HTR2A DNA Methylation as a Diagnostic Biomarker for Rheumatoid Arthritis: A Validation Study Using Targeted Sequencing and Machine Learning Algorithms

HTR2A DNA Methylation as a Diagnostic Biomarker for Rheumatoid Arthritis: A Validation Study Using Targeted Sequencing and Machine Learning Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HTR2A DNA Methylation as a Diagnostic Biomarker for Rheumatoid Arthritis: A Validation Study Using Targeted Sequencing and Machine Learning Algorithms Jianan Zhao, Binghen He, Yunshen Li, Yu Shan, Kai Wei, Ping Jiang, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4710847/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To validate the potential of HTR2A cg15692052 DNA methylation as a diagnostic biomarker for RA and its subtypes. Methods MethylTarget™ targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of HTR2A cg15692052 in RA, HC, ankylosing spondylitis (AS), psoriatic arthritis (PSA), gout, systemic lupus erythematosus (SLE), dermatomyositis (DM), and primary Sjögren's syndrome (SS) patients within the region of chr13:46898190 ~ chr13:46897976, spanning a total of 215 bp . Logistic regression, LASSO, random forests, and Xgboost algorithms were used in R software to screen for significant variables, construct models, visualize results, and perform statistical analysis. Multiple imputation was applied to handle missing values, and Spearman's method was used to calculate correlations. Results Compared to the HC group, RA patients and four serological subtypes of RA (RF-negative RA, RF/CCP double-positive, RF/CCP double-negative, and CCP-negative RA) exhibited significantly higher levels of HTR2A cg15692052 methylation at positions 75/125/143/149/163/185/187 and in average methylation ( P < 0.05). Methylation levels at all positions and average methylation in RA patients and its four serological subtypes were significantly positively correlated with erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP) ( P < 0.05). HTR2A cg15692052 displayed various haplotypes with differential proportions, among which the CCCCCCC haplotype was significantly elevated in RA ( P < 0.05) and positively correlated with ESR and CRP (r = 0.13 and 0.21, P = 0.001 and P < 0.001). Conversely, the TTTTTTT haplotype was significantly decreased in RA ( P < 0.05) and negatively correlated with CRP (r=-0.15, P = 0.002). Predictive models constructed using different machine learning algorithms, incorporating methylation levels of HTR2A cg15692052 at various positions combined with different clinical features, were able to significantly distinguish RA patients with AUCs ranging from 0.672 to 0.757, RF/CCP double-negative patients with AUCs from 0.825 to 0.966, RF/CCP double-positive RA patients with AUCs from 0.714 to 0.846, and RF-negative RA patients with AUCs from 0.928 to 0.932. Conclusions The DNA methylation level of HTR2A cg15692052 is associated with RA and can serve as a diagnostic biomarker for RA and its subtypes. rheumatoid arthritis DNA methylation HTR2A circulating methylation levels biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by chronic synovitis, the presence of autoantibodies, and persistent joint and bone damage. The etiology of RA is complex and heterogeneous, involving factors such as genetics, metabolism, and immunology( 1 ). Genetic factors, including the major histocompatibility complex (MHC) molecules, as well as epigenetics, are important areas of research in understanding RA. Diagnostic markers for RA include rheumatoid factor (RF) and cyclic citrullinated peptide (CCP), which are significant in identifying seropositive patients( 2 ). However, approximately one-third of patients are seronegative, posing challenges in early diagnosis and intervention, which are crucial in managing RA. Delayed or inaccurate diagnosis can lead to irreversible joint damage and a rapid decline in quality of life. Therefore, early diagnosis and intervention play a critical role in improving clinical outcomes and reducing disability rates in RA. DNA methylation is one of the important research areas in epigenetics, and its association with RA is being extensively investigated. Whole-genome analysis of monozygotic twins, with and without RA, has revealed increased DNA methylation variability, suggesting the involvement of epigenetics as a significant component in the development of autoimmune diseases ( 3 ). Differential DNA methylation regions may serve as potential extensions for RA diagnosis and drug response prediction. Multiple studies have identified differential DNA methylation regions in T cells as novel diagnostic markers for RA ( 4 ). RA patients treated with MTX exhibit cell-specific differential methylation patterns, which may be associated with their response to the drug ( 5 ). Peripheral blood mononuclear cells from anti-CCP positive RA patients exhibit distinct differential methylation features compared to anti-CCP negative patients( 6 ). The DNA methylation levels of HIPK3, CXCR5 in peripheral blood mononuclear cells could potentially serve as novel diagnostic markers for RA patients ( 7 – 9 ). The serotonin receptor encoded by HTR2A plays a significant role in a variety of diseases. It inhibits inflammatory cell responses in the lungs upon binding with serotonin( 10 ). In patients with human cardiac hypertrophy, the expression of HTR2A is upregulated, promoting disease progression through the activation of the phosphatidylinositol 3-kinase-phosphoinositide-dependent protein kinase 1- protein kinase B - mammalian target of rapamycin signaling pathway( 11 ). Research has also shown that upregulation of HTR2A can inhibit cell apoptosis and reactive oxygen species (ROS) production, protecting the intestinal barrier and improving disease outcomes( 12 ). There may be a link between HTR2A and RA; for instance, an increased frequency of the TT genotype of rs6313 polymorphism may reduce the incidence of RA in the Japanese population( 13 , 14 ). Significant differences in the rs6313 ( T102C polymorphism) between RA patients and control groups have also been observed ( 16 ). T cells from RA patients carrying the TC haplotype demonstrate increased production of pro-inflammatory cytokines (including tumor necrosis factor-alpha, interleukin-6, and interferon-gamma), while antagonists inhibiting HTR2A receptor activation can suppress cytokine production ( 14 ). Additionally, protective haplotypes in HTR2A show interactions with the human leukocyte antigen-DRB1 shared epitope alleles, which are associated with RA autoantibody positivity ( 15 ). Our preliminary findings indicated that DNA methylation levels of HTR2A could significantly differentiate between RA, OA, and HC patients. Consequently, we have designed a second set of experiments with an increased number of rheumatic disease-related conditions to continue to explore the potential of HTR2A as a biomarker for RA. 2 Materials and Methods 2.1 Participants and peripheral blood collection In the Guanghua Hospital Precision Medicine Research Cohort (PMRC) at Shanghai University of Traditional Chinese Medicine, we conducted patient recruitment. This is our second batch of methylation research data, where we combined the first batch of methylation data, which included the RA, HC, and OA groups. The results of principal component analysis and t-distributed stochastic neighbor embedding showed no significant batch effect between the two datasets (Fig. 1 A-B). A total of 671 patients participated, including 407 RA patients, 30 each of AS patients, PSA patients, gout patients, SLE patients, DM patients, as well as 60 HC (healthy controls) and 24 SS patients. The RA patients were further divided into four serological subtypes: RF/CCP double-positive RA patients (RA_DP), RF/CCP double-negative RA patients (RA_DN), RF single-negative RA patients (RA_RFN), and CCP single-negative RA patients (RA_CCPN). Additionally, there were patients who were responders and non-responders to anti-TNF-α therapy (RA_AJN_Y and RA_AJN_N). AS patients were also divided into responders and non-responders to anti-TNF-α therapy (AS_Y and AS_N). The inclusion criteria for RA were based on the 2010 American College of Rheumatology (ACR) criteria, the 1984 revised New York criteria for AS patients, the 2015 ACR/EULAR classification criteria for gout patients, the 2006 ACR classification criteria for PSA patients, the 2019 EULAR/ACR classification criteria for SLE patients, the 2017 ACR/EULAR classification criteria for DM patients, and the 2016 ACR/EULAR classification criteria for primary Sjögren's syndrome patients. All participants did not have a history of severe liver or kidney dysfunction, cardiovascular disease, or malignant tumors. Comprehensive clinical information was recorded for each individual, and whole blood samples were collected ( Table S1 ). All research participants provided informed consent, and the study was approved by the Ethics Committee of Guanghua Hospital (approval number: 2018-K-12) and completed clinical registration (NO. ChiCTR22400083234). 