Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways

Abstract

Various ‘omics’ technologies, including microarrays and gas chromatography mass spectrometry, can be used to identify hundreds of interesting genes, proteins and metabolites, such as differential genes, proteins and metabolites associated with diseases. Identifying metabolic pathways has become an invaluable aid to understanding the genes and metabolites associated with studying conditions. However, the classical methods used to identify pathways fail to accurately consider joint power of interesting gene/metabolite and the key regions impacted by them within metabolic pathways. In this study, we propose a powerful analytical method referred to as Subpathway-GM for the identification of metabolic subpathways. This provides a more accurate level of pathway analysis by integrating information from genes and metabolites, and their positions and cascade regions within the given pathway. We analyzed two colorectal cancer and one metastatic prostate cancer data sets and demonstrated that Subpathway-GM was able to identify disease-relevant subpathways whose corresponding entire pathways might be ignored using classical entire pathway identification methods. Further analysis indicated that the power of a joint genes/metabolites and subpathway strategy based on their topologies may play a key role in reliably recalling disease-relevant subpathways and finding novel subpathways.

Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence

“…Levels of metabolites including sarcosine, uracil, kynurenine, leucine, and proline (43) were shown to be increased during the progression to metastatic disease (32, 33, 43), as were pathways involved in nitrogen breakdown (43). Nitrogen metabolism is known to be altered in tumors to accommodate their enhanced glutamine requirements and the increase in nucleotide and protein synthesis (65).…”

“…These were employed by 15 of the selected studies, with the others using more classical statistical methods, including t-tests and regression modeling. One study used a novel feature selection algorithm termed associative voting (49), which integrates class association rule data mining and classification, and one utilized an entirely pathway based approach: subpathway-GM (33) which maps genes and metabolites of interest to metabolic pathways in order to identify biologically relevant subpathway regions (readers are referred to (49) and (33) for full methodological details). In fact, the subpathway-GM method revealed novel associations beyond the original analyses of the same dataset using a more classical statistical approach (52).…”

Identification of miRNA-Mediated Core Gene Module for Glioma Patient Prediction by Integrating High-Throughput miRNA, mRNA Expression and Pathway Structure

“…We applied Bioconductor package iSubpathwayMiner [37], [38] to obtain all the biological pathways, including 150 metabolic pathways and 150 non-metabolic pathways. We utilized these pathways to identify glioma survival related pathways.…”

Coordinate Regulation of Metabolite Glycosylation and Stress Hormone Biosynthesis by TT8 in Arabidopsis

“…2). These were then used for gene set enrichment analysis in PlantGSEA (Yi et al, 2013) and Subpathway enrichment using iSubpathwayMiner package (Li et al, 2013). Raw data have been deposited to the National Center for Biotechnology Information Gene Expression Omnibus database (Edgar et al, 2002).…”