ArticleJournal of inflammation research2021
Identification of Potential Early Diagnostic Biomarkers of Sepsis.
Article in Journal of inflammation research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 51 citations in OpenAlex.
- Early biomarkers for predicting sepsis-induced shock: insights from inflammatory pathways and immune response.Frontiers in pharmacology · 2026Review
- Single-cell RNA Sequencing Analysis Reveals the Molecular Mechanisms of Neutrophil Dysfunction in Chronic Bone Infection.Current medicinal chemistry · 2026Article
- Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.BMC geriatrics · 2025Article
- Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning.Discover oncology · 2025Article
- Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy.Scientific reports · 2025Article
- Identification and Verification of Necroptosis-Related Genes in Patients With Sepsis by Bioinformatic Analysis and Molecular Experiments.Journal of cellular and molecular medicine · 2025Article
- Comprehensive Pan-Cancer Analysis and Functional Studies Reveal SLC2A6 as a Ferroptosis Modulator in Hepatocellular carcinoma.Scientific reports · 2025Article
- A Web-Based Dynamic Nomogram for Early Diagnosis in Sepsis: Development and Validation with Real-World Clinical Utility.Infection and drug resistance · 2025Article
- Bioinformatics analysis of the role of RNA modification regulators in polycystic ovary syndrome.Heliyon · 2024Article
- USF2 activates RhoB/ROCK pathway by transcriptional inhibition of miR-206 to promote pyroptosis in septic cardiomyocytes.Molecular and cellular biochemistry · 2024Article
- Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication.Journal of inflammation research · 2024Article
- Article
- Exploring the use of cluster analysis to assess antibiotic stewardship in critically-ill neonates in a low resource setting.Antimicrobial resistance and infection control · 2023Article
- Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Urinary microRNAs in sepsis function as a novel prognostic marker.Experimental and therapeutic medicine · 2023Article
- Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis.International journal of molecular sciences · 2023Article
- Article
- Liang-Ge-San: a classic traditional Chinese medicine formula, attenuates acute inflammation via targeting GSK3β.Frontiers in pharmacology · 2023Article
- Classification of subtypes and identification of dysregulated genes in sepsis.Frontiers in cellular and infection microbiology · 2023Article
- Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival.
methodsWe analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples.
resultsA total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells.
conclusionIn summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.
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