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Trembley JH, Kren BT, Afzal M, Scaria GA, Klein MA, Ahmed K. Protein kinase CK2 – diverse roles in cancer cell biology and therapeutic promise. Mol Cell Biochem 2022; 478:899-926. [PMID: 36114992 PMCID: PMC9483426 DOI: 10.1007/s11010-022-04558-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022]
Abstract
The association of protein kinase CK2 (formerly casein kinase II or 2) with cell growth and proliferation in cells was apparent at early stages of its investigation. A cancer-specific role for CK2 remained unclear until it was determined that CK2 was also a potent suppressor of cell death (apoptosis); the latter characteristic differentiated its function in normal versus malignant cells because dysregulation of both cell growth and cell death is a universal feature of cancer cells. Over time, it became evident that CK2 exerts its influence on a diverse range of cell functions in normal as well as in transformed cells. As such, CK2 and its substrates are localized in various compartments of the cell. The dysregulation of CK2 is documented in a wide range of malignancies; notably, by increased CK2 protein and activity levels with relatively moderate change in its RNA abundance. High levels of CK2 are associated with poor prognosis in multiple cancer types, and CK2 is a target for active research and testing for cancer therapy. Aspects of CK2 cellular roles and targeting in cancer are discussed in the present review, with focus on nuclear and mitochondrial functions and prostate, breast and head and neck malignancies.
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Affiliation(s)
- Janeen H Trembley
- Research Service, Minneapolis VA Health Care System, Minneapolis, MN, 55417, USA.
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55455, USA.
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA.
| | - Betsy T Kren
- Research Service, Minneapolis VA Health Care System, Minneapolis, MN, 55417, USA
| | - Muhammad Afzal
- Department of Biochemistry, Riphah International University, Islamabad, Pakistan
| | - George A Scaria
- Hematology/Oncology Section, Primary Care Service Line, Minneapolis VA Health Care System, Minneapolis, MN, 55417, USA
| | - Mark A Klein
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA
- Hematology/Oncology Section, Primary Care Service Line, Minneapolis VA Health Care System, Minneapolis, MN, 55417, USA
- Department of Medicine, Division of Hematology, Oncology and Transplantation, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Khalil Ahmed
- Research Service, Minneapolis VA Health Care System, Minneapolis, MN, 55417, USA.
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, 55455, USA.
- Masonic Cancer Center, University of Minnesota, Minneapolis, MN, 55455, USA.
- Department of Urology, University of Minnesota, Minneapolis, MN, 55455, USA.
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2
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Vitali F, Li Q, Schissler AG, Berghout J, Kenost C, Lussier YA. Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes. Brief Bioinform 2019; 20:789-805. [PMID: 29272327 PMCID: PMC6585155 DOI: 10.1093/bib/bbx149] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/06/2017] [Indexed: 12/13/2022] Open
Abstract
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
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Affiliation(s)
| | - Qike Li
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
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Dimitrakopoulos CM, Beerenwinkel N. Computational approaches for the identification of cancer genes and pathways. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 9. [PMID: 27863091 PMCID: PMC5215607 DOI: 10.1002/wsbm.1364] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 07/26/2016] [Accepted: 08/23/2016] [Indexed: 12/27/2022]
Abstract
High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Christos M Dimitrakopoulos
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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4
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Börnigen D, Tyekucheva S, Wang X, Rider JR, Lee GS, Mucci LA, Sweeney C, Huttenhower C. Computational Reconstruction of NFκB Pathway Interaction Mechanisms during Prostate Cancer. PLoS Comput Biol 2016; 12:e1004820. [PMID: 27078000 PMCID: PMC4831844 DOI: 10.1371/journal.pcbi.1004820] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 02/19/2016] [Indexed: 12/21/2022] Open
Abstract
Molecular research in cancer is one of the largest areas of bioinformatic investigation, but it remains a challenge to understand biomolecular mechanisms in cancer-related pathways from high-throughput genomic data. This includes the Nuclear-factor-kappa-B (NFκB) pathway, which is central to the inflammatory response and cell proliferation in prostate cancer development and progression. Despite close scrutiny and a deep understanding of many of its members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. Here, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for ATF3, CXCL2, DUSP5, JUNB, NEDD9, SELE, TRIB1, and ZFP36 in this pathway, in addition to new mechanistic interactions between these genes and 10 known NFκB pathway members. A newly predicted interaction between NEDD9 and ZFP36 in particular was validated