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Yang W, Chen S, Cheng X, Xu B, Zeng H, Zou J, Su C, Chen Z. Characteristics of genomic mutations and signaling pathway alterations in thymic epithelial tumors. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1659. [PMID: 34988168 PMCID: PMC8667121 DOI: 10.21037/atm-21-5182] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/03/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND To elucidate the mechanisms of thymic epithelial tumor (TET) canceration by characterizing genomic mutations and signaling pathway alterations. METHODS Primary tumor and blood samples were collected from 21 patients diagnosed with TETs (thymoma and thymic cancer), 15 of whom were screened by nucleic acid extraction and whole exon sequencing. Bioinformatics was used to comprehensively analyze the sequencing data for these samples, including gene mutation information and the difference of tumor mutation burden (TMB) between thymoma and thymic carcinoma groups. We performed signaling pathway and functional enrichment analysis using the WebGestalt 2017 toolkit. RESULTS ZNF429 (36%) was the gene with the highest mutation frequency in thymic carcinoma. Mutations in BAP1 (14%), ABI1 (7%), BCL9L (7%), and CHEK2 (7%) were exclusively detected in thymic carcinoma, whereas ZNF721 mutations (14%) and PABPC1 (14%) were found exclusively in thymoma. The mean TMB values for thymic carcinoma and thymoma were 0.722 and 0.663 mutations per megabase (Mb), respectively, and these differences were not statistically significant. The ErbB signaling pathway was enriched in the thymoma and intersection groups, and pathways of central carbon metabolism in cancer, longevity regulating and MAPK signaling were only found in the thymoma group, while pathways in cancer (hsa05200) was found in the thymoma and thymic carcinoma groups. CONCLUSIONS Multiple differences in somatic genes and pathways have been identified. Our findings provide insights into differences between thymoma and thymic carcinoma that could aid in designing personalized clinical therapeutic strategies.
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Affiliation(s)
- Weilin Yang
- Department of Cardiothoracic Surgery of East Division, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sai Chen
- Center for Private Medical Service & Healthcare, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xinxin Cheng
- Department of Cardiothoracic Surgery of East Division, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bo Xu
- Department of Cardiothoracic Surgery of East Division, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huilan Zeng
- Department of Cardiothoracic Surgery of East Division, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianyong Zou
- Department of Thoracic Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chunhua Su
- Department of Thoracic Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhenguang Chen
- Department of Cardiothoracic Surgery of East Division, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Department of Thoracic Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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2
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Borisov N, Ilnytskyy Y, Byeon B, Kovalchuk O, Kovalchuk I. System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation. Int J Mol Sci 2020; 22:ijms22010388. [PMID: 33396562 PMCID: PMC7795809 DOI: 10.3390/ijms22010388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/26/2020] [Accepted: 12/28/2020] [Indexed: 12/19/2022] Open
Abstract
There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).
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Affiliation(s)
- Nicolas Borisov
- Moscow Institute of Physics and Technology, 9 Institutsky lane, Dolgoprudny, Moscow Region 141701, Russia;
| | - Yaroslav Ilnytskyy
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
| | - Boseon Byeon
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
- Biomedical and Health Informatics, Computer Science Department, State University of New York, 2 S Clinton St, Syracuse, NY 13202, USA
| | - Olga Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
| | - Igor Kovalchuk
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada; (Y.I.); (B.B.); (O.K.)
- Pathway Rx., 16 Sandstone Rd. S., Lethbridge, AB T1K 7X8, Canada
- Correspondence:
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3
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Barefoot ME, Varghese RS, Zhou Y, Poto CD, Ferrarini A, Ressom HW. Multi-omic Pathway and Network Analysis to Identify Biomarkers for Hepatocellular Carcinoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1350-1354. [PMID: 31946143 DOI: 10.1109/embc.2019.8856576] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
The threat of Hepatocellular Carcinoma (HCC) is a growing problem, with incidence rates anticipated to near double over the next two decades. The increasing burden makes discovery of novel diagnostic, prognostic, and therapeutic biomarkers distinguishing HCC from underlying cirrhosis a significant focus. In this study, we analyzed tissue and serum samples from 40 HCC cases and 25 patients with liver cirrhosis (CIRR) to better understand the mechanistic differences between HCC and CIRR. Through pathway and network analysis, we are able to take a systems biology approach to conduct multi-omic analysis of transcriptomic, glycoproteomic, and metabolomic data acquired through various platforms. As a result, we are able to identify the FXR/RXR Activation pathway as being represented by molecules spanning multiple molecular compartments in these samples. Specifically, serum metabolites deoxycholate and chenodeoxycholic acid and serum glycoproteins C4A/C4B, KNG1, and HPX are biomarker candidates identified from this analysis that are of interest for future targeted studies. These results demonstrate the integrative power of multi-omic analysis to prioritize clinically and biologically relevant biomarker candidates that can increase understanding of molecular mechanisms driving HCC and make an impact in patient care.
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4
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Kalamohan K, Gunasekaran P, Ibrahim S. Gene coexpression network analysis of multiple cancers discovers the varying stem cell features between gastric and breast cancer. Meta Gene 2019. [DOI: 10.1016/j.mgene.2019.100576] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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5
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Qi G, Kong W, Mou X, Wang S. A new method for excavating feature lncRNA in lung adenocarcinoma based on pathway crosstalk analysis. J Cell Biochem 2018; 120:9034-9046. [DOI: 10.1002/jcb.28177] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 11/08/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Guoqiang Qi
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
| | - Wei Kong
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
| | - Xiaoyang Mou
- Department of Biochemistry Rowan University and Guava Medicine Glassboro New Jersey
| | - Shuaiqun Wang
- Department of Electronic Engineering College of Information Engineering, Shanghai Maritime University Shanghai China
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6
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Kim M, Hwang D. Network-Based Protein Biomarker Discovery Platforms. Genomics Inform 2016; 14:2-11. [PMID: 27103885 PMCID: PMC4838525 DOI: 10.5808/gi.2016.14.1.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 01/06/2016] [Accepted: 01/07/2016] [Indexed: 02/06/2023] Open
Abstract
The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.
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Affiliation(s)
- Minhyung Kim
- Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea
| | - Daehee Hwang
- Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea
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7
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Amadoz A, Sebastian-Leon P, Vidal E, Salavert F, Dopazo J. Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. Sci Rep 2015; 5:18494. [PMID: 26678097 PMCID: PMC4683444 DOI: 10.1038/srep18494] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 11/19/2015] [Indexed: 12/22/2022] Open
Abstract
Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).
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Affiliation(s)
- Alicia Amadoz
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Patricia Sebastian-Leon
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
| | - Enrique Vidal
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, Spain
- Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, Spain
- Functional Genomics Node, (INB) at CIPF, Valencia, Spain
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8
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Lapatas V, Stefanidakis M, Jimenez RC, Via A, Schneider MV. Data integration in biological research: an overview. JOURNAL OF BIOLOGICAL RESEARCH (THESSALONIKE, GREECE) 2015; 22:9. [PMID: 26336651 PMCID: PMC4557916 DOI: 10.1186/s40709-015-0032-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/10/2015] [Indexed: 11/16/2022]
Abstract
Data sharing, integration and annotation are essential to ensure the reproducibility of the analysis and interpretation of the experimental findings. Often these activities are perceived as a role that bioinformaticians and computer scientists have to take with no or little input from the experimental biologist. On the contrary, biological researchers, being the producers and often the end users of such data, have a big role in enabling biological data integration. The quality and usefulness of data integration depend on the existence and adoption of standards, shared formats, and mechanisms that are suitable for biological researchers to submit and annotate the data, so it can be easily searchable, conveniently linked and consequently used for further biological analysis and discovery. Here, we provide background on what is data integration from a computational science point of view, how it has been applied to biological research, which key aspects contributed to its success and future directions.
