Abstract
BackgroundThe etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.ResultsWe implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular carcinoma samples (GSE14520), adenoma development in 32 colorectal cancer samples (GSE8671), and prognosis in 198 breast cancer patients (GSE7390). For each study, we find the best scoring partitions under a maximum cluster size of 200 nodes. Significant modules highlight groups of genes that are functionally related to cancer and show promise as therapeutic targets; these include interactions among transcription factors (SPIB, RPS6KA2 and RPS6KA6), cell-cycle regulatory genes (BRSK1, WEE1 and CDC25C), modulators of the cell-cycle and proliferation (CBLC and IRS2) and genes that regulate and participate in the map-kinase pathway (MAPK9, DUSP1, DUSP9, RIPK2). To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks. Compared with other highly cited module-finding tools, jActiveModules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.ConclusionsThese results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes. The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.
Highlights
The etiology of cancer involves a complex series of genetic and environmental conditions
A number of breast cancer prognosis (BC) modules are annotated with progesterone, estrogen and steroid hormone signaling; and levels of these hormones are known to correlate with BC risk
We use a random walk algorithm optimized to search for communities in large networks, to mine for disease genes in a weighted interaction network
Summary
The etiology of cancer involves a complex series of genetic and environmental conditions. GSEA focuses on the significance of groups of interacting genes rather than individual genes; and variations have been developed to improve statistical validity [2,3,4,5] and to use more granular methods to study pathway activity [6,7,8,9] Such approaches allow interpretation of significant genes in the setting of their pathway interactions and functional relevance; they are limited in their ability to search for significantly expressed genes that form a small component of large pathways or interact across multiple gene sets
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