by co-immunoprecipitation, as was NEDD9's potential biological role in prostate cancer cell growth regulation. We combined 651 gene expression datasets with 1.4M gene product interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction among pathway members were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in a total of 112 interactions in the fully reconstructed NFκB pathway: 13 (11%) previously known, 29 (26%) supported by existing literature, and 70 (63%) novel. This method is generalizable to other tissue types, cancers, and organisms, and this new information about the NFκB pathway will allow us to further understand prostate cancer and to develop more effective prevention and treatment strategies. In molecular research in cancer it remains challenging to uncover biomolecular mechanisms in cancer-related pathways from high-throughput genomic data, including the Nuclear-factor-kappa-B (NFκB) pathway. Despite close scrutiny and a deep understanding of many of the NFκB pathway members’ biomolecular activities, the current list of pathway members and a systems-level understanding of their interactions remains incomplete. In this study, we provide the first steps toward computational reconstruction of interaction mechanisms of the NFκB pathway in prostate cancer. We identified novel roles for 8 genes in this pathway and new mechanistic interactions between these genes and 10 known pathway members. We combined 651 gene expression datasets with 1.4M interactions to predict the inclusion of 40 additional genes in the pathway. Molecular mechanisms of interaction were inferred using recent advances in Bayesian data integration to simultaneously provide information specific to biological contexts and individual biomolecular activities, resulting in 112 interactions in the fully reconstructed NFκB pathway. This method is generalizable, and this new information about the NFκB pathway will allow us to further understand prostate cancer.
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Affiliation(s)
- Daniela Börnigen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Svitlana Tyekucheva
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
| | - Xiaodong Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jennifer R Rider
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Gwo-Shu Lee
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
| | - Christopher Sweeney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.,The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
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Multi-Leu PACE4 Inhibitor Retention within Cells Is PACE4 Dependent and a Prerequisite for Antiproliferative Activity. BIOMED RESEARCH INTERNATIONAL 2015; 2015:824014. [PMID: 26114115 PMCID: PMC4465654 DOI: 10.1155/2015/824014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 01/15/2015] [Indexed: 01/01/2023]
Abstract
The overexpression as well as the critical implication of the proprotein convertase PACE4 in prostate cancer progression has been previously reported and supported the development of peptide inhibitors. The multi-Leu peptide, a PACE4-specific inhibitor, was further generated and its capability to be uptaken by tumor xenograft was demonstrated with regard to its PACE4 expression status. To investigate whether the uptake of this inhibitor was directly dependent of PACE4 levels, uptake and efflux from cancer cells were evaluated and correlations were established with PACE4 contents on both wild type and PACE4-knockdown cell lines. PACE4-knockdown associated growth deficiencies were established on the knockdown HepG2, Huh7, and HT1080 cells as well as the antiproliferative effects of the multi-Leu peptide supporting the growth capabilities of PACE4 in cancer cells.
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Abstract
In spite of the expensive preclinical testing, the consistent failure to translate many promising targeted drugs from the laboratory bench to the clinic raises the question of whether the single-pathway drug-discovery strategies offer the correct perspective. As revealed by network biology, cancers harbor robust biological networks that are inherently resistant to changes, such as those induced by drugs with very narrow mechanisms of action. Therefore, network pharmacology strategies, the treatment of cancer by modulating more than one target, are needed. Different promiscuous approaches targeting multiple avenues within cancer-associated networks, such as the pleiotropic natural products, are emerging. Nevertheless, there is a long way before such 'proof-of-concept strategies' can be successfully applied in the clinical setting. This article provides a perspective on the current challenges in drug discovery, the reasons for high failure rates and how network pharmacology can aid the successful design of agents against cancer.
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7
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Lee YJ, Boyd AD, Li JJ, Gardeux V, Kenost C, Saner D, Li H, Abraham I, Krishnan JA, Lussier YA. COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:855-64. [PMID: 25954392 PMCID: PMC4419951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p<0.004). Therefore, we suggest that network topology is a scalable and generalizable method predictive of hospitalization.
