ArticleGlobal challenges (Hoboken, NJ)2025
An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell RNA Sequencing Analyses.
Article in Global challenges (Hoboken, NJ), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Advances in Single-Cell Sequencing for Infectious Diseases: Progress and Perspectives.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Development of a Mitochondrial Permeability Transition-Driven Necrosis-Related Prognostic Signature in Cervical Cancer: Integrating Bulk Transcriptomic and Single-Cell Data.Cancer medicine · 2025Article
- An In-Silico Study to Identify Relevant Biomarkers in Sepsis Applying Integrated Bulk RNA Sequencing and Single-Cell RNA Sequencing Analyses.Global challenges (Hoboken, NJ) · 2025Article
- Multi-Omics Analysis Identifies Immune Regulatory Networks in Sepsis-Associated Liver Injury: Experimental Validation and Clinical Relevance.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to discover sepsis-related biomarkers via in-silico analyses. The single-cell sequencing RNA (sc-RNA) data and metabolism-related genes are obtained from public databases and previous studies, respectively. Cell subpopulations are identified and annotated, followed by performing single-sample geneset enrichment analysis (ssGSEA and identification of differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) is applied to classify specific gene modules, and the key module is subjected to immune infiltration analysis. The communication between the subclusters of monocytes is visualized. Five cell subpopulations (subcluster C1-5) containing a relatively higher percentage of monocytes are identified, with subcluster C4 having the lowest enrichment score of metabolism-related genes. Genes with a higher expression in the subclusters are enriched for antigen processing and presentation of exogenous antigen, lymphocyte differentiation, and leukocyte activation. Subcluster C5 affected other subclusters through galectin 9 (LGALS9)-CD45 and LGALS9-CD44, while other subclusters affected subcluster C5 through MIF-(CD74+C-X-C motif chemokine receptor 4 (CXCR4)) and MIF-(CD74+CD44). Six genes (F-Box Protein 4,
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Registered trials
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