Pathway enrichment analysis approach based on topological structure and updated annotation of pathway

Abstract

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.

BNrich: A Bayesian network approach to the pathway enrichment analysis

“…TPEA, BLMA, and SPIA, were selected among the topology-based pathway analysis methods to be compared with the proposed method. The topology-based pathway enrichment analysis (TPEA) method, integrates topological properties and takes into account global upstream/downstream positions of genes in pathways [16]. BLMA [15,34] uses the gene expression data directly.…”

“…The second generation, the functional class scoring (FCS) methods, utilize all available expression data, even when information about the network structure of the pathways is missed [9][10][11]. The last generation, the topology-based pathway analysis (TPA), exploits biological (causal/noncausal) relation between pathway components [12][13][14][15][16]. In most cases, TPA methods provide a more accurate analysis [6].…”

“…We evaluate the proposed method on 187 human non-metabolic KEGG pathways using eight various gene expression datasets. Eventually, we compare the proposed framework with the other pathway topology-based methods, namely, SPIA [12], BLAM [15,34] and TPEA [16].…”

The histone deacetylase inhibitor panobinostat exerts anticancer effects on esophageal squamous cell carcinoma cells by inducing cell cycle arrest

“…We screened DEGs according to the following default criteria: fold change ≥2 or ≤0.5, and diverge probability ≥0.8. GO and KEGG analyses were used to perform ontology enrichment analysis and pathway enrichment analysis of the DEGs as described …”

“…GO and KEGG analyses were used to perform ontology enrichment analysis and pathway enrichment analysis of the DEGs as described. 34 Table 1.…”