Cytokine comparisons between women with breast cancer and women with a negative breast biopsy
Cytokine comparisons between women with breast cancer and women with a negative breast biopsy
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DOI：
10.1097/01.nnr.0000280655.58266.6c
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发表时间：
2008-01-01
期刊：
影响因子：
2.5
通讯作者：
Schubert, Christine
中科院分区：
文献类型：
--
作者：
Lyon, Debra E.;McCain, Nancy L.;Schubert, Christine
背景：了解与疾病状态相关的生物环境对生物行为研究具有重要意义。细胞因子是介导和调节免疫、炎症和造血的信号分子，是与乳腺癌相关的生物学环境的重要组成部分。细胞因子已被用作预后研究中的生物标志物，并与多种疾病（包括乳腺癌）的症状和不良结局相关。然而，迄今为止，细胞因子模式的检查受到传统实验室方法的限制。蛋白质组学技术的进步现在允许在单个标本中表征更广泛的细胞因子。由于细胞因子在整合的网络中发挥作用，因此可以检查多种细胞因子以了解可能与症状和预后相关的反应模式，从而获得更全面的了解。目的：使用蛋白质组学技术（a）检查乳腺癌患者与对照组相比，细胞因子水平和模式是否存在差异，（B）定义和比较标准细胞因子分类的受试者操作特征曲线，和（c）确定细胞因子的最佳拟合经验模型，以区分发现患有乳腺癌的妇女组和具有阴性活检的妇女组。35名最近被诊断为乳腺癌的妇女的细胞因子水平与24名可疑乳腺肿块的妇女进行了比较，这些妇女随后被发现有阴性乳腺活检。多重微珠阵列分析允许同时测量小体积血清中的多种标志物。使用非参数程序来确定每种细胞因子的中值和分布的差异。定义的接收器操作员特征曲线，以确定模式的cytokin.Results：有显着较高的全身性细胞因子值的妇女与癌症的妇女相比，没有癌症的所有细胞因子测量，与例外的粒细胞集落刺激因子和干扰素-γ。细胞因子与年龄或种族之间唯一的显著相关性是白细胞介素-8（r = 0.53）和巨噬细胞炎性蛋白-1 β（r = 0.45）水平随着活检阴性女性年龄的增加而增加。三种细胞因子（粒细胞集落刺激因子，白细胞介素-6，白细胞介素-17）区分乳腺癌和nocancer组的曲线下的异常高的面积（0.981; SE= 0.017）。讨论：乳腺癌的妇女与那些没有乳腺癌的妇女相比，细胞因子的水平和它们的模式有显着不同。这项研究的结果强调了进一步研究的必要性，以检查可能作为临床研究生物标志物的细胞因子的水平和模式。蛋白质组学技术的创新对扩大生物行为研究具有重要意义。
Background: Understanding the biological milieu associated with disease states has important implications for biobehavioral research. Cytokines, signaling molecules that mediate and regulate immunity, inflammation, and hematopoiesis, are an important component of the biological milieu associated with breast cancer. Cytokines have been used as biomarkers in research for prognosis and have been associated with symptoms and adverse outcomes in multiple conditions, including breast cancer. To date, however, the examination of cytokine patterns has been limited by traditional laboratory methods. Advances in proteomic technology now permit the characterization of a broader array of cytokines in a single specimen. Because cytokines operate in integrated networks, a more complete understanding will be gained as multiple cytokines can be examined for patterns of response that may be associated with symptoms and prognosis.Objectives: To use proteomic technology (a) to examine whether there was a difference in cytokine levels and patterns in women with breast cancer compared with controls, (b) to define and compare the receiver operator characteristic curves for standard cytokine classifications, and (c) to identify the best-fitting empirical model of cytokines to distinguish groups of women found to have breast cancer from those with negative biopsies.Methods: The cytokine levels of 35 women who had been diagnosed recently with breast cancer were compared with 24 women with a suspicious breast mass who were found subsequently to have a negative breast biopsy. Multiplex bead array assays permitted the simultaneous measure of multiple markers in a small volume of serum. Nonparametric procedures were used to determine differences in the median values and the distributions for each cytokine. The receiver operator characteristic curves were defined to identify patterns of cytokines.Results: There were significantly higher systemic cytokine values in women with cancer in comparison with those in women without cancer for all cytokines measured, with the exception of granulocyte colony-stimulating factor and interferon-gamma. The only significant associations found between cytokines and age or race were increased levels of interleukin-8 (r =.53) and macrophage inflammatory protein-1 beta (r =.45) with increased age in women with a negative biopsy. Three cytokines (granulocyte colony-stimulating factor, interleukin-6, and interleukin-17) distinguished between the breast cancer and nocancer groups with an exceptionally high areas under the curve (0.981; SE= 0.017).Discussion: Levels of cytokines and their patterns were markedly different in women with breast cancer as compared with those in women who did not have breast cancer. Results from this study highlight the need for further research to examine the levels and patterns of cytokines that may serve as biomarkers in clinical research. Innovations in proteomic technology have implications for expanding biobehavioral research.