Ning Da-Sheng, Zhou Zi-Qing, Zhou Shu-Heng, Chen Ji-Mei
Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, PR China.
Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, PR China.
Heliyon. 2024 Jul 11;10(14):e34295. doi: 10.1016/j.heliyon.2024.e34295. eCollection 2024 Jul 30.
Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.
代谢综合征(MS)是动脉粥样硬化(AS)斑块进展的一个独立危险因素,但其背后的机制尚不清楚。免疫系统在这个过程中可能起重要作用。本研究旨在识别MS患者中发生和进展为AS风险较高的潜在诊断基因。从基因表达综合数据库(GEO)中检索数据集并识别差异表达基因。使用多种生物信息学分析、机器学习和一致性聚类相结合的方法识别枢纽基因、免疫细胞失调和AS亚型。使用列线图和ROC分析评估枢纽基因的诊断价值。最后,构建富集分析、竞争性内源性RNA(ceRNA)网络、单细胞RNA(scRNA)测序分析和药物-蛋白质相互作用预测,以识别枢纽基因的功能作用、潜在调节因子和分布。识别出四个枢纽基因和两种巨噬细胞相关亚型。验证了它们强大的诊断价值并确定了功能过程。ScRNA分析确定了F13A1的巨噬细胞分化调节功能。CeRNA网络和药物-蛋白质结合模式揭示了潜在的治疗方法。识别出四个与免疫相关的枢纽基因(F13A1、MMRN1、SLCO2A1和ZNF521)并评估了它们的诊断价值,其中发现F13A1与巨噬细胞分化密切相关,可能是MS患者AS进展的潜在诊断和治疗标志物。