ArticleFrontiers in immunology2025
Portfolio analysis of single-cell RNA-sequencing and transcriptomic data unravels immune cells and telomere-related biomarkers in sepsis.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integrative Multiomics Analysis Reveals a Cancer Stem Cell-Driven Prognostic Signature and Nominates Belinostat for Targeted Therapy in Hepatocellular Carcinoma.Stem cells international · 2026Article
- Integrated single-cell and bulk transcriptomic analyses reveal cDC1-centered ubiquitination dysregulation and identify UBE2F as a critical regulator in sepsis.Frontiers in immunology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Early diagnosis of sepsis is essential to reducing mortality. Immune cells and telomeres play important roles in sepsis, but their mechanisms were still unclear. This study aimed to explore the value of immune cells and telomere-related genes in sepsis. Methods: In this study, the transcriptomic data with sepsis and control samples were obtained from public database. Multiple methods including differential expression analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), 101-machine learning algorithm combinations were used to identify biomarkers which related to the immune cells and telomere. Afterwards, a nomogram was constructed to assess the clinical predictive value of biomarkers. In addition, gene set enrichment analysis (GSEA), regulatory network construction and drug prediction analysis were adopted to demonstrate the role of biomarkers in sepsis. The key cells were also identified using a single-cell dataset. Finally, the expression of biomarkers was further validated in clinical samples by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: This study obtained a total of 4 biomarkers ( Conclusion: This study identified 4 biomarkers, namely
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