从动态模块网络预测慢性肝病患者的肝细胞癌风险

StephenW

Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks

    Yinying Chen, Wei Yang, Qilong Chen, Qiong Liu, Jun Liu, Yingying Zhang, Bing Li, Dongfeng Li, Jingyi Nan, Xiaodong Li, Huikun Wu, Xinghua Xiang, Yehui Peng, Jie Wang, Shibing Su & Zhong Wang

Journal of Translational Medicine volume 19, Article number: 122 (2021) Cite this article

    Metrics details

Abstract
Background

Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease.
Methods

In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs.
Results

We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes.
Conclusions

These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.

StephenW

从动态模块网络预测慢性肝病患者的肝细胞癌风险

    陈寅英，杨伟，陈其龙，刘琼，刘军，张颖颖，李冰，李东风，南经义，李晓东，吴慧坤，项兴华，彭业辉，王杰，苏士兵和王忠

转化医学杂志第19卷，文章号：122（2021）引用本文

    指标详细信息

抽象的
背景

在正常范式下，不可能在没有任何临床表现的情况下在肝癌的超癌前期发现潜在的预测风险，但对于控制这种复杂疾病至关重要。
方法

在这项研究中，我们利用了基于序列变构模块（AMs）的拟议方法，并定量计算了这些AMs的拓扑结构变异。
结果

我们发现使用SimiNEF的慢性乙型肝炎（CHB），肝硬化和HCC网络中共有13种致癌变构模块（OAM）。我们从12个OAM中获得了11个高度相关的基因对，涉及15个基因（r> 0.8，P <0.001）（袋外（OOB）分类错误率<0.5），部分与独立临床微阵列数据中的一致，然后优化三基因组（cyp1a2-cyp2c19-il6），以使用曲线下平均面积（AUC）为0.973的随机森林将HCC与非肿瘤肝组织区分开。此外，我们发现用该化合物处理的斑马鱼对Bel-7402，Hep 3B和Huh7细胞系的肿瘤生长具有明显的抑制作用，从而影响了这三个基因。
结论

这些发现表明，基于顺序AMs的方法可以检测慢性肝病患者的HCC风险，并且可以应用于任何时间依赖性的癌症风险。

StephenW

https://translational-medicine.b ... /s12967-021-02791-9