ETS2 alleviates acute‐on‐chronic liver failure by suppressing excessive inflammation

He, Lulu; Cai, Qun; Liang, Xi; Xin, Jiaojiao; Shi, Dongyan; Ren, Keke; Li, Yun; Chen, Jiaxian; Sun, Suwan; Guo, Beibei; Yang, Hui; Li, Bingqi; Ma, Shiwen; Luo, Jinjin; Hu, Meiqian; Li, Jiaqi; Hu, Wen; Li, Peng; Yao, Heng; Li, Jun; Chen, Xin; Jiang, Jing; Jun, Li

doi:10.1002/jmv.28710

Cited by 5 publications

(1 citation statement)

References 35 publications

(80 reference statements)

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“…Michelle V. Eidt et al demonstrated that IL-18 has an accuracy in predicting fatal outcomes up to 88.9% and 90% in the severe sepsis and septic shock groups, respectively, with a sensitivity of 80% and 83% and a specificity of 100% ( Eidt et al, 2016 ). ETS2 has been recognized as an effective prognostic biomarker for patients suffering from acute-on-chronic liver failure, and it mitigates liver failure through the downregulation of the HMGB1/lipopolysaccharide-induced inflammatory response ( He et al, 2023 ). A high level of HGF is firmly associated with poor outcomes in sepsis patients ( Peng et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Tao,

Zhu,

et al. 2024

Front. Mol. Biosci.

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Background: Sepsis is a pathological state resulting from dysregulated immune response in host during severe infection, leading to persistent organ dysfunction and ultimately death. Senescence-associated genes (SAGs) have manifested their potential in controlling the proliferation and dissemination of a variety of diseases. Nevertheless, the correlation between sepsis and SAGs remains obscure and requires further investigation.Methods: Two RNA expression datasets (GSE28750 and GSE57065) specifically related to sepsis were employed to filter hub SAGs, based on which a diagnostic model predictive of the incidence of sepsis was developed. The association between the expression of the SAGs identified and immune-related modules was analyzed employing Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Microenvironment Cell Populations-counter (MCP-counter) analysis. The identified genes in each cohort were clustered by unsupervised agreement clustering analysis and weighted gene correlation network analysis (WGCNA).Results: A diagnostic model for sepsis established based on hub genes (IGFBP7, GMFG, IL10, IL18, ETS2, HGF, CD55, and MMP9) exhibited a strong clinical reliability (AUC = 0.989). Sepsis patients were randomly assigned and classified by WGCNA into two clusters with distinct immune statuses. Analysis on the single-cell RNA sequencing (scRNA-seq) data revealed high scores of SAGs in the natural killer (NK) cells of the sepsis cohort than the healthy cohort.Conclusion: These findings suggested a close association between SAGs and sepsis alterations. The identified hub genes had potential to serve as a viable diagnostic marker for sepsis.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Tao,

Zhu,

et al. 2024

Front. Mol. Biosci.

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Pesticide avermectin-induced hepatotoxicity and growth inhibition in carp: Ameliorative capacity and potential mechanisms of quercetin as a dietary additive

Xin,

Li,

Ping

et al. 2024

Aquatic Toxicology

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The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad,

Vastrad

2024

Preprint

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Cardiac arrest (CA) is a common cause of death world wide. The disease has lacks effective treatment. Efforts have been made to elucidate the molecular pathogenesis of CA, but the molecular mechanisms remain elusive. To identify key genes and pathways in CA, the next generation sequencing (NGS) GSE200117 dataset was downloaded from the Gene Expression Omnibus (GEO) database. DESeq2 tool was used to recognize differentially expressed genes (DEGs). Gene ontology (GO) and REACTOME pathway enrichment analyses were performed to analyze the DEGs and associated signal pathways in the g:Profiler database. The IID database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. A miRNA-hub gene regulatory network and TF-hub gene regulatory network were then constructed to screen miRNAs, TFs and hub genes by miRNet and NetworkAnalyst database and Cityscape software. Receiver operating characteristic curve (ROC) analysis used to verified the hub genes. In total, 844 DEGs were identified, comprising 414 up regulated genes and 430 down regulated genes. GO and REACTOME pathway enrichment analyses indicated that the DEGs for CA were mainly enriched in organonitrogen compound metabolic process, response to stimulus, translation and immune system. Ten hub genes (up-regulated: HSPA8, HOXA1, INCA1 and TP53; down-regulated: HSPB1, LMNA, SNCA, ADAMTSL4 and PDLIM7) were screened. We also predicted miRNAs (hsa-mir-1914-5p and hsa-mir-598-3p) and TFs (JUN and PRRX2) targeting hub genes. This study uses a series of bioinformatics technologies to obtain hug genes, miRNAs and TFs, and key pathways related to CA. These analysis results provide us with new ideas for finding biomarkers and treatment methods for CA.

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ETS2 alleviates acute‐on‐chronic liver failure by suppressing excessive inflammation

Cited by 5 publications

References 35 publications

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Pesticide avermectin-induced hepatotoxicity and growth inhibition in carp: Ameliorative capacity and potential mechanisms of quercetin as a dietary additive

The Identification of Key Genes and Biological Pathways in Cardiac Arrest by Integrated Bioinformatics and Next Generation Sequencing Data Analysis

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