Abstract
In the era of network medicine, pathway analysis methods play a central role in the prediction of phenotype from high throughput experiments. In this paper, we present a network-based systems biology approach capable of extracting disease-perturbed subpathways within pathway networks in connection with expression data taken from The Cancer Genome Atlas (TCGA). Our system extends pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The framework enables the extraction, visualization, and analysis of statistically significant disease-specific subpathways through an easy to use web interface. Our analysis shows that the methodology is able to fill the gap in current techniques, allowing a more comprehensive analysis of the phenomena underlying disease states.
Highlights
Knowledge base-driven pathway analysis [1] has become a popular approach in complex disease study in order to improve biological insights for a deeper understanding of the molecular mechanisms underlying specific phenotypes.“Omics” technologies are capable of identifying differentially expressed genes and metabolites associated with specific diseases
The identification of cancer sub-type relies on the expression of the estrogen receptor (ER), progesterone receptor (PR), epidermal growth factor receptor 2 (ERBB2), and cytokeratin (CK) protein [31]
We proposed a novel methodology, SPECifIC (SubPathway ExtraCtor and enrICher), for extracting, visualizing, and enriching substructures obtained from a meta-pathway built by combining Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways
Summary
Knowledge base-driven pathway analysis [1] has become a popular approach in complex disease study in order to improve biological insights for a deeper understanding of the molecular mechanisms underlying specific phenotypes.“Omics” technologies are capable of identifying differentially expressed genes and metabolites associated with specific diseases. Several applications have been implemented to visualize and analyze “Omics” data in the context of known biological pathways [1]. To this purpose, several statistical tests in connection with known biological databases have been used to detect significant pathways. The gap between current analysis techniques and the ability to obtain accurate knowledge is broad. Using such information to better understand the underlying biological phenomena remains a challenge
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