Abstract
BackgroundRecent discovery in tumor development indicates that the tumor microenvironment (mostly stroma cells) plays an important role in cancer development. To understand how the tumor microenvironment (TME) interacts with the tumor, we explore the correlation of the gene expressions between tumor and stroma. The tumor and stroma gene expression data are modeled as a weighted bipartite network (tumor-stroma coexpression network) where the weight of an edge indicates the correlation between the expression profiles of the corresponding tumor gene and stroma gene. In order to efficiently mine this weighted bipartite network, we developed the Bipartite subnetwork Component Mining algorithm (BCM), and we show that the BCM algorithm can efficiently mine weighted bipartite networks for dense Bipartite sub-Networks (BiNets) with density guarantees.ResultsWe applied BCM to the tumor-stroma coexpression network and find 372 BiNets that demonstrate statistical significance in survival tests. A good number of these BiNets demonstrate strong prognosis powers on at least one breast cancer patient cohort, which suggests that these BiNets are potential biomarkers for breast cancer prognosis. Further study on these 372 BiNets by the network merging approach reveals that they form 10 macro bipartite networks which show orchestrated key biological processes in both tumor and stroma. In addition, by further examining the BiNets that are significant in ER-negative breast cancer patient prognosis, we discovered a ubiquitin C (UBC) gene network that demonstrates strong prognosis power in nearly all types of breast cancer subtypes we used in this study.ConclusionsThe results support our hypothesis that the UBC gene network plays an important role in breast cancer prognosis and therapy and it is a potential prognostic biomarker for multiple breast cancer subtypes.
Highlights
The initiation, development and metastasis of tumor are complicated biological processes
In this paper we propose a novel weighted Bipartite network Component Mining algorithm Bipartite subnetwork Component Mining algorithm (BCM) which guarantees a lower bound on the densities of the identified components, i.e., Bipartite sub-Networks (BiNets)
BiNets in tumor-stroma co-expression network Using the BCM algorithm described in the Materials and Methods, we obtained 826 BiNets with a bounded density
Summary
The initiation, development and metastasis of tumor are complicated biological processes. Recently it has been shown that genetic changes in the stroma (e.g., in fibroblast) can lead to the development of epithelial tumor [2]. An important issue in cancer research is to understand how TME components interact with the tumor. Recent research results provide further evidences that tumor-stroma interaction plays an important role in breast cancer tumor growth [4,5]. Despite these progresses and intensive research efforts, many issues still remain unclear, including how such interactions lead to the intracellular changes in tumor and TME components. Recent discovery in tumor development indicates that the tumor microenvironment (mostly stroma cells) plays an important role in cancer development. In order to efficiently mine this weighted bipartite network, we developed the Bipartite subnetwork Component Mining algorithm (BCM), and we show that the BCM algorithm can efficiently mine weighted bipartite networks for dense Bipartite sub-Networks (BiNets) with density guarantees
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