Cargando…
A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts
BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted t...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476321/ https://www.ncbi.nlm.nih.gov/pubmed/37661250 http://dx.doi.org/10.1186/s40001-023-01307-z |
_version_ | 1785100903517257728 |
---|---|
author | Peng, Yaojun Wu, Qiyan Ding, Xinhuan Wang, Lingxiong Gong, Hanpu Feng, Cong Liu, Tianyi Zhu, Haiyan |
author_facet | Peng, Yaojun Wu, Qiyan Ding, Xinhuan Wang, Lingxiong Gong, Hanpu Feng, Cong Liu, Tianyi Zhu, Haiyan |
author_sort | Peng, Yaojun |
collection | PubMed |
description | BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis. METHODS: Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature. RESULTS: We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature. CONCLUSIONS: Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01307-z. |
format | Online Article Text |
id | pubmed-10476321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104763212023-09-05 A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts Peng, Yaojun Wu, Qiyan Ding, Xinhuan Wang, Lingxiong Gong, Hanpu Feng, Cong Liu, Tianyi Zhu, Haiyan Eur J Med Res Research BACKGROUND: High throughput gene expression profiling is a valuable tool in providing insight into the molecular mechanism of human diseases. Hypoxia- and lactate metabolism-related genes (HLMRGs) are fundamentally dysregulated in sepsis and have great predictive potential. Therefore, we attempted to build an HLMRG signature to predict the prognosis of patients with sepsis. METHODS: Three publicly available transcriptomic profiles of peripheral blood mononuclear cells from patients with sepsis (GSE65682, E-MTAB-4421 and E-MTAB-4451, total n = 850) were included in this study. An HLMRG signature was created by employing Cox regression and least absolute shrinkage and selection operator estimation. The CIBERSORT method was used to analyze the abundances of 22 immune cell subtypes based on transcriptomic data. Metascape was used to investigate pathways related to the HLMRG signature. RESULTS: We developed a prognostic signature based on five HLMRGs (ERO1L, SIAH2, TGFA, TGFBI, and THBS1). This classifier successfully discriminated patients with disparate 28-day mortality in the discovery cohort (GSE65682, n = 479), and consistent results were observed in the validation cohort (E-MTAB-4421 plus E-MTAB-4451, n = 371). Estimation of immune infiltration revealed significant associations between the risk score and a subset of immune cells. Enrichment analysis revealed that pathways related to antimicrobial immune responses, leukocyte activation, and cell adhesion and migration were significantly associated with the HLMRG signature. CONCLUSIONS: Identification of a prognostic signature suggests the critical role of hypoxia and lactate metabolism in the pathophysiology of sepsis. The HLMRG signature can be used as an efficient tool for the risk stratification of patients with sepsis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-023-01307-z. BioMed Central 2023-09-04 /pmc/articles/PMC10476321/ /pubmed/37661250 http://dx.doi.org/10.1186/s40001-023-01307-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Peng, Yaojun Wu, Qiyan Ding, Xinhuan Wang, Lingxiong Gong, Hanpu Feng, Cong Liu, Tianyi Zhu, Haiyan A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title | A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title_full | A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title_fullStr | A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title_full_unstemmed | A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title_short | A hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
title_sort | hypoxia- and lactate metabolism-related gene signature to predict prognosis of sepsis: discovery and validation in independent cohorts |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476321/ https://www.ncbi.nlm.nih.gov/pubmed/37661250 http://dx.doi.org/10.1186/s40001-023-01307-z |
work_keys_str_mv | AT pengyaojun ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT wuqiyan ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT dingxinhuan ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT wanglingxiong ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT gonghanpu ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT fengcong ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT liutianyi ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT zhuhaiyan ahypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT pengyaojun hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT wuqiyan hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT dingxinhuan hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT wanglingxiong hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT gonghanpu hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT fengcong hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT liutianyi hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts AT zhuhaiyan hypoxiaandlactatemetabolismrelatedgenesignaturetopredictprognosisofsepsisdiscoveryandvalidationinindependentcohorts |