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Kusonmano K, Halle MK, Wik E, Hoivik EA, Krakstad C, Mauland KK, Tangen IL, Berg A, Werner HMJ, Trovik J, Øyan AM, Kalland KH, Jonassen I, Salvesen HB, Petersen K. Identification of highly connected and differentially expressed gene subnetworks in metastasizing endometrial cancer. PLoS One 2018; 13:e0206665. [PMID: 30383835 PMCID: PMC6211718 DOI: 10.1371/journal.pone.0206665] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 10/17/2018] [Indexed: 12/22/2022] Open
Abstract
We have identified nine highly connected and differentially expressed gene subnetworks between aggressive primary tumors and metastatic lesions in endometrial carcinomas. We implemented a novel pipeline combining gene set and network approaches, which here allows integration of protein-protein interactions and gene expression data. The resulting subnetworks are significantly associated with disease progression across tumor stages from complex atypical hyperplasia, primary tumors to metastatic lesions. The nine subnetworks include genes related to metastasizing features such as epithelial-mesenchymal transition (EMT), hypoxia and cell proliferation. TCF4 and TWIST2 were found as central genes in the subnetwork related to EMT. Two of the identified subnetworks display statistically significant association to patient survival, which were further supported by an independent validation in the data from The Cancer Genome Atlas data collection. The first subnetwork contains genes related to cell proliferation and cell cycle, while the second contains genes involved in hypoxia such as HIF1A and EGLN3. Our findings provide a promising context to elucidate the biological mechanisms of metastasis, suggest potential prognostic markers and further identify therapeutic targets. The pipeline R source code is freely available, including permutation tests to assess statistical significance of the identified subnetworks.
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Affiliation(s)
- Kanthida Kusonmano
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
- * E-mail:
| | - Mari K. Halle
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Elisabeth Wik
- Centre for Cancer Biomarkers, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Pathology, The Gade Institute, Haukeland University Hospital, Bergen, Norway
| | - Erling A. Hoivik
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Camilla Krakstad
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Karen K. Mauland
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ingvild L. Tangen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anna Berg
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Henrica M. J. Werner
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jone Trovik
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anne M. Øyan
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - Karl-Henning Kalland
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Microbiology, Haukeland University Hospital, Bergen, Norway
| | - Inge Jonassen
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Informatics, University of Bergen, Bergen, Norway
| | - Helga B. Salvesen
- Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Kjell Petersen
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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Abstract
In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. This chapter features two case studies, both of which utilize Cytoscape, an open source and user-friendly network visualization and analysis tool. In the first example, we demonstrate the basic functionalities of Cytoscape by building an interaction network from a publicly available database, analyzing its topological features, and performing gene ontology enrichment. For the second section, we constructed a network from scratch starting with an experimental gene expression dataset. From there, we implement more advanced visual annotations of the network and perform subnetwork enrichment. The methods described are applicable to larger networks that can be collected from various resources.
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Affiliation(s)
- Danica Wiredja
- Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA.,Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA.,Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA
| | - Gurkan Bebek
- Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA. .,Center for Proteomics and Bioinformatics, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA. .,Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA. .,Department of Electrical Engineering and Computer Science, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA.
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3
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Shi L, Lin H, Li G, Jin RA, Xu J, Sun Y, Ma WL, Yeh S, Cai X, Chang C. Targeting Androgen Receptor (AR)→IL12A Signal Enhances Efficacy of Sorafenib plus NK Cells Immunotherapy to Better Suppress HCC Progression. Mol Cancer Ther 2016; 15:731-742. [PMID: 26939703 DOI: 10.1158/1535-7163.mct-15-0706] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Accepted: 02/03/2016] [Indexed: 12/22/2022]
Abstract
Gender disparity has long been considered as a key to fully understand hepatocellular carcinoma (HCC) development. At the same time, immunotherapy related to IL12 still need more investigation before being applied in clinical settings. The aim of this study is to investigate the influence of the androgen receptor (AR) on natural killer (NK) cell-related innate immune surveillance in liver cancer, and provide a novel therapeutic approach to suppress HCC via altering IL12A. By using in vitro cell cytotoxicity test and in vivo liver orthotopic xenograft mouse model, we identified the role of AR in modulating NK cell cytotoxicity. Luciferase report assay and chromatin immunoprecipitation assay were applied for mechanism dissection. IHC was performed for sample staining. Our results showed AR could suppress IL12A expression at the transcriptional level via direct binding to the IL12A promoter region that resulted in repressing efficacy of NK cell cytotoxicity against HCC, and sorafenib treatment could enhance IL12A signals via suppressing AR signals. These results not only help to explain the AR roles in the gender disparity of HCC but also provide a potential new therapy to better suppress HCC via combining sorafenib with NK cell-related immunotherapy. Mol Cancer Ther; 15(4); 731-42. ©2016 AACR.
