Table 1_Portfolio analysis of single-cell RNA-sequencing and transcriptomic data unravels immune cells and telomere-related biomarkers in sepsis.docx

BackgroundEarly 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.
MethodsIn 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).
ResultsThis 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.
ConclusionThis 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.