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Meta-analysis of liver transcriptomic data identifies accurate disease classifiers and disease perturbed networks

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Title: Meta-analysis of liver transcriptomic data identifies accurate disease classifiers and disease perturbed networks
Author(s): Wang, Yuliang
Advisor(s): Price, Nathan
Department / Program: Chemical & Biomolecular Engr
Discipline: Chemical Engineering
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: M.S.
Genre: Thesis
Subject(s): microarray meta-analysis disease transcriptomic signature network perturbation
Abstract: Chronic liver diseases are a major health problem. Previous DNA microarray studies of different liver diseases have improved our knowledge of the molecular pathogenesis of liver diseases and produced potential biomarkers. However, these studies typically rely on binary phenotype comparisons (e.g. cancer vs. normal) to identify disease signatures. It is possible that the resulting signatures may be partially shared by other liver diseases not included in the binary comparison. In this study, we took a comprehensive and organ-specific approach, where we studied all liver pathophysiological states in a single unifying context, and found a specific transcriptomic signature for each phenotype with respect to all the other phenotypes, instead of just one. The resulting 36-gene disease signature had 85% accuracy in 10 fold cross validation. Through stringent leave-one-lab out independent validation, we found that high classification accuracy was achieved when there was a total of around 100 samples from 2 independent contributing labs. We also identified perturbed networks in liver diseases in general and hepatocellular carcinoma in particular. Many of the classifier genes and perturbed networks are involved in important biological processes in liver disease pathogenesis, including immune response and inflammation, fibrogenesis, metabolism and its regulation, apoptosis, and cellular signaling. The disease classifiers and perturbed networks identified in this study may be potential candidates for novel diagnostic approaches to multiple liver diseases.
Issue Date: 2011-05-25
URI: http://hdl.handle.net/2142/24023
Rights Information: Copywright 2011 Yuliang Wang
Date Available in IDEALS: 2011-05-25
Date Deposited: 2011-05
 

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