Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan.
Human Health Sciences, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Kyoto, Japan.
Sci Rep. 2019 Feb 27;9(1):2924. doi: 10.1038/s41598-019-39476-9.
Systemic inflammation has been associated with aggressive tumor growth, invasion, and metastasis. Here we performed a comprehensive analysis of 26 kinds of inflammatory cytokine expression patterns among 185 patients with breast cancer and 54 healthy volunteers followed by chemometric analysis. We identified the specific cytokine expression patterns of breast cancer patients compared to healthy volunteers with (1) VEGF, IL-9, GM-CSF, IL-13, IL-4, and IFNγ, (2) IL-8, IL-10, IL-12, IL-5, IL-7, IL-1α, GCSF, IL-1β, and TNFα and (3) IL-2, Eotaxin, MIP1β, MIP1α, IL-17, and bFGF. Among the patients with breast cancer, we identified the specific cytokine signature of metastatic patients compared to non-metastatic patients. We also established a mathematical model for distinguishing patients with breast cancer from healthy volunteers and metastatic patients from non-metastatic patients. This cytokine network analysis could provide new insights into early intervention and effective therapeutic strategy for patients with breast cancer.
系统性炎症与侵袭性肿瘤生长、浸润和转移有关。在这里，我们对 185 例乳腺癌患者和 54 名健康志愿者的 26 种炎性细胞因子表达模式进行了综合分析，并进行了化学计量学分析。与健康志愿者相比，我们确定了乳腺癌患者的特定细胞因子表达模式：（1）VEGF、IL-9、GM-CSF、IL-13、IL-4 和 IFNγ；（2）IL-8、IL-10、IL-12、IL-5、IL-7、IL-1α、GCSF、IL-1β 和 TNFα；（3）IL-2、Eotaxin、MIP1β、MIP1α、IL-17 和 bFGF。在乳腺癌患者中，我们确定了转移性患者与非转移性患者的特定细胞因子特征。我们还建立了一个数学模型，用于区分乳腺癌患者和健康志愿者以及转移性患者和非转移性患者。这种细胞因子网络分析可以为乳腺癌患者的早期干预和有效治疗策略提供新的见解。
