Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio.
Gastroenterology. 2013 Dec;145(6):1289-99. doi: 10.1053/j.gastro.2013.08.046. Epub 2013 Aug 23.
BACKGROUND & AIMS: Gene expression profiling provides an opportunity for definitive diagnosis but has not yet been well applied to inflammatory diseases. Here we describe an approach for diagnosis of an emerging form of esophagitis, eosinophilic esophagitis (EoE), which is currently diagnosed by histology and clinical symptoms.
We developed an EoE diagnostic panel (EDP) comprising a 96-gene quantitative polymerase chain reaction array and an associated dual-algorithm that uses cluster analysis and dimensionality reduction using a cohort of randomly selected esophageal biopsy samples from pediatric patients with EoE (n = 15) or without EoE (non-EoE controls, n = 14) and subsequently vetted the EDP using a separate cohort of 194 pediatric and adult patient samples derived from both fresh or formalin-fixed, paraffin-embedded tissue: active EoE (n = 91), control (non-EoE and EoE remission, n = 57), histologically ambiguous (n = 34), and reflux (n = 12) samples.
The EDP identified adult and pediatric patients with EoE with approximately 96% sensitivity and approximately 98% specificity, and distinguished patients with EoE in remission from controls, as well as identified patients exposed to swallowed glucorticoids. The EDP could be used with formalin-fixed, paraffin-embedded tissue RNA and distinguished patients with EoE from those with reflux esophagitis, identified by pH-impedance testing. Preliminary evidence showed that the EDP could identify patients likely to have disease relapse after treatment.
We developed a molecular diagnostic test (referred to as the EDP) that identifies patients with esophagitis in a fast, objective, and mechanistic manner, offering an opportunity to improve diagnosis and treatment, and a platform approach for other inflammatory diseases.
基因表达谱分析为明确诊断提供了机会，但尚未很好地应用于炎症性疾病。在这里，我们描述了一种用于诊断一种新出现的食管炎，嗜酸性食管炎（EoE）的方法，该方法目前通过组织学和临床症状进行诊断。
我们开发了一种嗜酸性食管炎诊断面板（EDP），该面板包含 96 个基因的定量聚合酶链反应阵列和一个相关的双算法，该算法使用聚类分析和降维，使用来自患有 EoE 的儿科患者（n = 15）或没有 EoE 的随机选择的食管活检样本队列（非 EoE 对照，n = 14），并随后使用来自新鲜或福尔马林固定，石蜡包埋组织的 194 名儿科和成年患者样本的独立队列验证 EDP：活动性 EoE（n = 91），对照（非 EoE 和 EoE 缓解，n = 57），组织学上不明确（n = 34）和反流（n = 12）样本。
EDP 可识别出约 96％敏感性和约 98％特异性的 EoE 成年和儿科患者，并可将缓解期的 EoE 患者与对照组区分开，以及识别出暴露于吞咽糖皮质激素的患者。EDP 可用于福尔马林固定，石蜡包埋组织 RNA，并可将 EoE 患者与 pH 阻抗测试识别的反流性食管炎患者区分开。初步证据表明，EDP 可以识别出在治疗后可能疾病复发的患者。
我们开发了一种分子诊断测试（称为 EDP），该测试可快速，客观且以机械方式识别出患有食管炎的患者，为改善诊断和治疗提供了机会，并为其他炎症性疾病提供了平台方法。
