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Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

BACKGROUND: With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic net...

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Autores principales: Jiang, Wei, Li, Xia, Rao, Shaoqi, Wang, Lihong, Du, Lei, Li, Chuanxing, Wu, Chao, Wang, Hongzhi, Wang, Yadong, Yang, Baofeng
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2535780/
https://www.ncbi.nlm.nih.gov/pubmed/18691435
http://dx.doi.org/10.1186/1752-0509-2-72
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author Jiang, Wei
Li, Xia
Rao, Shaoqi
Wang, Lihong
Du, Lei
Li, Chuanxing
Wu, Chao
Wang, Hongzhi
Wang, Yadong
Yang, Baofeng
author_facet Jiang, Wei
Li, Xia
Rao, Shaoqi
Wang, Lihong
Du, Lei
Li, Chuanxing
Wu, Chao
Wang, Hongzhi
Wang, Yadong
Yang, Baofeng
author_sort Jiang, Wei
collection PubMed
description BACKGROUND: With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. RESULTS: The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p ≈ 0), desmin (DES) (p = 2.71 × 10(-6)) and enolase 1 (ENO1) (p = 4.19 × 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 × 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 × 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that consisted of three-way gene interactions suggested that tumourigenesis in colon cancer resulted from dysfunction in protein biosynthesis and categories associated with ribonucleoprotein complex which are well supported by multiple lines of experimental evidence. CONCLUSION: This study demonstrated that IL8, DES and ENO1 act as the central elements in colon cancer susceptibility, and protein biosynthesis and the ribosome-associated function categories largely account for the colon cancer tumuorigenesis. Thus, the newly developed relevancy-based networking approach offers a powerful means to reverse-engineer the disease-specific network, a promising tool for systematic dissection of complex diseases.
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spelling pubmed-25357802008-09-15 Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements Jiang, Wei Li, Xia Rao, Shaoqi Wang, Lihong Du, Lei Li, Chuanxing Wu, Chao Wang, Hongzhi Wang, Yadong Yang, Baofeng BMC Syst Biol Research Article BACKGROUND: With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. RESULTS: The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p ≈ 0), desmin (DES) (p = 2.71 × 10(-6)) and enolase 1 (ENO1) (p = 4.19 × 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 × 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 × 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that consisted of three-way gene interactions suggested that tumourigenesis in colon cancer resulted from dysfunction in protein biosynthesis and categories associated with ribonucleoprotein complex which are well supported by multiple lines of experimental evidence. CONCLUSION: This study demonstrated that IL8, DES and ENO1 act as the central elements in colon cancer susceptibility, and protein biosynthesis and the ribosome-associated function categories largely account for the colon cancer tumuorigenesis. Thus, the newly developed relevancy-based networking approach offers a powerful means to reverse-engineer the disease-specific network, a promising tool for systematic dissection of complex diseases. BioMed Central 2008-08-10 /pmc/articles/PMC2535780/ /pubmed/18691435 http://dx.doi.org/10.1186/1752-0509-2-72 Text en Copyright © 2008 Jiang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Jiang, Wei
Li, Xia
Rao, Shaoqi
Wang, Lihong
Du, Lei
Li, Chuanxing
Wu, Chao
Wang, Hongzhi
Wang, Yadong
Yang, Baofeng
Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title_full Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title_fullStr Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title_full_unstemmed Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title_short Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
title_sort constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2535780/
https://www.ncbi.nlm.nih.gov/pubmed/18691435
http://dx.doi.org/10.1186/1752-0509-2-72
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