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      Identification of the susceptible genes and mechanism underlying the comorbid presence of coronary artery disease and rheumatoid arthritis: a network modularization analysis

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          Abstract

          Objective

          The comorbidities of coronary artery disease (CAD) and rheumatoid arthritis (RA) are mutual risk factors, which lead to higher mortality, but the biological mechanisms connecting the two remain unclear. Here, we aimed to identify the risk genes for the comorbid presence of these two complex diseases using a network modularization approach, to offer insights into clinical therapy and drug development for these diseases.

          Method

          The expression profile data of patients CAD with and without RA were obtained from the GEO database (GSE110008). Based on the differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA) was used to construct a gene network, detect co-expression modules, and explore their relation to clinical traits. The Z summary index, gene significance (GS), and module membership (MM) were utilized to screen the important differentiated modules and hub genes. The GO and KEGG pathway enrichment analysis were applied to analyze potential mechanisms.

          Result

          Based on the 278 DEGs obtained, 41 modules were identified, of which 17 and 24 modules were positively and negatively correlated with the comorbid occurrence of CAD and RA (CAD&RA), respectively. Thirteen modules with Z summary < 2 were found to be the underlying modules, which may be related to CAD&RA. With GS ≥ 0.5 and MM ≥ 0.8, 49 hub genes were identified, such as ADO, ABCA11P, POT1, ZNF141, GPATCH8, ATF6 and MIA3, etc. The area under the curve values of the representative seven hub genes under the three models (LR, KNN, SVM) were greater than 0.88. Enrichment analysis revealed that the biological functions of the targeted modules were mainly involved in cAMP-dependent protein kinase activity, demethylase activity, regulation of calcium ion import, positive regulation of tyrosine, phosphorylation of STAT protein, and tissue migration, etc.

          Conclusion

          Thirteen characteristic modules and 49 susceptibility hub genes were identified, and their corresponding molecular functions may reflect the underlying mechanism of CAD&RA, hence providing insights into the development of clinical therapies against these diseases.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09519-7.

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          Most cited references67

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              KEGG for taxonomy-based analysis of pathways and genomes

              KEGG ( https://www.kegg.jp ) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups.
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                Author and article information

                Contributors
                libingtcm@163.com
                hmzhang@icmm.ac.cn
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                20 July 2023
                20 July 2023
                2023
                : 24
                : 411
                Affiliations
                [1 ]GRID grid.410318.f, ISNI 0000 0004 0632 3409, Institute of Chinese Materia Medica, , China Academy of Chinese Medical Sciences, ; Beijing, 100700 China
                [2 ]GRID grid.410318.f, ISNI 0000 0004 0632 3409, Institute of Information on Traditional Chinese Medicine, , China Academy of Chinese Medical Sciences, ; Beijing, 100700 China
                Article
                9519
                10.1186/s12864-023-09519-7
                10360345
                37474895
                edefd8a0-330a-484b-8ee6-7044a9422fc4
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 8 March 2023
                : 16 July 2023
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: No. 81904064 PW
                Award ID: No. 81873199 to HZ
                Funded by: Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences
                Award ID: NO. CI2021A05052 to B.Li
                Funded by: Fundamental Research Funds for Central public welfare research institutes
                Award ID: ZXKT21024 to B.Li
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                coronary artery disease,rheumatoid arthritis,comorbidity mechanism,network module,susceptibility gene

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