2.2 Targeted DNA Methylation Analysis In the targeted DNA methylation detection, the process includes sample quality control, PCR primer design and optimization, bisulfite treatment, PCR amplification with specific barcodes, and high-throughput sequencing. Firstly, genomic DNA is extracted from peripheral blood of different sample groups and subjected to sample quality control, with a concentration requirement of ≥ 20 ng/µL and a total amount of ≥ 400 ng, purity of OD260/280 = 1.7 ~ 1.9, and OD260/230 ≥ 2.0. Primers are designed and optimized using the software "Methylation FastTarget V4.1", with the primer sequences being PrimerF: GGGGTAGGAGGGTGGTAGG and PrimerR: CCACCTCTTCAAACAACTACTATTATCC , targeting the cg site cg15692052. The sequencing length range is from chr13:46898190 to chr13:46897976, totaling 215 bp . After amplification, the primers undergo bisulfite treatment, which converts unmethylated cytosine C to uracil U. Then, PCR amplification is performed using primers with Index sequences to introduce specific barcode sequences to the ends of the library, compatible with the Illumina platform. Finally, high-throughput sequencing is conducted using Illumina Hiseq (Illumina, CA, USA) with a 2×150 bp paired-end sequencing mode to obtain FastQ data. 2.3 Statistical Methods During the data analysis process, the R software (Version 4.2) were utilized for least absolute shrinkage and selection operator (LASSO) 、logtistic、eXtreme gradient boosting (xgboost) and random forest modeling, visualization, and statistical analysis. The analysis incorporated packages such as "patchwork", "ggplot2", "readxl", "tidyverse", "reshape2", "ggrepel", "mice", "Hmisc", "pheatmap", "ggtree", "aplot", "tidyr", "ggcor", "ggpubr", "ggthemes", "caret", "pROC", "shapviz", "xgboost", "ROCit", and "randomForest". Both the random forest and XGBoost models employed a 5-fold cross-validation with a 7:3 random split between the training and testing sets. Multiple imputation methods were applied for handling missing values. Correlations were calculated and visualized using the Spearman method. Data presentation followed the median (Q1, Q3) format, with multiple group comparisons conducted using the Kruskal-Wallis test. Significant differences were determined and visualized based on a statistical significance threshold of P < 0.05. 3 Results 3.1 Methylation Levels of HTR2A Significantly Elevated in RA Patients We examined the methylation status of HTR2A cg15692052 in HC, AS, OA, gout, SS, RA, PSA, SLE, DM. We detected seven CG sites, including cg15692052_75, cg15692052_125, cg15692052_143, cg15692052_149, cg15692052_167, cg15692052_185, and cg15692052_187. We compared the methylation level differences among other groups compared to the healthy control group. The results showed: Excluding the OA group, the methylation levels of cg15692052_75/125/143 were significantly increased ( P < 0.05); the methylation level of cg15692052_149 was significantly elevated in all groups except for the OA and PSA groups ( P < 0.05); the methylation level of cg15692052_163 was significantly higher in all groups except for the OA, SS, and Gout groups ( P < 0.05);the methylation levels of cg15692052_185/187 were significantly elevated in the RA, SLE, DM groups ( P < 0.05);the average methylation level was significantly higher in all groups except for the OA and SS groups ( P < 0.05) (Fig. 1 C-J). On further examining the methylation differences of HTR2A between HC and various serological subtypes of RA, the results showed: Compared to the normal group, the methylation levels of the seven sites of cg15692052 and the average methylation level were significantly increased in all four RA subtypes (RF-negative RA patients, RF/CCP double positive, RF/CCP double negative, CCP single negative) ( P < 0.05); Compared to RF/CCP double negative RA patients or RF/CCP double positive RA patients, the methylation level of cg15692052_75 in CCP single negative RA patients was significantly increased ( P < 0.05); Compared to RF-negative RA patients or RF/CCP double positive RA patients, the methylation levels of cg15692052_125/143/149 and the average methylation level in CCP single negative RA patients were significantly increased ( P < 0.05); Compared to RF/CCP double positive RA patients, the methylation levels of cg15692052_167/185 in CCP single negative RA patients were significantly increased ( P < 0.05), and the methylation level of cg15692052_187 in RF/CCP double negative RA patients was significantly increased ( P < 0.05) (Fig. 1 K-Q). 3.2 Changes in Haplotype Methylation Proportion of HTR2A We compared the changes in the haplotype proportion of HTR2A cg15692052 methylation in other groups compared to normal individuals. The results showed that CCCCCCC was significantly increased in the Gout, PSA, RA, SLE, and DM groups ( P < 0.05); TTCCCCC was significantly decreased in the RA, SLE, and DM groups ( P < 0.05); TTTTTTT was significantly decreased in the AS, PSA, RA, SLE, and DM groups ( P < 0.05); CCCTCCC was significantly increased in the PSA, RA, and DM groups ( P < 0.05); CTCCCCC was significantly increased in the RA group ( P < 0.05); TTTTTCC was significantly decreased in the RA and DM groups ( P < 0.05); CCTCCCC was significantly increased in the RA, OA, Gout, and DM groups ( P < 0.05); TTTTTCCC was significantly decreased in the Gout, RA, SLE, and DM groups ( P < 0.05); TTTTTTC was significantly decreased in the OA, RA, SLE, and DM groups ( P < 0.05); CCCCCCT was significantly increased in the RA, SLE, and DM groups ( P < 0.05); CCCCCTC was significantly increased in the PSA, RA, and DM groups ( P < 0.05) (Fig. 2 A-K). Further examination of the differences in the haplotype methylation proportion of HTR2A between HC and various serological subtypes of RA revealed that compared to HC, CCCCCCC/CCCTCCC was significantly increased in all four subtypes of RA (RF single-negative RA patients, RF/CCP double-positive, RF/CCP double-negative, CCP single-negative) ( P < 0.05); CCCCCCC was significantly increased in CCP single-negative RA patients compared to RF single-negative, RF/CCP double-negative, and RF/CCP double-positive RA patients ( P < 0.05); TCCCCCC was significantly increased in RF/CCP double-negative patients compared to the HC group, RF single-negative or RF/CCP double-positive RA patients ( P < 0.05); TTCCCCC was significantly decreased in RF single-negative, RF/CCP double-positive, and CCP single-negative RA patients compared to the HC group ( P < 0.05); TTCCCCC was significantly increased in RF/CCP double-positive and RF/CCP double-negative RA patients compared to RF single-negative RA patients ( P < 0.05); TTCCCCC was significantly decreased in CCP single-negative RA patients compared to RF/CCP double-positive RA patients ( P = 0.004); TTCCCCC was significantly increased in RF/CCP double-negative RA patients compared to CCP single-negative RA patients ( P = 0.002); TTTTTTT/TTTTTCC was significantly decreased in all four subtypes of RA (RF single-negative RA patients, RF/CCP double-positive, RF/CCP double-negative, CCP single-negative) compared to HC ( P < 0.05); CTCCCCC/CCCCCTC was significantly increased in RF/CCP double-positive, CCP single-negative, and RF/CCP double-negative RA patients compared to HC ( P < 0.05); TTTTTCCC was significantly decreased in RF single-negative, RF/CCP double-positive, and CCP single-negative RA patients compared to HC ( P < 0.05); TTTTTCCC was significantly increased in RF single-negative, RF/CCP double-positive, and RF/CCP double-negative RA patients compared to CCP single-negative RA patients ( P < 0.05); TTTTTTC was significantly decreased in RF/CCP double-positive, RF/CCP double-negative, and CCP single-negative RA patients compared to HC ( P < 0.05); TTTTTTC was significantly increased in RF single-negative and RF/CCP double-positive RA patients compared to CCP single-negative RA patients ( P < 0.05); TTTTTTC was significantly decreased in RF/CCP double-negative RA patients compared to RF single-negative RA patients ( P = 0.028); CCCCCT was significantly increased in CCP single-negative and RF/CCP double-positive RA patients compared to HC ( P < 0.05) (Fig. 2 L-V). 3.3 Correlation between HTR2A Methylation Levels and Common Clinical Indicators in RA Patients We further investigated the correlation between the methylation levels of HTR2A cg15692052 at individual sites and the average methylation level with common clinical indicators in RA patients, including gender, age, height, weight, ESR, CRP, RF, CCP, presence of hypertension, and presence of interstitial lung disease. The results showed: cg15692052_75 was significantly positively correlated with ESR, CRP, and the presence of interstitial lung disease (r = 0.15, 0.22, and 0.10, P = 0.002, P < 0.001, and P = 0.044); cg15692052_125 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r = 0.11, 0.13, 0.20, and 0.15, P = 0.021, 0.010, P < 0.001, and P = 0.003); cg15692052_143 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r = 0.12, 0.10, 0.18, and 0.11, P = 0.013, 0.039, P < 0.001, and P = 0.025);cg15692052_149 was significantly positively correlated with gender, CRP, and the presence of interstitial lung disease (r = 0.10, 0.17, and 0.13, P = 0.041, 0.001, and 0.008); cg15692052_167 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r = 0.13, 0.12, 0.19, and 0.13, P = 0.010, 0.019, P < 0.001, and P = 0.009); cg15692052_185 was significantly positively correlated with gender, CRP, and the presence of interstitial lung disease (r = 0.14, 0.15, and 0.13, P = 0.005, 0.003, and 0.009); cg15692052_187 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r = 0.17, 0.10, 0.16, and 0.19, P = 0.001, 0.049, 0.001, and P < 0.001); the average methylation level was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r = 0.12, 0.12, 0.20, and 0.13, P = 0.015, 0.012, P < 0.001, and P = 0.008) (Fig. 3 A). 