Expression of CD163 and major histocompatibility complex class I as diagnostic markers for idiopathic inflammatory myopathies

IF 4.9 2区 医学 Q1 Medicine
Byeongzu Ghang, So Hye Nam, Wonho Choi, Hwa Jung Kim, Jungsun Lee, Doo-Ho Lim, Soo Min Ahn, Ji Seon Oh, Seokchan Hong, Yong-Gil Kim, Chang-Keun Lee, Jinseok Kim, Bin Yoo, Soo Jeong Nam
{"title":"Expression of CD163 and major histocompatibility complex class I as diagnostic markers for idiopathic inflammatory myopathies","authors":"Byeongzu Ghang, So Hye Nam, Wonho Choi, Hwa Jung Kim, Jungsun Lee, Doo-Ho Lim, Soo Min Ahn, Ji Seon Oh, Seokchan Hong, Yong-Gil Kim, Chang-Keun Lee, Jinseok Kim, Bin Yoo, Soo Jeong Nam","doi":"10.1186/s13075-024-03364-z","DOIUrl":null,"url":null,"abstract":"To develop an inflammation-related immunohistochemistry marker-based algorithm that confers higher diagnostic ability for idiopathic inflammatory myopathies (IIMs) than IIM-related histopathologic features. Muscle biopsy tissues from 129 IIM patients who met the 2017 EULAR/ACR criteria and 73 control tissues from patients with non-inflammatory myopathies or healthy muscle specimens were evaluated for histological features and immunostaining results of CD3, CD4, CD8, CD20, CD68, CD163, MX1, MHC class I, MHC class II, and HLA-DR. Diagnostic algorithms for IIM were developed based on the results of the classification and regression tree (CART) analysis, which used immunostaining results as predictor variables for classifying patients with IIMs. In the analysis set (IIM, n = 129; control, n = 73), IIM-related histopathologic features had a diagnostic accuracy of 87.6% (sensitivity 80.6%; specificity 100.0%) for IIMs. Notably, muscular expression of CD163 (99.2% vs. 20.8%, p < 0.001) and MHC class I (87.6% vs. 23.1%, p < 0.001) was significantly higher in the IIM group than in controls. Based on the CART analysis results, we developed an algorithm combining CD163 and MHC class I expression that conferred a diagnostic accuracy of 95.5% (sensitivity 96.1%; specificity 94.5%). In addition, our algorithm was able to correctly diagnose IIM in 94.1% (16/17) of patients who did not meet the 2017 EUALR/ACR criteria but were diagnosed as having IIMs by an expert physician. Combination of CD163 and MHC class I muscular expression may be useful in diagnosing IIMs.","PeriodicalId":8419,"journal":{"name":"Arthritis Research & Therapy","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arthritis Research & Therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s13075-024-03364-z","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

To develop an inflammation-related immunohistochemistry marker-based algorithm that confers higher diagnostic ability for idiopathic inflammatory myopathies (IIMs) than IIM-related histopathologic features. Muscle biopsy tissues from 129 IIM patients who met the 2017 EULAR/ACR criteria and 73 control tissues from patients with non-inflammatory myopathies or healthy muscle specimens were evaluated for histological features and immunostaining results of CD3, CD4, CD8, CD20, CD68, CD163, MX1, MHC class I, MHC class II, and HLA-DR. Diagnostic algorithms for IIM were developed based on the results of the classification and regression tree (CART) analysis, which used immunostaining results as predictor variables for classifying patients with IIMs. In the analysis set (IIM, n = 129; control, n = 73), IIM-related histopathologic features had a diagnostic accuracy of 87.6% (sensitivity 80.6%; specificity 100.0%) for IIMs. Notably, muscular expression of CD163 (99.2% vs. 20.8%, p < 0.001) and MHC class I (87.6% vs. 23.1%, p < 0.001) was significantly higher in the IIM group than in controls. Based on the CART analysis results, we developed an algorithm combining CD163 and MHC class I expression that conferred a diagnostic accuracy of 95.5% (sensitivity 96.1%; specificity 94.5%). In addition, our algorithm was able to correctly diagnose IIM in 94.1% (16/17) of patients who did not meet the 2017 EUALR/ACR criteria but were diagnosed as having IIMs by an expert physician. Combination of CD163 and MHC class I muscular expression may be useful in diagnosing IIMs.
CD163 和主要组织相容性复合体 I 类的表达作为特发性炎症性肌病的诊断标志物
目的:开发一种基于炎症相关免疫组化标记物的算法,与特发性炎症性肌病(IIM)相关组织病理学特征相比,该算法具有更高的特发性炎症性肌病诊断能力。对符合2017年EULAR/ACR标准的129例特发性炎症性肌病患者的肌肉活检组织和非炎症性肌病患者或健康肌肉标本的73例对照组织进行了组织学特征和CD3、CD4、CD8、CD20、CD68、CD163、MX1、MHC I类、MHC II类和HLA-DR的免疫染色结果评估。根据分类和回归树(CART)分析的结果制定了 IIM 的诊断算法,该算法将免疫染色结果作为 IIM 患者分类的预测变量。在分析集(IIM,n = 129;对照组,n = 73)中,IIM 相关组织病理学特征对 IIM 的诊断准确率为 87.6%(灵敏度 80.6%;特异性 100.0%)。值得注意的是,IIM 组中 CD163(99.2% 对 20.8%,P<0.001)和 MHC I 类(87.6% 对 23.1%,P<0.001)的肌肉表达明显高于对照组。根据 CART 分析结果,我们开发了一种结合 CD163 和 MHC I 类表达的算法,其诊断准确率为 95.5%(灵敏度 96.1%;特异性 94.5%)。此外,我们的算法还能对94.1%(16/17)不符合2017年EUALR/ACR标准但被专家医生诊断为患有IIM的患者正确诊断出IIM。结合CD163和MHC I类肌肉表达可能有助于诊断IIM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
8.60
自引率
2.00%
发文量
261
审稿时长
14 weeks
期刊介绍: Established in 1999, Arthritis Research and Therapy is an international, open access, peer-reviewed journal, publishing original articles in the area of musculoskeletal research and therapy as well as, reviews, commentaries and reports. A major focus of the journal is on the immunologic processes leading to inflammation, damage and repair as they relate to autoimmune rheumatic and musculoskeletal conditions, and which inform the translation of this knowledge into advances in clinical care. Original basic, translational and clinical research is considered for publication along with results of early and late phase therapeutic trials, especially as they pertain to the underpinning science that informs clinical observations in interventional studies.
文献相关原料
公司名称 产品信息 采购帮参考价格
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信