Yang, Y., \u0026amp; Chen, Z. Downregulation of the m6A reader protein YTHDC1 leads to islet \u0026beta;-cell failure and diabetes. \u003cem\u003eMetabolism: clinical and experimental\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e, 155339. https://doi.org/10.1016/j.metabol.2022.155339 (2023).\u003c/li\u003e\n\u003cli\u003eYan, H., Zhang, L., Cui, X., Zheng, S., \u0026amp; Li, R.Roles and mechanisms of the m6A reader YTHDC1 in biological processes and diseases. \u003cem\u003eCell death discovery\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 237. https://doi.org/10.1038/s41420-022-01040-2 (2022). \u003c/li\u003e\n\u003cli\u003eShen, X.et al.YTHDC1-dependent m6A modification modulated FOXM1 promotes glycolysis and tumor progression through CENPA in triple-negative breast cancer. \u003cem\u003eCancer science\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(6), 1881\u0026ndash;1895. https://doi.org/10.1111/cas.16137 (2024). \u003c/li\u003e\n\u003cli\u003eZhou, Q.et al.YTHDC1 aggravates high glucose-induced retinal vascular endothelial cell injury via m6A modification of CDK6. \u003cem\u003eBiology direct\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 54. https://doi.org/10.1186/s13062-024-00498-7 (2024).\u003c/li\u003e\n\u003cli\u003eZhou, L.et al.YTHDC1 inhibits autophagy-dependent NF-\u0026kappa;B signaling by stabilizing Beclin1 mRNA in macrophages. \u003cem\u003eJournal of inflammation (London, England)\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 22. https://doi.org/10.1186/s12950-024-00393-y (2024). \u003c/li\u003e\n\u003cli\u003eHui, L.et al.CBLL1 is highly expressed in non-small cell lung cancer and promotes cell proliferation and invasion. \u003cem\u003eThoracic cancer\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(6), 1479\u0026ndash;1488. https://doi.org/10.1111/1759-7714.13097 (2019). \u003c/li\u003e\n\u003cli\u003eLiu, X.et al.CBLL1 promotes endometrial stromal cell senescence via inhibiting PTEN in recurrent spontaneous abortion. \u003cem\u003eFASEB journal : official publication of the Federation of American Societies for Experimental Biology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(14), e23833. https://doi.org/10.1096/fj.202400972R (2024). \u003c/li\u003e\n\u003cli\u003eLi, H., Liu, F., \u0026amp; Qin, W.Circ_0072083 interference enhances growth-inhibiting effectsof cisplatin in non-small-cell lung cancer cells via miR-545-3p/CBLL1 axis. \u003cem\u003eCancer cell international\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e, 78. https://doi.org/10.1186/s12935-020-1162-x (2020). \u003c/li\u003e\n\u003cli\u003eUzawa, A.et al.Roles of cytokines and T cells in the pathogenesis of myasthenia gravis. \u003cem\u003eClinical and experimental immunology\u003c/em\u003e, \u003cem\u003e203\u003c/em\u003e(3), 366\u0026ndash;374. https://doi.org/10.1111/cei.13546 (2021). \u003c/li\u003e\n\u003cli\u003eJing, S.et al.Effect of low-dose rituximab treatment on T- and B-cell lymphocyte imbalance in refractory myasthenia gravis. \u003cem\u003eJournal of neuroimmunology\u003c/em\u003e, \u003cem\u003e332\u003c/em\u003e, 216\u0026ndash;223. https://doi.org/10.1016/j.jneuroim.2019.05.004 (2019). \u003c/li\u003e\n\u003cli\u003eZhang, Q.et al.Therapeutic potential of natural killer cells in neuroimmunological diseases. \u003cem\u003eBiomedicine \u0026amp; pharmacotherapy = Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e, \u003cem\u003e173\u003c/em\u003e, 116371. https://doi.org/10.1016/j.biopha.2024.116371 (2024). \u003c/li\u003e\n\u003cli\u003eLiu, R. T.et al. Naturalkiller cells promote the differentiation of follicular helper T cells instead of inducing apoptosis in myasthenia gravis. \u003cem\u003eInternational immunopharmacology\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e, 107880. https://doi.org/10.1016/j.intimp.2021.107880 (2021). \u003c/li\u003e\n\u003cli\u003eLi, Y.et al.Imbalance in T follicular helper cells producing IL-17 promotes pro-inflammatory responses in MuSK antibody positive myasthenia gravis. \u003cem\u003eJournal of neuroimmunology\u003c/em\u003e, \u003cem\u003e345\u003c/em\u003e, 577279. https://doi.org/10.1016/j.jneuroim.2020.577279 (2020). \u003c/li\u003e\n\u003cli\u003eWu, N.et al.Central Role of T Follicular Helper Cells in Myasthenia Gravis. \u003cem\u003eNoro psikiyatri arsivi\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), 68\u0026ndash;72. https://doi.org/10.29399/npa.27193 (2021). \u003c/li\u003e\n\u003cli\u003eWeng, S.et al.Astragaloside IV ameliorates experimental autoimmune myasthenia gravis by regulating CD4\u0026thinsp;+\u0026thinsp;T cells and altering gut microbiota. \u003cem\u003eChinese medicine\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 97. https://doi.org/10.1186/s13020-023-00798-6 (2023). \u003c/li\u003e\n\u003cli\u003eSong, J.et al.Inhibition of ROCK activity regulates the balance of Th1, Th17 and Treg cells in myasthenia gravis. \u003cem\u003eClinical immunology (Orlando,Fla.)