Huang Jiazhuo, Shi Zhentao, Huang Zhifeng, Lai Shaobin
Department of Cardiology, The First People's Hospital of Zhaoqing City, No.9 Donggang East Road, Zhaoqing, 526040, Guangdong, China.
Biochem Genet. 2024 Oct 10. doi: 10.1007/s10528-024-10937-9.
Mounting evidence indicates that myocardial fibrosis (MF) is frequently intertwined with immune and metabolic disorders. This comprehensive review aims to delve deeply into the crucial role of immune-related signature genes in the pathogenesis and progression of MF. This exploration holds significant importance as understanding the underlying mechanisms of MF is essential for developing effective diagnostic and therapeutic strategies. The dataset GSE9735 about myocardial fibrosis and non-fibrosis was downloaded from GEO database. Differentially expressed genes (DEGs) were identified by 'limma' package in R software. Then, the biological function of DEG was determined by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. XCell was used to estimate the composition pattern of matrix and immune cells. Protein-protein interaction (PPI) network was constructed based on STRING analysis software, and Hub genes were screened and functional modules were analyzed. The correlation between hub genes and immune cell subtypes was analyzed. Hub genes with |correlation coefficient|> 0.45 and p-value < 0.05 were used as characteristic biomarkers. Finally, the logistic regression model is used to verify the three markers in the training set and verification set (GSE97358 and GSE225336). A total of 635 DEGs were identified. Functional enrichment analysis shows that inflammation and immune response, extracellular matrix and structural remodeling play an important role in the pathological mechanism of MF. Immune cell infiltration analysis showed that immune cells (Plasma cells, Eosinophils, Chondrocytes and Th2 cells) significantly changed in MF pathological conditions. In PPI network analysis, IL1β, TTN, PTPRC, IGF1, ALDH1A1, CYP26A1, ALDH1A3, MYH11, CSF1R and CD80 were identified as hub genes, among which IL1β, CYP26A1 and GNG2 were regarded as immune-related characteristic markers. The AUC scores of the three biomarkers are all above 0.65, which proves that they have a good discrimination effect in MF. In this study, three immune-related genes were identified as diagnostic biomarkers of MF, which provided a new perspective for exploring the molecular mechanism of MF. This study takes a comprehensive approach to understanding the intricate relationship between myocardial fibrosis and immune metabolism. By identifying key immune-related biomarkers, this study not only reveals the molecular basis of myocardial fibrosis but also paves the way for the development of novel diagnostic tools and therapeutic strategies. These findings are critical for improving patient prognosis and may have broader implications for studying and treating other cardiovascular diseases associated with immune dysregulation.
越来越多的证据表明，心肌纤维化（MF）常与免疫和代谢紊乱相互交织。本综述旨在深入探讨免疫相关特征基因在MF发病机制和进展中的关键作用。由于了解MF的潜在机制对于制定有效的诊断和治疗策略至关重要，因此这一探索具有重要意义。从GEO数据库下载了关于心肌纤维化和非纤维化的数据集GSE9735。通过R软件中的“limma”包识别差异表达基因（DEG）。然后，通过基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析确定DEG的生物学功能。使用XCell估计基质和免疫细胞的组成模式。基于STRING分析软件构建蛋白质-蛋白质相互作用（PPI）网络，并筛选枢纽基因并分析功能模块。分析枢纽基因与免疫细胞亚型之间的相关性。将|相关系数|> 0.45且p值< 0.05的枢纽基因用作特征生物标志物。最后，使用逻辑回归模型在训练集和验证集（GSE97358和GSE225336）中验证这三个标志物。共鉴定出635个DEG。功能富集分析表明，炎症和免疫反应、细胞外基质和结构重塑在MF的病理机制中起重要作用。免疫细胞浸润分析表明，免疫细胞（浆细胞、嗜酸性粒细胞、软骨细胞和Th2细胞）在MF病理状态下有显著变化。在PPI网络分析中，IL1β、TTN、PTPRC、IGF1、ALDH1A1、CYP26A1、ALDH1A3、MYH11、CSF1R和CD80被鉴定为枢纽基因，其中IL1β、CYP26A1和GNG2被视为免疫相关特征标志物。这三个生物标志物的AUC评分均高于0.65，证明它们在MF中具有良好的鉴别效果。在本研究中，三个免疫相关基因被鉴定为MF的诊断生物标志物，为探索MF的分子机制提供了新的视角。本研究采用综合方法来理解心肌纤维化与免疫代谢之间的复杂关系。通过识别关键的免疫相关生物标志物，本研究不仅揭示了心肌纤维化的分子基础，还为新型诊断工具和治疗策略的开发铺平了道路。这些发现对于改善患者预后至关重要，可能对研究和治疗其他与免疫失调相关的心血管疾病具有更广泛的意义。
