To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models. We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis. Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73. We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

中文翻译：

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滑膜 RNA 测序的网络分析确定了预测类风湿性关节炎反应的基因-基因相互作用

确定早期类风湿关节炎 (RA) 滑膜活检的 RNA 测序 (RNA-Seq) 的基因-基因相互作用网络分析是否可以告知我们对 RA 发病机制的理解，并产生改进的治疗反应预测模型。我们利用了四个精心策划的通路存储库，获得了 10,537 个经过实验评估的基因-基因相互作用。我们在滑膜 RNA-Seq 中提取了特定的基因-基因相互作用网络，以表征早期 RA 中组织学定义的病理类型，并利用这些滑膜特异性基因-基因网络来预测对基于甲氨蝶呤的疾病缓解抗风湿药物 (DMARD) 治疗的反应早期关节炎队列 (PEAC) 的病理生物学。通过稳健的线性回归模型对每个网络中确定的差异交互进行统计评估。通过接受者操作特征 (ROC) 曲线分析评估预测对 DMARD 治疗反应的能力。比较不同组织学病理类型的分析显示了与组织学变化相匹配的连贯分子特征，并突出了新的病理类型特异性基因相互作用和机制。对反应者与非反应者的分析表明，在对常规合成 DMARD 反应良好的患者中，细胞凋亡调节基因-基因相互作用的表达更高。对网络连锁基因对之间相互作用的详细分析确定 SOCS2/STAT2 比率可预测治疗成功，将 ROC 曲线下面积 (AUC) 从 0.62 提高到 0.78。我们确定了血管生成的关键作用，在比较响应者和非响应者时，观察 NOS3 (eNOS) 与 CAMK1 和 eNOS 激活剂 AKT3 之间的显着统计相互作用。CAMKD2/NOS3 的比率增强了将 ROC AUC 从 0.63 提高到 0.73 的响应预测模型。我们展示了一种新颖、强大的方法，该方法利用基因相互作用网络来利用生物学相关的基因-基因相互作用，从而改进预测治疗反应的模型。