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Galindez G, List M, Baumbach J, Völker U, Mäder U, Blumenthal DB, Kacprowski T. Inference of differential gene regulatory networks using boosted differential trees. BIOINFORMATICS ADVANCES 2024; 4:vbae034. [PMID: 38505804 PMCID: PMC10948285 DOI: 10.1093/bioadv/vbae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/24/2024] [Accepted: 02/27/2024] [Indexed: 03/21/2024]
Abstract
Summary Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. Availability and implementation BoostDiff is available at https://github.com/scibiome/boostdiff_inference.
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Affiliation(s)
- Gihanna Galindez
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, 38106, Germany
| | - Markus List
- Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, 85354, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
- Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, Odense, 5230, Denmark
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany
| | - Ulrike Mäder
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, 17475, Germany
| | - David B Blumenthal
- Biomedical Network Science Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91052, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, 38106, Germany
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Savino A, Provero P, Poli V. Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression. Int J Mol Sci 2020; 21:ijms21249461. [PMID: 33322692 PMCID: PMC7764314 DOI: 10.3390/ijms21249461] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/02/2020] [Accepted: 12/09/2020] [Indexed: 02/02/2023] Open
Abstract
Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.
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Affiliation(s)
- Aurora Savino
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
| | - Paolo Provero
- Department of Neurosciences “Rita Levi Montalcini”, University of Turin, Corso Massimo D’Ázeglio 52, 10126 Turin, Italy;
- Center for Omics Sciences, Ospedale San Raffaele IRCCS, Via Olgettina 60, 20132 Milan, Italy
| | - Valeria Poli
- Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Via Nizza 52, 10126 Turin, Italy
- Correspondence: (A.S.); (V.P.)
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Bhuva DD, Cursons J, Smyth GK, Davis MJ. Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer. Genome Biol 2019; 20:236. [PMID: 31727119 PMCID: PMC6857226 DOI: 10.1186/s13059-019-1851-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 10/02/2019] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. RESULTS In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. CONCLUSIONS Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
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Affiliation(s)
- Dharmesh D Bhuva
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Joseph Cursons
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Gordon K Smyth
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Melissa J Davis
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia. .,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia. .,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia.
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Liany H, Rajapakse JC, Karuturi RKM. MultiDCoX: Multi-factor analysis of differential co-expression. BMC Bioinformatics 2017; 18:576. [PMID: 29297310 PMCID: PMC5751780 DOI: 10.1186/s12859-017-1963-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Background Differential co-expression (DCX) signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression. Results We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Conclusions MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression. Electronic supplementary material The online version of this article (10.1186/s12859-017-1963-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Herty Liany
- School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, 119077, Singapore.,Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore
| | - R Krishna Murthy Karuturi
- Computational and System Biology, Genome Institute of Singapore, A-STAR, 60 Biopolis Street, Singapore, 138672, Singapore. .,The Jackson Laboratory, 10 Discovery Dr, Farmington, CT, 06032, USA.
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Liu W. Structural similarity and difference testing on multiple sparse Gaussian graphical models. Ann Stat 2017. [DOI: 10.1214/17-aos1539] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Kaushik A, Bhatia Y, Ali S, Gupta D. Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma. PLoS One 2015; 10:e0142443. [PMID: 26558755 PMCID: PMC4641706 DOI: 10.1371/journal.pone.0142443] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 10/21/2015] [Indexed: 01/19/2023] Open
Abstract
Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.
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Affiliation(s)
- Abhinav Kaushik
- Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India
| | - Yashuma Bhatia
- Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India
| | - Shakir Ali
- Department of Biochemistry, Faculty of Science, Jamia Hamdard, New Delhi, 110062, India
| | - Dinesh Gupta
- Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, 110067, India
- * E-mail:
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Chu JH, Hersh CP, Castaldi PJ, Cho MH, Raby BA, Laird N, Bowler R, Rennard S, Loscalzo J, Quackenbush J, Silverman EK. Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD. BMC SYSTEMS BIOLOGY 2014; 8:78. [PMID: 24964944 PMCID: PMC4105829 DOI: 10.1186/1752-0509-8-78] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Accepted: 06/19/2014] [Indexed: 11/22/2022]
Abstract
Background The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.
