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Miao R, Dang Q, Cai J, Huang HH, Xie SL, Liang Y. Sparse principal component analysis based on genome network for correcting cell type heterogeneity in epigenome-wide association studies. Med Biol Eng Comput 2022; 60:2601-2618. [DOI: 10.1007/s11517-022-02599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 04/30/2022] [Indexed: 10/17/2022]
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2
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Nazarenko T, Whitwell HJ, Blyuss O, Zaikin A. Parenclitic and Synolytic Networks Revisited. Front Genet 2021; 12:733783. [PMID: 34745212 PMCID: PMC8564045 DOI: 10.3389/fgene.2021.733783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/28/2021] [Indexed: 11/14/2022] Open
Abstract
Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches.
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Affiliation(s)
- Tatiana Nazarenko
- Department of Mathematics and Institute for Women’s Health, University College London, London, United Kingdom
| | - Harry J. Whitwell
- National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, Hammersmith Campus, London, United Kingdom
- Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion, Imperial College London, South Kensington Campus, London, United Kingdom
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Oleg Blyuss
- Department of Mathematics and Institute for Women’s Health, University College London, London, United Kingdom
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- School of Physics, Astronomy and Mathematics, University of Hertfordshire, Harfield, United Kingdom
- Department of Pediatrics and Pediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Alexey Zaikin
- Department of Mathematics and Institute for Women’s Health, University College London, London, United Kingdom
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
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3
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Levy JJ, Chen Y, Azizgolshani N, Petersen CL, Titus AJ, Moen EL, Vaickus LJ, Salas LA, Christensen BC. MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks. NPJ Syst Biol Appl 2021; 7:33. [PMID: 34417465 PMCID: PMC8379254 DOI: 10.1038/s41540-021-00193-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/01/2021] [Indexed: 02/07/2023] Open
Abstract
DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.
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Affiliation(s)
- Joshua J Levy
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
| | - Youdinghuan Chen
- Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Nasim Azizgolshani
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Curtis L Petersen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
| | - Alexander J Titus
- Department of Life Sciences, University of New Hampshire, Manchester, NH, USA
| | - Erika L Moen
- The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Louis J Vaickus
- Emerging Diagnostic and Investigative Technologies, Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA
| | - Lucas A Salas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
- Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
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4
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Nazarenko T, Blyuss O, Whitwell H, Zaikin A. Ensemble of correlation, parenclitic and synolitic graphs as a tool to detect universal changes in complex biological systems: Comment on "Dynamic and thermodynamic models of adaptation" by A.N. Gorban et al. Phys Life Rev 2021; 38:120-123. [PMID: 34090824 DOI: 10.1016/j.plrev.2021.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 05/24/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Tatiana Nazarenko
- Department of Mathematics and Institute for Women's Health, University College London, London, UK
| | - Oleg Blyuss
- Department of Mathematics and Institute for Women's Health, University College London, London, UK; School of Physics, Astronomy and Mathematics, University of Hertfordshire, Harfield, UK; Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia; Department of Pediatrics and Pediatric Infectious Diseases, Institute of Child's Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Harry Whitwell
- National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, IRDB Building, Imperial College London, Hammersmith Campus, London, W12 0NN, UK; Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK; Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia; Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Alexey Zaikin
- Department of Mathematics and Institute for Women's Health, University College London, London, UK; Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia; Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
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5
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Sarno F, Benincasa G, List M, Barabasi AL, Baumbach J, Ciardiello F, Filetti S, Glass K, Loscalzo J, Marchese C, Maron BA, Paci P, Parini P, Petrillo E, Silverman EK, Verrienti A, Altucci L, Napoli C. Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside. Clin Epigenetics 2021; 13:66. [PMID: 33785068 PMCID: PMC8010949 DOI: 10.1186/s13148-021-01047-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/01/2021] [Indexed: 02/07/2023] Open
Abstract
Despite impressive efforts invested in epigenetic research in the last 50 years, clinical applications are still lacking. Only a few university hospital centers currently use epigenetic biomarkers at the bedside. Moreover, the overall concept of precision medicine is not widely recognized in routine medical practice and the reductionist approach remains predominant in treating patients affected by major diseases such as cancer and cardiovascular diseases. By its' very nature, epigenetics is integrative of genetic networks. The study of epigenetic biomarkers has led to the identification of numerous drugs with an increasingly significant role in clinical therapy especially of cancer patients. Here, we provide an overview of clinical epigenetics within the context of network analysis. We illustrate achievements to date and discuss how we can move from traditional medicine into the era of network medicine (NM), where pathway-informed molecular diagnostics will allow treatment selection following the paradigm of precision medicine.
