基于复杂网络理论的肺癌特异性基因识别研究

基于复杂网络理论分别构建以肺癌组织和健康肺组织基因为节点,基因间调控关系为边的基因调控网络,并从拓扑结构、分布特征、节点中心性三方面分析网络特性,挖掘网络核心节点生物功能差异性以识别出肺癌特异性基因。结果发现,肺癌组织和健康肺组织基因调控网络拓扑参数极其相似且两者都为无标度网络,两网络核心节点集高度重叠,但非重叠部分核心节点的生物功能十分特殊,并据此识别出肺癌特异性基因。该方法识别出的肺癌特异性基因,能够成为潜在肺癌生物标记物,为肺癌的早期诊断提供帮助,同时该方法能够适用于其他疾病特异性基因的识别。

Research on Specific Gene Identification of Lung Cancer Based on Complex Network Theory

Based on complex network theory, the study constructs lung cancer tissue and healthy lung tissue gene regulatory networks with genes as nodes and inter-gene regulatory relationships as edges. The paper analyzes network characteristics from three aspects: topological structure, distribution characteristics, and node centrality. By comparing the biological function of the key nodes of networks to identify lung cancer-specific genes, it is found that the topological parameters of the two gene regulatory networks are extremely similar and they are both scale-free networks. The key nodes set of the two networks are highly overlapping, but the non-overlapping key nodes’ biological functions are very special, and then the lung cancer-specific genes are identified base on this. The lung cancer-specific genes identified by this method may be potential biomarkers for lung cancer, which promotes the early diagnosis of lung cancer. The method can also be applied in the identification of other disease-specific genes.