Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Linfeng Tao; Yue Zhu; Lifang Wu; Jun Liu

doi:10.3389/fmolb.2023.1322221

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data

Front Mol Biosci. 2024 Jan 8:10:1322221. doi: 10.3389/fmolb.2023.1322221. eCollection 2023.

Authors

Linfeng Tao^#¹, Yue Zhu^#², Lifang Wu³, Jun Liu¹

Affiliations

¹ Gusu School of Nanjing Medical University, Department of Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Clinical Medical Center of Critical Care Medicine, Suzhou, China.
² Department of Breast and Thyroid Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, Suzhou, China.
³ Department of Critical Care Medicine of Kunshan Third People's Hospital, Suzhou, China.

^# Contributed equally.

Abstract

Background: Sepsis is a pathological state resulting from dysregulated immune response in host during severe infection, leading to persistent organ dysfunction and ultimately death. Senescence-associated genes (SAGs) have manifested their potential in controlling the proliferation and dissemination of a variety of diseases. Nevertheless, the correlation between sepsis and SAGs remains obscure and requires further investigation. Methods: Two RNA expression datasets (GSE28750 and GSE57065) specifically related to sepsis were employed to filter hub SAGs, based on which a diagnostic model predictive of the incidence of sepsis was developed. The association between the expression of the SAGs identified and immune-related modules was analyzed employing Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Microenvironment Cell Populations-counter (MCP-counter) analysis. The identified genes in each cohort were clustered by unsupervised agreement clustering analysis and weighted gene correlation network analysis (WGCNA). Results: A diagnostic model for sepsis established based on hub genes (IGFBP7, GMFG, IL10, IL18, ETS2, HGF, CD55, and MMP9) exhibited a strong clinical reliability (AUC = 0.989). Sepsis patients were randomly assigned and classified by WGCNA into two clusters with distinct immune statuses. Analysis on the single-cell RNA sequencing (scRNA-seq) data revealed high scores of SAGs in the natural killer (NK) cells of the sepsis cohort than the healthy cohort. Conclusion: These findings suggested a close association between SAGs and sepsis alterations. The identified hub genes had potential to serve as a viable diagnostic marker for sepsis.

Keywords: WGCNA (weighted gene co-expression network analyses); biomarker; senescence-associated genes; sepsis; single-cell analysis.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Scientific Project of Gusu School, Nanjing Medical University (GSKY20210225).