Achiron A, Gurevich M, Snir Y, Segal E, Mandel M
Multiple Sclerosis Center, Sheba Medical Center, Tel-Hashomer, Sackler School of Medicine, Tel-Aviv University, Israel.
Clin Exp Immunol. 2007 Aug;149(2):235-42. doi: 10.1111/j.1365-2249.2007.03405.x. Epub 2007 May 4.
Multiple sclerosis (MS) is a demyelinating disease characterized by an unpredictable clinical course with intermittent relapses that lead over time to significant neurological disability. Clinical and radiological variables are limited in the ability to predict disease course. Peripheral blood genome scale analyses were used to characterize MS patients with different disease types, but not for prediction of outcome. Using complementary-DNA microarrays we studied peripheral-blood gene expression patterns in 53 relapsing-remitting MS patients. Patients were classified into good, intermediate and poor clinical outcome established after 2-year follow-up. A training set of 26 samples was used to identify clinical outcome differentiating gene-expression signature. Supervised learning and feature selection algorithms were applied to identify a predictive signature that was validated in an independent group of 27 patients. Key genes within the predictive signature were confirmed by quantitative reverse transcription-polymerase chain reaction in an additional 10 patients. The analysis identified 431 differentiating genes between patients with good and poor clinical outcome (change in neurological disability by the expanded disability status scale was -0.33 +/- 0.24 and 1.6 +/- 0.35, P = 0.0002, total number of relapses were 0 and 1.80 +/- 0.35, P = 0.00009, respectively). An optimal set of 29 genes was depicted as a clinical outcome predictive gene expression signature and classified appropriately 88.9% of patients. This predictive signature was enriched by genes related biologically to zinc-ion binding and cytokine activity regulation pathways involved in inflammation and apoptosis. Our findings provide a basis for monitoring patients by prediction of disease outcome and can be incorporated into clinical decision-making in relapsing-remitting MS.
多发性硬化症（MS）是一种脱髓鞘疾病，其临床病程不可预测，具有间歇性复发特点，随着时间推移会导致严重的神经功能残疾。临床和放射学变量在预测疾病进程方面能力有限。外周血基因组规模分析曾用于对不同疾病类型的MS患者进行特征描述，但未用于预测预后。我们使用互补DNA微阵列研究了53例复发缓解型MS患者外周血基因表达模式。患者根据2年随访后确定的良好、中等和不良临床预后进行分类。使用26个样本的训练集来识别区分临床预后的基因表达特征。应用监督学习和特征选择算法来识别一个预测特征，并在另外27例患者的独立组中进行验证。在另外10例患者中通过定量逆转录聚合酶链反应确认了预测特征中的关键基因。分析确定了临床预后良好和不良患者之间的431个差异基因（扩展残疾状态量表评估的神经功能残疾变化分别为-0.33±0.24和1.6±0.35，P = 0.0002；复发总数分别为0和1.80±0.35，P = 0.00009）。一组由29个基因组成的最佳基因集被描绘为临床预后预测基因表达特征，能正确分类88.9%的患者。该预测特征富含与锌离子结合以及参与炎症和凋亡的细胞因子活性调节途径在生物学上相关的基因。我们的研究结果为通过预测疾病预后监测患者提供了依据，可纳入复发缓解型MS的临床决策制定中。
