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

BackgroundWith 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.ResultsThe 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.ConclusionThis 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.

[1]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[2]  H. Huland,et al.  The expression of angiopoietins and their receptor Tie-2 in human prostate carcinoma. , 2000, Anticancer research.

[3]  S. Dhanasekaran,et al.  Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.

[4]  Daniel Q. Naiman,et al.  Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data , 2005, Bioinform..

[5]  Y. Liu,et al.  Fatty acid oxidation is a dominant bioenergetic pathway in prostate cancer , 2006, Prostate Cancer and Prostatic Diseases.

[6]  J. Doyle,et al.  Reverse Engineering of Biological Complexity , 2002, Science.

[7]  Li Jin,et al.  Variants in the HEPSIN gene are associated with prostate cancer in men of European origin , 2006, Human Genetics.

[8]  Gregory D. Schuler,et al.  Database resources of the National Center for Biotechnology , 2003, Nucleic Acids Res..

[9]  Edi Brogi,et al.  Id4 messenger RNA and estrogen receptor expression: inverse correlation in human normal breast epithelium and carcinoma. , 2006, Human pathology.

[10]  M. Bittner,et al.  Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. , 2001, Cancer research.

[11]  John T. Wei,et al.  Integrative molecular concept modeling of prostate cancer progression , 2007, Nature Genetics.

[12]  T. Barrette,et al.  Mining for regulatory programs in the cancer transcriptome , 2005, Nature Genetics.

[13]  Li Li,et al.  BMC Bioinformatics Methodology article Discovery of time-delayed gene regulatory networks based on temporal , 2006 .

[14]  A. Subramanian,et al.  Structural analysis of alpha-enolase. Mapping the functional domains involved in down-regulation of the c-myc protooncogene. , 2000, The Journal of biological chemistry.

[15]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[16]  Yun Xiao,et al.  A systematic method for mapping multiple loci: an application to construct a genetic network for rheumatoid arthritis. , 2008, Gene.

[17]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[18]  Lorella Pascolo,et al.  Molecular Determinants in the Transport of a Bile Acid-Derived Diagnostic Agent in Tumoral and Nontumoral Cell Lines of Human Liver , 2006, Journal of Pharmacology and Experimental Therapeutics.

[19]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[20]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[21]  A. Blann,et al.  Plasma angiopoietin‐1, angiopoietin‐2 and Tie‐2 in breast and prostate cancer: a comparison with VEGF and Flt‐1 , 2003, European journal of clinical investigation.

[22]  Xiaoxing Liu,et al.  An Entropy-based gene selection method for cancer classification using microarray data , 2005, BMC Bioinformatics.

[23]  I. Khalil,et al.  Systems biology for cancer , 2005, Current opinion in oncology.

[24]  Vladimir Pavlovic,et al.  RankGene: identification of diagnostic genes based on expression data , 2003, Bioinform..

[25]  Jeffrey A. Magee,et al.  Expression profiling reveals hepsin overexpression in prostate cancer. , 2001, Cancer research.

[26]  C. V. van Noorden,et al.  Promotion of colon cancer metastases in rat liver by fish oil diet is not due to reduced stroma formation , 2004, Clinical & Experimental Metastasis.

[27]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[28]  Toshiro Ono,et al.  Identification of the antigens predominantly reacted with serum from patients with hepatocellular carcinoma , 2003, Cancer.

[29]  Daniel R. Richards,et al.  Corrigendum: A network-based analysis of systemic inflammation in humans , 2005, Nature.

[30]  Gopalakrishnapillai Anilkumar,et al.  Association of prostate-specific membrane antigen with caveolin-1 and its caveolae-dependent internalization in microvascular endothelial cells: implications for targeting to tumor vasculature. , 2006, Microvascular research.

[31]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[32]  S. Dhanasekaran,et al.  Integrative analysis of genomic aberrations associated with prostate cancer progression. , 2007, Cancer research.

[33]  Hiroaki Kitano,et al.  Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biology , 2002, Current Genetics.

[34]  Xia Li,et al.  Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. , 2004, Nucleic acids research.