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Affiliation(s)
- Vasileios Lapatas
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | - Michalis Stefanidakis
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | | | - Allegra Via
- />Biocomputing Group, Sapienza University, Piazzale Aldo Moro 5, Rome, 00185 Italy
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9
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Kulkarni YM, Dutta S, Iyer AKV, Venkatadri R, Kaushik V, Ramesh V, Wright CA, Semmes OJ, Yakisich JS, Azad N. A proteomics approach to identifying key protein targets involved in VEGF inhibitor mediated attenuation of bleomycin-induced pulmonary fibrosis. Proteomics 2015; 16:33-46. [PMID: 26425798 DOI: 10.1002/pmic.201500171] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/07/2015] [Accepted: 09/25/2015] [Indexed: 12/18/2022]
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a life expectancy of less than 5 years post diagnosis for most patients. Poor molecular characterization of IPF has led to insufficient understanding of the pathogenesis of the disease, resulting in lack of effective therapies. In this study, we have integrated a label-free LC-MS based approach with systems biology to identify signaling pathways and regulatory nodes within protein interaction networks that govern phenotypic changes that may lead to IPF. Ingenuity Pathway Analysis of proteins modulated in response to bleomycin treatment identified PI3K/Akt and Wnt signaling as the most significant profibrotic pathways. Similar analysis of proteins modulated in response to vascular endothelial growth factor (VEGF) inhibitor (CBO-P11) treatment identified natural killer cell signaling and PTEN signaling as the most significant antifibrotic pathways. Mechanistic/mammalian target of rapamycin (mTOR) and extracellular signal-regulated kinase (ERK) were identified to be key mediators of pro- and antifibrotic response, where bleomycin (BLM) treatment resulted in increased expression and VEGF inhibitor treatment attenuated expression of mTOR and ERK. Using a BLM mouse model of pulmonary fibrosis and VEGF inhibitor CBO-P11 as a therapeutic measure, we identified a comprehensive set of signaling pathways and proteins that contribute to the pathogenesis of pulmonary fibrosis that can be targeted for therapy against this fatal disease.
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Affiliation(s)
- Yogesh M Kulkarni
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Sucharita Dutta
- Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA.,Leroy T. Canoles Jr, Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Anand Krishnan V Iyer
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Rajkumar Venkatadri
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Vivek Kaushik
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Vani Ramesh
- Department of Obstetrics and Gynecology, The Jones Institute for Reproductive Medicine, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Clayton A Wright
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Oliver John Semmes
- Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA, USA.,Leroy T. Canoles Jr, Cancer Research Center, Eastern Virginia Medical School, Norfolk, VA, USA
| | - Juan S Yakisich
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
| | - Neelam Azad
- Department of Pharmaceutical Sciences, School of Pharmacy, Hampton University, Hampton, VA, USA
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10
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Wang J, Zuo Y, Liu L, Man Y, Tadesse MG, Ressom HW. Identification of functional modules by integration of multiple data sources using a Bayesian network classifier. ACTA ACUST UNITED AC 2015; 7:206-17. [PMID: 24736851 DOI: 10.1161/circgenetics.113.000087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. METHODS AND RESULTS In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. CONCLUSIONS The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.
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Affiliation(s)
- Jinlian Wang
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
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11
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Linde J, Schulze S, Henkel SG, Guthke R. Data- and knowledge-based modeling of gene regulatory networks: an update. EXCLI JOURNAL 2015; 14:346-78. [PMID: 27047314 PMCID: PMC4817425 DOI: 10.17179/excli2015-168] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 02/10/2015] [Indexed: 02/01/2023]
Abstract
Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.
