Interaction between smoking history and gene expression levels impacts survival of breast cancer patients
Interaction between smoking history and gene expression levels impacts survival of breast cancer patients
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DOI：
10.1007/s10549-015-3507-z
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发表时间：
2015-08-01
影响因子：
3.8
通讯作者：
Wittliff, James L.
中科院分区：
文献类型：
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作者：
Andres, Sarah A.;Bickett, Katie E.;Wittliff, James L.
与以往关注吸烟与乳腺癌发生风险的研究不同，本研究探讨了吸烟对乳腺癌复发和进展的影响。目的是评估吸烟史和基因表达水平对乳腺癌患者复发和总体生存的影响。分别对48名吸烟者、50名非吸烟者和总人口拟合多变量Cox比例风险模型，以确定哪些基因表达和基因表达/吸烟相互作用项在预测乳腺癌患者的总体生存和无病生存方面具有显著意义。采用类似Andres等人（BMC Cancer 13:326, 2013a; Horm Cancer 4:208- 221,2013 3b）的方法，多变量分析显示，CENPN、CETN1、CYP1A1、IRF2、LECT2和NCOA1是吸烟者乳腺癌复发和死亡率的重要预测因子。此外，COMT对复发有重要影响，NAT1和RIPK1对死亡率有重要影响。相比之下，在非吸烟者中，只有IRF2、CETN1和CYP1A1对疾病复发和死亡率有显著影响，而NAT2对生存率也有显著影响。利用组合样本分析吸烟状况与基因表达值之间的相互作用，发现吸烟状况与CYP1A1、LECT2和CETN1之间存在显著的相互作用。由7-8个基因组成的特征对吸烟者的乳腺癌复发和总生存具有高度预测性，总生存和复发的中位c指数分别为0.8和0.73。相比之下，非吸烟者的中位c指数仅为0.59。因此，基因表达与吸烟状况之间的显著相互作用在预测乳腺癌患者预后方面发挥了关键作用。
In contrast to studies focused on cigarette smoking and risk of breast cancer occurrence, this study explored the influence of smoking on breast cancer recurrence and progression. The goal was to evaluate the interaction between smoking history and gene expression levels on recurrence and overall survival of breast cancer patients. Multivariable Cox proportional hazards models were fitted for 48 cigarette smokers, 50 non-smokers, and the total population separately to determine which gene expressions and gene expression/cigarette usage interaction terms were significant in predicting overall and disease-free survival in breast cancer patients. Using methods similar to Andres et al. (BMC Cancer 13:326, 2013a; Horm Cancer 4:208-221, 2013b), multivariable analyses revealed CENPN, CETN1, CYP1A1, IRF2, LECT2, and NCOA1 to be important predictors for both breast carcinoma recurrence and mortality among smokers. Additionally, COMT was important for recurrence, and NAT1 and RIPK1 were important for mortality. In contrast, only IRF2, CETN1, and CYP1A1 were significant for disease recurrence and mortality among non-smokers, with NAT2 additionally significant for survival. Analysis of interaction between smoking status and gene expression values using the combined samples revealed significant interactions between smoking status and CYP1A1, LECT2, and CETN1. Signatures consisting of 7-8 genes were highly predictive for breast cancer recurrence and overall survival among smokers, with median C-index values of 0.8 and 0.73 for overall survival and recurrence, respectively. In contrast, median C-index values for non-smokers was only 0.59. Hence, significant interactions between gene expression and smoking status can play a key role in predicting breast cancer patient outcomes.