综合转录组分析揭示了用于建立腹主动脉瘤诊断模型和分子治疗框架的巨噬细胞相关基因。

Comprehensive transcriptomic analysis unveils macrophage-associated genes for establishing an abdominal aortic aneurysm diagnostic model and molecular therapeutic framework.

作者信息

Wu Zhen, Yu Weiming, Luo Jie, Shen Guanghui, Cui Zhongqi, Ni Wenxuan, Wang Haiyang

机构信息

Department of Vascular and Interventional Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, Heilongjiang, China.

General Surgery, Thyroid Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510000, Guangdong, China.

出版信息

Eur J Med Res. 2024 Jun 12;29(1):323. doi: 10.1186/s40001-024-01900-w.

DOI:10.1186/s40001-024-01900-w

PMID:38867262

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11167832/

Abstract

BACKGROUND

Abdominal aortic aneurysm (AAA) is a highly lethal cardiovascular disease. The aim of this research is to identify new biomarkers and therapeutic targets for the treatment of such deadly diseases.

METHODS

Single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were used to identify distinct immune cell infiltration types between AAA and normal abdominal aortas. Single-cell RNA sequencing data were used to analyse the hallmark genes of AAA-associated macrophage cell subsets. Six macrophage-related hub genes were identified through weighted gene co-expression network analysis (WGCNA) and validated for expression in clinical samples and AAA mouse models. We screened potential therapeutic drugs for AAA through online Connectivity Map databases (CMap). A network-based approach was used to explore the relationships between the candidate genes and transcription factors (TFs), lncRNAs, and miRNAs. Additionally, we also identified hub genes that can effectively identify AAA and atherosclerosis (AS) through a variety of machine learning algorithms.

RESULTS

We obtained six macrophage hub genes (IL-1B, CXCL1, SOCS3, SLC2A3, G0S2, and CCL3) that can effectively diagnose abdominal aortic aneurysm. The ROC curves and decision curve analysis (DCA) were combined to further confirm the good diagnostic efficacy of the hub genes. Further analysis revealed that the expression of the six hub genes mentioned above was significantly increased in AAA patients and mice. We also constructed TF regulatory networks and competing endogenous RNA networks (ceRNA) to reveal potential mechanisms of disease occurrence. We also obtained two key genes (ZNF652 and UBR5) through a variety of machine learning algorithms, which can effectively distinguish abdominal aortic aneurysm and atherosclerosis.

CONCLUSION

Our findings depict the molecular pharmaceutical network in AAA, providing new ideas for effective diagnosis and treatment of diseases.

摘要

背景

腹主动脉瘤（AAA）是一种具有高度致死性的心血管疾病。本研究旨在识别用于治疗此类致命疾病的新生物标志物和治疗靶点。

方法

采用单样本基因集富集分析（ssGSEA）和CIBERSORT算法来识别AAA与正常腹主动脉之间不同的免疫细胞浸润类型。利用单细胞RNA测序数据来分析与AAA相关的巨噬细胞亚群的标志性基因。通过加权基因共表达网络分析（WGCNA）鉴定出六个与巨噬细胞相关的枢纽基因，并在临床样本和AAA小鼠模型中验证其表达情况。我们通过在线连通性图谱数据库（CMap）筛选用于AAA的潜在治疗药物。采用基于网络的方法来探索候选基因与转录因子（TFs）、长链非编码RNA（lncRNAs）和微小RNA（miRNAs）之间的关系。此外，我们还通过多种机器学习算法鉴定出能够有效区分AAA和动脉粥样硬化（AS）的枢纽基因。

结果

我们获得了六个能够有效诊断腹主动脉瘤的巨噬细胞枢纽基因（IL-1B、CXCL1、SOCS3、SLC2A3、G0S2和CCL3）。结合ROC曲线和决策曲线分析（DCA）进一步证实了这些枢纽基因具有良好的诊断效能。进一步分析显示，上述六个枢纽基因在AAA患者和小鼠中的表达显著增加。我们还构建了TF调控网络和竞争性内源性RNA网络（ceRNA）以揭示疾病发生的潜在机制。我们还通过多种机器学习算法获得了两个关键基因（ZNF652和UBR5），它们能够有效区分腹主动脉瘤和动脉粥样硬化。