抗dsdna IgG糖基化与treatment-naïve系统性红斑狼疮患者器官受累相关。

IF 3.5 2区 医学 Q1 RHEUMATOLOGY
Zhuochao Zhou, Yuhong Liu, Xiaotong Gu, Haowen Zhang, Panan Zhang, Yue Sun, Honglei Liu, Xiaobing Cheng, Yutong Su, Hui Shi, Qiongyi Hu, Huihui Chi, Jianfen Meng, Jinchao Jia, Tingting Liu, Mengyan Wang, Cui Lu, Yunping Cai, Yijun You, Dehao Zhu, Shifang Ren, Jialin Teng, Jingyi Wu, Chengde Yang, Junna Ye
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引用次数: 0

摘要

目的:本研究旨在利用机器学习算法探索SLE患者抗双链DNA (anti-dsDNA)免疫球蛋白G (IgG)糖基化与器官受累程度的关系。方法和分析:我们在2017年至2019年期间从上海瑞金医院风湿病与免疫科连续入组86例抗dsdna抗体阳性的SLE患者treatment-naïve。我们对SLE患者的器官受累程度进行了量化和分类,并分析了纯化的抗dsdna IgG的每种糖型和糖型组合。随机森林分类器和人工神经网络应用于评估糖型对水平与器官受累程度之间的相关性。结果:Pearson相关分析显示SLE患者受累和未受累器官之间有很强的相关性。随机森林分析结果表明,IgG1Gal与IgG3/4Bis的组合精度最高(0.7692),曲线下面积最高(0.8187)。在人工神经网络预测参与程度方面,IgG3/4Bis和IgG1Gal的均方误差最低(0.0244)。结论:我们的研究显示了联合糖型对SLE脏器受累程度的分类和预测的有效性。不同糖型与受累程度有不同程度的相关性,其中抗dsdna IgG3/4Bis与IgG1Gal组合与受累器官的相关性最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.

Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.

Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.

Glycosylation of anti-dsDNA IgG correlates with organ involvement in treatment-naïve patients with systemic lupus erythematosus.

Objective: This study aimed to leverage machine learning algorithms to explore the relationship between anti-double-stranded DNA (anti-dsDNA) immunoglobulin G (IgG) glycosylation and the degree of organ involvement in patients with SLE.

Methods and analysis: We enrolled 86 consecutive treatment-naïve patients with SLE positive for anti-dsDNA antibodies from the Department of Rheumatology and Immunology at Ruijin Hospital, Shanghai, between 2017 and 2019. We quantified and classified the degree of organ involvement in patients with SLE and analysed each glycoform and a combination of glycoforms of purified anti-dsDNA IgG. A random forest classifier and artificial neural network were applied to evaluate the correlation between the levels of glycoform pairs and the degree of organ involvement.

Results: Pearson's correlation analysis revealed a strong connection between the involved and uninvolved organs in patients with SLE. Random forest analysis showed that the combination of IgG1Gal and IgG3/4Bis had the highest accuracy (0.7692) and area under the curve (0.8187). In terms of predicting the degree of involvement using an artificial neural network, IgG3/4Bis and IgG1Gal showed the lowest mean squared error (0.0244).

Conclusions: Our study showed the effectiveness of combining glycoforms to classify and predict the degree of SLE organ involvement. Different glycoforms were correlated with the involvement degree to various extents, and the combination of anti-dsDNA IgG3/4Bis and IgG1Gal exhibited the best correlation with organ involvement.

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来源期刊
Lupus Science & Medicine
Lupus Science & Medicine RHEUMATOLOGY-
CiteScore
5.30
自引率
7.70%
发文量
88
审稿时长
15 weeks
期刊介绍: Lupus Science & Medicine is a global, peer reviewed, open access online journal that provides a central point for publication of basic, clinical, translational, and epidemiological studies of all aspects of lupus and related diseases. It is the first lupus-specific open access journal in the world and was developed in response to the need for a barrier-free forum for publication of groundbreaking studies in lupus. The journal publishes research on lupus from fields including, but not limited to: rheumatology, dermatology, nephrology, immunology, pediatrics, cardiology, hepatology, pulmonology, obstetrics and gynecology, and psychiatry.
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