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Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis
BACKGROUND: Sepsis is a life-threatening disease with a high mortality in the intensive care unit (ICU), and autophagy plays an essential role in the development of sepsis. The purpose of this study was to identify potential autophagy-related genes in sepsis and their relationship with immune cell i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183527/ https://www.ncbi.nlm.nih.gov/pubmed/37197531 http://dx.doi.org/10.21037/jtd-23-312 |
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author | Di, Chong Du, Yingying Zhang, Renlingzi Zhang, Lei Wang, Sheng |
author_facet | Di, Chong Du, Yingying Zhang, Renlingzi Zhang, Lei Wang, Sheng |
author_sort | Di, Chong |
collection | PubMed |
description | BACKGROUND: Sepsis is a life-threatening disease with a high mortality in the intensive care unit (ICU), and autophagy plays an essential role in the development of sepsis. The purpose of this study was to identify potential autophagy-related genes in sepsis and their relationship with immune cell infiltration by bioinformatics analysis. METHODS: The messenger RNA (mRNA) expression profile of the GSE28750 data set was collected from the Gene Expression Omnibus (GEO) database. The potential differentially expressed autophagy-related genes of sepsis were screened with the “limma” package in R (The Foundation for Statistical Computing). The hub genes were selected by weighted gene coexpression network analysis (WGCNA) networks with Cytoscape, and functional enrichment analysis was performed. The expression level and diagnostic value of the hub genes were validated by Wilcoxon test and receiver operating characteristic (ROC) curve analysis of the GSE95233 data set. The compositional patterns of immune cell infiltration in sepsis were estimated using the CIBERSORT algorithm. Spearman rank correlation analysis was used to associate the identified biomarkers with infiltrating immune cells. A competing endogenous (ceRNA) network was constructed to predict related noncoding RNAs of identified biomarkers with the miRWalk platform. RESULTS: In all, 80 differential autophagy-related genes were obtained. GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 were identified as hub genes and diagnostic biomarker groups for sepsis. In addition, 7 differentially infiltrated immune cells correlated with the hub autophagy-related genes were identified. The ceRNA network predicted 23 microRNAs and 122 long noncoding RNAs related to 5 hub autophagy-related genes. CONCLUSIONS: GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 may influence the development of sepsis and have a vital impact on sepsis immune regulation as autophagy-related genes. |
format | Online Article Text |
id | pubmed-10183527 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-101835272023-05-16 Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis Di, Chong Du, Yingying Zhang, Renlingzi Zhang, Lei Wang, Sheng J Thorac Dis Original Article BACKGROUND: Sepsis is a life-threatening disease with a high mortality in the intensive care unit (ICU), and autophagy plays an essential role in the development of sepsis. The purpose of this study was to identify potential autophagy-related genes in sepsis and their relationship with immune cell infiltration by bioinformatics analysis. METHODS: The messenger RNA (mRNA) expression profile of the GSE28750 data set was collected from the Gene Expression Omnibus (GEO) database. The potential differentially expressed autophagy-related genes of sepsis were screened with the “limma” package in R (The Foundation for Statistical Computing). The hub genes were selected by weighted gene coexpression network analysis (WGCNA) networks with Cytoscape, and functional enrichment analysis was performed. The expression level and diagnostic value of the hub genes were validated by Wilcoxon test and receiver operating characteristic (ROC) curve analysis of the GSE95233 data set. The compositional patterns of immune cell infiltration in sepsis were estimated using the CIBERSORT algorithm. Spearman rank correlation analysis was used to associate the identified biomarkers with infiltrating immune cells. A competing endogenous (ceRNA) network was constructed to predict related noncoding RNAs of identified biomarkers with the miRWalk platform. RESULTS: In all, 80 differential autophagy-related genes were obtained. GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 were identified as hub genes and diagnostic biomarker groups for sepsis. In addition, 7 differentially infiltrated immune cells correlated with the hub autophagy-related genes were identified. The ceRNA network predicted 23 microRNAs and 122 long noncoding RNAs related to 5 hub autophagy-related genes. CONCLUSIONS: GABARAPL2, GAPDH, WDFY3, MAP1LC3B, DRAM1, WIPI1, and ULK3 may influence the development of sepsis and have a vital impact on sepsis immune regulation as autophagy-related genes. AME Publishing Company 2023-04-25 2023-04-28 /pmc/articles/PMC10183527/ /pubmed/37197531 http://dx.doi.org/10.21037/jtd-23-312 Text en 2023 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Di, Chong Du, Yingying Zhang, Renlingzi Zhang, Lei Wang, Sheng Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title | Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title_full | Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title_fullStr | Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title_full_unstemmed | Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title_short | Identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
title_sort | identification of autophagy-related genes and immune cell infiltration characteristics in sepsis via bioinformatic analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183527/ https://www.ncbi.nlm.nih.gov/pubmed/37197531 http://dx.doi.org/10.21037/jtd-23-312 |
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