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Affiliation(s)
- Young Ji Lee
- Department of Medicine, University of Illinois at Chicago, Chicago, IL
| | - Andrew D Boyd
- Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, IL ; Departments of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL ; University of Illinois Hospital and Health Science System, University of Illinois at Chicago, Chicago, IL
| | - Jianrong John Li
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Vincent Gardeux
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Colleen Kenost
- Department of Medicine, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA
| | - Don Saner
- Cancer Center, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA
| | - Haiquan Li
- Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Ivo Abraham
- Department of Pharmacy Practice and Science, The University of Arizona, Tucson, AZ, USA
| | - Jerry A Krishnan
- Department of Medicine, University of Illinois at Chicago, Chicago, IL ; University of Illinois Hospital and Health Science System, University of Illinois at Chicago, Chicago, IL
| | - Yves A Lussier
- Department of Medicine, The University of Arizona, Tucson, AZ, USA ; Cancer Center, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA ; Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA
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8
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Gene expression in rat models for inter-generational transmission of islet dysfunction and obesity. GENOMICS DATA 2014; 2:351-3. [PMID: 26484128 PMCID: PMC4536069 DOI: 10.1016/j.gdata.2014.09.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Accepted: 09/29/2014] [Indexed: 11/01/2022]
Abstract
Paternal high fat diet (HFD) consumption triggers unique gene signatures, consistent with premature aging and chronic degenerative disorders, in both white adipose tissue (RpWAT) and pancreatic islets of daughters. In addition to published data in Nature, 2010, 467, 963-966 (GSE: 19877, islet) and FASEB J 2014, 28, 1830-1841 (GSE: 33551, RpWAT), we describe here additional details on systems-based approaches and analysis to develop our observations. Our data provides a resource for exploring the complex molecular mechanisms that underlie intergenerational transmission of obesity.
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9
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Gardeux V, Achour I, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JGN, Lussier YA. 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine. J Am Med Inform Assoc 2014; 21:1015-25. [PMID: 25301808 PMCID: PMC4215042 DOI: 10.1136/amiajnl-2013-002519] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Background The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method ‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). Conclusions The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. Software http://lussierlab.org/publications/N-of-1-pathways
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Affiliation(s)
- Vincent Gardeux
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Department of Informatics, School of Engineering, EISTI (École Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise, France Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Ikbel Achour
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Jianrong Li
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Mark Maienschein-Cline
- Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Haiquan Li
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Lorenzo Pesce
- Computation Institute, Argonne National Laboratory & University of Chicago, Chicago, Illinois, USA
| | - Gurunadh Parinandi
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Neil Bahroos
- Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Robert Winn
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA University of Illinois Cancer Center, Chicago, Illinois, USA
| | - Ian Foster
- Computation Institute, Argonne National Laboratory & University of Chicago, Chicago, Illinois, USA Department of Computer Science, University of Chicago, Chicago, Illinois, USA Mathematics and Computer Science Division, Argonne National Laboratory, Chicago, Illinois, USA
| | - Joe G N Garcia
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA
| | - Yves A Lussier
- Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA Computation Institute, Argonne National Laboratory & University of Chicago, Chicago, Illinois, USA University of Illinois Cancer Center, Chicago, Illinois, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA Department of Biopharmaceutical Science, College of Pharmacy, University of Illinois at Chicago, Illinois, USA Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, USA Department of Pharmacology, University of Illinois at Chicago, Chicago, Illinois, USA
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Boland MR, Hripcsak G, Shen Y, Chung WK, Weng C. Defining a comprehensive verotype using electronic health records for personalized medicine. J Am Med Inform Assoc 2013; 20:e232-8. [PMID: 24001516 PMCID: PMC3861934 DOI: 10.1136/amiajnl-2013-001932] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 08/12/2013] [Indexed: 11/04/2022] Open
Abstract
The burgeoning adoption of electronic health records (EHR) introduces a golden opportunity for studying individual manifestations of myriad diseases, which is called 'EHR phenotyping'. In this paper, we break down this concept by: relating it to phenotype definitions from Johannsen; comparing it to cohort identification and disease subtyping; introducing a new concept called 'verotype' (Latin: vere = true, actually) to represent the 'true' population of similar patients for treatment purposes through the integration of genotype, phenotype, and disease subtype (eg, specific glucose value pattern in patients with diabetes) information; analyzing the value of the 'verotype' concept for personalized medicine; and outlining the potential for using network-based approaches to reverse engineer clinical disease subtypes.
