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A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

dc.creatorVíctor Manuel Treviño Alvarado
dc.date2016
dc.date.accessioned2018-10-19T14:22:12Z
dc.date.available2018-10-19T14:22:12Z
dc.identifier.issn1553734X
dc.identifier.doi10.1371/journal.pcbi.1004884
dc.identifier.urihttp://hdl.handle.net/11285/630657
dc.descriptionAbstract: The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems. © 2016 Trevino et al.
dc.languageeng
dc.publisherPublic Library of Science
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84964702648&doi=10.1371%2fjournal.pcbi.1004884&partnerID=40&md5=36bf44a4ca133eea43cb4dbaff2ed11d
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourcePLoS Computational Biology
dc.subjectArticle
dc.subjectbioengineering
dc.subjectcancer survival
dc.subjectcell communication
dc.subjectcell function
dc.subjectcell interaction
dc.subjectDNA damage
dc.subjectgene dosage
dc.subjectgene expression
dc.subjectgene overexpression
dc.subjectgene regulatory network
dc.subjectgenetic association
dc.subjectgenetic variability
dc.subjecthuman
dc.subjectnetwork reverse engineering
dc.subjectoutcome assessment
dc.subjectpolarization
dc.subjectprediction
dc.subjectprognosis
dc.subjectprostate cancer cell line
dc.subjectprostate carcinoma
dc.subjectprostate epithelium cell
dc.subjectsignal transduction
dc.subjecttranscription regulation
dc.subjectvalidation process
dc.subjectBayes theorem
dc.subjectbiological model
dc.subjectbiology
dc.subjectcell line
dc.subjectcoculture
dc.subjectcytology
dc.subjectepithelium cell
dc.subjectgene expression profiling
dc.subjectgene regulatory network
dc.subjectgenetics
dc.subjectmale
dc.subjectmetabolism
dc.subjectpathology
dc.subjectprostate
dc.subjectprostate tumor
dc.subjecttumor cell line
dc.subjectBayes Theorem
dc.subjectCell Communication
dc.subjectCell Line
dc.subjectCell Line, Tumor
dc.subjectCoculture Techniques
dc.subjectComputational Biology
dc.subjectEpithelial Cells
dc.subjectGene Expression Profiling
dc.subjectGene Regulatory Networks
dc.subjectHumans
dc.subjectMale
dc.subjectModels, Biological
dc.subjectProstate
dc.subjectProstatic Neoplasms
dc.subjectSignal Transduction
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleA Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells
dc.typeArtículo
dc.identifier.volume12
dc.identifier.issue4
refterms.dateFOA2018-10-19T14:22:12Z


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