[35]  J. Xu,et al.  Ribosomal proteins and colorectal cancer. , 2007, Current genomics.

[36]  A. Butte,et al.  Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[37]  R. Tibshirani,et al.  Gene expression profiling identifies clinically relevant subtypes of prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[38]  O. Klezovitch,et al.  Hepsin promotes prostate cancer progression and metastasis. , 2004, Cancer cell.

[39]  W. Catalona,et al.  Analysis of Candidate Genes for Prostate Cancer , 2004, Human Heredity.

[40]  P. Nelson,et al.  Predicting prostate cancer behavior using transcript profiles. , 2004, The Journal of urology.

[41]  Hong-Wen Deng,et al.  Gene selection for classification of microarray data based on the Bayes error , 2007, BMC Bioinformatics.

[42]  Manish Gala,et al.  Induction of interleukin-8 preserves the angiogenic response in HIF-1α–deficient colon cancer cells , 2005, Nature Medicine.

[43]  O. Fiehn,et al.  Interpreting correlations in metabolomic networks. , 2003, Biochemical Society transactions.

[44]  Angel R. Martinez,et al.  Computational Statistics Handbook with MATLAB , 2001 .

[45]  J. Collins,et al.  Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks , 2005, Nature Biotechnology.

[46]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[47]  Ian M. Fingerman,et al.  Database resources of the National Center for Biotechnology Information , 2011, Nucleic Acids Res..

[48]  D Husmeier,et al.  Reverse engineering of genetic networks with Bayesian networks. , 2003, Biochemical Society transactions.

[49]  David Botstein,et al.  SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data , 2003, Nucleic Acids Res..

[50]  Grzegorz Sowa,et al.  The phosphorylation of caveolin-2 on serines 23 and 36 modulates caveolin-1-dependent caveolae formation , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Jianzhong Li,et al.  A stable gene selection in microarray data analysis , 2006, BMC Bioinformatics.

[52]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[53]  Sergei Egorov,et al.  Pathway studio - the analysis and navigation of molecular networks , 2003, Bioinform..

[54]  伊藤 雄介,et al.  IL-8 promotes cell proliferation and migration through metalloproteinase-cleavage proHB-EGF in human colon carcinoma cells , 2005 .

[55]  T. Stamey,et al.  Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia. , 2001, The Journal of urology.

[56]  A. Ghazalpour,et al.  Thematic Review toward a Biological Network for Atherosclerosis Human Studies , 2022 .

[57]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[58]  Olivier Cussenot,et al.  Extensive analysis of the 7q31 region in human prostate tumors supports TES as the best candidate tumor suppressor gene , 2004, International journal of cancer.

[59]  K. Chan,et al.  Id proteins expression in prostate cancer: high-level expression of Id-4 in primary prostate cancer is associated with development of metastases , 2006, Modern Pathology.

[60]  Thomas D. Schmittgen,et al.  Expression of prostate specific membrane antigen and three alternatively spliced variants of PSMA in prostate cancer patients , 2003, International journal of cancer.

[61]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[62]  D. McDonnell,et al.  A negative coregulator for the human ER. , 2002, Molecular endocrinology.

[63]  Marco Grzegorczyk,et al.  Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks , 2006, Bioinform..

[64]  I. Wool,et al.  Ribosomal protein genes are overexpressed in colorectal cancer: isolation of a cDNA clone encoding the human S3 ribosomal protein , 1991, Molecular and cellular biology.

[65]  David A. Bell,et al.  A Formalism for Relevance and Its Application in Feature Subset Selection , 2000, Machine Learning.

[66]  Peter Robin Hiesinger,et al.  Genetics in the Age of Systems Biology , 2005, Cell.

[67]  Chris S. Haley,et al.  Epistasis: too often neglected in complex trait studies? , 2004, Nature Reviews Genetics.

[68]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[69]  J. Collins,et al.  A network biology approach to prostate cancer , 2007, Molecular systems biology.

[70]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[71]  Falk Schreiber,et al.  MAVisto: a tool for the exploration of network motifs , 2005, Bioinform..

[72]  T. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2006, Nucleic Acids Res..