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Affiliation(s)
- Mary Regina Boland
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Yufeng Shen
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University, New York, New York, USA
- Department of Medicine, Columbia University, New York, New York, USA
- The Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- The Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, USA
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11
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Chen JL, Hsu A, Yang X, Li J, Lee Y, Parinandi G, Li H, Lussier YA. Curation-free biomodules mechanisms in prostate cancer predict recurrent disease. BMC Med Genomics 2013; 6 Suppl 2:S4. [PMID: 23819917 PMCID: PMC3654873 DOI: 10.1186/1755-8794-6-s2-s4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023] Open
Abstract
Motivation Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease. Results Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated "Cancer Module" mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank_p = 0.019). Conclusion Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.
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Affiliation(s)
- James L Chen
- Center for Biomed Informatics and Department of Medicine, The University of Chicago, Chicago, IL, USA
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12
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Vangala RK, Ravindran V, Ghatge M, Shanker J, Arvind P, Bindu H, Shekar M, Rao VS. Integrative bioinformatics analysis of genomic and proteomic approaches to understand the transcriptional regulatory program in coronary artery disease pathways. PLoS One 2013; 8:e57193. [PMID: 23468932 PMCID: PMC3585295 DOI: 10.1371/journal.pone.0057193] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2012] [Accepted: 01/18/2013] [Indexed: 11/19/2022] Open
Abstract
Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.
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Affiliation(s)
- Rajani Kanth Vangala
- Tata Proteomics and Coagulation Department, Thrombosis Research Institute, Bangalore, Karnataka, India.
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13
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Babaei S, Hulsman M, Reinders M, de Ridder J. Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion. BMC Bioinformatics 2013; 14:29. [PMID: 23343428 PMCID: PMC3626877 DOI: 10.1186/1471-2105-14-29] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Accepted: 01/04/2013] [Indexed: 11/13/2022] Open
Abstract
Background Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. Results We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. Conclusions The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.
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Affiliation(s)
- Sepideh Babaei
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
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Perez-Rathke A, Li H, Lussier YA. Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq data. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2013:159-170. [PMID: 23424121 PMCID: PMC3595401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR<5%, Precision=75%, Recall =92%), and (iii) to predict phenotypes in an independent dataset and expression platform (RNA-seq vs microarrays, Fisher Exact Test p<10(-6)). Measuring at a single-sample level, FAIME could differentiate cancer samples from normal ones; furthermore, it achieved comparative performance in identifying differentially expressed pathways as compared to state-of-the-art cross-sample methods. These results motivate future work on mechanism-level biomarker discovery predictive of diagnoses, treatment, and therapy.
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Affiliation(s)
- Alan Perez-Rathke
- Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA.
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Azuaje FJ, Dewey FE, Brutsaert DL, Devaux Y, Ashley EA, Wagner DR. Systems-based approaches to cardiovascular biomarker discovery. ACTA ACUST UNITED AC 2012; 5:360-7. [PMID: 22715280 DOI: 10.1161/circgenetics.112.962977] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Francisco J Azuaje
- Department of Cardiovascular Diseases, Public Research Centre for Health, Luxembourg, Luxembourg.
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Chen JL, Li J, Kiriluk KJ, Rosen AM, Paner GP, Antic T, Lussier YA, Vander Griend DJ. Deregulation of a Hox protein regulatory network spanning prostate cancer initiation and progression. Clin Cancer Res 2012; 18:4291-302. [PMID: 22723371 PMCID: PMC3479663 DOI: 10.1158/1078-0432.ccr-12-0373] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PURPOSE The aberrant activity of developmental pathways in prostate cancer may provide significant insight into predicting tumor initiation and progression, as well as identifying novel therapeutic targets. To this end, despite shared androgen-dependence and functional similarities to the prostate gland, seminal vesicle cancer is exceptionally rare. EXPERIMENTAL DESIGN We conducted genomic pathway analyses comparing patient-matched normal prostate and seminal vesicle epithelial cells to identify novel pathways for tumor initiation and progression. Derived gene expression profiles were grouped into cancer biomodules using a protein-protein network algorithm to analyze their relationship to known oncogenes. Each resultant biomodule was assayed for its prognostic ability against publically available prostate cancer patient gene array datasets. RESULTS Analyses show that the embryonic developmental biomodule containing four homeobox gene family members (Meis1, Meis2, Pbx1, and HoxA9) detects a survival difference in a set of watchful-waiting patients (n = 172, P = 0.05), identify men who are more likely to recur biochemically postprostatectomy (n = 78, P = 0.02), correlate with Gleason score (r = 0.98, P = 0.02), and distinguish between normal prostate, primary tumor, and metastatic disease. In contrast to other cancer types, Meis1, Meis2, and Pbx1 expression is decreased in poor-prognosis tumors, implying that they function as tumor suppressor genes for prostate cancer. Immunohistochemical staining documents nuclear basal-epithelial and stromal Meis2 staining, with loss of Meis2 expression in prostate tumors. CONCLUSION These data implicate deregulation of the Hox protein cofactors Meis1, Meis2, and Pbx1 as serving a critical function to suppress prostate cancer initiation and progression.
