Subpathway Analysis based on Signaling-Pathway Impact Analysis of Signaling Pathway

Abstract

Pathway analysis is a common approach to gain insight from biological experiments. Signaling-pathway impact analysis (SPIA) is one such method and combines both the classical enrichment analysis and the actual perturbation on a given pathway. Because this method focuses on a single pathway, its resolution generally is not very high because the differentially expressed genes may be enriched in a local region of the pathway. In the present work, to identify cancer-related pathways, we incorporated a recent subpathway analysis method into the SPIA method to form the “sub-SPIA method.” The original subpathway analysis uses the k-clique structure to define a subpathway. However, it is not sufficiently flexible to capture subpathways with complex structure and usually results in many overlapping subpathways. We therefore propose using the minimal-spanning-tree structure to find a subpathway. We apply this approach to colorectal cancer and lung cancer datasets, and our results show that sub-SPIA can identify many significant pathways associated with each specific cancer that other methods miss. Based on the entire pathway network in the Kyoto Encyclopedia of Genes and Genomes, we find that the pathways identified by sub-SPIA not only have the largest average degree, but also are more closely connected than those identified by other methods. This result suggests that the abnormality signal propagating through them might be responsible for the specific cancer or disease.

A Scoring System to Evaluate the Impact of SNPs in a Path Related Context to Study Behçet’s Disease Aetiology in Japanese Population

“…We compared our results with the results of Signaling Pathway Impact Analysis (SPIA) method proposed by Tarca et al (2009) and Sub-SPIA method proposed by Li et al (2015). In Table 2 and Table 3, most significant 20 pathways found by SPIA and Sub-SPIA are given respectively.…”

“…In active subnetwork search and biclustering methods, a list of genes related to the condition is obtained but these methods do not clarify the impact on signal flow cascades. In network propagation methods (Draghici et al 2007;Tarca et al 2009;Vaske et al 2010;Dutta et al 2012;Haynes et al 2013;Sebastian-Leon et al 2014;Li et al 2015;Bokanizad et al 2016), molecular profiles are used with directed gene networks to determine their effects on the signal flow. The advantage of network propagation methods over other methods is considering the interactions among genes to understand how an alteration affects signaling taking into account the position of the signature gene in the network.…”

“…A circuit's activation probability is calculated by multiplying probabilities of nodes on the same path and calculating the probability of activation of at least one path. Li et al (2015) proposed Sub-SPIA method that applies SPIA to subpathways. These subpathways are acquired by starting with significant nodes in the pathway, connecting the ones that have at most n non-significant genes in-between, applying Kruskal algorithm to find minimum spanning tree and removing unnecessary non-significant genes.…”

A Note on Stochastic Modeling of Biological Systems: Automatic Generation of an Optimized Gillepsie Algorithm

“…This discussion illustrates the importance of being able to simulate the dynamics of relatively large systems including those eliciting properties such as stochasticity and multiple scale dynamics. Although other computational methods such as constraint-based methods or pathway perturbation analysis methods using pathway maps can handle models of large dimensionality, kinetic-based models are the most appropriate for a detailed study of the dynamical features of the model [37,38,39]. Many works have been published regarding stochastic kinetic modeling of biological systems.…”

Signaling Pathway Activities Improve Prognosis for Breast Cancer

“…Given that the inferred activity of the pathway should be closely related to its cellular mechanism for disease progression, its use to guide cancer prognosis seems promising. Recently, a number of pathway activity inference methods have been proposed (Hidalgo, et al, 2017;Jacob, et al, 2012;Li, et al, 2015;Martini, et al, 2013). Here, we use the canonical circuit activity analysis method, which has demonstrated to have a superior performance (Hidalgo, et al, 2017) finding significant associations of specific circuit activities, directly responsible for triggering the prominent cancer hallmarks (Hanahan and Weinberg, 2011), to patient survival.…”