3.4 Correlation between HTR2A Haplotypes and Common Clinical Indicators in RA Patients Given the differences in the proportion of HTR2A cg15692052 methylation haplotypes, we further analyzed their correlation with common clinical indicators in RA. The results showed that CCCCCCC was significantly positively correlated with ESR and CRP (r = 0.13 and 0.21, P = 0.001 and P < 0.001), and significantly negatively correlated with age (r=-0.10, P = 0.047). TCCCCCC was significantly positively correlated with Gender, age, and the presence of interstitial lung disease (r = 0.12, 0.11, and 0.18, P = 0.012, 0.022, and P < 0.001), and significantly negatively correlated with RF (r=-0.11, P = 0.028). TTCCCCC was significantly positively correlated with age (r = 0.15, P = 0.003) and significantly negatively correlated with CRP (r=-0.13, P = 0.007). TTTTTTT was significantly negatively correlated with Gender, CRP, and the presence of interstitial lung disease (r=-0.17, -0.15, and − 0.14, P = 0.001, 0.002, and 0.006). TTTTTCC was significantly negatively correlated with ESR, CRP, and the presence of interstitial lung disease (r=-0.12, -0.20, and − 0.13, P = 0.012, P < 0.001, and P = 0.011). TCCTCCC was significantly positively correlated with age and weight (r = 0.12 and 0.11, P = 0.013 and 0.024). TTTTCCC was significantly negatively correlated with ESR, CRP, the presence of hypertension, and the presence of interstitial lung disease (r=-0.14, -0.21, -0.10, and − 0.13, P = 0.004, P < 0.001, P = 0.049, and 0.008). TTTTTTC was significantly negatively correlated with CRP (r=-0.13, P = 0.007). CCCCCTC was significantly negatively correlated with height (r=-0.11, P = 0.025) (Fig. 3 B). 3.5 Methylation Level of HTR2A as an Auxiliary Diagnostic Marker for RA We further assessed whether the methylation level of HTR2A could serve as a diagnostic biomarker for RA and its subtypes. We combined LASSO and random forest methods to jointly screen for important variables, and then used Logistic, random forest, and Xgboost methods to construct clinical prediction models, with all patients except RA serving as the control group(Figure S1 ). The group of CCP single-negative RA patients was excluded from subsequent analysis due to insufficient sample size. For distinguishing between RA and non-RA patients, the variables included in the model identified by LASSO and random forest were age, CRP, ESR, height, Gender, and cg15692052_185. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.757/0.734/0.672, with F1 scores of 0.632/0.745/0.516(Fig. 4 and TableS2 ). For distinguishing between RF/CCP double-negative RA and non-RF/CCP double-negative RA patients, the variables included in the model were CCP, RF, CRP, weight, cg15692052_143, age, cg15692052_187, Gender, and height. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.912/0.966/0.825, with F1 scores of 0.969/0.994/0.948(Fig. 4 and TableS2 ). For distinguishing between RF/CCP double-positive RA and non-RF/CCP double-positive RA patients, the variables included in the model were CCP, RF, CRP, Gender, cg15692052_187, age, and cg15692052_185. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.832/0.846/0.714, with F1 scores of 0.712/0.826/0.574. For distinguishing between RF single-negative RA and non-RF single-negative RA patients, the variables included in the model were CCP, RF, ESR, Gender, cg15692052_185, age, and cg15692052_75. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.928/0.923/0.932, with F1 scores of 0.939/0.984/0.930 (Fig. 4 and TableS2 ). 4 Discussion In our previous study, we discovered that the DNA methylation level of HTR2A in peripheral blood mononuclear cells could significantly distinguish patients with RA, OA, and HC. In the current study, we observed similar results, with elevated methylation levels of HTR2A cg15692052 in the RA group, and this elevation was present across various serological subtypes of RA. The methylation cg sites of HTR2A cg15692052 detected in this study were all located in the 5-UTR or promoter region, which may further affect gene expression, potentially leading to abnormal translation and post-translational regulation, influencing disease development. A recent study found that HTR2A is highly expressed in RA synovial tissue ( 16 ). We further investigated the correlation between the methylation level of HTR2A cg15692052 in RA patients and common clinical indicators. We found that the methylation cg sites of HTR2A cg15692052 were significantly correlated with ESR and CRP, and may be associated with RA complications. This suggests that the differential methylation level of HTR2A may be linked to the inflammatory response in RA patients. Secondly, we examined the changes in the methylation haplotype proportions of HTR2A cg15692052, and the results were consistent with those of the first batch. The haplotype representing full methylation, CCCCCCC, or haplotypes containing most Cs were significantly elevated in RA, while the haplotype representing full un-methylation, TTTTTTT, was significantly reduced in RA. The changes in the proportions of different methylation haplotypes may have an overall guiding significance for the DNA methylation level and gene expression. We further observed the correlation between the changes in the methylation haplotype proportions of HTR2A cg15692052 and clinical characteristics. We found that with the methylation of the CG sites of HTR2A cg15692052, there may be a positive correlation trend with ESR or CRP, implying that the overall hypermethylation state of HTR2A cg15692052 may promote the inflammatory response in RA. Finally, we used logistic, LASSO, XGBoost, and random forest methods to jointly screen important feature variables for diagnosing RA and its subtypes, and combined multiple cross-validation to stabilize the results. We found that the overall methylation level of HTR2A cg15692052, in combination with different clinical feature variables, could significantly distinguish RA patients and RA subpopulation patients, especially for seronegative RA patients. Early diagnosis and intervention for these patients could significantly improve their prognosis and disease condition. The overall methylation level of HTR2A cg15692052 may be one of the important diagnostic indicators for future seronegative RA patients. We acknowledge that this study still has some limitations that need to be improved in the future. First, the relationship between DNA methylation of HTR2A cg15692052 and gene expression needs to be further explored. Although current research supports that hypermethylation in synovial tissue can promote disease progression, in-depth study of its biological function remains an urgent research topic to be clarified. In the future, we plan to conduct comprehensive in-depth research on the gene function of HTR2A by combining cell biology, molecular biology, bioinformatics, and epigenetics, expecting to publish valuable results. Secondly, our correlations all showed a weak correlation trend, which still requires a larger sample size for validation, but it is undeniable that we obtained statistical support, which is also a result with some guiding significance. Finally, our results still need to be validated on a larger scale in multi-center clinical trials, which is also a key plan for our future. In our results, we also found that the methylation levels of DM, and AS were significantly abnormal, but due to the limitation of the sample size in the current results, it is still necessary to collect a large number of specific disease samples in the future to clarify the potential of HTR2A in diagnosing other diseases. In summary, we found that the overall methylation level of HTR2A cg15692052 is differentially altered in RA and is correlated with various clinical indicators, potentially serving as a novel diagnostic biomarker for RA, RA subtypes, and RA complications. Declarations Ethics approval and consent to participate : All research participants provided informed consent, and the study was approved by the Ethics Committee of Guanghua Hospital (approval number: 2018-K-12) and completed clinical registration (NO. ChiCTR22400083234). Consent for publication: The author claim that none of the material in the paper has been published or is under consideration for publication elsewhere. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding: This work was funded by the National Natural Science Funds of China (82074234 and 82073901), State Administration of Traditional Chinese Medicine, Shanghai Municipal Health Commission, East China Region-based Chinese and Western Medicine Joint Disease Specialist Alliance, and the National Key Research and Development Program of China (2021YFE0200900), Shanghai Municipal Health Commission (202340274). Authors’ contributions: JZ, HB, YL, YS is responsible for the collection, collation, and writing of the original manuscript. KW, PJ, YS, CC, LX, YZ, FZ, GY, QL, MZ is responsible for the collection, collation of the original data. SG is responsible for concept development and manuscript review. LL, YZ, JJ, and RW are responsible for the concept development, revision, and manuscript review. All authors reviewed and accepted the final version. Acknowledgements: Not applicable References Zhao J, Guo S, Schrodi SJ, He D. Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications. Front Immunol. 2021;12:790122. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-81. Webster AP, Plant D, Ecker S, Zufferey F, Bell JT, Feber A, et al. Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins. Genome Med. 2018;10(1):64. Zhao J, Wei K, Chang C, Xu L, Jiang P, Guo S, et al. DNA Methylation of T Lymphocytes as a Therapeutic Target: Implications for Rheumatoid Arthritis Etiology. Front Immunol. 2022;13:863703. Adams C, Nair N, Plant D, Verstappen SMM, Quach HL, Quach DL, et al. Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis. Arthritis Rheumatol. 2023;75(7):1088-97. Shao X, Hudson M, Colmegna I, Greenwood CMT, Fritzler MJ, Awadalla P, et al. Rheumatoid arthritis-relevant DNA methylation changes identified in ACPA-positive asymptomatic individuals using methylome capture sequencing. Clin Epigenetics. 2019;11(1):110. Zhao J, Xu L, Chang C, Jiang P, Wei K, Shi Y, et al. Circulating methylation level of HTR2A is associated with inflammation and disease activity in rheumatoid arthritis. Front Immunol. 2022;13:1054451. Jiang P, Wei K, Xu L, Chang C, Zhang R, Zhao J, et al. DNA methylation change of HIPK3 in Chinese rheumatoid arthritis and its effect on inflammation. Front Immunol. 2022;13:1087279. Shi Y, Chang C, Xu L, Jiang P, Wei K, Zhao J, et al. Circulating DNA methylation level of CXCR5 correlates with inflammation in patients with rheumatoid arthritis. Immun Inflamm Dis. 2023;11(6):e902. Wang Z, Yan C, Du Q, Huang Y, Li X, Zeng D, et al. HTR2A agonists play a therapeutic role by restricting ILC2 activation in papain-induced lung inflammation. Cell Mol Immunol. 2023;20(4):404-18. Gao W, Guo N, Zhao S, Chen Z, Zhang W, Yan F, et al. HTR2A promotes the development of cardiac hypertrophy by activating PI3K-PDK1-AKT-mTOR signaling. Cell Stress Chaperones. 2020;25(6):899-908. Guo NK, She H, Tan L, Zhou YQ, Tang CQ, Peng XY, et al. Nano Parthenolide Improves Intestinal Barrier Function of Sepsis by Inhibiting Apoptosis and ROS via 5-HTR2A. Int J Nanomedicine. 2023;18:693-709. Peroutka SJ. Serotonin receptor variants in disease: new therapeutic opportunities? Ann N Y Acad Sci. 1998;861:16-25. Kling A, Seddighzadeh M, Arlestig L, Alfredsson L, Rantapää-Dahlqvist S, Padyukov L. Genetic variations in the serotonin 5-HT2A receptor gene (HTR2A) are associated with rheumatoid arthritis. Ann Rheum Dis. 2008;67(8):1111-5. Seddighzadeh M, Korotkova M, Källberg H, Ding B, Daha N, Kurreeman FA, et al. Evidence for interaction between 5-hydroxytryptamine (serotonin) receptor 2A and MHC type II molecules in the development of rheumatoid arthritis. Eur J Hum Genet. 2010;18(7):821-6. Xiang C, Hong SM, Zhao B, Pi H, Du F, Lu X, et al. Fibroblast expression of neurotransmitter receptor HTR2A associates with inflammation in rheumatoid arthritis joint. Clin Exp Med. 2024;24(1):84. Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Figure S1: Feature Selection for Modeling RA and RA Subtypes Using LASSO and Random Forest Algorithms supplymentalmaterials.xlsx Table S1: General Information of Patients Table S2: Prediction Results of RA and RA Subtypes Using Logistic, Random Forest, and XGBoost Algorithms Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4710847","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326763156,"identity":"9b10b547-20a1-4024-b5ac-9d58e6d5cad4","order_by":0,"name":"Jianan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYNCCAgkgwXzgwIcfRGsxAGlhSzw4s4d4LSCCx/gwBxsRivnbzz58XGBgkdgvfebDYQYeBnl+sQP4tUicSTc2nmEgkTizL3fD4QILBsOZsxMIWHMgjU2ax0DC2OAM74bDM3gYEgxuE9Aif/4ZRIv9GZ4Hh3nYiNBicANii5wBDw8DcVoMbzxjNgZpkTjDZgAMZAnCfpE7n8b4mKeijoe/h/nxhw8/bOT5pQloQQcSpCkfBaNgFIyCUYAdAADzqjuMJ4F1/QAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jianan","middleName":"","lastName":"Zhao","suffix":""},{"id":326763157,"identity":"12594a9e-c1b7-432f-89ff-2a930a23b1ed","order_by":1,"name":"Binghen He","email":"","orcid":"","institution":"Tongren Hospital Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Binghen","middleName":"","lastName":"He","suffix":""},{"id":326763158,"identity":"1c8b7de3-fbdd-431d-8d2c-fcc8f0a1f8f5","order_by":2,"name":"Yunshen Li","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yunshen","middleName":"","lastName":"Li","suffix":""},{"id":326763159,"identity":"8070753c-056e-4b76-83d1-3dda8953ec5e","order_by":3,"name":"Yu Shan","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shan","suffix":""},{"id":326763161,"identity":"debbc56e-7699-48c4-9437-f8a3d24c1039","order_by":4,"name":"Kai Wei","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wei","suffix":""},{"id":326763163,"identity":"2c389fa0-5ce4-409d-9061-c67edcac6e09","order_by":5,"name":"Ping Jiang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Jiang","suffix":""},{"id":326763165,"identity":"36515512-dae5-45f4-9fa9-c3fd999e0591","order_by":6,"name":"Yiming Shi","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Shi","suffix":""},{"id":326763166,"identity":"7f6b166c-4e47-40c2-9a17-bb871fd41df3","order_by":7,"name":"Cen Chang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Cen","middleName":"","lastName":"Chang","suffix":""},{"id":326763167,"identity":"db7a7f0a-1140-45f0-9b61-5ca03a903d51","order_by":8,"name":"Yixin Zheng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Zheng","suffix":""},{"id":326763168,"identity":"0776f9a8-6b2a-45d0-b702-76637a7fc832","order_by":9,"name":"Fuyu Zhao","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fuyu","middleName":"","lastName":"Zhao","suffix":""},{"id":326763169,"identity":"5532f1bc-18dc-4074-a6bc-3ae6ae59fd11","order_by":10,"name":"Guizhen Yang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guizhen","middleName":"","lastName":"Yang","suffix":""},{"id":326763170,"identity":"19bf6f92-65ca-4a9c-972c-93dd3cb6c08b","order_by":11,"name":"Qianqian Li","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Li","suffix":""},{"id":326763171,"identity":"9092161e-5a94-4f87-8f30-25877c668194","order_by":12,"name":"Mi Zhou","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mi","middleName":"","lastName":"Zhou","suffix":""},{"id":326763172,"identity":"20502424-98f1-4c8c-a892-2ce4d4acb7fe","order_by":13,"name":"Shicheng Guo","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shicheng","middleName":"","lastName":"Guo","suffix":""},{"id":326763173,"identity":"a38064a4-cb3d-46ed-81a4-f7c00dc9a890","order_by":14,"name":"Liangjing Lv","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liangjing","middleName":"","lastName":"Lv","suffix":""},{"id":326763174,"identity":"32ca0890-57bd-4259-b36a-8b48398b5842","order_by":15,"name":"Yuejuan Zheng","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuejuan","middleName":"","lastName":"Zheng","suffix":""},{"id":326763175,"identity":"61c9bfcf-6da7-43aa-87e3-bf62978390af","order_by":16,"name":"Juan Jiao","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Jiao","suffix":""},{"id":326763176,"identity":"d8da72e4-8b2d-4bab-a442-8262d24adf06","order_by":17,"name":"Rongsheng Wang","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rongsheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-09 09:30:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4710847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4710847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62159306,"identity":"63176b58-b54f-4450-9cfd-d8fd9453549d","added_by":"auto","created_at":"2024-08-09 21:42:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":508265,"visible":true,"origin":"","legend":"\u003cp\u003eMethylation Levels of the HTR2A cg15692052 CpG Site\u003c/p\u003e\n\u003cp\u003e(A-B) Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (tSNE) Plots\u003c/p\u003e\n\u003cp\u003e(C-J) Differential Methylation Levels of the HTR2A cg15692052 CpG Site among Multiple Groups\u003c/p\u003e\n\u003cp\u003e(K-Q) Differential Methylation Levels of the HTR2A cg15692052 CpG Site across Different Serological Subtypes of Rheumatoid Arthritis (RA)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/6faa7a4b71bf7fbe5f30373f.png"},{"id":62158730,"identity":"3ee5f518-ba51-41a2-8d6c-09af0de79099","added_by":"auto","created_at":"2024-08-09 21:34:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":507914,"visible":true,"origin":"","legend":"\u003cp\u003eHaplotype Methylation Proportion Differences of HTR2A cg15692052\u003c/p\u003e\n\u003cp\u003e(A-K) Haplotype Methylation Proportion Differences of HTR2A cg15692052 among Multiple Groups\u003c/p\u003e\n\u003cp\u003e(L-V) Haplotype Methylation Proportion Differences of HTR2A cg15692052 across Different Serological Subtypes of RA\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/e868e29560b12dbc2bb0e779.png"},{"id":62158728,"identity":"7f0c6ba8-0c01-45fa-96b8-6995870070b9","added_by":"auto","created_at":"2024-08-09 21:34:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":88714,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between Methylation Levels of HTR2A cg15692052 and Common Clinical Parameters\u003c/p\u003e\n\u003cp\u003e(A) Correlation between Methylation Levels of Different CpG Sites of HTR2A cg15692052 and Common Clinical Parameters\u003c/p\u003e\n\u003cp\u003e(B) Correlation between Haplotype Methylation Proportions of HTR2A cg15692052 and Common Clinical Parameters\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/42f890aa16631074af6d7ffd.png"},{"id":62158732,"identity":"6e9861b7-d0a0-49f2-afb0-4d86eae8ed59","added_by":"auto","created_at":"2024-08-09 21:34:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48824,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction Results of RA and RA Subtypes Using Logistic, Random Forest, and XGBoost Algorithms\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/a62d163370e5b2aea5830189.png"},{"id":69077888,"identity":"7099d866-1168-4833-8e60-d0a7fcd1f971","added_by":"auto","created_at":"2024-11-15 11:24:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1991759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/100026c2-8bcb-49a7-8466-44d0ec13163d.pdf"},{"id":62159307,"identity":"52d32d22-1a72-46c9-9ec8-54c6fe90325e","added_by":"auto","created_at":"2024-08-09 21:42:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":444458,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Feature Selection for Modeling RA and RA Subtypes Using LASSO and Random Forest Algorithms\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/ce5362052b8c6339828850ea.pdf"},{"id":62158727,"identity":"1bc596a4-9832-4ada-9711-c95029e4f5b1","added_by":"auto","created_at":"2024-08-09 21:34:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19897,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: General Information of Patients\u003c/p\u003e\n\u003cp\u003eTable S2: Prediction Results of RA and RA Subtypes Using Logistic, Random Forest, and XGBoost Algorithms\u003c/p\u003e","description":"","filename":"supplymentalmaterials.