Wang Jie, Lou Yi, Lu Jianmin, Luo Yuxiao, Lu Anqian, Chen Anna, Fu Jiantao, Liu Jing, Zhou Xiang, Yang Jin
Department of Translational Medicine, Affiliated Hospital of Hangzhou Normal University, Institute of Hepatology and Metabolic Diseases of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Department of Liver Disease, Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang, China.
J Clin Transl Hepatol. 2021 Feb 28;9(1):22-31. doi: 10.14218/JCTH.2020.00084. Epub 2021 Jan 4.
Great efforts have been made towards increasing our understanding of the pathogenesis involved in hepatocellular carcinoma (HCC), but the rapid growth inherent to such tumor development remains to be explored.
We identified distinct gene coexpression modes upon liver tumor growth using weighted gene coexpression network analysis. Modeling of tumor growth as signaling activity was employed to understand the main cascades responsible for the growth. Hub genes in the modules were determined, examined , and further assembled into the growth signature.
We revealed modules related to the different growth states in HCC, especially the fastest growth module, which is preserved among different HCC cohorts. Moreover, signaling flux in the cell cycle pathway was found to act as a driving force for rapid growth. Twenty hub genes in the module were identified and assembled into the growth signature, and two genes (, and ) were tested for their growth potential . Genetic alteration of the growth signature affected the global gene expression. The activity of the signature was associated with tumor metabolism and immunity in HCC. Finally, the prognosis effect of the growth signature was reproduced in nine cancers.
These results collectively demonstrate the molecule organization of rapid tumor growth in HCC, which is a highly synergistic process, with implications for the future management of patients.
为增进对肝细胞癌（HCC）发病机制的理解，人们已付出巨大努力，但这种肿瘤发展所固有的快速生长仍有待探索。
我们使用加权基因共表达网络分析确定了肝脏肿瘤生长过程中不同的基因共表达模式。将肿瘤生长建模为信号活动，以了解负责生长的主要级联反应。确定、检查模块中的枢纽基因，并进一步将其组装成生长特征。
我们揭示了与HCC不同生长状态相关的模块，特别是在不同HCC队列中均保留的最快生长模块。此外，发现细胞周期途径中的信号通量是快速生长的驱动力。确定了模块中的20个枢纽基因并将其组装成生长特征，并测试了两个基因（，和）的生长潜力。生长特征的基因改变影响整体基因表达。该特征的活性与HCC中的肿瘤代谢和免疫相关。最后，在9种癌症中重现了生长特征的预后效应。
这些结果共同证明了HCC中肿瘤快速生长的分子组织，这是一个高度协同的过程，对未来患者的管理具有启示意义。
