Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers.
Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers.
复制标题
通过动态网络生物标志物寻找乳腺癌内分泌抵抗过程的临界点
DOI：
10.1093/jmcb/mjy059
复制
发表时间：
2019-08-19
影响因子：
5.5
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Liu R;Wang J;Ukai M;Sewon K;Chen P;Suzuki Y;Wang H;Aihara K;Okada-Hatakeyama M;Chen L
摘要获得性耐药是肿瘤治疗无效的主要原因。确定内分泌抵抗的临界点并检测相关分子是一项具有挑战性的任务。动态网络生物标志物（DNB）方法源自新系统生物学理论，旨在定量识别剧烈系统转变的临界点，并在理论上可以识别在获得耐药性中发挥关键作用的DNB基因。我们分析了从他莫昔芬处理的雌激素受体（ER）阳性MCF-7细胞系产生的时间进程mRNA序列数据，并确定了内分泌抵抗的临界点及其主要分子。结果表明，基因突变和DNB基因之间存在相互作用，其中累积的突变最终影响DNB基因，随后引起转录景观的改变，使全面的耐药性成为可能。基于临床数据集的生存分析验证了DNB基因与乳腺癌患者的不良生存相关。结果提供了抵抗前状态或内分泌抵抗早期迹象的检测。我们的预测方法可能会大大有利于复杂疾病的治疗安排，其中患者暴露于相当不同的药物，并可能成为耐药性。
Abstract Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
基于动态网络生物标志物检测复杂疾病的组织特异性早期预警信号：通过跨组织分析研究 2 型糖尿病
影响因子：
9.5
作者：
Li, Meiyi;Zeng, Tao;Chen, Luonan
通讯作者：
Chen, Luonan
使用下一代 DNA 测序数据进行变异发现和基因分型的框架。
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
3.3
作者：
Cardone, RA;Bagorda, A;Reshkin, SJ
通讯作者：
Reshkin, SJ
影响因子：
4.8
作者：
Knowlden, JM;Hutcheson, IR;Nicholson, RI
通讯作者：
Nicholson, RI