Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.
Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.
复制标题
滑膜RNA测序的网络分析鉴定了预测类风湿关节炎反应的基因-基因相互作用。
DOI：
10.1186/s13075-022-02803-z
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发表时间：
2022-07-11
影响因子：
4.9
通讯作者：
中科院分区：
文献类型：
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作者：
为了确定早期类风湿关节炎（RA）滑膜活检组织RNA测序（RNA-Seq）的基因-基因相互作用网络分析是否可以为我们了解RA发病机制提供信息，并产生改进的治疗反应预测模型。我们利用四个精心策划的途径库，获得了10，537个实验评估的基因-基因相互作用。我们在滑液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，并利用这些滑液特异性基因-基因网络来预测病理生物学中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）。通过稳健的线性回归模型对每个网络中识别的差异相互作用进行统计学评估。通过受试者工作特征（ROC）曲线分析评估预测DMARD治疗反应的能力。比较不同组织病理类型的分析显示了与组织学变化相匹配的一致的分子特征，并突出了新的病理类型特异性基因相互作用和机制。对反应者与无反应者的分析显示，对常规合成DMARD反应良好的患者中凋亡调节基因-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了SOCS 2/STAT 2比值作为治疗成功的预测指标，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一个关键作用，在比较应答者和非应答者时，观察到NOS 3（eNOS）与CAMK 1和eNOS激活剂AKT 3之间的显著统计学相互作用。CAMKD 2/NOS 3的比值增强了反应的预测模型，将ROC AUC从0.63提高到0.73。我们展示了一种新的，强大的方法，利用基因相互作用网络，利用生物相关的基因-基因相互作用，从而改进模型，预测治疗反应。在线版本包含补充材料，可通过10.1186/s13075-022-02803-z获得。
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. The online version contains supplementary material available at 10.1186/s13075-022-02803-z.
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影响因子：
--
作者：
Alaimo S;Giugno R;Acunzo M;Veneziano D;Ferro A;Pulvirenti A
通讯作者：
Pulvirenti A
影响因子：
27.4
作者：
Cherlin S;Lewis MJ;Plant D;Nair N;Goldmann K;Tzanis E;Barnes MR;McKeigue P;Barrett JH;Pitzalis C;Barton A;MATURA Consortium;Cordell HJ
通讯作者：
Cordell HJ
DOI：
10.1016/j.bbrc.2006.05.075
发表时间：
2006-07-14
影响因子：
3.1
作者：
Imai, Kazushi;Morikawa, Masako;Okada, Yasunori
通讯作者：
Okada, Yasunori
影响因子：
2.3
作者：
Fatel, Elis Carolina de Souza;Rosa, Flávia Troncon;Dichi, Isaias
通讯作者：
Dichi, Isaias
影响因子：
20.1
作者：
Chawla A
通讯作者：
Chawla A