Wellcome Trust Centre for Cell-Matrix Research, Division of Cell-Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Department of Orthopaedic Surgery, Stockport NHS Foundation Trust, Stockport, UK.
Ann Rheum Dis. 2018 Mar;77(3):423. doi: 10.1136/annrheumdis-2017-212603. Epub 2017 Dec 22.
Osteoarthritis (OA) is a heterogeneous and complex disease. We have used a network biology approach based on genome-wide analysis of gene expression in OA knee cartilage to seek evidence for pathogenic mechanisms that may distinguish different patient subgroups.
Results from RNA-Sequencing (RNA-Seq) were collected from intact knee cartilage at total knee replacement from 44 patients with OA, from 16 additional patients with OA and 10 control patients with non-OA. Results were analysed to identify patient subsets and compare major active pathways.
The RNA-Seq results showed 2692 differentially expressed genes between OA and non-OA. Analysis by unsupervised clustering identified two distinct OA groups: Group A with 24 patients (55%) and Group B with 18 patients (41%). A 10 gene subgroup classifier was validated by RT-qPCR in 16 further patients with OA. Pathway analysis showed increased protein expression in both groups. PhenomeExpress analysis revealed group differences in complement activation, innate immune responses and altered Wnt and TGFβ signalling, but no activation of inflammatory cytokine expression. Both groups showed suppressed circadian regulators and whereas matrix changes in Group A were chondrogenic, in Group B they were non-chondrogenic with changes in mechanoreceptors, calcium signalling, ion channels and in cytoskeletal organisers. The gene expression changes predicted 478 potential biomarkers for detection in synovial fluid to distinguish patients from the two groups.
Two subgroups of knee OA were identified by network analysis of RNA-Seq data with evidence for the presence of two major pathogenic pathways. This has potential importance as a new basis for the stratification of patients with OA for drug trials and for the development of new targeted treatments.
骨关节炎(OA)是一种异质且复杂的疾病。我们使用基于全基因组分析 OA 膝关节软骨基因表达的网络生物学方法,寻求可能区分不同患者亚组的致病机制的证据。
从 44 例 OA 患者的全膝关节置换术中完整膝关节软骨中收集 RNA-Seq(RNA-Seq)结果,从另外 16 例 OA 患者和 10 例非 OA 对照患者中收集结果。对结果进行分析,以确定患者亚组并比较主要的活跃途径。
RNA-Seq 结果显示 OA 和非 OA 之间有 2692 个差异表达基因。通过无监督聚类分析鉴定出两个不同的 OA 组:A 组有 24 例患者(55%)和 B 组有 18 例患者(41%)。通过对另外 16 例 OA 患者进行 RT-qPCR 验证了 10 个基因亚群分类器。途径分析显示两组蛋白表达均增加。PhenomeExpress 分析显示补体激活、固有免疫反应和 Wnt 和 TGFβ信号改变存在组间差异,但炎症细胞因子表达无激活。两组均显示昼夜节律调节剂受抑制,而 A 组的基质变化呈软骨形成性,B 组的基质变化呈非软骨形成性,伴有机械感受器、钙信号、离子通道和细胞骨架组织者的变化。基因表达变化预测了 478 个潜在的生物标志物,可用于区分滑液中的患者和两个亚组。
通过 RNA-Seq 数据的网络分析确定了两个亚组的膝骨关节炎,有证据表明存在两种主要的致病途径。这对于 OA 患者的药物试验分层和新的靶向治疗的开发具有重要意义。