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Affiliation(s)
- Federica Sarno
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albert-Lazlo Barabasi
- Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, Hamburg, Germany
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | | | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Bradley A Maron
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paola Paci
- Department of Computer, Control, and Management Engineering, Sapienza University, Rome, Italy
| | - Paolo Parini
- Department of Laboratory Medicine and Department of Medicine, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
| | - Enrico Petrillo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Antonella Verrienti
- Department of Translational and Precision Medicine, Sapienza University, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania "Luigi Vanvitelli", Napoli, Italy.
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy
- Clinical Department of Internal Medicine and Specialistic Units, AOU, University of Campania "Luigi Vanvitelli", Naples, Italy
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6
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Affiliation(s)
- Simón Lunagómez
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Sofia C. Olhede
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Statistical Science, UCL, London, UK
| | - Patrick J. Wolfe
- Department of Statistics, Purdue University, West Lafayette, IN
- Department of Computer Science, Purdue University, West Lafayette, IN
- Department of Electrical & Computer Engineering, Purdue University, West Lafayette, IN
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7
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Affiliation(s)
- Jinyuan Chang
- School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
| | - Eric D. Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston, MA
| | - Qiwei Yao
- Department of Statistics, London School of Economics and Political Science, London, UK
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8
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Whitwell HJ, Bacalini MG, Blyuss O, Chen S, Garagnani P, Gordleeva SY, Jalan S, Ivanchenko M, Kanakov O, Kustikova V, Mariño IP, Meyerov I, Ullner E, Franceschi C, Zaikin A. The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging. Front Aging Neurosci 2020; 12:136. [PMID: 32523526 PMCID: PMC7261843 DOI: 10.3389/fnagi.2020.00136] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022] Open
Abstract
Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
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Affiliation(s)
- Harry J Whitwell
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
| | | | - Oleg Blyuss
- School of Physics, Astronomy and Mathematics, University of Hertfordshire, Harfield, United Kingdom.,Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shangbin Chen
- Britton Chance Centre for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Susan Yu Gordleeva
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sarika Jalan
- Complex Systems Laboratory, Discipline of Physics, Indian Institute of Technology Indore, Indore, India.,Centre for Bio-Science and Bio-Medical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Oleg Kanakov
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Valentina Kustikova
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ines P Mariño
- Department of Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Madrid, Spain
| | - Iosif Meyerov
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ekkehard Ullner
- Department of Physics (SUPA), Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Zaikin
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Mathematics, Institute for Women's Health, University College London, London, United Kingdom
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Weighill D, Tschaplinski TJ, Tuskan GA, Jacobson D. Data Integration in Poplar: 'Omics Layers and Integration Strategies. Front Genet 2019; 10:874. [PMID: 31608114 PMCID: PMC6773870 DOI: 10.3389/fgene.2019.00874] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.
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Affiliation(s)
- Deborah Weighill
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Timothy J Tschaplinski
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Daniel Jacobson
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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10
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R V, Nazeer KAA. Multi-network approach to identify differentially methylated gene communities in cancer. Gene 2019; 697:227-237. [PMID: 30797996 DOI: 10.1016/j.gene.2019.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/02/2019] [Accepted: 02/05/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND AND OBJECTIVE High-throughput Next Generation Sequencing tools have generated immense quantity of genome-wide methylation and expression profiling data, resulting in an unprecedented opportunity to unravel the epigenetic regulatory mechanisms underlying cancer. Identifying differentially methylated regions within gene networks is an important step towards revealing the cancer epigenome blueprint. Approaches that integrate gene methylation and expression profiles assume their negative correlation and build a single scaffold network to cluster. However, the exact regulatory mechanism between gene expression and methylation is not precisely deciphered. METHODS A consensus-based clustering framework, namely, Differentially Methylated Gene Communities based on Multi-network (DMGC-M) is proposed, that takes multiple gene networks as input and builds a community structure out of evidences from all network types. RESULTS Experiments on six cancer datasets from The Cancer Genome Atlas (TCGA) reveal that multi-network approaches produce more discriminative gene communities than integrated approaches. CONCLUSION The proposed method will be useful to a number of researchers who seek to identify epigenetic dysregulations in pathways or molecular networks. The findings can also advance recent research efforts in Molecular Pathologic Epidemiology.
