ArticleMedical science monitor : international medical journal of experimental and clinical research2020
Bioinformatics Analysis for Multiple Gene Expression Profiles in Sepsis.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 21 citations in OpenAlex.
- Identification and validation of MYC as an autophagy-related gene for diagnosis and immune infiltration in sepsis-induced myocardial dysfunction.Journal of cardiothoracic surgery · 2026Article
- Longitudinal blood transcriptome profiling reveals immune dynamics in sepsis.BMC infectious diseases · 2026Article
- Single-cell RNA transcriptomics divulged altered gene expressions in monocytes and HLA variations in decompensated cirrhosis patients with sepsis.Scientific reports · 2025Article
- Article
- Systematic analysis based on bioinformatics and experimental validation identifiesAnnals of medicine · 2024Article
- HP promotes neutrophil inflammatory activation by regulating PFKFB2 in the glycolytic metabolism of sepsis.PloS one · 2024Article
- Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis.International journal of molecular sciences · 2023Article
- Landscape of co-expressed genes between the myocardium and blood in sepsis and ceRNA network construction: a bioinformatic approach.Scientific reports · 2023Article
- An immune genes signature for predicting mortality in sepsis patients.Frontiers in immunology · 2023Article
- Extracellular histones induce inflammation and senescence of vascular smooth muscle cells by activating the AMPK/FOXO4 signaling pathway.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2022Article
- Article
- Exploration of the Shared Gene Signatures between Myocardium and Blood in Sepsis: Evidence from Bioinformatics Analysis.BioMed research international · 2022Article
- LPIN1 Is a Regulatory Factor Associated With Immune Response and Inflammation in Sepsis.Frontiers in immunology · 2022Article
- Identification of Hub Genes With Differential Correlations in Sepsis.Frontiers in genetics · 2022Article
- Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets.Frontiers in physiology · 2022Article
- Article
- Identification of potential biomarkers and available drugs for oral squamous cell carcinoma.Translational cancer research · 2021Article
- Combined Transcriptome and Proteome Leukocyte's Profiling Reveals Up-Regulated Module of Genes/Proteins Related to Low Density Neutrophils and Impaired Transcription and Translation Processes in Clinical Sepsis.Frontiers in immunology · 2021Article
- Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis.Journal of inflammation research · 2021Article
- Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.EBioMedicine · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND This work aimed to screen key biomarkers related to sepsis progression by bioinformatics analyses. MATERIAL AND METHODS The microarray datasets of blood and neutrophils from patients with sepsis or septic shock were downloaded from Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) from 4 groups (sepsis versus normal blood samples; septic shock versus normal blood samples; sepsis neutrophils versus normal controls and septic shock neutrophils versus controls) were respectively identified followed by functional analyses. Subsequently, protein-protein network was constructed, and key functional sub-modules were extracted. Finally, receiver operating characteristic analysis was conducted to evaluate diagnostic values of key genes. RESULTS There were 2082 DEGs between blood samples of sepsis patients and controls, 2079 DEGs between blood samples of septic shock patients and healthy individuals, 6590 DEGs between neutrophils from sepsis and controls, and 1056 DEGs between neutrophils from septic shock patients and normal controls. Functional analysis showed that numerous DEGs were significantly enriched in ribosome-related pathway, cell cycle, and neutrophil activation involved in immune response. In addition, TRIM25 and MYC acted as hub genes in protein-protein interaction (PPI) analyses of DEGs from microarray datasets of blood samples. Moreover, MYC (AUC=0.912) and TRIM25 (AUC=0.843) had great diagnostic values for discriminating septic shock blood samples and normal controls. RNF4 was a hub gene from PPI analyses based on datasets from neutrophils and RNF4 (AUC=0.909) was capable of distinguishing neutrophil samples from septic shock samples and controls. CONCLUSIONS Our findings identified several key genes and pathways related to sepsis development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.