Publication:
Identification of interconnected markers for T-cell acute lymphoblastic leukemia

dc.contributor.coauthorNg, Özden Hatırnaz
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Chemical and Biological Engineering
dc.contributor.departmentCCBB (The Center for Computational Biology and Bioinformatics)
dc.contributor.kuauthorGürsoy, Attila
dc.contributor.kuauthorKeskin, Özlem
dc.contributor.kuauthorMaiorov, Emine Güven
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteResearch Center
dc.date.accessioned2024-11-09T13:11:34Z
dc.date.issued2013
dc.description.abstractT-cell acute lymphoblastic leukemia (T-ALL) is a complex disease, resulting from proliferation of differentially arrested immature T cells. The molecular mechanisms and the genes involved in the generation of T-ALL remain largely undefined. In this study, we propose a set of genes to differentiate individuals with T-ALL from the nonleukemia/healthy ones and genes that are not differential themselves but interconnected with highly differentially expressed ones. We provide new suggestions for pathways involved in the cause of T-ALL and show that network-based classification techniques produce fewer genes with more meaningful and successful results than expression-based approaches. We have identified 19 significant subnetworks, containing 102 genes. The classification/prediction accuracies of subnetworks are considerably high, as high as 98%. Subnetworks contain 6 nondifferentially expressed genes, which could potentially participate in pathogenesis of T-ALL. Although these genes are not differential, they may serve as biomarkers if their loss/gain of function contributes to generation of T-ALL via SNPs. We conclude that transcription factors, zinc-ion-binding proteins, and tyrosine kinases are the important protein families to trigger T-ALL. These potential disease-causing genes in our subnetworks may serve as biomarkers, alternative to the traditional ones used for the diagnosis of T-ALL, and help understand the pathogenesis of the disease.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipN/A
dc.description.versionPublisher version
dc.description.volume2013
dc.identifier.doi10.1155/2013/210253
dc.identifier.eissn2314-6141
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR00040
dc.identifier.issn2314-6133
dc.identifier.quartileQ3
dc.identifier.scopus2-s2.0-84897367794
dc.identifier.urihttps://hdl.handle.net/20.500.14288/2865
dc.identifier.wos322474800001
dc.keywordsBiotechnology and applied microbiology
dc.keywordsHedgehog-interacting protein
dc.keywordsFocal-adhesion kinase
dc.keywordsChronic lymphocytic-leukemia
dc.keywordsAcute myeloid-leukemia
dc.keywordsP2X7 receptor gene
dc.keywordsExpression profiles
dc.keywordsCancer-theraphy
dc.keywordsHepatocellular-carcinoma
dc.language.isoeng
dc.publisherHindawi
dc.relation.grantnoNA
dc.relation.ispartofBiomed Research International
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/1071
dc.subjectMedicine
dc.subjectResearch and experimental medicine
dc.titleIdentification of interconnected markers for T-cell acute lymphoblastic leukemia
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorMaiorov, Emine Güven
local.contributor.kuauthorKeskin, Özlem
local.contributor.kuauthorGürsoy, Attila
local.publication.orgunit1College of Engineering
local.publication.orgunit1Research Center
local.publication.orgunit2CCBB (The Center for Computational Biology and Bioinformatics)
local.publication.orgunit2Department of Chemical and Biological Engineering
local.publication.orgunit2Department of Computer Engineering
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