Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

BackgroundWhole-genome duplication (WGD) events have shaped the genomes of eukaryotic organisms. Relaxed selection after duplication along with inherent functional constraints are thought to determine the fate of the paralogs and, ultimately, the evolution of gene function. Here, we investigated the rate of protein evolution (as measured by dN/dS ratios) before and after the WGD in the hemiascomycete yeasts, and the way in which changes in such rates relate to molecular and biological function.ResultsFor most groups of orthologous genes (81%) we observed a change in the rates of evolution after genome duplication. Genes with atypically-low dN/dS ratio before the WGD were prone to increase their rates of evolution after duplication. Importantly, the paralogs were often different in their rates of evolution after the WGD (50% cases), however, this was more consistent with an asymmetric deceleration in the protein-evolution rates, rather than an asymmetric increase of the initial rates. Functional-category analysis showed that regulatory proteins such as protein kinases and transcription factors were enriched in genes that increase their rates of evolution after the WGD. While changes in the rate of protein-sequence evolution were associated to protein abundance, content of disordered regions, and contribution to fitness, these features were an attribute of specific functional classes.ConclusionsOur results indicate that strong purifying selection in ancestral pre-duplication sequences is a strong predictor of increased rates after the duplication in yeasts and that asymmetry in evolution rate is established during the deceleration phase. In addition, changes in the rates at which paralogous sequences evolve before and after WGD are different for specific protein functions; increased rates of protein evolution after duplication occur preferentially in specific protein functions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-017-0895-1) contains supplementary material, which is available to authorized users.

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Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.