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      Exploration of Potential Biomarker Genes and Pathways in Kawasaki Disease: An Integrated in-Silico Approach

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          Abstract

          Kawasaki disease (KD) is a common childhood systemic vasculitis with a special predilection for coronary arteries. Even after more than five decades of the initial description of the disease, the etiology of KD remains an enigma. This transcriptome data re-analysis study aimed to elucidate the underlying pathogenesis of KD using a bioinformatic approach to identify differentially expressed genes (DEGs) to delineate common pathways involved in KD. Array datasets from the Gene Expression Omnibus database were extracted and subjected to comparative meta-analysis for the identification of prominent DEGs. Fifteen hub genes with high connectivity were selected from these DEGs ( IL1B, ITGAM, TLR2, CXCL8, SPI1, S100A12, MMP9, PRF1, TLR8, TREM1, CD44, UBB, FCER1G, IL7R, and FCGR1A ). Of these 15 genes, five genes ( CXCL8, FCGR1A, IL1B, TLR2, and TLR8) were found to be involved in neutrophil degranulation. To gain further insight into the molecular mechanism, a protein–protein network was established. Significantly enriched pathways based on the above-mentioned genes were mainly centered on biological regulation and signaling events. In addition, the pathway analysis also indicated that the majority of the DEGs in KD were enriched in systemic lupus erythematosus, suggesting a strong interplay between immunological and genetic factors in the pathogenesis of KD. These findings could significantly aid in identifying therapeutic targets and understanding KD biosignatures to design a biomarker panel for early diagnosis and severity of the disease.

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          Most cited references61

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              The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                09 May 2022
                2022
                : 13
                : 849834
                Affiliations
                [1] 1 Genetic Metabolic Unit , Department of Pediatrics , Advanced Pediatric Centre , Post Graduate Institute of Medical Education & Research , Chandigarh, India
                [2] 2 Allergy Immunology Unit , Department of Pediatrics , Advanced Pediatric Centre , Post Graduate Institute of Medical Education & Research , Chandigarh, India
                Author notes

                Edited by: Sajad Ahmad Dar, Jazan University, Saudi Arabia

                Reviewed by: Arshad Jawed, GE Healthcare Life Sciences, India

                Mohd Wahid, Jazan University, Saudi Arabia

                *Correspondence: Priyanka Srivastava, srivastavapriy@ 123456gmail.com

                This article was submitted to Evolutionary and Population Genetics, a section of the journal Frontiers in Genetics

                Article
                849834
                10.3389/fgene.2022.849834
                9124956
                35615376
                2e7a1982-6594-4ee2-b86f-09c4c7f7e506
                Copyright © 2022 Srivastava, Bamba, Pilania, Kumari, Kumrah, Sil and Singh.

                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.

                History
                : 06 January 2022
                : 11 April 2022
                Categories
                Genetics
                Original Research

                Genetics
                bioinformatics,biomarkers,hub genes,in-silico analysis,kawasaki disease,microarray,transcriptomics analysis

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