123
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Pan-Cancer Modular Regulatory Network Analysis to Identify Common and Cancer-Specific Network Components

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.

          Related collections

          Most cited references51

          • Record: found
          • Abstract: not found
          • Article: not found

          Stability selection

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A genomic code for nucleosome positioning.

            Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome-DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain approximately 50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An atlas of combinatorial transcriptional regulation in mouse and man.

              Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution. (c) 2010 Elsevier Inc. All rights reserved.
                Bookmark

                Author and article information

                Journal
                Cancer Inform
                Cancer Inform
                Cancer Informatics
                Cancer Informatics
                Libertas Academica
                1176-9351
                2014
                28 October 2014
                : 13
                : Suppl 5
                : 69-84
                Affiliations
                [1 ]Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA.
                [2 ]Department of Computer Sciences, University of Wisconsin, Madison, WI, USA.
                [3 ]Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
                Author notes

                *These authors contributed equally.

                Article
                cin-suppl.5-2014-069
                10.4137/CIN.S14058
                4213198
                25374456
                84c73e69-a895-4314-8902-e4ed400992ea
                © 2014 the author(s), publisher and licensee Libertas Academica Ltd.

                This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

                History
                : 07 July 2014
                : 22 September 2014
                : 24 September 2014
                Categories
                Original Research

                Oncology & Radiotherapy
                the cancer genome atlas (tcga),regulatory modules,transcriptional regulatory networks,pan-cancer analysis,stability selection,modular regulatory network inference,probabilistic graphical models

                Comments

                Comment on this article