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Affiliation(s)
- Jörg Linde
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
| | - Sylvie Schulze
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
| | | | - Reinhard Guthke
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
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12
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Kim KJ, Lee S, Kim WU. Applications of systems approaches in the study of rheumatic diseases. Korean J Intern Med 2015; 30:148-60. [PMID: 25750554 PMCID: PMC4351319 DOI: 10.3904/kjim.2015.30.2.148] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 12/23/2014] [Indexed: 12/27/2022] Open
Abstract
The complex interaction of molecules within a biological system constitutes a functional module. These modules are then acted upon by both internal and external factors, such as genetic and environmental stresses, which under certain conditions can manifest as complex disease phenotypes. Recent advances in high-throughput biological analyses, in combination with improved computational methods for data enrichment, functional annotation, and network visualization, have enabled a much deeper understanding of the mechanisms underlying important biological processes by identifying functional modules that are temporally and spatially perturbed in the context of disease development. Systems biology approaches such as these have produced compelling observations that would be impossible to replicate using classical methodologies, with greater insights expected as both the technology and methods improve in the coming years. Here, we examine the use of systems biology and network analysis in the study of a wide range of rheumatic diseases to better understand the underlying molecular and clinical features.
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Affiliation(s)
- Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Saseong Lee
- POSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea, Seoul, Korea
| | - Wan-Uk Kim
- POSTECH-CATHOLIC BioMedical Engineering Institute, The Catholic University of Korea, Seoul, Korea
- Division of Rheumatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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13
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Wang J, Zuo Y, Man YG, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW. Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer 2015; 6:54-65. [PMID: 25553089 PMCID: PMC4278915 DOI: 10.7150/jca.10631] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 11/14/2014] [Indexed: 12/12/2022] Open
Abstract
The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.
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Affiliation(s)
- Jinlian Wang
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
- 7. Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yiming Zuo
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
- 6. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA
| | - Yan-gao Man
- 2. Bon Secours Cancer Institute, Richmond VA, USA
| | | | - Alexander Stojadinovic
- 2. Bon Secours Cancer Institute, Richmond VA, USA
- 3. Division of Surgical Oncology, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Meng Liu
- 4. Department of Public Health School of Hunter College, City University of New York, NYC, USA
| | - Xiaowei Yang
- 4. Department of Public Health School of Hunter College, City University of New York, NYC, USA
| | - Rency S. Varghese
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Mahlet G Tadesse
- 5. Department of Mathematics and Statistics, Georgetown University, Washington DC, USA
| | - Habtom W Ressom
- 1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
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14
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MacArthur L, Ressom H, Shah S, Federoff HJ. Network modeling to identify new mechanisms and therapeutic targets for Parkinson’s disease. Expert Rev Neurother 2013; 13:685-93. [DOI: 10.1586/ern.13.59] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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15
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Wang T, Gu J, Yuan J, Tao R, Li Y, Li S. Inferring pathway crosstalk networks using gene set co-expression signatures. MOLECULAR BIOSYSTEMS 2013; 9:1822-8. [PMID: 23591523 DOI: 10.1039/c3mb25506a] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Constructing molecular interaction networks in cells is important for understanding the underlying mechanisms of biological processes. Except for single gene analysis, several gene set-based methods have been proposed to infer pathway crosstalk by analyzing large-scale gene expression data. But most of them take all pathway genes as a whole to infer the crosstalk. Biological evidence suggests that the pathway crosstalk usually occurs between some subsets rather than the whole sets of pathway genes. In this study, we propose a novel method, sGSCA (signature-based gene set co-expression analysis) which can use the co-expression correlations between subsets of pathway genes to infer the pathway crosstalk networks. The method applies sparse canonical correlation analysis (sCCA) to measure the pathway level co-expression and simultaneously obtain the subsets or signature genes that contribute to the co-expression of pathways. On simulated datasets, sGSCA can efficiently detect pathway crosstalk and the corresponding highly correlated signature genes. We applied sGSCA to two cancer gene expression datasets (one for hepatocellular cancer and the other for lung cancer). In the inferred networks, we found several important pathway crosstalks related to the cancers. The identified signature genes also show high enrichment for the cancer related genes. sGSCA can infer pathway crosstalk networks using large-scale gene expression data, and should be a useful tool for systematically studying the molecular mechanisms of complex diseases on both pathway and gene levels at the same time.
