ORIGINAL RESEARCH article
Front. Immunol.
Sec. Systems Immunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1638156
Portfolio analysis of single-cell RNA-sequencing and transcriptomic data unravels immune cells and telomere-related biomarkers in sepsis
Provisionally accepted
- 1The Centre Hospital of Wuhan, Wuhan, China
- 2Wuhan University of Science and Technology, Wuhan, China
- 3Shantou University Medical College, Shantou, China
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Background: Early diagnosis of sepsis isessential 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 (MYO10, SULT1B1, MKI67, and CREB5), and the analysis of nomogram showed that the biomarkers had good clinical predictive value to sepsis. The enrichment analysis results revealed that the four biomarkers were enriched in the ribosome pathway. Besides, a lncRNAs-miRNAs-biomarkers network was constructed for the four biomarkers. Finally, we obtained a candidate drug (MS-275) and a key cell (CD16+ and CD14+ monocytes) respectively based on drug prediction and cell identification analysis. In addition, we found that the expression levels of CREB5 and SULT1B1 had significant changes during the process of key cell differentiation.The RT-qPCR results showed biomarkers were upregulated in the sepsis group, consistent with the bioinformatics analysis results. Conclusion: This study identified 4 biomarkers, namely MYO10, SULT1B1, MKI67, and CREB5 and explored the pathogenesis of sepsis, providing new insights for potential treatment strategies by integrating transcriptomic data and single-cell analysis.
Keywords: Sepsis, single-cell RNA sequencing, 101-machine learning, Telomere, immune cells
Received: 17 Jun 2025; Accepted: 17 Oct 2025.
Copyright: © 2025 Chen, Huang, Wang, Zheng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yunhao Liu, yunhaoliu@wust.edu.cn
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