Role of immune-related endoplasmic reticulum stress genes in sepsis-induced cardiomyopathy: Novel insights from bioinformatics analysis.

Autor: Zhen WJ; Department of Anesthesiology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China., Zhang Y; Department of Anesthesiology, Zhuzhou Central Hospital (Zhuzhou Hospital Affiliated to Xiangya School of Medicine), Zhuzhou, Hunan Province, China., Fu WD; Department of Anesthesiology, Zhuzhou Central Hospital (Zhuzhou Hospital Affiliated to Xiangya School of Medicine), Zhuzhou, Hunan Province, China., Fu XL; Department of Cardiovascular Medicine, Zhuzhou Central Hospital (Zhuzhou Hospital Affiliated to Xiangya School of Medicine), Zhuzhou, Hunan Province, China., Yan X; Department of Cardiovascular Medicine, Zhuzhou Central Hospital (Zhuzhou Hospital Affiliated to Xiangya School of Medicine), Zhuzhou, Hunan Province, China.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Dec 13; Vol. 19 (12), pp. e0315582. Date of Electronic Publication: 2024 Dec 13 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0315582
Abstrakt: Background: The current study aims to elucidate the key molecular mechanisms linked to endoplasmic reticulum stress (ERS) in the pathogenesis of sepsis-induced cardiomyopathy (SIC) and offer innovative therapeutic targets for SIC.
Methods: The study downloaded dataset GSE79962 from the Gene Expression Omnibus database and acquired the ERS-related gene set from GeneCards. It utilized weighted gene co-expression network analysis (WGCNA) and conducted differential expression analysis to identify key modules and genes associated with SIC. The SIC hub genes were determined by the intersection of WGCNA-based hubs, DEGs, and ERS-related genes, followed by protein-protein interaction (PPI) network construction. Enrichment analyses, encompassing GO, KEGG, GSEA, and GSVA, were performed to elucidate potential biological pathways. The CIBERSORT algorithm was employed to analyze immune infiltration patterns. Diagnostic and prognostic models were developed to assess the clinical significance of hub genes in SIC. Additionally, in vivo experiments were conducted to validate the expression of hub genes.
Results: Differential analysis revealed 1031 differentially expressed genes (DEGs), while WGCNA identified a hub module with 1327 key genes. Subsequently, 13 hub genes were pinpointed by intersecting with ERS-related genes. NOX4, PDHB, SCP2, ACTC1, DLAT, EDN1, and NSDHL emerged as hub ERS-related genes through the protein-protein interaction network, with their diagnostic values confirmed via ROC curves. Diagnostic models incorporating five genes (NOX4, PDHB, ACTC1, DLAT, NSDHL) were validated using the LASSO algorithm, highlighting only the prognostic significance of serum PDHB levels in predicting the survival of septic patients. Additionally, decreased PDHB mRNA and protein expression levels were observed in the cardiac tissue of septic mice compared to control mice.
Conclusions: This study elucidated the interplay between metabolism and the immune microenvironment in SIC, providing fresh perspectives on the investigation of potential SIC pathogenesis. PDHB emerged as a significant biomarker of SIC, with implications on its progression through the regulation of ERS and metabolism.
Competing Interests: No potential conflict of interest was reported by the author(s).
(Copyright: © 2024 Zhen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje