Broderick Gordon, Craddock R Cameron, Whistler Toni, Taylor Renee, Klimas Nancy, Unger Elizabeth R
University of Alberta, Institute for Biomolecular Design, Edmonton, Alberta, T6G 2H7, Canada.
Pharmacogenomics. 2006 Apr;7(3):407-19. doi: 10.2217/14622416.7.3.407.
To examine the potential of multivariate projection methods in identifying common patterns of change in clinical and gene expression data that capture the illness state of subjects with unexplained fatigue and nonfatigued control participants.
Data for 111 female subjects was examined. A total of 59 indicators, including multidimensional fatigue inventory (MFI), medical outcome Short Form 36 (SF-36), Centers for Disease Control and Prevention (CDC) symptom inventory and cognitive response described illness. Partial least squares (PLS) was used to construct two feature spaces: one describing the symptom space from gene expression in peripheral blood mononuclear cells (PBMC) and one based on 117 clinical variables. Multiplicative scatter correction followed by quantile normalization was applied for trend removal and range adjustment of microarray data. Microarray quality was assessed using mean Pearson correlation between samples. Benjamini-Hochberg multiple testing criteria served to identify significantly expressed probes.
A single common trend in 59 symptom constructs isolates of nonfatigued subjects from the overall group. This segregation is supported by two co-regulation patterns representing 10% of the overall microarray variation. Of the 39 principal contributors, the 17 probes annotated related to basic cellular processes involved in cell signaling, ion transport and immune system function. The single most influential gene was sestrin 1 (SESN1), supporting recent evidence of oxidative stress involvement in chronic fatigue syndrome (CFS). Dominant variables in the clinical feature space described heart rate variability (HRV) during sleep. Potassium and free thyroxine (T4) also figure prominently.
Combining multiple symptom, gene or clinical variables into composite features provides better discrimination of the illness state than even the most influential variable used alone. Although the exact mechanism is unclear, results suggest a common link between oxidative stress, immune system dysfunction and potassium imbalance in CFS patients leading to impaired sympatho-vagal balance strongly reflected in abnormal HRV.
研究多元投影方法在识别临床和基因表达数据中共同变化模式的潜力，这些模式能够反映不明原因疲劳受试者和非疲劳对照参与者的疾病状态。
对111名女性受试者的数据进行了检查。共有59项指标，包括多维疲劳量表（MFI）、医学结局简明健康调查问卷（SF-36）、疾病控制和预防中心（CDC）症状清单以及认知反应来描述病情。采用偏最小二乘法（PLS）构建两个特征空间：一个从外周血单核细胞（PBMC）中的基因表达描述症状空间，另一个基于117个临床变量。对微阵列数据进行乘性散射校正，随后进行分位数归一化，以去除趋势并调整范围。使用样本间的平均皮尔逊相关系数评估微阵列质量。采用Benjamini-Hochberg多重检验标准来识别显著表达的探针。
59个症状结构中的单一共同趋势将非疲劳受试者与整个组区分开来。这种分离得到了两种共调控模式的支持，这两种模式占微阵列总变异的10%。在39个主要贡献者中，17个注释探针与参与细胞信号传导、离子转运和免疫系统功能的基本细胞过程相关。最具影响力的单个基因是 sestrin 1（SESN1），这支持了氧化应激参与慢性疲劳综合征（CFS）的最新证据。临床特征空间中的主导变量描述了睡眠期间的心率变异性（HRV）。钾和游离甲状腺素（T4）也很突出。
将多个症状、基因或临床变量组合成复合特征，比单独使用最具影响力的变量能更好地区分疾病状态。尽管确切机制尚不清楚，但结果表明，氧化应激、免疫系统功能障碍和钾失衡之间存在共同联系，在CFS患者中导致交感 - 迷走神经平衡受损，这在异常的HRV中得到强烈反映。
