Cargando…
Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis
BACKGROUND: Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance. METHODS: Gene Set Enrichm...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108839/ https://www.ncbi.nlm.nih.gov/pubmed/37077915 http://dx.doi.org/10.3389/fimmu.2023.1110070 |
_version_ | 1785026926083047424 |
---|---|
author | Li, Qiaoke Sun, Mingze Zhou, Qi Li, Yulong Xu, Jinmei Fan, Hong |
author_facet | Li, Qiaoke Sun, Mingze Zhou, Qi Li, Yulong Xu, Jinmei Fan, Hong |
author_sort | Li, Qiaoke |
collection | PubMed |
description | BACKGROUND: Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their 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. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed 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. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between 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. |
format | Online Article Text |
id | pubmed-10108839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101088392023-04-18 Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis Li, Qiaoke Sun, Mingze Zhou, Qi Li, Yulong Xu, Jinmei Fan, Hong Front Immunol Immunology BACKGROUND: Sepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their 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. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed 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. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between 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. Frontiers Media S.A. 2023-04-03 /pmc/articles/PMC10108839/ /pubmed/37077915 http://dx.doi.org/10.3389/fimmu.2023.1110070 Text en Copyright © 2023 Li, Sun, Zhou, Li, Xu and Fan https://creativecommons.org/licenses/by/4.0/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) and the copyright owner(s) 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. |
spellingShingle | Immunology Li, Qiaoke Sun, Mingze Zhou, Qi Li, Yulong Xu, Jinmei Fan, Hong Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title | Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title_full | Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title_fullStr | Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title_full_unstemmed | Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title_short | Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis |
title_sort | integrated analysis of multi-omics data reveals t cell exhaustion in sepsis |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108839/ https://www.ncbi.nlm.nih.gov/pubmed/37077915 http://dx.doi.org/10.3389/fimmu.2023.1110070 |
work_keys_str_mv | AT liqiaoke integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis AT sunmingze integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis AT zhouqi integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis AT liyulong integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis AT xujinmei integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis AT fanhong integratedanalysisofmultiomicsdatarevealstcellexhaustioninsepsis |