BACKGROUND Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. METHODS In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. RESULTS We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. CONCLUSIONS The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

中文翻译：

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一种基于多癌症集成数据的共表达网络信息挖掘新方法。

背景技术基因共表达网络是揭示疾病性质的有利方法。随着癌症的发展，基于癌症数据构建基因共表达网络的方法已成为热点。然而，用于多癌症网络建设的当前节点测量方法和节点挖掘策略仍然数量有限。方法在本文中，我们介绍了一种基于多癌症集成数据的共表达网络信息挖掘新方法，即PMN。我们基于癌症基因组图谱（TCGA）的多癌综合基因表达数据，通过组合针对不同节点的不同类型的相关度量（线性和非线性规则）来构建网络。对于挖掘基因，我们结合了节点的不同属性（局部和全局特征）。结果我们发现了更多可疑的异常表达基因和不同癌症的共有途径。而且我们还发现了一些可靠的基因和途径。当然，有些可疑因素和分子需要临床验证。结论结果表明，我们的方法在挖掘多癌基因共表达基因方面非常有效。