EGR1 and KLF4 as Diagnostic Markers for Abdominal Aortic Aneurysm and Associated With Immune Infiltration.
EGR1 and KLF4 as Diagnostic Markers for Abdominal Aortic Aneurysm and Associated With Immune Infiltration.
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EGR1 和 KLF4 作为腹主动脉瘤的诊断标志物并与免疫浸润相关
DOI：
10.3389/fcvm.2022.781207
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发表时间：
2022
影响因子：
3.6
通讯作者：
Li Z
中科院分区：
文献类型：
--
作者：
Guo C;Liu Z;Yu Y;Zhou Z;Ma K;Zhang L;Dang Q;Liu L;Wang L;Zhang S;Hua Z;Han X;Li Z
背景腹主动脉瘤（abdominal aortic aneurysm，AAA）的形成和破裂是一种致死性疾病，其形成和发展的病理过程和分子机制尚不清楚。血管周围脂肪组织（PVAT）作为一种新定义的分泌器官引起了广泛的关注，我们的目的是探讨PVAT和AAA之间的潜在联系。方法对30例腹主动脉瘤周围PVAT和30例正常腹主动脉周围PVAT的基因表达和临床资料进行分析。通过WGCNA、CIBERSORT、PPI和多种机器学习算法（包括LASSO、RF和SVM）进一步研究PVAT的诊断标志物和免疫细胞浸润。随后，8周龄C57 BL/6雄性小鼠（n = 10）用于构建AAA模型，并收集主动脉样品用于分子验证。同时，我们医院的55例患者（AAA与正常：40：15）的外周静脉血样本被用作内部队列，以通过qRT-PCR验证诊断标志物。通过受试者工作特征（ROC）曲线、ROC下面积（AUC）和一致性指数（C-index）评估生物标志物的诊断效力。结果WGCNA共鉴定出Grey 60模块中的75个基因。为了在grey 60模块中选择与PVAT最相关的基因，应用三种算法（包括LASSO、RF和SVM）和PPI。EGR 1和KLF 4被确定为PVAT的诊断标志物，具有0.916、0.926和0.948的高准确度AUC（组合两种标志物）。此外，这两种生物标志物在小鼠和室内队列中也显示出准确的诊断功效，AUC和C指数均>0.8。与NAA组相比，AAA周围PVAT多为免疫细胞浸润。最终，免疫相关分析显示，EGR 1和KLF 4与肥大细胞、T细胞和浆细胞相关。结论EGR 1和KLF 4是AAA周围PVAT的诊断标志物，与多种免疫细胞相关。
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 (n = 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. EGR1 and KLF4 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 EGR1 and KLF4 were associated with mast cells, T cells, and plasma cells. Conclusion EGR1 and KLF4 were diagnostic markers of PVAT around AAA and associated with multiple immune cells.
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