Cancer Genetic Network Inference Using Gaussian Graphical Models

Abstract

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

Keywords: Computational biology; machine learning; network meta-analysis.

Figure 1.

Pipeline of inferring gene-interaction networks.

Figure 2.

A network of cross-cancer gene interactions that are unique to the inferred cancer networks. A node in the network represents a gene, and an edge indicates the conditional dependence of the two incident genes. The conditional dependence depicts the interaction of the genes at the expression level. The thickness of an edge represents the degree of consensus of the interaction among the cancer networks. The edges in this network are shared by at least five cancer networks but absent in all normal networks.

Figure 3.

The map of consensus gene interactions that are appeared in at least 5 of the 15 cancer networks but not present in any normal network. This map depicts a portion of the precision matrices, indicating the conditional dependence between a pair of genes in a specific cancer. The higher the value in the map is, the stronger the conditional dependence (interaction) of the pair of genes.

Figure 4.

A network of cross-cancer interactions that are mostly unique to the inferred cancer networks. A node in the network represents a gene and an edge indicates the conditional dependence of the two incident genes. The conditional dependence depicts the interaction of the genes at the expression level. The thickness of an edge represent the degree of consensus of the interaction among the cancer networks. Edges in this network represent the interactions identified in at least five cancer networks but also appeared in one normal network.

Figure 5.

The map of consensus gene interactions that are appeared in at least 5 of the 15 cancer networks; these interactions are also present in one normal network. The higher the value in the map is, the stronger the conditional dependence (interaction) of the pair of genes. A black square denotes the interaction is also appeared in the normal network of the corresponding organ.

Figure 6.

A network of strong cross-cancer interactions. A node in the network represents a gene and an edge indicates the conditional dependence of the two incident genes. The conditional dependence depicts the interaction of the genes at the expression level. The thickness of an edge represents the degree of consensus of the interaction among the cancer networks. Edges in this network represent the interactions identified in at least five cancer networks, and they are either absent in any normal network or appear in just one normal network.