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      Transcriptomic analysis reveals the potential crosstalk genes and immune relationship between IgA nephropathy and periodontitis


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          It is well known that periodontitis has an important impact on systemic diseases. The aim of this study was to investigate potential crosstalk genes, pathways and immune cells between periodontitis and IgA nephropathy (IgAN).


          We downloaded periodontitis and IgAN data from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify shared genes. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. Hub genes were further screened using least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was drawn according to the screening results. Finally, single-sample GSEA (ssGSEA) was used to analyze the infiltration level of 28 immune cells in the expression profile and its relationship with shared hub genes.


          By taking the intersection of WGCNA important module genes and DEGs, we found that the SPAG4, CCDC69, KRT10, CXCL12, HPGD, CLDN20 and CCL187 genes were the most important cross-talk genes between periodontitis and IgAN. GO analysis showed that the shard genes were most significantly enriched in kinase regulator activity. The LASSO analysis results showed that two overlapping genes ( CCDC69 and CXCL12) were the optimal shared diagnostic biomarkers for periodontitis and IgAN. The immune infiltration results revealed that T cells and B cells play an important role in the pathogenesis of periodontitis and IgAN.


          This study is the first to use bioinformatics tools to explore the close genetic relationship between periodontitis and IgAN. The SPAG4, CCDC69, KRT10, CXCL12, HPGD, CLDN20 and CCL187 genes were the most important cross-talk genes between periodontitis and IgAN. T-cell and B-cell-driven immune responses may play an important role in the association between periodontitis and IgAN.

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          Most cited references40

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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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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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              Periodontal diseases

              Periodontal diseases comprise a wide range of inflammatory conditions that affect the supporting structures of the teeth (the gingiva, bone and periodontal ligament), which could lead to tooth loss and contribute to systemic inflammation. Chronic periodontitis predominantly affects adults, but aggressive periodontitis may occasionally occur in children. Periodontal disease initiation and propagation is through a dysbiosis of the commensal oral microbiota (dental plaque), which then interacts with the immune defences of the host, leading to inflammation and disease. This pathophysiological situation persists through bouts of activity and quiescence, until the affected tooth is extracted or the microbial biofilm is therapeutically removed and the inflammation subsides. The severity of the periodontal disease depends on environmental and host risk factors, both modifiable (for example, smoking) and non-modifiable (for example, genetic susceptibility). Prevention is achieved with daily self-performed oral hygiene and professional removal of the microbial biofilm on a quarterly or bi-annual basis. New treatment modalities that are actively explored include antimicrobial therapy, host modulation therapy, laser therapy and tissue engineering for tissue repair and regeneration.

                Author and article information

                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                30 January 2023
                : 14
                : 1062590
                [1] Department of Stomatology, Beijing Chaoyang Hospital, Capital Medical University , Beijing, China
                Author notes

                Edited by: Mitesh Dwivedi, Uka Tarsadia University, India

                Reviewed by: Devang Khambholja, Sardar Patel University, India; Pradeep Kumar, All India Institute of Medical Sciences, India; Guanglin He, Sichuan University, China

                *Correspondence: Zuomin Wang, wzuomin@ 123456sina.cn ; Zhiqiang Liu, kokorora@ 123456sina.com

                This article was submitted to Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders, a section of the journal Frontiers in Immunology

                Copyright © 2023 Gao, Guo, Wang, Liu and Wang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 06 October 2022
                : 18 January 2023
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 40, Pages: 10, Words: 3916
                This work was supported by the National Natural Science Foundation of China [grant no. 82170956 and no.81870763].
                Original Research

                periodontitis,iga nephropathy,transcriptomic analysis,crosstalk genes,immune infiltration


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