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Affiliation(s)
- Jen-hwa Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
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Kommadath A, Bao H, Arantes AS, Plastow GS, Tuggle CK, Bearson SMD, Guan LL, Stothard P. Gene co-expression network analysis identifies porcine genes associated with variation in Salmonella shedding. BMC Genomics 2014; 15:452. [PMID: 24912583 PMCID: PMC4070558 DOI: 10.1186/1471-2164-15-452] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 05/27/2014] [Indexed: 01/22/2023] Open
Abstract
Background Salmonella enterica serovar Typhimurium is a gram-negative bacterium that can colonise the gut of humans and several species of food producing farm animals to cause enteric or septicaemic salmonellosis. While many studies have looked into the host genetic response to Salmonella infection, relatively few have used correlation of shedding traits with gene expression patterns to identify genes whose variable expression among different individuals may be associated with differences in Salmonella clearance and resistance. Here, we aimed to identify porcine genes and gene co-expression networks that differentiate distinct responses to Salmonella challenge with respect to faecal Salmonella shedding. Results Peripheral blood transcriptome profiles from 16 pigs belonging to extremes of the trait of faecal Salmonella shedding counts recorded up to 20 days post-inoculation (low shedders (LS), n = 8; persistent shedders (PS), n = 8) were generated using RNA-sequencing from samples collected just before (day 0) and two days after (day 2) Salmonella inoculation. Weighted gene co-expression network analysis (WGCNA) of day 0 samples identified four modules of co-expressed genes significantly correlated with Salmonella shedding counts upon future challenge. Two of those modules consisted largely of innate immunity related genes, many of which were significantly up-regulated at day 2 post-inoculation. The connectivity at both days and the mean gene-wise expression levels at day 0 of the genes within these modules were higher in networks constructed using LS samples alone than those using PS alone. Genes within these modules include those previously reported to be involved in Salmonella resistance such as SLC11A1 (formerly NRAMP1), TLR4, CD14 and CCR1 and those for which an association with Salmonella is novel, for example, SIGLEC5, IGSF6 and TNFSF13B. Conclusions Our analysis integrates gene co-expression network analysis, gene-trait correlations and differential expression to provide new candidate regulators of Salmonella shedding in pigs. The comparatively higher expression (also confirmed in an independent dataset) and the significantly higher connectivity of genes within the Salmonella shedding associated modules in LS compared to PS even before Salmonella challenge may be factors that contribute to the decreased faecal Salmonella shedding observed in LS following challenge. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-452) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | | | - Le Luo Guan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada.
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Sordillo J, Raby BA. Gene expression profiling in asthma. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2014; 795:157-81. [PMID: 24162908 DOI: 10.1007/978-1-4614-8603-9_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Joanne Sordillo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA,
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Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 2012; 518:209-14. [PMID: 23246976 DOI: 10.1016/j.gene.2012.11.028] [Citation(s) in RCA: 113] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2012] [Accepted: 11/27/2012] [Indexed: 10/27/2022]
Abstract
Large-scale "omics" data, such as microarrays, can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable. We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based "eigen-molecules" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. We illustrated its utility by demonstrating biologically relevant, differentially correlated molecules in transcriptome coexpression and metabolite-to-metabolite correlation networks. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates. The package can be downloaded from the following website: http://diffcorr.sourceforge.net/.
