Cargando…

Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we sti...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Juexing, Zhou, Lei, Li, Zhenhua, Yang, Shangneng, Tang, Liangyue, Gong, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740124/
https://www.ncbi.nlm.nih.gov/pubmed/35003233
http://dx.doi.org/10.3389/fgene.2021.812509
_version_ 1784629246304452608
author Li, Juexing
Zhou, Lei
Li, Zhenhua
Yang, Shangneng
Tang, Liangyue
Gong, Hui
author_facet Li, Juexing
Zhou, Lei
Li, Zhenhua
Yang, Shangneng
Tang, Liangyue
Gong, Hui
author_sort Li, Juexing
collection PubMed
description Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.
format Online
Article
Text
id pubmed-8740124
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87401242022-01-08 Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis Li, Juexing Zhou, Lei Li, Zhenhua Yang, Shangneng Tang, Liangyue Gong, Hui Front Genet Genetics Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8740124/ /pubmed/35003233 http://dx.doi.org/10.3389/fgene.2021.812509 Text en Copyright © 2021 Li, Zhou, Li, Yang, Tang and Gong. 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 Genetics
Li, Juexing
Zhou, Lei
Li, Zhenhua
Yang, Shangneng
Tang, Liangyue
Gong, Hui
Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title_full Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title_fullStr Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title_full_unstemmed Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title_short Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis
title_sort identification of crucial genes and infiltrating immune cells underlying sepsis-induced cardiomyopathy via weighted gene co-expression network analysis
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740124/
https://www.ncbi.nlm.nih.gov/pubmed/35003233
http://dx.doi.org/10.3389/fgene.2021.812509
work_keys_str_mv AT lijuexing identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis
AT zhoulei identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis
AT lizhenhua identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis
AT yangshangneng identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis
AT tangliangyue identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis
AT gonghui identificationofcrucialgenesandinfiltratingimmunecellsunderlyingsepsisinducedcardiomyopathyviaweightedgenecoexpressionnetworkanalysis