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Affiliation(s)
- Visakh R
- Department of Computer Science & Engineering, National Institute of Technology Calicut, Kerala - 673601, India.
| | - K A Abdul Nazeer
- Department of Computer Science & Engineering, National Institute of Technology Calicut, Kerala - 673601, India.
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11
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Jiang Z, Cinti C, Taranta M, Mattioli E, Schena E, Singh S, Khurana R, Lattanzi G, Tsinoremas NF, Capobianco E. Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures. PLoS One 2018; 13:e0206686. [PMID: 30485296 PMCID: PMC6261551 DOI: 10.1371/journal.pone.0206686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 10/17/2018] [Indexed: 02/07/2023] Open
Abstract
Background In melanoma, like in other cancers, both genetic alterations and epigenetic underlie the metastatic process. These effects are usually measured by changes in both methylome and transcriptome profiles, whose cross-correlation remains uncertain. We aimed to assess at systems scale the significance of epigenetic treatment in melanoma cells with different metastatic potential. Methods and findings Treatment by DAC demethylation with 5-Aza-2’-deoxycytidine of two melanoma cell lines endowed with different metastatic potential, SKMEL-2 and HS294T, was performed and high-throughput coupled RNA-Seq and RRBS-Seq experiments delivered differential profiles (DiP) of both transcriptomes and methylomes. Methylation levels measured at both TSS and gene body were studied to inspect correlated patterns with wide-spectrum transcript abundance levels quantified in both protein coding and non-coding RNA (ncRNA) regions. The DiP were then mapped onto standard bio-annotation sources (pathways, biological processes) and network configurations were obtained. The prioritized associations for target identification purposes were expected to elucidate the reprogramming dynamics induced by the epigenetic therapy. The interactomic connectivity maps of each cell line were formed to support the analysis of epigenetically re-activated genes. i.e. those supposedly silenced by melanoma. In particular, modular protein interaction networks (PIN) were used, evidencing a limited number of shared annotations, with an example being MAPK13 (cascade of cellular responses evoked by extracellular stimuli). This gene is also a target associated to the PANDAR ncRNA, therapeutically relevant because of its aberrant expression observed in various cancers. Overall, the non-metastatic SKMEL-2 map reveals post-treatment re-activation of a richer pathway landscape, involving cadherins and integrins as signatures of cell adhesion and proliferation. Relatively more lncRNAs were also annotated, indicating more complex regulation patterns in view of target identification. Finally, the antigen maps matched to DiP display other differential signatures with respect to the metastatic potential of the cell lines. In particular, as demethylated melanomas show connected targets that grow with the increased metastatic potential, also the potential target actionability seems to depend to some degree on the metastatic state. However, caution is required when assessing the direct influence of re-activated genes over the identified targets. In light of the stronger treatment effects observed in non-metastatic conditions, some limitations likely refer to in silico data integration tools and resources available for the analysis of tumor antigens. Conclusion Demethylation treatment strongly affects early melanoma progression by re-activating many genes. This evidence suggests that the efficacy of this type of therapeutic intervention is potentially high at the pre-metastatic stages. The biomarkers that can be assessed through antigens seem informative depending on the metastatic conditions, and networks help to elucidate the assessment of possible targets actionability.
