Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis

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Abstract
Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage. Diseases such as cancer are often associated with malfunctioning pathways involving several genes. Identifying modules of such genes and how the genes in each module interact with each other is helpful toward understanding the nature of these diseases. Here the authors provide a novel computational method for discovering such modules of genes merely from two sets of gene expression data, one from healthy tissues and one from tissues suffering from a particular disease. The method is based on the concept of identifying sets of genes whose joint expression state predicts the presence or absence of a particular disease with minimum uncertainty. Once such gene sets have been identified, we can then further use the microarray data to determine the “logic” that connects the genes' individual expression states related to the outcome of the disease. In turn, this logic may give us valuable insight into the nature of the pathways and how we may target some elements of these pathways for therapeutic purposes. The authors apply this methodology in a particular example and conclude that prostate cancer is often associated with cellular damage from oxidative stress combined with the inhibition of the apoptotic mechanisms normally activated by such damage.