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4710847/v1/f53076be94c51a89917e9fcf.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HTR2A DNA Methylation as a Diagnostic Biomarker for Rheumatoid Arthritis: A Validation Study Using Targeted Sequencing and Machine Learning Algorithms","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by chronic synovitis, the presence of autoantibodies, and persistent joint and bone damage. The etiology of RA is complex and heterogeneous, involving factors such as genetics, metabolism, and immunology(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Genetic factors, including the major histocompatibility complex (MHC) molecules, as well as epigenetics, are important areas of research in understanding RA. Diagnostic markers for RA include rheumatoid factor (RF) and cyclic citrullinated peptide (CCP), which are significant in identifying seropositive patients(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, approximately one-third of patients are seronegative, posing challenges in early diagnosis and intervention, which are crucial in managing RA. Delayed or inaccurate diagnosis can lead to irreversible joint damage and a rapid decline in quality of life. Therefore, early diagnosis and intervention play a critical role in improving clinical outcomes and reducing disability rates in RA.\u003c/p\u003e \u003cp\u003eDNA methylation is one of the important research areas in epigenetics, and its association with RA is being extensively investigated. Whole-genome analysis of monozygotic twins, with and without RA, has revealed increased DNA methylation variability, suggesting the involvement of epigenetics as a significant component in the development of autoimmune diseases (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Differential DNA methylation regions may serve as potential extensions for RA diagnosis and drug response prediction. Multiple studies have identified differential DNA methylation regions in T cells as novel diagnostic markers for RA (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). RA patients treated with MTX exhibit cell-specific differential methylation patterns, which may be associated with their response to the drug (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Peripheral blood mononuclear cells from anti-CCP positive RA patients exhibit distinct differential methylation features compared to anti-CCP negative patients(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The DNA methylation levels of HIPK3, CXCR5 in peripheral blood mononuclear cells could potentially serve as novel diagnostic markers for RA patients (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe serotonin receptor encoded by HTR2A plays a significant role in a variety of diseases. It inhibits inflammatory cell responses in the lungs upon binding with serotonin(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In patients with human cardiac hypertrophy, the expression of \u003cem\u003eHTR2A\u003c/em\u003e is upregulated, promoting disease progression through the activation of the phosphatidylinositol 3-kinase-phosphoinositide-dependent protein kinase 1- protein kinase B - mammalian target of rapamycin signaling pathway(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Research has also shown that upregulation of \u003cem\u003eHTR2A\u003c/em\u003e can inhibit cell apoptosis and reactive oxygen species (ROS) production, protecting the intestinal barrier and improving disease outcomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). There may be a link between HTR2A and RA; for instance, an increased frequency of the \u003cem\u003eTT\u003c/em\u003e genotype of \u003cem\u003ers6313\u003c/em\u003e polymorphism may reduce the incidence of RA in the Japanese population(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Significant differences in the \u003cem\u003ers6313\u003c/em\u003e (\u003cem\u003eT102C\u003c/em\u003e polymorphism) between RA patients and control groups have also been observed (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). T cells from RA patients carrying the \u003cem\u003eTC\u003c/em\u003e haplotype demonstrate increased production of pro-inflammatory cytokines (including tumor necrosis factor-alpha, interleukin-6, and interferon-gamma), while antagonists inhibiting \u003cem\u003eHTR2A\u003c/em\u003e receptor activation can suppress cytokine production (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, protective haplotypes in \u003cem\u003eHTR2A\u003c/em\u003e show interactions with the \u003cem\u003ehuman leukocyte antigen-DRB1\u003c/em\u003e shared epitope alleles, which are associated with RA autoantibody positivity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Our preliminary findings indicated that DNA methylation levels of \u003cem\u003eHTR2A\u003c/em\u003e could significantly differentiate between RA, OA, and HC patients. Consequently, we have designed a second set of experiments with an increased number of rheumatic disease-related conditions to continue to explore the potential of HTR2A as a biomarker for RA.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and peripheral blood collection\u003c/h2\u003e \u003cp\u003eIn the Guanghua Hospital Precision Medicine Research Cohort (PMRC) at Shanghai University of Traditional Chinese Medicine, we conducted patient recruitment. This is our second batch of methylation research data, where we combined the first batch of methylation data, which included the RA, HC, and OA groups. The results of principal component analysis and t-distributed stochastic neighbor embedding showed no significant batch effect between the two datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). A total of 671 patients participated, including 407 RA patients, 30 each of AS patients, PSA patients, gout patients, SLE patients, DM patients, as well as 60 HC (healthy controls) and 24 SS patients. The RA patients were further divided into four serological subtypes: RF/CCP double-positive RA patients (RA_DP), RF/CCP double-negative RA patients (RA_DN), RF single-negative RA patients (RA_RFN), and CCP single-negative RA patients (RA_CCPN). Additionally, there were patients who were responders and non-responders to anti-TNF-α therapy (RA_AJN_Y and RA_AJN_N). AS patients were also divided into responders and non-responders to anti-TNF-α therapy (AS_Y and AS_N). The inclusion criteria for RA were based on the 2010 American College of Rheumatology (ACR) criteria, the 1984 revised New York criteria for AS patients, the 2015 ACR/EULAR classification criteria for gout patients, the 2006 ACR classification criteria for PSA patients, the 2019 EULAR/ACR classification criteria for SLE patients, the 2017 ACR/EULAR classification criteria for DM patients, and the 2016 ACR/EULAR classification criteria for primary Sj\u0026ouml;gren's syndrome patients. All participants did not have a history of severe liver or kidney dysfunction, cardiovascular disease, or malignant tumors. Comprehensive clinical information was recorded for each individual, and whole blood samples were collected (\u003cb\u003eTable\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). All research participants provided informed consent, and the study was approved by the Ethics Committee of Guanghua Hospital (approval number: 2018-K-12) and completed clinical registration (NO. ChiCTR22400083234).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Targeted DNA Methylation Analysis\u003c/h2\u003e \u003cp\u003eIn the targeted DNA methylation detection, the process includes sample quality control, PCR primer design and optimization, bisulfite treatment, PCR amplification with specific barcodes, and high-throughput sequencing. Firstly, genomic DNA is extracted from peripheral blood of different sample groups and subjected to sample quality control, with a concentration requirement of \u0026ge;\u0026thinsp;20 ng/\u0026micro;L and a total amount of \u0026ge;\u0026thinsp;400 ng, purity of OD260/280\u0026thinsp;=\u0026thinsp;1.7\u0026thinsp;~\u0026thinsp;1.9, and OD260/230\u0026thinsp;\u0026ge;\u0026thinsp;2.0. Primers are designed and optimized using the software \"Methylation FastTarget V4.1\", with the primer sequences being PrimerF: \u003cem\u003eGGGGTAGGAGGGTGGTAGG\u003c/em\u003e and PrimerR: \u003cem\u003eCCACCTCTTCAAACAACTACTATTATCC\u003c/em\u003e, targeting the cg site cg15692052. The sequencing length range is from chr13:46898190 to chr13:46897976, totaling 215\u003cem\u003ebp\u003c/em\u003e. After amplification, the primers undergo bisulfite treatment, which converts unmethylated cytosine C to uracil U. Then, PCR amplification is performed using primers with Index sequences to introduce specific barcode sequences to the ends of the library, compatible with the Illumina platform. Finally, high-throughput sequencing is conducted using Illumina