\u003c/em\u003e, \u003cem\u003e203\u003c/em\u003e, 142\u0026ndash;153. https://doi.org/10.1016/j.clim.2019.05.006 (2019). \u003c/li\u003e\n\u003cli\u003eLink, J.et al.Organ-specific autoantigens induce interferon-gamma and interleukin-4 mRNA expression in mononuclear cells in multiple sclerosis and myasthenia gravis. \u003cem\u003eNeurology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(4), 728\u0026ndash;734. https://doi.org/10.1212/wnl.44.4.728 (1994). \u003c/li\u003e\n\u003cli\u003eWang, Z., Wang, W., Chen, Y., \u0026amp; Wei, D.T helper type 17 cells expand in patients with myasthenia-associated thymoma. \u003cem\u003eScandinavian journal of immunology\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(1), 54\u0026ndash;61. https://doi.org/10.1111/j.1365-3083.2012.02703.x (2012). \u003c/li\u003e\n\u003cli\u003eXu, W. H.et al.Changes of Treg-associated moleculeson CD4+CD25 +Treg cells in myasthenia gravis and effects of immunosuppressants. \u003cem\u003eJournal of clinical immunology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(5), 975\u0026ndash;983. https://doi.org/10.1007/s10875-012-9685-0 (2012). \u003c/li\u003e\n\u003cli\u003eDalle, S., Rossmeislova, L., \u0026amp; Koppo, K.The Role of Inflammation in Age-Related Sarcopenia. \u003cem\u003eFrontiers in physiology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 1045. https://doi.org/10.3389/fphys.2017.01045 (2017). \u003c/li\u003e\n\u003cli\u003eMolin, C. J., Westerberg, E., \u0026amp; Punga, A. R.Profile of upregulated inflammatory proteins in sera of Myasthenia Gravis patients. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 39716. https://doi.org/10.1038/srep39716 (2017). \u003c/li\u003e\n\u003cli\u003eHuda R.New Approaches to Targeting B Cells for Myasthenia Gravis Therapy. \u003cem\u003eFrontiers in immunology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 240. https://doi.org/10.3389/fimmu.2020.00240 (2020).\u003c/li\u003e\n\u003cli\u003eXiong, J.et al.Association of Sarcopenia and Expression of Interleukin-16 in Gastric Cancer Survival. \u003cem\u003eNutrients\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 838. https://doi.org/10.3390/nu14040838 (2022). \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":"myasthenia gravis, N6-methyladenosine regulation, prediction model, immunity","lastPublishedDoi":"10.21203/rs.3.rs-5264805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5264805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMyasthenia gravis (MG), a rare autoimmune disorder, presents a complex pathogenesis involving various immune molecules. The modification of N6-methyladenosine (m6A) regulates diverse immune metabolic and immunopathological processes; however, its role in MG remains unclear. We downloaded dataset GSE85452 from the GEO database to identify differentially expressed genes regulated by m6A. The Random Forest (RF) method was utilized to identify pivotal regulatory genes associated with m6A modification. Subsequently, a prognostic model was crafted and confirmed using this gene set. Patients with MG were stratified according to the expression levels of these key regulatory genes. Additionally, MG-specific immune signatures were delineated by examining immune cell infiltration patterns and their correlations. Further functional annotation, protein-protein interaction mapping, and molecular docking analyses were performed on these immune biomarkers, leading to the discovery of three genes that exhibited significant differential expression within the dataset: RBM15, CBLL1, and YTHDF1.The random forest algorithm confirmed these as key regulatory genes of m6A in MG, validated by constructing a clinical prediction model. Based on key regulatory gene expression, we divided MG patients into two groups, revealing two distinct m6A modification patterns with varying immune cell abundances. We also discovered 61 genes associated with the m6A phenotype and conducted an in-depth exploration of their biological roles. RBM15, CBLL1, and YTHDF1 were found positively correlated with CD56dim natural killer cells, natural killer T cells, and type 1 helper T cells. These genes were stable diagnostic m6A-related markers in both discovery and validation cohorts. Our findings suggest RBM15, CBLL1, and YTHDF1 as immune markers for MG. Further analysis of these genes may elucidate their roles in the immune microenvironment of MG.\u003c/p\u003e","manuscriptTitle":"Identification of key regulatory factors for m6A in myasthenia gravis and characteristics of the immune characteristics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:25:46","doi":"10.21203/rs.3.rs-5264805/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":"a7c8752c-ab85-468f-a493-1e5292cee0e8","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T09:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:25:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5264805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5264805","identity":"rs-5264805","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}