Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis - Li, Qiaoke; Sun, Mingze; Zhou, Qi; Li, Yulong; Xu, Jinmei; Fan, Hong (Frontiers in Immunology, 2023)
Background Sepsis is a heterogeneous disease, thus the single-gene based biomarker is not enough. Higher level biomarkers should be explored. It is necessary to identify important pathways related to sepsis and evaluate its clinical significance. Methods Gene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. Spearman correlation coefficient was used to find the relationship between pathways and immune cell abundance. Methylation and single cell transcriptome data were also used to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction. Results Eighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Among them, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance. Five pathways can be used to distinguish systemic inflammatory response syndrome (SIRS), bacterial sepsis and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways. Conclusion Sepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis and drug screening.