The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis

Frontiers in Immunology

Immunology

Yuxin Yao¹  Bingqing Bai¹  Qiao Wang¹  Jiahui Li¹  Meng Chen¹  Ruobing Liao¹  Zhijun Fan¹  Yulan Chen¹  Fangran Xin¹  Tingting Wei¹  Lingyu Fu²  Bingbing Dai³  Jintao Zhang³  Xin Feng⁴  Xiangnan Jin⁴  Lei Jin⁵  Haina Liu⁶ 

[1]Department of Clinical Epidemiology and Evidence-Based Medicine, the First Affiliated Hospital, China Medical University, Shenyang, China

[2]Department of Clinical Epidemiology and Evidence-Based Medicine, the First Affiliated Hospital, China Medical University, Shenyang, China

[3]Department of Medical Record Management Center, the First Affiliated Hospital, China Medical University, Shenyang, China

[4]Department of Rheumatology and Immunology, Dalian Municipal Central Hospital, Dalian, China

[5]Department of Rheumatology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China

[6]Department of Rheumatology, ShengJing Hospital Affiliated of China Medical University, Shenyang, China

[7]Department of Rheumatology, The First Affiliated Hospital, China Medical University, Shenyang, China

关键词: rheumatoid arthritis;    Leflunomide;    methylation;    machine learning;    prognostic;   

DOI  :  10.3389/fimmu.2023.1173187

 received in 2023-02-24, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveAlthough Leflunomide (LEF) is effective in treating rheumatoid arthritis (RA), there are still a considerable number of patients who respond poorly to LEF treatment. Till date, few LEF efficacy-predicting biomarkers have been identified. Herein, we explored and developed a DNA methylation-based predictive model for LEF-treated RA patient prognosis.MethodsTwo hundred forty-five RA patients were prospectively enrolled from four participating study centers. A whole-genome DNA methylation profiling was conducted to identify LEF-related response signatures via comparison of 40 samples using Illumina 850k methylation arrays. Furthermore, differentially methylated positions (DMPs) were validated in the 245 RA patients using a targeted bisulfite sequencing assay. Lastly, prognostic models were developed, which included clinical characteristics and DMPs scores, for the prediction of LEF treatment response using machine learning algorithms.ResultsWe recognized a seven-DMP signature consisting of cg17330251, cg19814518, cg20124410, cg21109666, cg22572476, cg23403192, and cg24432675, which was effective in predicting RA patient’s LEF response status. In the five machine learning algorithms, the support vector machine (SVM) algorithm provided the best predictive model, with the largest discriminative ability, accuracy, and stability. Lastly, the AUC of the complex model(the 7-DMP scores with the lymphocyte and the diagnostic age) was higher than the simple model (the seven-DMP signature, AUC:0.74 vs 0.73 in the test set).ConclusionIn conclusion, we constructed a prognostic model integrating a 7-DMP scores with the clinical patient profile to predict responses to LEF treatment. Our model will be able to effectively guide clinicians in determining whether a patient is LEF treatment sensitive or not.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Wang, Liu, Jin, Feng, Dai, Chen, Xin, Wei, Bai, Fan, Li, Yao, Liao, Zhang, Jin and Fu