Jarvis James N, Dozmorov Igor, Jiang Kaiyu, Frank Mark Barton, Szodoray Peter, Alex Philip, Centola Michael
Department of Pediatrics, University of Oklahoma College of Medicine, Oklahoma City, OK, USA.
Arthritis Res Ther. 2004;6(1):R15-R32. doi: 10.1186/ar1018. Epub 2003 Nov 6.
Juvenile rheumatoid arthritis (JRA) has a complex, poorly characterized pathophysiology. Modeling of transcriptosome behavior in pathologic specimens using microarrays allows molecular dissection of complex autoimmune diseases. However, conventional analyses rely on identifying statistically significant differences in gene expression distributions between patients and controls. Since the principal aspects of disease pathophysiology vary significantly among patients, these analyses are biased. Genes with highly variable expression, those most likely to regulate and affect pathologic processes, are excluded from selection, as their distribution among healthy and affected individuals may overlap significantly. Here we describe a novel method for analyzing microarray data that assesses statistically significant changes in gene behavior at the population level. This method was applied to expression profiles of peripheral blood leukocytes from a group of children with polyarticular JRA and healthy control subjects. Results from this method are compared with those from a conventional analysis of differential gene expression and shown to identify discrete subsets of functionally related genes relevant to disease pathophysiology. These results reveal the complex action of the innate and adaptive immune responses in patients and specifically underscore the role of IFN-gamma in disease pathophysiology. Discriminant function analysis of data from a cohort of patients treated with conventional therapy identified additional subsets of functionally related genes; the results may predict treatment outcomes. While data from only 9 patients and 12 healthy controls was used, this preliminary investigation of the inflammatory genomics of JRA illustrates the significant potential of utilizing complementary sets of bioinformatics tools to maximize the clinical relevance of microarray data from patients with autoimmune disease, even in small cohorts.
青少年类风湿性关节炎（JRA）具有复杂且特征不明的病理生理学。使用微阵列对病理标本中的转录组行为进行建模，能够对复杂的自身免疫性疾病进行分子剖析。然而，传统分析依赖于识别患者与对照之间基因表达分布的统计学显著差异。由于疾病病理生理学的主要方面在患者之间存在显著差异，这些分析存在偏差。那些表达高度可变、最有可能调节和影响病理过程的基因被排除在选择之外，因为它们在健康个体和患病个体中的分布可能有显著重叠。在此，我们描述了一种分析微阵列数据的新方法，该方法评估群体水平上基因行为的统计学显著变化。此方法应用于一组多关节型JRA儿童和健康对照受试者外周血白细胞的表达谱。将该方法的结果与差异基因表达的传统分析结果进行比较，结果显示可识别与疾病病理生理学相关的功能相关基因的离散子集。这些结果揭示了患者先天和适应性免疫反应的复杂作用，并特别强调了IFN-γ在疾病病理生理学中的作用。对接受传统治疗的一组患者的数据进行判别函数分析，确定了功能相关基因的其他子集；结果可能预测治疗结果。虽然仅使用了9名患者和12名健康对照的数据，但这项对JRA炎症基因组学的初步研究表明，利用互补的生物信息学工具集来最大化自身免疫性疾病患者微阵列数据的临床相关性具有巨大潜力，即使在小样本队列中也是如此。
Zotero 插件
