The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Abstract

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.

Gene co-expression is distance-dependent in breast cancer

“…In this sense, gene expression profiles and co-expression patterns might provide insight about shared transcriptional regulatory mechanisms [1,2]. Among the elements involved in those regulatory mechanisms there are proteins that constitute the transcriptional machinery such as the RNA polymerase II and its associated enzymes [3], transcription factors and their cofactors, sequences identified by those transcription factors such as promoters [4] and enhancers [5,6], histone modifications , both repressing or promoting gene expression, and structural proteins associated with chromatin architecture [7] (for a comprehensive review see [8,9]).…”

Strategies for Functional Interrogation of Big Cancer Data Using Drosophila Cancer Models

“…In addition to changes in the expression levels of protein coding genes, sequencing studies also revealed global changes in other types of RNA in tumor cells, microRNAs, and long noncoding RNAs (lncRNA) in particular [8,37]. These cancer associated RNAs-sometimes referred to as oncomiRs and onco-lncRNAs-can have global effects on gene expression and signaling pathway output, affecting a wide number of cancer relevant cellular processes, often in a tissue specific manner [8,[38][39][40][41][42].…”

“…Multi-omics integration studies and network-based models use sophisticated computational approaches to integrate data obtained by different high-throughput omics technologies [8,37,59]. These approaches explore interactions between different levels of alterations (DNA, RNA, protein, chromatin, and so on) to evaluate systems-level changes in tumor cells and identify integrated molecular signatures that correlate with different aspects of tumorigenesis, patient outcome, and drug response.…”

On a Class of Tensor Markov Fields

“…After having considered some of the properties of this class of Tensor Markov Fields, it may become evident that aside from purely theoretical importance, there is a number of important applications that may arise as probabilistic graphical models in tensor valued problems, among the ones that are somewhat evident are the following: The analysis of multidimensional biomolecular networks such as the ones arising from multi-omic experiments (For a real-life example, see Figure 4 ) [ 8 , 9 , 10 ]; Probabilistic graphical models in computer vision (especially 3D reconstructions and 4D [3D+time] rendering) [ 11 ]; The study of fracture mechanics in continuous deformable media [ 12 ]; Probabilistic network models for seismic dynamics [ 13 ]; Boolean networks in control theory [ 14 ]. …”

“…The analysis of multidimensional biomolecular networks such as the ones arising from multi-omic experiments (For a real-life example, see Figure 4 ) [ 8 , 9 , 10 ];…”