2019
DOI: 10.3390/genes10110865
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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 wi… Show more

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Cited by 28 publications
(27 citation statements)
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References 300 publications
(350 reference statements)
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“…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]).…”
Section: Introductionmentioning
confidence: 99%
“…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]).…”
Section: Introductionmentioning
confidence: 99%
“…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].…”
Section: Functional Exploration Of Tumor Transcriptomesmentioning
confidence: 99%
“…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.…”
Section: Functional Exploration Of Integrated Multi-omics Analyses Anmentioning
confidence: 99%
“…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 ]. …”
Section: Specific Applicationsmentioning
confidence: 99%
“…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 ];…”
Section: Specific Applicationsmentioning
confidence: 99%