Hiseq (Illumina, CA, USA) with a 2\u0026times;150 \u003cem\u003ebp\u003c/em\u003e paired-end sequencing mode to obtain FastQ data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Methods\u003c/h2\u003e \u003cp\u003eDuring the data analysis process, the R software (Version 4.2) were utilized for least absolute shrinkage and selection operator (LASSO) 、logtistic、eXtreme gradient boosting (xgboost) and random forest modeling, visualization, and statistical analysis. The analysis incorporated packages such as \"patchwork\", \"ggplot2\", \"readxl\", \"tidyverse\", \"reshape2\", \"ggrepel\", \"mice\", \"Hmisc\", \"pheatmap\", \"ggtree\", \"aplot\", \"tidyr\", \"ggcor\", \"ggpubr\", \"ggthemes\", \"caret\", \"pROC\", \"shapviz\", \"xgboost\", \"ROCit\", and \"randomForest\". Both the random forest and XGBoost models employed a 5-fold cross-validation with a 7:3 random split between the training and testing sets. Multiple imputation methods were applied for handling missing values. Correlations were calculated and visualized using the Spearman method. Data presentation followed the median (Q1, Q3) format, with multiple group comparisons conducted using the Kruskal-Wallis test. Significant differences were determined and visualized based on a statistical significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Methylation Levels of \u003cem\u003eHTR2A\u003c/em\u003e Significantly Elevated in RA Patients\u003c/h2\u003e \u003cp\u003eWe examined the methylation status of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 in HC, AS, OA, gout, SS, RA, PSA, SLE, DM. We detected seven CG sites, including cg15692052_75, cg15692052_125, cg15692052_143, cg15692052_149, cg15692052_167, cg15692052_185, and cg15692052_187.\u003c/p\u003e \u003cp\u003eWe compared the methylation level differences among other groups compared to the healthy control group. The results showed: Excluding the OA group, the methylation levels of cg15692052_75/125/143 were significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the methylation level of cg15692052_149 was significantly elevated in all groups except for the OA and PSA groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the methylation level of cg15692052_163 was significantly higher in all groups except for the OA, SS, and Gout groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05);the methylation levels of cg15692052_185/187 were significantly elevated in the RA, SLE, DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05);the average methylation level was significantly higher in all groups except for the OA and SS groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-J).\u003c/p\u003e \u003cp\u003eOn further examining the methylation differences of \u003cem\u003eHTR2A\u003c/em\u003e between HC and various serological subtypes of RA, the results showed: Compared to the normal group, the methylation levels of the seven sites of cg15692052 and the average methylation level were significantly increased in all four RA subtypes (RF-negative RA patients, RF/CCP double positive, RF/CCP double negative, CCP single negative) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Compared to RF/CCP double negative RA patients or RF/CCP double positive RA patients, the methylation level of cg15692052_75 in CCP single negative RA patients was significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Compared to RF-negative RA patients or RF/CCP double positive RA patients, the methylation levels of cg15692052_125/143/149 and the average methylation level in CCP single negative RA patients were significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Compared to RF/CCP double positive RA patients, the methylation levels of cg15692052_167/185 in CCP single negative RA patients were significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the methylation level of cg15692052_187 in RF/CCP double negative RA patients was significantly increased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK-Q).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in Haplotype Methylation Proportion of \u003cem\u003eHTR2A\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eWe compared the changes in the haplotype proportion of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 methylation in other groups compared to normal individuals. The results showed that CCCCCCC was significantly increased in the Gout, PSA, RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTCCCCC was significantly decreased in the RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTTT was significantly decreased in the AS, PSA, RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CCCTCCC was significantly increased in the PSA, RA, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CTCCCCC was significantly increased in the RA group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTCC was significantly decreased in the RA and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CCTCCCC was significantly increased in the RA, OA, Gout, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTCCC was significantly decreased in the Gout, RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTTC was significantly decreased in the OA, RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CCCCCCT was significantly increased in the RA, SLE, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CCCCCTC was significantly increased in the PSA, RA, and DM groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-K).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther examination of the differences in the haplotype methylation proportion of HTR2A between HC and various serological subtypes of RA revealed that compared to HC, CCCCCCC/CCCTCCC was significantly increased in all four subtypes of RA (RF single-negative RA patients, RF/CCP double-positive, RF/CCP double-negative, CCP single-negative) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CCCCCCC was significantly increased in CCP single-negative RA patients compared to RF single-negative, RF/CCP double-negative, and RF/CCP double-positive RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TCCCCCC was significantly increased in RF/CCP double-negative patients compared to the HC group, RF single-negative or RF/CCP double-positive RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTCCCCC was significantly decreased in RF single-negative, RF/CCP double-positive, and CCP single-negative RA patients compared to the HC group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTCCCCC was significantly increased in RF/CCP double-positive and RF/CCP double-negative RA patients compared to RF single-negative RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTCCCCC was significantly decreased in CCP single-negative RA patients compared to RF/CCP double-positive RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004); TTCCCCC was significantly increased in RF/CCP double-negative RA patients compared to CCP single-negative RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002); TTTTTTT/TTTTTCC was significantly decreased in all four subtypes of RA (RF single-negative RA patients, RF/CCP double-positive, RF/CCP double-negative, CCP single-negative) compared to HC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); CTCCCCC/CCCCCTC was significantly increased in RF/CCP double-positive, CCP single-negative, and RF/CCP double-negative RA patients compared to HC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTCCC was significantly decreased in RF single-negative, RF/CCP double-positive, and CCP single-negative RA patients compared to HC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTCCC was significantly increased in RF single-negative, RF/CCP double-positive, and RF/CCP double-negative RA patients compared to CCP single-negative RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTTC was significantly decreased in RF/CCP double-positive, RF/CCP double-negative, and CCP single-negative RA patients compared to HC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTTC was significantly increased in RF single-negative and RF/CCP double-positive RA patients compared to CCP single-negative RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); TTTTTTC was significantly decreased in RF/CCP double-negative RA patients compared to RF single-negative RA patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028); CCCCCT was significantly increased in CCP single-negative and RF/CCP double-positive RA patients compared to HC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL-V).