Yu Ling-Hao, Huang Qin-Wei, Zhou Xiong-Hui
College of Science, Huazhong Agricultural University, Wuhan, China.
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Front Genet. 2019 Feb 19;10:99. doi: 10.3389/fgene.2019.00099. eCollection 2019.
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in cancer hallmarks are likely to be cancer drivers. However, there is no appropriate method in the current literature for identifying genetic cancer hallmarks, especially considering the interrelationships among the genes. Here, we hypothesized that "dense clusters" (or "communities") in the gene co-expression networks of cancer patients may represent functional units regarding cancer formation and progression, and the communities present in the co-expression networks of multiple types of cancer may be cancer hallmarks. Consequently, we mined the conserved communities in the gene co-expression networks of seven cancers in order to identify candidate hallmarks. Functional annotation of the communities showed that they were mainly related to immune response, the cell cycle and the biological processes that maintain basic cellular functions. Survival analysis using the genes involved in the conserved communities verified that two of these hallmarks could predict the survival risks of cancer patients in multiple types of cancer. Furthermore, the genes involved in these hallmarks, one of which was related to the cell cycle, could be useful in screening for cancer drugs.
识别癌症的特征对于癌症研究至关重要，而参与癌症特征的基因很可能是癌症驱动因素。然而，当前文献中没有合适的方法来识别遗传性癌症特征，尤其是考虑到基因之间的相互关系。在此，我们假设癌症患者基因共表达网络中的“密集簇”（或“群落”）可能代表与癌症形成和进展相关的功能单元，并且多种癌症共表达网络中存在的群落可能是癌症特征。因此，我们挖掘了七种癌症基因共表达网络中的保守群落，以识别候选特征。群落的功能注释表明，它们主要与免疫反应、细胞周期以及维持基本细胞功能的生物学过程相关。使用参与保守群落的基因进行生存分析证实，其中两个特征可以预测多种癌症患者的生存风险。此外，参与这些特征的基因（其中一个与细胞周期相关）可用于筛选癌症药物。
