Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis
Identification of Early Warning Signals at the Critical Transition Point of Colorectal Cancer Based on Dynamic Network Analysis
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基于动态网络分析的结直肠癌关键转变点预警信号识别
DOI：
10.3389/fbioe.2020.00530
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发表时间：
2020-05
影响因子：
5.7
通讯作者：
Chen Xiujie
中科院分区：
文献类型：
--
作者：
Liu Lei;Shao Zhuo;Lv Jiaxuan;Xu Fei;Ren Sibo;Jin Qing;Yang Jingbo;Ma Weifang;Xie Hongbo;Zhang Denan;Chen Xiujie
结直肠癌（CRC）是全球癌症相关死亡的主要原因之一。由于大肠癌早期诊断方法和预警信号的缺乏以及大肠癌的异质性强，确定准确的治疗方法和识别特异性的预警信号仍然是研究者迫切需要解决的问题。本研究将28例结直肠癌患者的癌组织表达谱和癌旁组织表达谱结合到人类蛋白质-蛋白质相互作用（PPI）网络中，以构建针对每个患者的特定网络。采用网络传播方法获得一个包含一个患者90%以上突变信息的突变巨簇（GC）。接下来，将突变选择规则应用于GC以挖掘每个CRC患者中驱动基因的突变序列。整合来自同类型CRC患者的突变序列，获得不同类型CRC驱动基因的突变序列，为临床CRC疾病进展的诊断提供参考。最后，采用动态网络分析法挖掘结直肠癌患者的动态网络生物标志物（DNB）。通过临床分期数据验证这些DNB，以确定肿瘤进展中疾病前状态和疾病状态之间的关键转变点。在DNB中发现了12个已知的药物靶点，其中6个已被用作临床治疗抗癌药物的靶点。本研究为结直肠癌的预后、诊断和治疗提供了重要信息，尤其是为早期治疗提供了重要依据。对降低结直肠癌的发病率和死亡率具有重要意义。
Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumor-adjacent tissues in 28 CRC patients were combined into a human protein–protein interaction (PPI) network to construct a specific network for each patient. A network propagation method was used to obtain a mutant giant cluster (GC) containing more than 90% of the mutation information of one patient. Next, mutation selection rules were applied to the GC to mine the mutation sequence of driver genes in each CRC patient. The mutation sequences from patients with the same type CRC were integrated to obtain the mutation sequences of driver genes of different types of CRC, which provide a reference for the diagnosis of clinical CRC disease progression. Finally, dynamic network analysis was used to mine dynamic network biomarkers (DNBs) in CRC patients. These DNBs were verified by clinical staging data to identify the critical transition point between the pre-disease state and the disease state in tumor progression. Twelve known drug targets were found in the DNBs, and 6 of them have been used as targets for anticancer drugs for clinical treatment. This study provides important information for the prognosis, diagnosis and treatment of CRC, especially for pre-emptive treatments. It is of great significance for reducing the incidence and mortality of CRC.
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