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Affiliation(s)
- Liang Shi
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.,George Whipple Lab for Cancer Research, Departments of Pathology, Urology and Radiation Oncology, and The Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Hui Lin
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
| | - Gonghui Li
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
| | - Ren-An Jin
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
| | - Junjie Xu
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China.,George Whipple Lab for Cancer Research, Departments of Pathology, Urology and Radiation Oncology, and The Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Yin Sun
- George Whipple Lab for Cancer Research, Departments of Pathology, Urology and Radiation Oncology, and The Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Wen-Lung Ma
- Sex Hormone Research Center, China Medical University/Hospital, Taichung 404, Taiwan
| | - Shuyuan Yeh
- George Whipple Lab for Cancer Research, Departments of Pathology, Urology and Radiation Oncology, and The Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Xiujun Cai
- Chawnshang Chang Liver Cancer Center, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, China
| | - Chawnshang Chang
- George Whipple Lab for Cancer Research, Departments of Pathology, Urology and Radiation Oncology, and The Wilmot Cancer Center, University of Rochester Medical Center, Rochester, NY 14642, USA.,Sex Hormone Research Center, China Medical University/Hospital, Taichung 404, Taiwan
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Ayati M, Erten S, Chance MR, Koyutürk M. MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2015; 2015:7. [PMID: 28194175 PMCID: PMC5270451 DOI: 10.1186/s13637-015-0025-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2015] [Accepted: 06/02/2015] [Indexed: 11/23/2022]
Abstract
Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association. Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.
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Affiliation(s)
- Marzieh Ayati
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Eucid Ave., Cleveland, 44106 OH USA
| | - Sinan Erten
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Eucid Ave., Cleveland, 44106 OH USA
| | - Mark R Chance
- Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Eucid Ave., Cleveland, 44106 OH USA
| | - Mehmet Koyutürk
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Eucid Ave., Cleveland, 44106 OH USA.,Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Eucid Ave., Cleveland, 44106 OH USA
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Santiago JA, Potashkin JA. A network approach to clinical intervention in neurodegenerative diseases. Trends Mol Med 2014; 20:694-703. [PMID: 25455073 DOI: 10.1016/j.molmed.2014.10.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Revised: 09/30/2014] [Accepted: 10/08/2014] [Indexed: 02/07/2023]
Abstract
Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases.
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Affiliation(s)
- Jose A Santiago
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL 60064-3037, USA
| | - Judith A Potashkin
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, 3333 Green Bay Road, North Chicago, IL 60064-3037, USA.
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Guan D, Shao J, Zhao Z, Wang P, Qin J, Deng Y, Boheler KR, Wang J, Yan B. PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data. Nucleic Acids Res 2014; 42:W130-6. [PMID: 24875471 PMCID: PMC4086064 DOI: 10.1093/nar/gku471] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Interactions among transcriptional factors (TFs), cofactors and other proteins or enzymes can affect transcriptional regulatory capabilities of eukaryotic organisms. Post-translational modifications (PTMs) cooperate with TFs and epigenetic alterations to constitute a hierarchical complexity in transcriptional gene regulation. While clearly implicated in biological processes, our understanding of these complex regulatory mechanisms is still limited and incomplete. Various online software have been proposed for uncovering transcriptional and epigenetic regulatory networks, however, there is a lack of effective web-based software capable of constructing underlying interactive organizations between post-translational and transcriptional regulatory components. Here, we present an open web server, post-translational hierarchical gene regulatory network (PTHGRN) to unravel relationships among PTMs, TFs, epigenetic modifications and gene expression. PTHGRN utilizes a graphical Gaussian model with partial least squares regression-based methodology, and is able to integrate protein–protein interactions, ChIP-seq and gene expression data and to capture essential regulation features behind high-throughput data. The server provides an integrative platform for users to analyze ready-to-use public high-throughput Omics resources or upload their own data for systems biology study. Users can choose various parameters in the method, build network topologies of interests and dissect their associations with biological functions. Application of the software to stem cell and breast cancer demonstrates that it is an effective tool for understanding regulatory mechanisms in biological complex systems. PTHGRN web server is publically available at web site http://www.byanbioinfo.org/pthgrn.
