基于滑膜组织学特征和 RNA 测序数据的机器学习整合鉴定三种类风湿关节炎疾病亚型。
Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.
机构信息
Hospital for Special Surgery, The Rockefeller University, and New York Genome Center, New York, New York.
New York Genome Center, New York, New York.
出版信息
Arthritis Rheumatol. 2018 May;70(5):690-701. doi: 10.1002/art.40428. Epub 2018 Apr 2.
OBJECTIVE
In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning.
METHODS
Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes.
RESULTS
Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others.
CONCLUSION
Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.
目的
本研究通过使用基因表达数据和机器学习对类风湿关节炎（RA）滑膜组织的组织学评分进行优化。
方法
对 129 个滑膜组织样本（n=123 例 RA 患者和 n=6 例骨关节炎[OA]患者）中的 20 种组织学特征进行评估。对 45 个滑膜样本的基因表达数据进行一致性聚类。使用组织学数据作为输入，使用支持向量机学习来预测基因表达亚型。比较各亚型的相应临床数据。
结果
基因表达数据的一致性聚类揭示了 3 种不同的滑膜亚型，包括以白细胞广泛浸润为特征的高炎症亚型、以转化生长因子β、糖蛋白和神经元基因富集为特征的低炎症亚型以及混合亚型。将组织学特征应用于机器学习，以基因表达亚型作为标签，生成了组织学特征评分的算法。高炎症滑膜亚型患者的全身性炎症标志物和自身抗体水平更高。C 反应蛋白（CRP）水平在高炎症亚组中与疼痛严重程度显著相关，但在其他亚组中则不相关。
结论
对 RA 和 OA 滑膜组织的基因表达分析揭示了 3 种不同的滑膜亚型。这些标签被用于生成组织学评分算法，其中组织学评分与全身性炎症参数相关，包括红细胞沉降率、CRP 水平和自身抗体水平。基因表达模式与临床特征的比较揭示了一个潜在的临床重要区别：不同滑膜亚型患者的疼痛机制可能不同。
Zotero 插件