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Affiliation(s)
- Ting Wang
- Bioinformatics Division/Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology (TNLIST), Department of Automation, Tsinghua University, Beijing, 100084, China.
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16
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Furlong LI. Human diseases through the lens of network biology. Trends Genet 2013; 29:150-9. [DOI: 10.1016/j.tig.2012.11.004] [Citation(s) in RCA: 150] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2012] [Revised: 10/24/2012] [Accepted: 11/09/2012] [Indexed: 12/13/2022]
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Kim KJ, Hwang D, Kim WU. Systems Approach to Rheumatoid Arthritis. JOURNAL OF RHEUMATIC DISEASES 2013. [DOI: 10.4078/jrd.2013.20.6.348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Ki-Jo Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Korea
| | - Daehee Hwang
- Center for Systems Biology of Plant Senescence and Life History, Daegu Gyeongbuk Institute of Science & Technology, Daegu, Korea
| | - Wan-Uk Kim
- Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Korea
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Chakrabarti A, Verbridge S, Stroock AD, Fischbach C, Varner JD. Multiscale models of breast cancer progression. Ann Biomed Eng 2012; 40:2488-500. [PMID: 23008097 DOI: 10.1007/s10439-012-0655-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2012] [Accepted: 09/04/2012] [Indexed: 12/13/2022]
Abstract
Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.
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Affiliation(s)
- Anirikh Chakrabarti
- School of Chemical and Biomolecular Engineering, 244 Olin Hall, Cornell University, Ithaca, NY 14853, USA
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Hoehndorf R, Dumontier M, Gkoutos GV. Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics. Bioinformatics 2012; 28:2169-75. [PMID: 22711793 PMCID: PMC3493115 DOI: 10.1093/bioinformatics/bts350] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 06/11/2012] [Accepted: 06/12/2012] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large-scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several pharmacogenomics databases. An integrated analysis of the information in these databases can reveal disease pathways and facilitate novel biomedical analyses. RESULTS We demonstrate how to integrate pharmacogenomics databases through integration of the biomedical ontologies that are used as meta-data in these databases. The additional background knowledge in these ontologies can then be used to enable novel analyses. We identify disease pathways using a novel multi-ontology enrichment analysis over the Human Disease Ontology, and we identify significant associations between chemicals and pathways using an enrichment analysis over a chemical ontology. The drug-pathway and disease-pathway associations are a valuable resource for research in disease and drug mechanisms and can be used to improve computational drug repurposing. AVAILABILITY http://pharmgkb-owl.googlecode.com
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Affiliation(s)
- Robert Hoehndorf
- Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK.
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Scholz SW, Mhyre T, Ressom H, Shah S, Federoff HJ. Genomics and bioinformatics of Parkinson's disease. Cold Spring Harb Perspect Med 2012; 2:a009449. [PMID: 22762024 PMCID: PMC3385936 DOI: 10.1101/cshperspect.a009449] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Within the last two decades, genomics and bioinformatics have profoundly impacted our understanding of the molecular mechanisms of Parkinson's disease (PD). From the description of the first PD gene in 1997 until today, we have witnessed the emergence of new technologies that have revolutionized our concepts to identify genetic mechanisms implicated in human health and disease. Driven by the publication of the human genome sequence and followed by the description of detailed maps for common genetic variability, novel applications to rapidly scrutinize the entire genome in a systematic, cost-effective manner have become a reality. As a consequence, about 30 genetic loci have been unequivocally linked to the pathogenesis of PD highlighting essential molecular pathways underlying this common disorder. Herein we discuss how neurogenomics and bioinformatics are applied to dissect the nature of this complex disease with the overall aim of developing rational therapeutic interventions.
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Affiliation(s)
- Sonja W Scholz
- Department of Neuroscience, Georgetown University, Washington, DC, USA
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