Key module genes.

BackgroundThe 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.MethodsThe 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.ResultsDifferential 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.ConclusionsThis 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.背景 本研究旨在阐明脓毒症心肌病（sepsis-induced cardiomyopathy, SIC）发病过程中与内质网应激（endoplasmic reticulum stress, ERS）相关的关键分子机制，并为SIC提供创新性治疗靶点。方法 本研究从基因表达综合（Gene Expression Omnibus, GEO）数据库下载数据集GSE79962，并从GeneCards数据库获取ERS相关基因集。采用加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）并开展差异表达分析，以筛选与SIC相关的关键模块及基因。通过取基于WGCNA的核心基因、差异表达基因（differentially expressed genes, DEGs）与ERS相关基因的交集，确定SIC核心基因，随后构建蛋白质-蛋白质相互作用（protein-protein interaction, PPI）网络。开展涵盖基因本体（Gene Ontology, GO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）、基因集富集分析（Gene Set Enrichment Analysis, GSEA）及基因集变异分析（Gene Set Variation Analysis, GSVA）的富集分析，以阐明潜在生物学通路。采用CIBERSORT算法分析免疫浸润模式。构建诊断与预后模型，以评估核心基因在SIC中的临床意义。此外，开展体内实验以验证核心基因的表达水平。结果 差异表达分析共筛选得到1031个差异表达基因（DEGs），WGCNA鉴定出一个包含1327个关键基因的核心模块。随后通过与ERS相关基因取交集，共确定13个核心基因。通过蛋白质-蛋白质相互作用网络筛选得到NOX4、PDHB、SCP2、ACTC1、DLAT、EDN1及NSDHL作为核心ERS相关基因，并经ROC曲线验证了其诊断价值。采用LASSO算法构建包含NOX4、PDHB、ACTC1、DLAT、NSDHL共5个基因的诊断模型，验证后发现仅血清PDHB水平的预后意义可用于预测脓毒症患者生存情况。此外，与对照组小鼠相比，脓毒症小鼠心肌组织中PDHB的mRNA及蛋白表达水平均显著降低。结论 本研究阐明了SIC中代谢与免疫微环境的相互作用，为SIC潜在发病机制的研究提供了全新视角。PDHB可作为SIC的重要生物标志物，其通过调控内质网应激及代谢过程影响SIC的疾病进展。