Department of Cardiology, The First Clinical Medicine College of Jinan University, China.
Department of hematology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Cell Mol Biol (Noisy-le-grand). 2023 Dec 31;69(15):204-209. doi: 10.14715/cmb/2023.69.15.35.
Aortic valve stenosis (AS) is the most common clinical valvular heart disease. Without effective pharmaceutical therapy at present, identifying effective therapeutic targets is critical. However, the pathological and molecular mechanisms of aortic stenosis are complex, including inflammatory infiltration, oxidative stress and so on. In this study, we investigated how oxidative stress interacts with immune cell infiltration in aortic stenosis using bioinformatics analysis, and provide a better understanding of aortic valve stenosis at the pathophysiologic level. After obtaining the datasets, including GSE153555, GSE51472 and GSE12644, from the Gene Expression Omnibus (GEO) database, the package 'limma' was applied to identify the differentially expressed genes (DEGs) in GSE153555. The GeneCards database searched for oxidative stress-related genes. We evaluated the expression of 22 immune cells using Cibersort. Clustering differentially expressed genes into different modules via Weighted gene correlation network analysis (WGCNA) and exploring the relationship among modules and immune cell types. The genes in modules intersected with oxidative stress-related genes to find oxidative stress genes related to immune infiltration. Finally, the GSE51472 and GSE12644 datasets were used to initially verify oxidative stress-related genes in aortic valve stenosis. A total of 1213 differentially expressed genes were identified in the GSE153555 dataset, and 279 of them were oxidative stress-related genes. Increased infiltration of B cell navie and Macrophages M1 in aortic stenosis was found. Using WGCNA, we clustered 15 modules. The brown module was identified as the most significant template positively correlated with T-cell regulatory Treg, and the magenta module was identified as a critical module associated with M1 macrophages with the highest negative correlation coefficient. The results verified by other datasets showed that in comparison to normal people, the aortic stenosis patients exhibited dramatically high IGFBP2 and SPHK1 expression. Both IGFBP2 and SPHK1 may be significantly involved in the mechanism of aortic stenosis pathophysiologically and can be used for aortic stenosis early detection, therapy, and therapeutic targets.
主动脉瓣狭窄（AS）是最常见的临床瓣膜性心脏病。目前尚无有效的药物治疗方法，因此确定有效的治疗靶点至关重要。然而，主动脉瓣狭窄的病理和分子机制复杂，包括炎症浸润、氧化应激等。在这项研究中，我们使用生物信息学分析研究了氧化应激如何与主动脉瓣狭窄中的免疫细胞浸润相互作用，并在病理生理水平更好地了解主动脉瓣狭窄。从基因表达综合数据库（GEO）中获得数据集，包括 GSE153555、GSE51472 和 GSE12644，应用“limma”包鉴定 GSE153555 中的差异表达基因（DEGs）。使用 GeneCards 数据库搜索氧化应激相关基因。使用 Cibersort 评估 22 种免疫细胞的表达。通过加权基因相关网络分析（WGCNA）将差异表达基因聚类到不同模块，并探索模块与免疫细胞类型之间的关系。将模块中的基因与氧化应激相关基因进行交集，以找到与免疫浸润相关的氧化应激基因。最后，使用 GSE51472 和 GSE12644 数据集初步验证主动脉瓣狭窄中的氧化应激相关基因。在 GSE153555 数据集中鉴定出 1213 个差异表达基因，其中 279 个是氧化应激相关基因。发现主动脉瓣狭窄患者中 B 细胞幼稚和巨噬细胞 M1 的浸润增加。使用 WGCNA，我们聚类了 15 个模块。棕色模块被鉴定为与 T 细胞调节性 Treg 呈正相关的最显著模板，而洋红色模块被鉴定为与 M1 巨噬细胞呈最高负相关系数的关键模块。其他数据集的验证结果表明，与正常人相比，主动脉瓣狭窄患者的 IGFBP2 和 SPHK1 表达明显升高。IGFBP2 和 SPHK1 可能都显著参与了主动脉瓣狭窄的病理生理机制，可用于主动脉瓣狭窄的早期检测、治疗和治疗靶点。
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