Department of Cardiovascular, Shaanxi Provincial People's Hospital, Xi'an, China.
Department of Cardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming, China.
Front Immunol. 2022 Jun 13;13:907309. doi: 10.3389/fimmu.2022.907309. eCollection 2022.
Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 () (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, may be an effective diagnostic biomarker for AAA, whereas may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.
识别腹主动脉瘤 (AAA) 的生物标志物对于了解其发病机制、开发新型靶向治疗方法以及可能改善患者预后和破裂风险至关重要。在这里，我们使用单细胞 RNA 测序、加权共表达网络 (WGCNA) 和差异表达分析从公共数据库中识别 AAA 生物标志物。此外，我们还使用多种机器学习方法来识别区分大 AAA 和小 AAA 的生物标志物。使用 GEO 数据集验证生物标志物。使用 CIBERSORT 评估 AAA 组织中免疫细胞浸润情况，并研究生物标志物与浸润免疫细胞之间的关系。因此，筛选了 288 个差异表达基因 (DEG) 用于 AAA 和正常样本。鉴定的 DEG 主要与炎症反应、脂质和动脉粥样硬化有关。对于大 AAA 和小 AAA 样本，筛选了 17 个主要与坏死性凋亡有关的 DEG。G0/G1 开关 2 () 作为 AAA 的生物标志物 (在 GSE57691、GSE47472 和 GSE7284 中的 AUC 分别为 0.861、0.875 和 0.911)，HPSE 作为大 AAA 的生物标志物 (在 GSE57691 和 GSE98278 中的 AUC 分别为 0.669 和 0.754)，并通过 qRT-PCR 进一步验证。免疫细胞浸润分析表明，AAA 过程可能由滤泡辅助性 T (Tfh) 细胞介导，大 AAA 过程也可能由 Tfh 细胞、M1 和 M2 巨噬细胞介导。此外， 表达与中性粒细胞、激活和静止肥大细胞、M0 和 M1 巨噬细胞、调节性 T 细胞 (Tregs)、静止树突状细胞和静止 CD4 记忆 T 细胞相关。此外， 表达与 M0 和 M1 巨噬细胞、激活和静止肥大细胞、Tregs 和静止 CD4 记忆 T 细胞相关。此外， 可能是 AAA 的有效诊断生物标志物，而 可能用于预测大 AAA 的破裂风险。免疫细胞在 AAA 的发生和发展中起作用，这可能改善其诊断和治疗。
