Lyon Debra E, McCain Nancy L, Walter Jeanne, Schubert Christine
Virginia Commonwealth University School of Nursing, PO Box 980567, 1100 East Leigh St., Richmond, VA 23219-0567, USA.
Nurs Res. 2008 Jan-Feb;57(1):51-8. doi: 10.1097/01.NNR.0000280655.58266.6c.
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.
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.
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.
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 no-cancer groups with an exceptionally high areas under the curve (0.981; SE = 0.017).
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.
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、炎症及造血作用的信号分子,是与乳腺癌相关的生物环境的重要组成部分。细胞因子已在预后研究中用作生物标志物,并与多种疾病(包括乳腺癌)的症状及不良结局相关。然而,迄今为止,细胞因子模式的检测一直受传统实验室方法的限制。蛋白质组学技术的进展现在使得在单个样本中能够对更广泛的细胞因子进行表征。由于细胞因子在整合网络中发挥作用,当可以检测多种细胞因子以寻找可能与症状及预后相关的反应模式时,将能获得更全面的理解。
运用蛋白质组学技术(a)检测乳腺癌女性与对照女性在细胞因子水平及模式上是否存在差异,(b)定义并比较标准细胞因子分类的受试者工作特征曲线,以及(c)识别最适合的细胞因子经验模型,以区分经活检确诊患有乳腺癌的女性群体与活检结果为阴性的女性群体。
将35名近期被诊断为乳腺癌的女性的细胞因子水平与24名乳房有可疑肿块但随后乳房活检结果为阴性的女性进行比较。多重微珠阵列分析允许在少量血清中同时检测多种标志物。采用非参数检验程序来确定每种细胞因子中位数及分布的差异。定义受试者工作特征曲线以识别细胞因子模式。
在所有检测的细胞因子中,除粒细胞集落刺激因子和干扰素-γ外,癌症女性的全身细胞因子值显著高于无癌症女性。在活检结果为阴性的女性中,细胞因子与年龄或种族之间仅发现的显著关联是白细胞介素-8(r = 0.53)和巨噬细胞炎性蛋白-1β(r = 0.45)水平随年龄增加而升高。三种细胞因子(粒细胞集落刺激因子、白细胞介素-6和白细胞介素-17)在区分乳腺癌组和无癌组方面具有极高的曲线下面积(0.981;标准误 = 0.017)。
与未患乳腺癌的女性相比,乳腺癌女性的细胞因子水平及其模式明显不同。本研究结果凸显了进一步开展研究以检测可能作为临床研究生物标志物的细胞因子水平及模式的必要性。蛋白质组学技术的创新对扩展生物行为研究具有重要意义。