Dong Guanglong, Yu Xiangwen, Zhao Mingyu, Lin Shusen, Meng Qingyu
Vascular Surgery Department, the Third Affiliated Hospital of Qiqihar Medical University, Heilongjiang 161000, China.
Iran J Public Health. 2024 Jul;53(7):1517-1527. doi: 10.18502/ijph.v53i7.16046.
There is increasing evidence that macrophages are involved in the development of carotid atherosclerosis (CAS), but the specific mechanism is still unclear. We aimed to explore the key genes that play a regulatory role on macrophages in the progression of CAS.
From 2021 August to 2023 August, GEO datasets GSE100927 and GSE43292 were downloaded and the key gene modules related to CAS were identified by weighted Gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genes (KEGG) pathway analysis was performed on the genes of the key modules to identify common gene enrichment pathways. Differential expression analysis of pathway-related genes was performed by the "limma" package of R software. Case groups were categorized into high and low expression groups based on the expression levels of key genes, and ssGSEA immune infiltration analysis was performed.
The turquoise module of GSE100924 (threshold=12) and the brown module of GSE43292 (threshold=7) were obtained through WGCNA analysis. The analysis of KEGG showed that the differentially expressed genes in the turquoise and brown modules were co-enriched in the staphylococcus aureus infection signaling pathway. Differential expression analysis identified 18 common differentially expressed genes, all of which were highly expressed in the case group. is the gene of interest. According to ssGSEA analysis, the high expression group of showed a significant increase in the number of macrophages (GSE43292, =0.0011; GSE100927, =0.025).
This study identified the key gene involved in regulating macrophage functional activity during the CAS process, providing new ideas for effective control of CAS.
越来越多的证据表明巨噬细胞参与颈动脉粥样硬化(CAS)的发展,但其具体机制仍不清楚。我们旨在探索在CAS进展过程中对巨噬细胞起调节作用的关键基因。
从2021年8月至2023年8月,下载GEO数据集GSE100927和GSE43292,并通过加权基因共表达网络分析(WGCNA)确定与CAS相关的关键基因模块。对关键模块的基因进行京都基因与基因组百科全书(KEGG)通路分析,以确定常见的基因富集通路。通过R软件的“limma”包对通路相关基因进行差异表达分析。根据关键基因的表达水平将病例组分为高表达组和低表达组,并进行单样本基因集富集分析(ssGSEA)免疫浸润分析。
通过WGCNA分析获得了GSE100924的绿松石模块(阈值 = 12)和GSE43292的棕色模块(阈值 = 7)。KEGG分析表明,绿松石和棕色模块中的差异表达基因在金黄色葡萄球菌感染信号通路中共同富集。差异表达分析确定了18个共同的差异表达基因,所有这些基因在病例组中均高表达。[感兴趣的基因名称]是感兴趣的基因。根据ssGSEA分析,[感兴趣的基因名称]高表达组的巨噬细胞数量显著增加(GSE43292,P = 0.0011;GSE100927,P = 0.025)。
本研究确定了在CAS过程中参与调节巨噬细胞功能活性的关键基因[感兴趣的基因名称],为有效控制CAS提供了新思路。