title: 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.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
authors.forename: Chunquan
authors.initials: C
authors.lastname: Li
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.forename: Qianlan
authors.initials: Q
authors.lastname: Yao
authors.forename: Chendan
authors.initials: C
authors.lastname: Zou
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Xu
authors.forename: Chunlong
authors.initials: C
authors.lastname: Zhang
authors.forename: Desi
authors.initials: D
authors.lastname: Shang
authors.forename: Lingyun
authors.initials: L
authors.lastname: Zhou
authors.forename: Chaoxia
authors.initials: C
authors.lastname: Zou
authors.forename: Zeguo
authors.initials: Z
authors.lastname: Sun
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Zhang
authors.forename: Haixiu
authors.initials: H
authors.lastname: Yang
authors.forename: Xu
authors.initials: X
authors.lastname: Gao
authors.forename: Xia
authors.initials: X
authors.lastname: Li
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism; D006632:Histamine / Q000378:metabolism; D006801:Humans; D008297:Male; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D055432:Metabolomics; D009362:Neoplasm Metastasis; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D059467:Transcriptome
chemical_list: D006632:Histamine