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癌相关基因在疾病发生与发展过程中的作用机理是非常重要的研究课题。现代数据分析方法,为从基因组数据中推断癌相关基因之间的关联,以及分析基因组的作用机理提供了有效的手段。本文根据基因表达谱数据分别建立了正常组织与神经胶质瘤和肾癌的患病组织的基因互信息网络。用以介数为基础的相继故障模型研究了基因网络结构与鲁棒性之间的关系。定义了网络相继故障节点百分比、平均相继故障规模和相继故障规模比例累积概率等衡量网络鲁棒性的结构参数。通过对照组与实验组之间的比对,我们发现实验组网络比对照组网络更加稳定,并将引起网络大规模相继故障的基因称为结构性关键基因。这些结构性关键基因中的一部分已经被证明与神经胶质瘤或肾癌的发生、发展有密切关系。大多数基因被预测在神经胶质瘤和肾癌的发生中起着激励或抑制作用,需要进一步的试验验证。预测信息为研究癌相关基因提供了新的方向。
The mechanism of action of cancer related genes in the process of disease occurrence and development is a very important research topic. Modern data analysis methods provide an effective means of inferring associations between cancer-related genes from genomic data and analyzing the mechanisms of action of the genome. In this paper, gene-expression data were used to establish a genetic mutual information network between normal tissues and diseased tissue of glioma and renal cell carcinoma. The relationship between the structure of genetic networks and robustness was studied by using the successive failure model based on meso-number. The structural parameters that measure the robustness of the network are defined, such as the percentage of nodes in succession, the average successive failure scale and the cumulative probability of successive failures. Through the comparison between the control group and the experimental group, we found that the experimental group network is more stable than the control group network, and the genes that cause large-scale network failure are called structural key genes. Some of these structurally-critical genes have been shown to be closely linked to the development and progression of gliomas or renal cancers. Most of the genes predicted to play a stimulating or suppressive role in the development of glioma and renal cell carcinoma require further experimental verification. Prediction information provides a new direction for the study of cancer-related genes.