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation between \u003cem\u003eHTR2A\u003c/em\u003e Methylation Levels and Common Clinical Indicators in RA Patients\u003c/h2\u003e \u003cp\u003eWe further investigated the correlation between the methylation levels of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 at individual sites and the average methylation level with common clinical indicators in RA patients, including gender, age, height, weight, ESR, CRP, RF, CCP, presence of hypertension, and presence of interstitial lung disease. The results showed: cg15692052_75 was significantly positively correlated with ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.15, 0.22, and 0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044); cg15692052_125 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.11, 0.13, 0.20, and 0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, 0.010, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003); cg15692052_143 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.12, 0.10, 0.18, and 0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, 0.039, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025);cg15692052_149 was significantly positively correlated with gender, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.10, 0.17, and 0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041, 0.001, and 0.008); cg15692052_167 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.13, 0.12, 0.19, and 0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, 0.019, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009); cg15692052_185 was significantly positively correlated with gender, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.14, 0.15, and 0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, 0.003, and 0.009); cg15692052_187 was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.17, 0.10, 0.16, and 0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, 0.049, 0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); the average methylation level was significantly positively correlated with gender, ESR, CRP, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.12, 0.12, 0.20, and 0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, 0.012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation between \u003cem\u003eHTR2A\u003c/em\u003e Haplotypes and Common Clinical Indicators in RA Patients\u003c/h2\u003e \u003cp\u003eGiven the differences in the proportion of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 methylation haplotypes, we further analyzed their correlation with common clinical indicators in RA. The results showed that CCCCCCC was significantly positively correlated with ESR and CRP (r\u0026thinsp;=\u0026thinsp;0.13 and 0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and significantly negatively correlated with age (r=-0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047). TCCCCCC was significantly positively correlated with Gender, age, and the presence of interstitial lung disease (r\u0026thinsp;=\u0026thinsp;0.12, 0.11, and 0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, 0.022, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and significantly negatively correlated with RF (r=-0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). TTCCCCC was significantly positively correlated with age (r\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and significantly negatively correlated with CRP (r=-0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). TTTTTTT was significantly negatively correlated with Gender, CRP, and the presence of interstitial lung disease (r=-0.17, -0.15, and \u0026minus;\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, 0.002, and 0.006). TTTTTCC was significantly negatively correlated with ESR, CRP, and the presence of interstitial lung disease (r=-0.12, -0.20, and \u0026minus;\u0026thinsp;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). TCCTCCC was significantly positively correlated with age and weight (r\u0026thinsp;=\u0026thinsp;0.12 and 0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013 and 0.024). TTTTCCC was significantly negatively correlated with ESR, CRP, the presence of hypertension, and the presence of interstitial lung disease (r=-0.14, -0.21, -0.10, and \u0026minus;\u0026thinsp;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, and 0.008). TTTTTTC was significantly negatively correlated with CRP (r=-0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). CCCCCTC was significantly negatively correlated with height (r=-0.11, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Methylation Level of HTR2A as an Auxiliary Diagnostic Marker for RA\u003c/h2\u003e \u003cp\u003eWe further assessed whether the methylation level of \u003cem\u003eHTR2A\u003c/em\u003e could serve as a diagnostic biomarker for RA and its subtypes. We combined LASSO and random forest methods to jointly screen for important variables, and then used Logistic, random forest, and Xgboost methods to construct clinical prediction models, with all patients except RA serving as the control group(Figure\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The group of CCP single-negative RA patients was excluded from subsequent analysis due to insufficient sample size. For distinguishing between RA and non-RA patients, the variables included in the model identified by LASSO and random forest were age, CRP, ESR, height, Gender, and cg15692052_185. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.757/0.734/0.672, with F1 scores of 0.632/0.745/0.516(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand TableS2\u003c/b\u003e). For distinguishing between RF/CCP double-negative RA and non-RF/CCP double-negative RA patients, the variables included in the model were CCP, RF, CRP, weight, cg15692052_143, age, cg15692052_187, Gender, and height. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.912/0.966/0.825, with F1 scores of 0.969/0.994/0.948(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand TableS2\u003c/b\u003e). For distinguishing between RF/CCP double-positive RA and non-RF/CCP double-positive RA patients, the variables included in the model were CCP, RF, CRP, Gender, cg15692052_187, age, and cg15692052_185. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.832/0.846/0.714, with F1 scores of 0.712/0.826/0.574. For distinguishing between RF single-negative RA and non-RF single-negative RA patients, the variables included in the model were CCP, RF, ESR, Gender, cg15692052_185, age, and cg15692052_75. The validation set AUCs of the models constructed by RF, Xgboost, and Logistic were 0.928/0.923/0.932, with F1 scores of 0.939/0.984/0.930 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand TableS2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn our previous study, we discovered that the DNA methylation level of \u003cem\u003eHTR2A\u003c/em\u003e in peripheral blood mononuclear cells could significantly distinguish patients with RA, OA, and HC. In the current study, we observed similar results, with elevated methylation levels of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 in the RA group, and this elevation was present across various serological subtypes of RA. The methylation cg sites of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 detected in this study were all located in the 5-UTR or promoter region, which may further affect gene expression, potentially leading to abnormal translation and post-translational regulation, influencing disease development. A recent study found that \u003cem\u003eHTR2A\u003c/em\u003e is highly expressed in RA synovial tissue (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). We further investigated the correlation between the methylation level of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 in RA patients and common clinical indicators. We found that the methylation cg sites of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 were significantly correlated with ESR and CRP, and may be associated with RA complications. This suggests that the differential methylation level of \u003cem\u003eHTR2A\u003c/em\u003e may be linked to the inflammatory response in RA patients.\u003c/p\u003e \u003cp\u003eSecondly, we examined the changes in the methylation haplotype proportions of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052, and the results were consistent with those of the first batch. The haplotype representing full methylation, CCCCCCC, or haplotypes containing most Cs were significantly elevated in RA, while the haplotype representing full un-methylation, TTTTTTT, was significantly reduced in RA. The changes in the proportions of different methylation haplotypes may have an overall guiding significance for the DNA methylation level and gene expression. We further observed the correlation between the changes in the methylation haplotype proportions of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 and clinical characteristics. We found that with the methylation of the CG sites of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052, there may be a positive correlation trend with ESR or CRP, implying that the overall hypermethylation state of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 may promote the inflammatory response in RA.\u003c/p\u003e \u003cp\u003eFinally, we used logistic, LASSO, XGBoost, and random forest methods to jointly screen important feature variables for diagnosing RA and its subtypes, and combined multiple cross-validation to stabilize the results. We found that the overall methylation level of HTR2A cg15692052, in combination with different clinical feature variables, could significantly distinguish RA patients and RA subpopulation patients, especially for seronegative RA patients. Early diagnosis and intervention for these patients could significantly improve their prognosis and disease condition. The overall methylation level of HTR2A cg15692052 may be one of the important diagnostic indicators for future seronegative RA patients. We acknowledge that this study still has some limitations that need to be improved in the future. First, the relationship between DNA methylation of HTR2A cg15692052 and gene expression needs to be further explored. Although current research supports that hypermethylation in synovial tissue can promote disease progression, in-depth study of its biological function remains an urgent research topic to be clarified. In the future, we plan to conduct comprehensive in-depth research on the gene function of HTR2A by combining cell biology, molecular biology, bioinformatics, and epigenetics, expecting to publish valuable results. Secondly, our correlations all showed a weak correlation trend, which still requires a larger sample size for validation, but it is undeniable that we obtained statistical support, which is also a result with some guiding significance. Finally, our results still need to be validated on a larger scale in multi-center clinical trials, which is also a key plan for our future. In our results, we also found that the methylation levels of DM, and AS were significantly abnormal, but due to the limitation of the sample size in the current results, it is still necessary to collect a large number of specific disease samples in the future to clarify the potential of HTR2A in diagnosing other diseases. In summary, we found that the overall methylation level of HTR2A cg15692052 is differentially altered in RA and is correlated with various clinical indicators, potentially serving as a novel diagnostic biomarker for RA, RA subtypes, and RA complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e All research participants provided informed consent, and the study was approved by the Ethics Committee of Guanghua Hospital (approval number: 2018-K-12) and completed clinical registration (NO. ChiCTR22400083234).