The identification of affected pathway subnetworks and pathway clusters in colon cancer
Creators: name: Gokhan Goy
name: Yazici, Miray Unlu
name: Bakir-Gungor, Burcu
Publisher: name: F1000 Research Limited
Year: 2019

description: Motivation:<br>Currently we encounter with omics revolution in which genome, epigenome, transcriptome, and other omes can be readily characterized. Traditional analyses attempted to untangle the molecular mechanisms of carcinogenesis using a single omic dataset which contributed towards the identification of cancer-specific mutations, epigenetic alterations, etc. However, the acquisition of cancer hallmarks requires molecular alterations at multiple levels. In this respect, combining multi-omic data is critical to understand the casual relationship between molecular signatures. <br>To facilitate the analysis of high-dimensional ‘omic’ data, protein-protein interaction (PPI) networks and biological pathways are widely used. Although several signaling pathways and driver genes crucial in the initiation and progression of cancer have been unveiled, a comprehensive picture of genomic and epigenomic changes in cancer development is far from being complete. <br><br>Method:<br>In this study, we proposed a novel method to detect affected pathway sub-networks and pathway clusters for a specific cancer type using multi-omics data. To test our method, transcriptomic and epigenomic data of colon adenocarcinoma (COAD) were obtained from TCGA. To analyze RNA-Seq and methylation data, edgeR Bioconductor package and ChAMP pipeline were used, respectively. The combined p-values of genes were calculated using Fisher’s combined test. Altered sub-networks which contain genes that are highly altered in transcriptomics and epigenomics datasets of colon cancer and are topologically close in the PPI network were identified using active sub-network search algorithms. Affected pathways were determined using hypergeometric test. <br>To create a pathway network, firstly, a binary gene-pathway matrix was generated. Secondly, a pathway-pathway matrix, which indicates the similarity between pathways was created from this gene-pathway matrix using kappa score. Then, affected pathway sub-networks and pathway clusters were identified on the generated pathway network.<br><br>Results:<br>COAD RNA-Seq data containing p-values of 9,426 genes and methylation data including 122,986 methylation changes of 18,299 genes were compiled for this study (p&lt;0.05). When RNA-Seq and methylation data were combined, 7,546 common genes and their p-values were obtained. 690 affected sub-networks in a human PPI were identified and functionally enriched. <br>A pathway sub-network including 93 pathways and 842 edges was identified from the generated pathway network with 288 KEGG pathway nodes and 10,904 derived connections. Within this pathway sub-network, 4 significant pathway clusters were obtained that included colorectal, pancreatic, endometrial, and prostate cancer, ErbB signaling, glioma, and melanoma pathways. Some of the affected genes within these identified pathways are already known in literature and we identified additional candidate genes.<br><br>Conclusions:<br>As the potency of single omics studies about the etiology of complex diseases is not sufficient, studies that combine multi-omics data and hence identify affected pathways have higher potential to discover cancer hallmarks. Since the pathways are strongly interrelated, here we proposed a novel approach that generates a pathway network using multi omics data, and identifies affected pathway sub-networks and pathway clusters. Our approach is based on both significance level of an affected pathway and its topological relationship with its neighbor pathways. The identified pathway sub-networks, pathway clusters and affected genes within these pathways helped us to illuminate colon cancer development mechanisms.