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Affiliation(s)
- Zhijie Jiang
- Center for Computational Science, University of Miami, Miami, FL, United States of America
| | | | | | - Elisabetta Mattioli
- CNR Institute of Molecular Genetics, Bologna, Italy
- IRCCS Rizzoli Orthopedic Institute, Bologna, Italy
| | - Elisa Schena
- CNR Institute of Molecular Genetics, Bologna, Italy
- Endocrinology Unit, Department of Medical & Surgical Sciences, Alma Mater Studiorum University of Bologna, S Orsola-Malpighi Hospital, Bologna, Italy
| | - Sakshi Singh
- Institute of Clinical Physiology, CNR, Siena, Italy
| | - Rimpi Khurana
- Center for Computational Science, University of Miami, Miami, FL, United States of America
| | - Giovanna Lattanzi
- CNR Institute of Molecular Genetics, Bologna, Italy
- IRCCS Rizzoli Orthopedic Institute, Bologna, Italy
| | - Nicholas F. Tsinoremas
- Center for Computational Science, University of Miami, Miami, FL, United States of America
- Department of Medicine, University of Miami, Miami, FL, United States of America
| | - Enrico Capobianco
- Center for Computational Science, University of Miami, Miami, FL, United States of America
- * E-mail:
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12
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Jelisavcic V, Stojkovic I, Milutinovic V, Obradovic Z. Fast learning of scale-free networks based on Cholesky factorization. INT J INTELL SYST 2018. [DOI: 10.1002/int.21984] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Vladisav Jelisavcic
- School of Electrical Engineering; University of Belgrade; Belgrade Serbia
- Mathematical Institute of the Serbian Academy of Sciences and Arts; Belgrade Serbia
| | - Ivan Stojkovic
- School of Electrical Engineering; University of Belgrade; Belgrade Serbia
- Center for Data Analytics & Biomedical Informatics; Temple University; Philadelphia USA
| | - Veljko Milutinovic
- School of Electrical Engineering; University of Belgrade; Belgrade Serbia
| | - Zoran Obradovic
- Center for Data Analytics & Biomedical Informatics; Temple University; Philadelphia USA
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13
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Abstract
BACKGROUND The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites. METHODS In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes. RESULTS We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures. CONCLUSIONS A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.
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Affiliation(s)
- Wei-Lin Hu
- College of Science, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China
| | - Xiong-Hui Zhou
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
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Parenclitic Network Analysis of Methylation Data for Cancer Identification. PLoS One 2017; 12:e0169661. [PMID: 28107365 PMCID: PMC5249089 DOI: 10.1371/journal.pone.0169661] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 12/20/2016] [Indexed: 12/20/2022] Open
Abstract
We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93−99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.
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15
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16
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Bartlett TE, Jones A, Goode EL, Fridley BL, Cunningham JM, Berns EMJJ, Wik E, Salvesen HB, Davidson B, Trope CG, Lambrechts S, Vergote I, Widschwendter M. Intra-Gene DNA Methylation Variability Is a Clinically Independent Prognostic Marker in Women's Cancers. PLoS One 2015; 10:e0143178. [PMID: 26629914 PMCID: PMC4667934 DOI: 10.1371/journal.pone.0143178] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 10/30/2015] [Indexed: 12/31/2022] Open
Abstract
We introduce a novel per-gene measure of intra-gene DNA methylation variability (IGV) based on the Illumina Infinium HumanMethylation450 platform, which is prognostic independently of well-known predictors of clinical outcome. Using IGV, we derive a robust gene-panel prognostic signature for ovarian cancer (OC, n = 221), which validates in two independent data sets from Mayo Clinic (n = 198) and TCGA (n = 358), with significance of p = 0.004 in both sets. The OC prognostic signature gene-panel is comprised of four gene groups, which represent distinct biological processes. We show the IGV measurements of these gene groups are most likely a reflection of a mixture of intra-tumour heterogeneity and transcription factor (TF) binding/activity. IGV can be used to predict clinical outcome in patients individually, providing a surrogate read-out of hard-to-measure disease processes.