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Improved outcome of high-risk early HER2 positive breast cancer with high CXCL13-CXCR5 messenger RNA expression. Clin Breast Cancer 2012; 12:183-93. [PMID: 22607768 DOI: 10.1016/j.clbc.2012.03.006] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2011] [Revised: 02/02/2012] [Accepted: 03/16/2012] [Indexed: 02/06/2023]
Abstract
UNLABELLED The CXCL13-CXCR5 is a chemokine axis that is activated in some breast cancers. A total of 321 tissue blocks from a group of patients who received adjuvant, dose-dense chemotherapy for high-risk early breast cancer were examined. Activation of this axis was found to be associated with determinants of poor prognosis but also with improved outcome in the human epidermal growth factor receptor 2 overexpressing subpopulation. BACKGROUND Chemokines are important in cell migration and are thought to play a key role in metastasis. We explored the prognostic significance of C-X-C ligand-motif (CXCL) 12, CXCL13, and receptor (CXCR) 5 on disease-free survival (DFS) and overall survival (OS) in early breast cancer. METHODS A total of 595 patients with high risk, [corrected] early breast cancer were treated in a 2-arm trial (HE10/97) with dose-dense sequential epirubicin followed by cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) with or without paclitaxel. RNA was extracted from 321 formalin-fixed paraffin-embedded primary tumor tissue samples and quantitative reverse-transcriptase polymerase chain reaction was used to assess messenger RNA (mRNA) expression of CXCL12, CXCL13, and CXCR5; estrogen receptor; progesterone receptor (PgR); microtubule-associated protein tau and human epidermal growth factor receptor 2 (HER2). RESULTS CXCL13 and CXCR5 were found to be negatively associated with estrogen receptor and microtubule-associated protein tau mRNA expression and with dense lymphocytic infiltration, and were positively associated with nuclear grade. Only CXCL13 was positively associated with HER2. Multivariate analysis revealed an association between high CXCL13 mRNA expression and improved DFS (hazard ratio [HR] 0.48 [95% CI, 0.25-0.90]; Wald, P = .023) but not OS; whereas high CXCL12 expression was significantly associated with increased OS (HR 0.53 [95% CI, 0.33-0.85]; Wald, P = .009). In the HER2 mRNA overexpressing subgroup, high CXCL13 mRNA expression was associated with improved DFS (P < .001), whereas high CXCR5 was associated with increased DFS and OS (P = .004 and P = .049, respectively). CONCLUSIONS The CXCL13-CXCR5 axis is associated with classic determinants of poor prognosis, such as high grade, hormone receptor negativity, and axillary node involvement. Interestingly, this chemokine axis seems to be strongly associated with improved outcome in patients with HER2(+) disease.
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Silverman EK, Loscalzo J. Network medicine approaches to the genetics of complex diseases. DISCOVERY MEDICINE 2012; 14:143-152. [PMID: 22935211 PMCID: PMC3545396] [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
Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.
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Affiliation(s)
- Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital
- Department of Medicine, Brigham and Women’s Hospital
- Harvard Medical School
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Brigham and Women’s Hospital
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital
- Department of Medicine, Brigham and Women’s Hospital
- Harvard Medical School
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Center DM, Schwartz DA, Solway J, Gail D, Laposky AD, Lin QS, Gan W. Genomic medicine and lung diseases. Am J Respir Crit Care Med 2012; 186:280-5. [PMID: 22652029 DOI: 10.1164/rccm.201203-0569ws] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The recent explosion of genomic data and technology points to opportunities to redefine lung diseases at the molecular level; to apply integrated genomic approaches to elucidate mechanisms of lung pathophysiology; and to improve early detection, diagnosis, and treatment of lung diseases. Research is needed to translate genomic discoveries into clinical applications, such as detecting preclinical disease, predicting patient outcomes, guiding treatment choices, and most of all identifying potential therapeutic targets for lung diseases. The Division of Lung Diseases in the National Heart, Lung, and Blood Institute convened a workshop, "Genomic Medicine and Lung Diseases," to discuss the potential for integrated genomics and systems approaches to advance 21st century pulmonary medicine and to evaluate the most promising opportunities for this next phase of genomics research to yield clinical benefit. Workshop sessions included (1) molecular phenotypes, molecular biomarkers, and therapeutics; (2) new technology and opportunity; (3) integrative genomics; (4) molecular anatomy of the lung; (5) novel data and information platforms; and (6) recommendations for exceptional research opportunities in lung genomics research.
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Affiliation(s)
- David M Center
- Pulmonary, Allergy, Sleep, and Critical Care Medicine, Boston University School of Medicine, Boston, Massachusetts, USA
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