A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

Abstract

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

High miR-205 expression in normal epithelium is associated with biochemical failure - an argument for epithelial crosstalk in prostate cancer?

“…Trevino et al . proposed that normal epithelial cells have the potential to revert some of the traits of tumor cells, effectively normalizing the phenotypic characteristics of the tumor cells 34 , 36 . The integrity and homeostasis of the epithelium are of vital importance to survival.…”

Gene Regulatory Cross Networks: Inferring Gene Level Cell-to-Cell Communications of Immune Cells

“…GCCN is similar to the widely used term cross-talk network but we make the distinction between the two as the following: GCCN only refers to gene level communications, whereas cross-talk network term is used to describe communication in any molecular levels between different cell-types. In summary, the defined GRCN and GCCN terms of this study are more specific than the well-established crosstalk networks (CTN) term that is widely used for any type of network that refers to communication between different cell types at any molecular level [10][11][12][13][14][15]. We consider that GRCN and GCCN are yet to be explored as a relatively new avenue of research and catch up with the classical gene networks inference research of single cell types.…”

“…However, different cell types are also interacting with each other, especially immune cell types, to manifest various complex biological functions. So far, GNI methods have not focused on the gene level interactions between different cell types [3]. In fact, it was stated in the very recent publication [3] that gene network approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue and until then, there has been no attempt to develop a completely data-driven systems biology approach to discover novel cell-communication networks.…”

“…GRNs are well studied to implement in the datasets of single cell types but, to our knowledge, we could not find a previous study that performs only GRNI, without the combination of other algorithms such as differential expression (DE), directly on the gene expression datasets of two different cell types to infer gene regulatory crossnetwork (GRCN) that contains causal direct interactions of genes between the two-cell types. We found a few other related studies but they are mainly the co-expression network based implementations on different cell types and those types of networks were named as cross-talk networks [3][4][5] in general. For example, in [3], a co-expression based network inference method, RELNET applied to link genes differentially expressed in two cell conditions and also introduced a gene connectivity metric to score the targets.…”

“…To the best of our knowledge, this is one of the first study that aims to infer causal direct regulatory interactions between genes of different cell types using only expression dataset and without the combination of any other algorithms such as DE. The closest approach might be considered as the method [3], but their algorithm is limited to infer differentially expressed (DE) genes between the two cell types. However, there are also genes that might be interacting with different genes in different cell conditions and since they express in both conditions, they may not be captured as DE genes.…”