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Affiliation(s)
- James L. Chen
- Department of Medicine, Section of Hematology/Oncology, The University of Chicago
| | - Jianrong Li
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois
| | - Kyle J. Kiriluk
- Department of Surgery, Section of Urology, The University of Chicago
| | - Alex M. Rosen
- Department of Surgery, Section of Urology, The University of Chicago
| | - Gladell P. Paner
- Department of Pathology, Section of Urology, The University of Chicago
| | - Tatjana Antic
- Department of Pathology, Section of Urology, The University of Chicago
| | - Yves A. Lussier
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
- Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois
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Pradhan MP, Prasad NKA, Palakal MJ. A systems biology approach to the global analysis of transcription factors in colorectal cancer. BMC Cancer 2012; 12:331. [PMID: 22852817 PMCID: PMC3539921 DOI: 10.1186/1471-2407-12-331] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2011] [Accepted: 06/21/2012] [Indexed: 02/08/2023] Open
Abstract
Background Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity’s own significance but also uncover relationships with novel biological entities. To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). Methods We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. Results The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. Conclusions Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.
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Affiliation(s)
- Meeta P Pradhan
- School of Informatics, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA
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18
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Shi M, Beauchamp RD, Zhang B. A network-based gene expression signature informs prognosis and treatment for colorectal cancer patients. PLoS One 2012; 7:e41292. [PMID: 22844451 PMCID: PMC3402487 DOI: 10.1371/journal.pone.0041292] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2012] [Accepted: 06/19/2012] [Indexed: 01/08/2023] Open
Abstract
Background Several studies have reported gene expression signatures that predict recurrence risk in stage II and III colorectal cancer (CRC) patients with minimal gene membership overlap and undefined biological relevance. The goal of this study was to investigate biological themes underlying these signatures, to infer genes of potential mechanistic importance to the CRC recurrence phenotype and to test whether accurate prognostic models can be developed using mechanistically important genes. Methods and Findings We investigated eight published CRC gene expression signatures and found no functional convergence in Gene Ontology enrichment analysis. Using a random walk-based approach, we integrated these signatures and publicly available somatic mutation data on a protein-protein interaction network and inferred 487 genes that were plausible candidate molecular underpinnings for the CRC recurrence phenotype. We named the list of 487 genes a NEM signature because it integrated information from Network, Expression, and Mutation. The signature showed significant enrichment in four biological processes closely related to cancer pathophysiology and provided good coverage of known oncogenes, tumor suppressors, and CRC-related signaling pathways. A NEM signature-based Survival Support Vector Machine prognostic model was trained using a microarray gene expression dataset and tested on an independent dataset. The model-based scores showed a 75.7% concordance with the real survival data and separated patients into two groups with significantly different relapse-free survival (p = 0.002). Similar results were obtained with reversed training and testing datasets (p = 0.007). Furthermore, adjuvant chemotherapy was significantly associated with prolonged survival of the high-risk patients (p = 0.006), but not beneficial to the low-risk patients (p = 0.491). Conclusions The NEM signature not only reflects CRC biology but also informs patient prognosis and treatment response. Thus, the network-based data integration method provides a convergence between biological relevance and clinical usefulness in gene signature development.