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Affiliation(s)
- Thomas E. Bartlett
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, United Kingdom
- Deparment of Mathematics, University College London, London, United Kingdom
- CoMPLEX, University College London, London, United Kingdom
| | - Allison Jones
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, United Kingdom
| | - Ellen L. Goode
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, United States of America
| | - Brooke L. Fridley
- Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, United States of America
| | - Julie M. Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States of America
| | - Els M. J. J. Berns
- Department of Medical Oncology, Erasmus MC-Cancer Center, Rotterdam, The Netherlands
| | - Elisabeth Wik
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
| | - Helga B. Salvesen
- Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway
| | - Ben Davidson
- Department of Pathology, Oslo University Hospital, Norwegian Radium Hospital, University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, Oslo, Norway
| | - Claes G. Trope
- Department of Gynaecological Oncology, Oslo University Hospital, Norwegian Radium Hospital, Oslo, Norway
| | - Sandrina Lambrechts
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology and Leuven Cancer Institute, University Hospitals Leuven, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Ignace Vergote
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology and Leuven Cancer Institute, University Hospitals Leuven, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Martin Widschwendter
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, United Kingdom
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17
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Ruan D, Young A, Montana G. Differential analysis of biological networks. BMC Bioinformatics 2015; 16:327. [PMID: 26453322 PMCID: PMC4600256 DOI: 10.1186/s12859-015-0735-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 08/18/2015] [Indexed: 12/13/2022] Open
Abstract
Background In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. Results We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. Conclusions We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.
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Affiliation(s)
- Da Ruan
- Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
| | - Alastair Young
- Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
| | - Giovanni Montana
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. .,Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK.
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18
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Zhang Y, Zhang J, Liu Z, Liu Y, Tuo S. A network-based approach to identify disease-associated gene modules through integrating DNA methylation and gene expression. Biochem Biophys Res Commun 2015; 465:437-42. [DOI: 10.1016/j.bbrc.2015.08.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2015] [Accepted: 08/09/2015] [Indexed: 11/28/2022]
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19
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Sun Y, Yuan K, Zhang P, Ma R, Zhang QW, Tian XS. Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes. Exp Ther Med 2015; 10:743-748. [PMID: 26622386 DOI: 10.3892/etm.2015.2527] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 05/07/2015] [Indexed: 12/30/2022] Open
Abstract
Multiple signal transduction pathways can affect each other considerably through crosstalk. However, the presence and extent of this phenomenon have not been rigorously studied. The aim of the present study was to identify strong and normal interactions between pathways in breast cancer and determine the main pathway. Five sets of breast cancer data were downloaded from the high-throughput Gene Expression Omnibus (GEO) and analyzed to identify differentially expressed (DE) genes using the Rank Product (RankProd) method. A list of pathways with differential expression was obtained by gene set enrichment analysis (GSEA) of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DE genes that overlapped between pathways were identified and a crosstalk network diagram based on the overlap of DE genes was constructed. A total of 1,464 DE genes and 26 pathways were identified. In addition, the number of DE genes that overlapped between specific pathways were determined, and the greatest degree of overlap was between the extracellular matrix (ECM)-receptor interaction and Focal adhesion pathways, which had 22 overlapping DE genes. Weighted pathway analysis of the crosstalk between pathways identified that Pathways in cancer was the main pathway in breast cancer.
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Affiliation(s)
- Yong Sun
- Department of Breast and Thyroid Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, P.R. China ; Department of General Surgery, Laiwu Hospital Affiliated to Taishan Medical College, Laiwu, Shandong 271100, P.R. China
| | - Kai Yuan
- Department of Breast Surgery, Shandong Provincial Qianfoshan Hospital, Shandong University, P.R. China
| | - Peng Zhang
- Department of General Surgery, Laiwu Hospital Affiliated to Taishan Medical College, Laiwu, Shandong 271100, P.R. China
| | - Rong Ma
- Department of Breast Surgery, Qilu Hospital, Shandong University, Jinan, Shandong 250014, P.R. China
| | - Qi-Wen Zhang
- Department of General Surgery, Laiwu Hospital Affiliated to Taishan Medical College, Laiwu, Shandong 271100, P.R. China
| | - Xing-Song Tian
- Department of Breast and Thyroid Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, P.R. China
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