Dynamic protein interaction modules in human hepatocellular carcinoma progression

Abstract

BackgroundGene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.ResultsCompared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.ConclusionsWe demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.

Fundamentals of protein interaction network mapping

“…An interaction may be diseaserelated if the two encoding genes are both expressed only in the disease state (or are upregulated in the disease state; Ideker et al, 2002), or the genes have correlated expression profiles in disease states (Camargo & Azuaje, 2007;Guo et al, 2007;Xiao et al, 2012). Molecular Systems Biology Protein interaction network mapping Jamie Snider et al Differential correlation of gene expression profiles can provide more specific information about an interaction: if two genes have significantly different correlation levels in two conditions, then the interaction of their protein products may change between conditions Yoon et al, 2011;Zhang et al, 2012b;Yu et al, 2013).…”

An integrative, genomic, transcriptomic and network-assisted study to identify genes associated with human cleft lip with or without cleft palate

“…EW_dmGWAS (Edge-Weighted dense module search for Genome-Wide Association Studies) is the upgraded algorithm of dmGWAS, which integrates not only GWAS signals but also gene expression profiles in order to identify dense modules in node-weighted and edge-weighted PPI network [19]. The importance of differential gene expression has been demonstrated in a study of hepatocellular carcinoma because it represents disease-associated transcriptional information [20]. Combination of gene expression profiles and PPI network could outperform traditional analysis in uncovering the mechanisms of disease.…”

Dense module searching for gene networks associated with multiple sclerosis

“…Additionally, differing genotyping platforms used by GWAS and other factors (ethnic background, sample size, statistical tests) can make it difficult to perform meta-analytical studies, but network-based analysis is a promising approach to detect combinatory association signals in network modules. This study aims to apply network approach to find joint association signals at the network modules, and thus, leading to biologically interpretable results [12,13]. A new version of the dense module searching algorithm (dmGWAS) is used to integrate GWAS signals with a comprehensive human protein-protein interaction (PPI) network so that MS candidate subnetworks can be identified [11].…”