iSubgraph: Integrative Genomics for Subgroup Discovery in Hepatocellular Carcinoma Using Graph Mining and Mixture Models

• Published in: PloS one Vol. 8; no. 11; p. e78624
• Main Authors: Ozdemir, Bahadir, Abd-Almageed, Wael, Roessler, Stephanie, Wang, Xin Wei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.11.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bioinformatics; Biology; Cancer; Carcinoma, Hepatocellular - classification; Carcinoma, Hepatocellular - genetics; Carcinoma, Hepatocellular - mortality; Carcinoma, Hepatocellular - pathology; Clustering; Computer Graphics; Cybernetics; Data Mining; Data processing; DNA methylation; DNA microarrays; Epidermal growth factor receptors; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene regulation; Gene Regulatory Networks; Genes; Genetic aspects; Genomes; Genomics; Genomics - methods; Graphical representations; Health aspects; Hepatocellular carcinoma; Hepatoma; Heterogeneity; Humans; International conferences; Kaplan-Meier Estimate; Laboratories; Liver cancer; Liver Neoplasms - classification; Liver Neoplasms - genetics; Liver Neoplasms - mortality; Liver Neoplasms - pathology; Male; Mathematical models; Medical research; MicroRNA; MicroRNAs; MicroRNAs - genetics; MicroRNAs - metabolism; Middle Aged; Mineral industry; Mining industry; miRNA; Models, Genetic; Modules; Mortality; Myc protein; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasm Staging; Networks; Patients; Pattern recognition; Prediction models; Ribonucleic acid; RNA; RNA, Messenger - genetics; RNA, Messenger - metabolism; Subgroups; Survival; Survival analysis; Therapeutic applications; Transforming growth factor-b1; Tumor necrosis factor; Tumors; United States > US; Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0078624

The high tumor heterogeneity makes it very challenging to identify key tumorigenic pathways as therapeutic targets. The integration of multiple omics data is a promising approach to identify driving regulatory networks in patient subgroups. Here, we propose a novel conceptual framework to discover patterns of miRNA-gene networks, observed frequently up- or down-regulated in a group of patients and to use such networks for patient stratification in hepatocellular carcinoma (HCC). We developed an integrative subgraph mining approach, called iSubgraph, and identified altered regulatory networks frequently observed in HCC patients. The miRNA and gene expression profiles were jointly analyzed in a graph structure. We defined a method to transform microarray data into graph representation that encodes miRNA and gene expression levels and the interactions between them as well. The iSubgraph algorithm was capable to detect cooperative regulation of miRNAs and genes even if it occurred only in some patients. Next, the miRNA-mRNA modules were used in an unsupervised class prediction model to discover HCC subgroups via patient clustering by mixture models. The robustness analysis of the mixture model showed that the class predictions are highly stable. Moreover, the Kaplan-Meier survival analysis revealed that the HCC subgroups identified by the algorithm have different survival characteristics. The pathway analyses of the miRNA-mRNA co-modules identified by the algorithm demonstrate key roles of Myc, E2F1, let-7, TGFB1, TNF and EGFR in HCC subgroups. Thus, our method can integrate various omics data derived from different platforms and with different dynamic scales to better define molecular tumor subtypes. iSubgraph is available as MATLAB code at http://www.cs.umd.edu/~ozdemir/isubgraph/.