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BioMed Research International
Volume 2013 (2013), Article ID 210253, 20 pages
Research Article

Identification of Interconnected Markers for T-Cell Acute Lymphoblastic Leukemia

1Center for Computational Biology and Bioinformatics and College of Engineering, Koç University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey
2Department of Genetics, Institute for Experimental Medicine (DETAE), Istanbul University, 34393 Istanbul, Turkey

Received 29 April 2013; Accepted 4 June 2013

Academic Editor: Tao Huang

Copyright © 2013 Emine Guven Maiorov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


T-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.