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Affiliation(s)
- Mingguang Shi
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - R. Daniel Beauchamp
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Surgery, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Bing Zhang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- * E-mail:
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Regan K, Wang K, Doughty E, Li H, Li J, Lee Y, Kann MG, Lussier YA. Translating Mendelian and complex inheritance of Alzheimer's disease genes for predicting unique personal genome variants. J Am Med Inform Assoc 2012; 19:306-16. [PMID: 22319180 PMCID: PMC3277633 DOI: 10.1136/amiajnl-2011-000656] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome. Materials and methods An analytical method, SPADE-gen, spanning three biological scales was developed to demonstrate the mechanistic concordance between Mendelian and complex inheritance of Alzheimer's disease (AD) genes: biological functions (BP), protein interaction modeling, and protein domain implicated in the disease-associated polymorphism. Results Among Gene Ontology (GO) biological processes (BP) enriched at a false detection rate <5% in 15 AD genes of Mendelian inheritance (Online Mendelian Inheritance in Man) and independently in those of complex inheritance (25 host genes of intragenic AD single-nucleotide polymorphisms confirmed in genome-wide association studies), 16 overlapped (empirical p=0.007) and 45 were similar (empirical p<0.009; information theory). SPAN network modeling extended the canonical pathway of AD (KEGG) with 26 new protein interactions (empirical p<0.0001). Discussion The study prioritized new AD-associated biological mechanisms and focused the analysis on previously unreported interactions associated with the biological processes of polymorphisms that affect specific protein domains within characterized AD genes and their direct interactors using (1) concordant GO-BP and (2) domain interactions within STRING protein–protein interactions corresponding to the genomic location of the AD polymorphism (eg, EPHA1, APOE, and CD2AP). Conclusion These results are in line with unique-event polymorphism theory, indicating how disease-associated polymorphisms of Mendelian or complex inheritance relate genetically to those observed as ‘unique personal variants’. They also provide insight for identifying novel targets, for repositioning drugs, and for personal therapeutics.
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Affiliation(s)
- Kelly Regan
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois 60637, USA
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Wang J, Chen G, Li M, Pan Y. Integration of breast cancer gene signatures based on graph centrality. BMC SYSTEMS BIOLOGY 2011; 5 Suppl 3:S10. [PMID: 22784616 PMCID: PMC3287565 DOI: 10.1186/1752-0509-5-s3-s10] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome. RESULTS In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well-known breast cancer genes, such as TP53 and BRCA1, are ranked extremely high in our results. Compared with previous results by functional enrichment analysis, graph centralities, especially the eigenvector centrality and subgraph centrality, based gene signatures are more tightly related to breast cancer. We validate these signatures on genome-wide microarray dataset and found strong association between the expression of these signature genes and pathologic parameters. CONCLUSIONS In summary, graph centralities provide a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is not only can be used on breast cancer, but also can be used on other gene expression related diseases and drug studies.
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Affiliation(s)
- Jianxin Wang
- School of Information Science and Engineering, Central South University, Changsha, 410083, China.
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Lussier YA, Stadler WM, Chen JL. Advantages of genomic complexity: bioinformatics opportunities in microRNA cancer signatures. J Am Med Inform Assoc 2011; 19:156-60. [PMID: 22101905 PMCID: PMC3277616 DOI: 10.1136/amiajnl-2011-000419] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
MicroRNAs, small non-coding RNAs, may act as tumor suppressors or oncogenes, and each regulate their own transcription and that of hundreds of genes, often in a tissue-dependent manner. This creates a tightly interwoven network regulating and underlying oncogenesis and cancer biology. Although protein-coding gene signatures and single protein pathway markers have proliferated over the past decade, routine adoption of the former has been hampered by interpretability, reproducibility, and dimensionality, whereas the single molecule–phenotype reductionism of the latter is often overly simplistic to account for complex phenotypes. MicroRNA-derived biomarkers offer a powerful alternative; they have both the flexibility of gene expression signature classifiers and the desirable mechanistic transparency of single protein biomarkers. Furthermore, several advances have recently demonstrated the robust detection of microRNAs from various biofluids, thus providing an additional opportunity for obtaining bioinformatically derived biomarkers to accelerate the identification of individual patients for personalized therapy.
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Affiliation(s)
- Yves A Lussier
- Dept of Medicine, School of Medicine, The University of Illinois at Chicago, Chicago, Illinois, USA.
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Butte AJ, Shah NH. Computationally translating molecular discoveries into tools for medicine: translational bioinformatics articles now featured in JAMIA. J Am Med Inform Assoc 2011; 18:352-3. [PMID: 21672904 DOI: 10.1136/amiajnl-2011-000343] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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