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Affiliation(s)
- Daogang Guan
- Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Jiaofang Shao
- Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Zhongying Zhao
- Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Panwen Wang
- Department of Biochemistry and HKU-SIRI, The University of Hong Kong, Hong Kong SAR, China
| | - Jing Qin
- Department of Biochemistry and HKU-SIRI, The University of Hong Kong, Hong Kong SAR, China
| | - Youping Deng
- Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, Illinois 60612, USA
| | - Kenneth R Boheler
- Stem Cell & Regenerative Medicine Consortium, LKS Faculty of Medicine and Department of Physiology, The University of Hong Kong, Hong Kong SAR, China
| | - Junwen Wang
- Department of Biochemistry and HKU-SIRI, The University of Hong Kong, Hong Kong SAR, China Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Bin Yan
- Department of Biology, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China Stem Cell & Regenerative Medicine Consortium, LKS Faculty of Medicine and Department of Physiology, The University of Hong Kong, Hong Kong SAR, China
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7
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Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA. INsPeCT: INtegrative Platform for Cancer Transcriptomics. Cancer Inform 2014; 13:59-66. [PMID: 24653643 PMCID: PMC3956744 DOI: 10.4137/cin.s13630] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Revised: 01/08/2014] [Accepted: 01/08/2014] [Indexed: 01/21/2023] Open
Abstract
The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.
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Affiliation(s)
- Piyush B Madhamshettiwar
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Stefan R Maetschke
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Melissa J Davis
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Antonio Reverter
- CSIRO Animal, Food and Health Sciences, St. Lucia, Brisbane, Queensland, Australia
| | - Mark A Ragan
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
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Chung FH, Lee HHC, Lee HC. ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer. PLoS One 2013; 8:e65683. [PMID: 23799036 PMCID: PMC3683052 DOI: 10.1371/journal.pone.0065683] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2012] [Accepted: 04/26/2013] [Indexed: 12/22/2022] Open
Abstract
Significantly expressed genes extracted from microarray gene expression data have proved very useful for identifying genetic biomarkers of diseases, including cancer. However, deriving a disease related inference from a list of differentially expressed genes has proven less than straightforward. In a systems disease such as cancer, how genes interact with each other should matter just as much as the level of gene expression. Here, in a novel approach, we used the network and disease progression properties of individual genes in state-specific gene-gene interaction networks (GGINs) to select cancer genes for human colorectal cancer (CRC) and obtain a much higher hit rate of known cancer genes when compared with methods not based on network theory. We constructed GGINs by integrating gene expression microarray data from multiple states--healthy control (Nor), adenoma (Ade), inflammatory bowel disease (IBD) and CRC--with protein-protein interaction database and Gene Ontology. We tracked changes in the network degrees and clustering coefficients of individual genes in the GGINs as the disease state changed from one to another. From these we inferred the state sequences Nor-Ade-CRC and Nor-IBD-CRC both exhibited a trend of (disease) progression (ToP) toward CRC, and devised a ToP procedure for selecting cancer genes for CRC. Of the 141 candidates selected using ToP, ∼50% had literature support as cancer genes, compared to hit rates of 20% to 30% for standard methods using only gene expression data. Among the 16 candidate cancer genes that encoded transcription factors, 13 were known to be tumorigenic and three were novel: CDK1, SNRPF, and ILF2. We identified 13 of the 141 predicted cancer genes as candidate markers for early detection of CRC, 11 and 2 at the Ade and IBD states, respectively.
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Affiliation(s)
- Feng-Hsiang Chung
- Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, Taiwan
- Center for Dynamical Biomarkers and Translational Medicine, National Central University, Zhongli, Taiwan
- * E-mail: (HCL); (FHC)
| | - Henry Hsin-Chung Lee
- Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, Taiwan
- Cathay Medical Research Institute, Cathay General Hospital, Taipei, Taiwan
| | - Hoong-Chien Lee
- Institute of Systems Biology and Bioinformatics, National Central University, Zhongli, Taiwan
- Cathay Medical Research Institute, Cathay General Hospital, Taipei, Taiwan
- * E-mail: (HCL); (FHC)
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Abstract
In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network's topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources.
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Affiliation(s)
- Gurkan Bebek
- Center for Proteomics and Bioinformatics, Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
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