Center for Genes, Environment and Health, National Jewish Health, Denver, Colorado, United States of America.
PLoS One. 2012;7(6):e37708. doi: 10.1371/journal.pone.0037708. Epub 2012 Jun 22.
Peripheral blood biomarkers are needed to identify and determine the extent of idiopathic pulmonary fibrosis (IPF). Current physiologic and radiographic prognostic indicators diagnose IPF too late in the course of disease. We hypothesize that peripheral blood biomarkers will identify disease in its early stages, and facilitate monitoring for disease progression.
Gene expression profiles of peripheral blood RNA from 130 IPF patients were collected on Agilent microarrays. Significance analysis of microarrays (SAM) with a false discovery rate (FDR) of 1% was utilized to identify genes that were differentially-expressed in samples categorized based on percent predicted D(L)CO and FVC.
At 1% FDR, 1428 genes were differentially-expressed in mild IPF (D(L)CO >65%) compared to controls and 2790 transcripts were differentially- expressed in severe IPF (D(L)CO >35%) compared to controls. When categorized by percent predicted D(L)CO, SAM demonstrated 13 differentially-expressed transcripts between mild and severe IPF (< 5% FDR). These include CAMP, CEACAM6, CTSG, DEFA3 and A4, OLFM4, HLTF, PACSIN1, GABBR1, IGHM, and 3 unknown genes. Principal component analysis (PCA) was performed to determine outliers based on severity of disease, and demonstrated 1 mild case to be clinically misclassified as a severe case of IPF. No differentially-expressed transcripts were identified between mild and severe IPF when categorized by percent predicted FVC.
These results demonstrate that the peripheral blood transcriptome has the potential to distinguish normal individuals from patients with IPF, as well as extent of disease when samples were classified by percent predicted D(L)CO, but not FVC.
需要外周血生物标志物来识别和确定特发性肺纤维化（IPF）的程度。目前的生理和影像学预后指标在疾病过程中诊断 IPF 为时已晚。我们假设外周血生物标志物将在疾病的早期阶段识别疾病，并有助于监测疾病的进展。
收集了 130 例 IPF 患者外周血 RNA 的基因表达谱，并在 Agilent 微阵列上进行了分析。使用错误发现率（FDR）为 1%的差异微阵列分析（SAM）来识别基于预测的 D（L）CO 和 FVC 百分比分类的样本中差异表达的基因。
在 FDR 为 1%时，与对照组相比，轻度 IPF（D（L）CO >65%）中有 1428 个基因差异表达，与对照组相比，严重 IPF（D（L）CO >35%）中有 2790 个转录物差异表达。当按预测的 D（L）CO 的百分比分类时，SAM 在轻度和重度 IPF 之间显示了 13 个差异表达的转录物（<5% FDR）。其中包括 CAMP、CEACAM6、CTSG、DEFA3 和 A4、OLFM4、HLTF、PACSIN1、GABBR1、IGHM 和 3 个未知基因。进行主成分分析（PCA）以根据疾病的严重程度确定离群值，结果显示 1 例轻度病例在临床上被错误地归类为严重的 IPF 病例。当按预测的 FVC 的百分比分类时，轻度和重度 IPF 之间没有发现差异表达的转录物。
这些结果表明，外周血转录组有可能区分正常个体和 IPF 患者，以及根据预测的 D（L）CO 百分比分类时疾病的严重程度，但不能根据 FVC 进行分类。
