以及作为腹主动脉瘤的诊断标志物并与免疫浸润相关。
and as Diagnostic Markers for Abdominal Aortic Aneurysm and Associated With Immune Infiltration.
作者信息
Guo Chunguang, Liu Zaoqu, Yu Yin, Zhou Zhibin, Ma Ke, Zhang Linfeng, Dang Qin, Liu Long, Wang Libo, Zhang Shuai, Hua Zhaohui, Han Xinwei, Li Zhen
机构信息
Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
出版信息
Front Cardiovasc Med. 2022 Feb 9;9:781207. doi: 10.3389/fcvm.2022.781207. eCollection 2022.
BACKGROUND
Formation and rupture of abdominal aortic aneurysm (AAA) is fatal, and the pathological processes and molecular mechanisms underlying its formation and development are unclear. Perivascular adipose tissue (PVAT) has attracted extensive attention as a newly defined secretory organ, and we aim to explore the potential association between PVAT and AAA.
METHODS
We analyzed gene expression and clinical data of 30 PVAT around AAA and 30 PVAT around normal abdominal aorta (NAA). The diagnostic markers and immune cell infiltration of PVAT were further investigated by WGCNA, CIBERSORT, PPI, and multiple machine learning algorisms (including LASSO, RF, and SVM). Subsequently, eight-week-old C57BL/6 male mice ( = 10) were used to construct AAA models, and aorta samples were collected for molecular validation. Meanwhile, fifty-five peripheral venous blood samples from patients (AAA vs. normal: 40:15) in our hospital were used as an inhouse cohort to validate the diagnostic markers by qRT-PCR. The diagnostic efficacy of biomarkers was assessed by receiver operating characteristic (ROC) curve, area under the ROC (AUC), and concordance index (C-index).
RESULTS
A total of 75 genes in the Grey60 module were identified by WGCNA. To select the genes most associated with PVAT in the grey60 module, three algorithms (including LASSO, RF, and SVM) and PPI were applied. and were identified as diagnostic markers of PVAT, with high accurate AUCs of 0.916, 0.926, and 0.948 (combined two markers). Additionally, the two biomarkers also displayed accurate diagnostic efficacy in the mice and inhouse cohorts, with AUCs and C-indexes all >0.8. Compared with the NAA group, PVAT around AAA was more abundant in multiple immune cell infiltration. Ultimately, the immune-related analysis revealed that and were associated with mast cells, T cells, and plasma cells.
CONCLUSION
and were diagnostic markers of PVAT around AAA and associated with multiple immune cells.
背景
腹主动脉瘤（AAA）的形成和破裂是致命的，其形成和发展的病理过程及分子机制尚不清楚。血管周围脂肪组织（PVAT）作为一个新定义的分泌器官已引起广泛关注，我们旨在探讨PVAT与AAA之间的潜在关联。
方法
我们分析了30例AAA周围PVAT和30例正常腹主动脉（NAA）周围PVAT的基因表达和临床数据。通过加权基因共表达网络分析（WGCNA）、CIBERSORT、蛋白质-蛋白质相互作用（PPI）和多种机器学习算法（包括套索回归、随机森林和支持向量机）进一步研究PVAT的诊断标志物和免疫细胞浸润情况。随后，使用8周龄的C57BL/6雄性小鼠（n = 10）构建AAA模型，并收集主动脉样本进行分子验证。同时，收集我院55例患者（AAA组与正常组：40:15）的外周静脉血样本作为内部队列，通过实时定量聚合酶链反应（qRT-PCR）验证诊断标志物。通过受试者工作特征（ROC）曲线、ROC曲线下面积（AUC）和一致性指数（C-index）评估生物标志物的诊断效能。
结果
通过WGCNA在Grey60模块中鉴定出总共75个基因。为了选择Grey60模块中与PVAT最相关的基因，应用了三种算法（包括套索回归、随机森林和支持向量机）和PPI。CXCL1和CCL20被鉴定为PVAT的诊断标志物，联合两个标志物时AUC分别为0.916、0.926和0.948，准确性较高。此外，这两种生物标志物在小鼠和内部队列中也显示出准确的诊断效能，AUC和C-index均>0.8。与NAA组相比，AAA周围的PVAT中有更多种类的免疫细胞浸润。最终，免疫相关分析显示CXCL1和CCL20与肥大细胞、T细胞和浆细胞有关。
结论
CXCL1和CCL20是AAA周围PVAT的诊断标志物，并与多种免疫细胞相关。
Zotero 插件