Abstract
BackgroundIdentifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.ResultsThrough the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.ConclusionWe showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.
Highlights
Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics
Along with many genetic and genomic studies aimed at identification of disease genes, several computational methods have been proposed to prioritize candidate genes based on various information including sequence similarity
acute myeloid leukemia (AML) subnetworks associated with key leukemogenic processes Through the search for sutnebworks perturbed in AML patients, we identified 269 subnetworks (p < 0.05) comprising of 859 genes whose functions are associated with AML development processes such as myeloid differentiation, cell signaling of growth and survival, cell cycle, cell and tissue remodeling
Summary
Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. The previous studies have incorporated topological characteristics of known disease genes such as degrees in networks [14], the overlap between interaction partners of candidate genes and those of known disease genes [6], the probability of candidate genes to participate in the same protein complexes with known disease-causing genes [10], or the distribution of distances from candidate genes to known disease genes [13] Despite their successful performance in general, for some specific diseases of our interest, such as acute myeloid leukemia (AML), the performance is not satisfactory (AUC = 0.55 by Radivojac et al [13]). Mani et al proposed another method predicting oncogenes in B-cell lymphomas integrating both molecular interactions and mRNA expressions [16]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