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e The author claim that none of the material in the paper has been published or is under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was funded by the National Natural Science Funds of China (82074234 and 82073901), State Administration of Traditional Chinese Medicine, Shanghai Municipal Health Commission, East China Region-based Chinese and Western Medicine Joint Disease Specialist Alliance, and the National Key Research and Development Program of China (2021YFE0200900), Shanghai Municipal Health Commission\u0026nbsp;(202340274).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e JZ, HB, YL,\u0026nbsp;YS is responsible for the collection, collation, and writing of the original manuscript. KW, PJ, YS, CC, LX, YZ, FZ, GY, QL, MZ is responsible for the collection, collation of the original data. SG is responsible for concept development and manuscript review. LL, YZ, JJ, and RW are responsible for the concept development, revision, and manuscript review. All authors reviewed and accepted the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao J, Guo S, Schrodi SJ, He D. Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications. Front Immunol. 2021;12:790122.\u003c/li\u003e\n\u003cli\u003eOkada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506(7488):376-81.\u003c/li\u003e\n\u003cli\u003eWebster AP, Plant D, Ecker S, Zufferey F, Bell JT, Feber A, et al. Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins. Genome Med. 2018;10(1):64.\u003c/li\u003e\n\u003cli\u003eZhao J, Wei K, Chang C, Xu L, Jiang P, Guo S, et al. DNA Methylation of T Lymphocytes as a Therapeutic Target: Implications for Rheumatoid Arthritis Etiology. Front Immunol. 2022;13:863703.\u003c/li\u003e\n\u003cli\u003eAdams C, Nair N, Plant D, Verstappen SMM, Quach HL, Quach DL, et al. Identification of Cell-Specific Differential DNA Methylation Associated With Methotrexate Treatment Response in Rheumatoid Arthritis. Arthritis Rheumatol. 2023;75(7):1088-97.\u003c/li\u003e\n\u003cli\u003eShao X, Hudson M, Colmegna I, Greenwood CMT, Fritzler MJ, Awadalla P, et al. Rheumatoid arthritis-relevant DNA methylation changes identified in ACPA-positive asymptomatic individuals using methylome capture sequencing. Clin Epigenetics. 2019;11(1):110.\u003c/li\u003e\n\u003cli\u003eZhao J, Xu L, Chang C, Jiang P, Wei K, Shi Y, et al. Circulating methylation level of HTR2A is associated with inflammation and disease activity in rheumatoid arthritis. Front Immunol. 2022;13:1054451.\u003c/li\u003e\n\u003cli\u003eJiang P, Wei K, Xu L, Chang C, Zhang R, Zhao J, et al. DNA methylation change of HIPK3 in Chinese rheumatoid arthritis and its effect on inflammation. Front Immunol. 2022;13:1087279.\u003c/li\u003e\n\u003cli\u003eShi Y, Chang C, Xu L, Jiang P, Wei K, Zhao J, et al. Circulating DNA methylation level of CXCR5 correlates with inflammation in patients with rheumatoid arthritis. Immun Inflamm Dis. 2023;11(6):e902.\u003c/li\u003e\n\u003cli\u003eWang Z, Yan C, Du Q, Huang Y, Li X, Zeng D, et al. HTR2A agonists play a therapeutic role by restricting ILC2 activation in papain-induced lung inflammation. Cell Mol Immunol. 2023;20(4):404-18.\u003c/li\u003e\n\u003cli\u003eGao W, Guo N, Zhao S, Chen Z, Zhang W, Yan F, et al. HTR2A promotes the development of cardiac hypertrophy by activating PI3K-PDK1-AKT-mTOR signaling. Cell Stress Chaperones. 2020;25(6):899-908.\u003c/li\u003e\n\u003cli\u003eGuo NK, She H, Tan L, Zhou YQ, Tang CQ, Peng XY, et al. Nano Parthenolide Improves Intestinal Barrier Function of Sepsis by Inhibiting Apoptosis and ROS via 5-HTR2A. Int J Nanomedicine. 2023;18:693-709.\u003c/li\u003e\n\u003cli\u003ePeroutka SJ. Serotonin receptor variants in disease: new therapeutic opportunities? Ann N Y Acad Sci. 1998;861:16-25.\u003c/li\u003e\n\u003cli\u003eKling A, Seddighzadeh M, Arlestig L, Alfredsson L, Rantap\u0026auml;\u0026auml;-Dahlqvist S, Padyukov L. Genetic variations in the serotonin 5-HT2A receptor gene (HTR2A) are associated with rheumatoid arthritis. Ann Rheum Dis. 2008;67(8):1111-5.\u003c/li\u003e\n\u003cli\u003eSeddighzadeh M, Korotkova M, K\u0026auml;llberg H, Ding B, Daha N, Kurreeman FA, et al. Evidence for interaction between 5-hydroxytryptamine (serotonin) receptor 2A and MHC type II molecules in the development of rheumatoid arthritis. Eur J Hum Genet. 2010;18(7):821-6.\u003c/li\u003e\n\u003cli\u003eXiang C, Hong SM, Zhao B, Pi H, Du F, Lu X, et al. Fibroblast expression of neurotransmitter receptor HTR2A associates with inflammation in rheumatoid arthritis joint. Clin Exp Med. 2024;24(1):84.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rheumatoid arthritis, DNA methylation, HTR2A, circulating methylation levels, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-4710847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4710847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo validate the potential of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 DNA methylation as a diagnostic biomarker for RA and its subtypes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMethylTarget\u0026trade; targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 in RA, HC, ankylosing spondylitis (AS), psoriatic arthritis (PSA), gout, systemic lupus erythematosus (SLE), dermatomyositis (DM), and primary Sj\u0026ouml;gren's syndrome (SS) patients within the region of chr13:46898190\u0026thinsp;~\u0026thinsp;chr13:46897976, spanning a total of 215\u003cem\u003ebp\u003c/em\u003e. Logistic regression, LASSO, random forests, and Xgboost algorithms were used in R software to screen for significant variables, construct models, visualize results, and perform statistical analysis. Multiple imputation was applied to handle missing values, and Spearman's method was used to calculate correlations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to the HC group, RA patients and four serological subtypes of RA (RF-negative RA, RF/CCP double-positive, RF/CCP double-negative, and CCP-negative RA) exhibited significantly higher levels of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 methylation at positions 75/125/143/149/163/185/187 and in average methylation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Methylation levels at all positions and average methylation in RA patients and its four serological subtypes were significantly positively correlated with erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 displayed various haplotypes with differential proportions, among which the CCCCCCC haplotype was significantly elevated in RA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and positively correlated with ESR and CRP (r\u0026thinsp;=\u0026thinsp;0.13 and 0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the TTTTTTT haplotype was significantly decreased in RA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and negatively correlated with CRP (r=-0.15, P\u0026thinsp;=\u0026thinsp;0.002). Predictive models constructed using different machine learning algorithms, incorporating methylation levels of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 at various positions combined with different clinical features, were able to significantly distinguish RA patients with AUCs ranging from 0.672 to 0.757, RF/CCP double-negative patients with AUCs from 0.825 to 0.966, RF/CCP double-positive RA patients with AUCs from 0.714 to 0.846, and RF-negative RA patients with AUCs from 0.928 to 0.932.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe DNA methylation level of \u003cem\u003eHTR2A\u003c/em\u003e cg15692052 is associated with RA and can serve as a diagnostic biomarker for RA and its subtypes.\u003c/p\u003e","manuscriptTitle":"HTR2A DNA Methylation as a Diagnostic Biomarker for Rheumatoid Arthritis: A Validation Study Using Targeted Sequencing and Machine Learning Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 21:34:39","doi":"10.21203/rs.3.rs-4710847/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":"4ab5bc08-0054-4881-914d-04dac406bca4","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-15T11:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 21:34:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4710847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4710847","identity":"rs-4710847","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}