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

解读脓毒症:基于GEO数据集GSE123731的粒细胞基因表达的观察性生物信息学分析

阅读:1

Abstract

Sepsis 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。