A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis.
A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis.
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DOI：
10.1186/s12890-015-0142-8
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发表时间：
2015-11-21
影响因子：
3.1
通讯作者：
Noth I
中科院分区：
文献类型：
--
作者：
Huang Y;Ma SF;Vij R;Oldham JM;Herazo-Maya J;Broderick SM;Strek ME;White SR;Hogarth DK;Sandbo NK;Lussier YA;Gibson KF;Kaminski N;Garcia JG;Noth I
特发性肺纤维化(IPF)患者的病程具有高度的异质性。预后模型依赖于人口统计学和临床特征，并且不能重现。整合来自基因组分析的数据可能识别新的预后模型，并提供对IPF的机械性见解。外周血单个核细胞的总RNA在一次培训(45名IPF个体)和两个独立的验证队列(分别为21名IPF/10对照和75名IPF个体)中进行微阵列分析。为了确定预测IPF预后的基因集，我们结合了来自训练队列的基因组、临床和结果数据。如果满足以下所有标准，则选择预测基因：1)存在于加权基因共表达网络分析中与肺功能相关的基因共表达模块(p <； 0.05)；2)在观察到的预后良好与预后差之间存在差异表达，折叠变化(FC)>；1.5和假发现率(Fdr) <； 2%；以及3)在单变量COX回归分析中预测死亡率(p <； 0.05)。采用“生存风险组预测”来构建功能基因组模型，该模型使用IPF预后预测基因集来推导每个患者的预后指数(PI)，以区分生存结果的高风险或低风险。用重复的10倍交叉验证算法评估预测准确性，并通过多变量COX回归生存分析在两个验证队列中独立评估预测准确性。一组118个IPF预后预测基因被用来推导功能基因组模型和PI。在训练队列中，PI预测的高危肺间质纤维化患者的生存时间显著短于那些被标记为低风险患者的患者(LOG RANK p <； 0.001)。在两个独立的队列(对数等级p <； 0.001和0.002)中进一步验证了预测的准确性。功能通路分析表明，IPF预后预测基因集丰富的典型通路参与T细胞生物学，包括ICOS、T细胞受体和CD28信号转导。使用监督和非监督分析，我们确定了一组IPF预后预测基因，并得出了一个功能基因组模型，可以高精度地预测高风险和低风险的IPF患者。这种基因组模型可以补充目前的预后工具，为IPF患者提供更个性化的护理。本文的在线版本(doi：10.1186/s12890-0150142-8)包含补充材料，授权用户可以使用。
The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed “good” vs. “poor” prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. “Survival risk group prediction” was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients. The online version of this article (doi:10.1186/s12890-015-0142-8) contains supplementary material, which is available to authorized users.