• Medicine · Nov 2024

    Observational Study

    Deciphering sepsis: An observational bioinformatic analysis of gene expression in granulocytes from GEO dataset GSE123731.

    • Li Jin, Xiaowei He, Yuanyuan Wang, Feng Shao, Jun Qian, Mengxiao Jiang, Shengjie Zhang, and Wenjie Liao.
    • Department of Emergency, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, Jiangsu, China.
    • Medicine (Baltimore). 2024 Nov 15; 103 (46): e40559e40559.

    AbstractSepsis triggers severe inflammatory responses leading to organ dysfunction and demands early diagnostic and therapeutic intervention. This study identifies differentially expressed genes (DEGs) in sepsis patients using the Gene Expression Omnibus database to find potential diagnostic and therapeutic markers. We analyzed the dataset GSE123731 via GEO2R to detect DEGs, constructed protein-protein interaction networks, and performed transcription factor analyses using Cytoscape. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted using R and FunRich software. Key genes were validated by Quantitative Reverse Transcription Polymerase Chain and co-immunoprecipitation assays in granulocytes from sepsis patients. We identified 59 DEGs significantly involved in neutrophil degranulation and immune system activation. Cytokine signaling pathways were highlighted in Kyoto Encyclopedia of Genes and Genomes analysis. Co-immunoprecipitation assays confirmed interactions involving matrix metallopeptidase 8, matrix metallopeptidase 9, and arginase 1, supporting their roles as biomarkers. The identified DEGs and validated interactions reveal crucial molecular mechanisms in sepsis, offering new avenues for diagnostic and therapeutic strategies, potentially enhancing patient outcomes.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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