AUTHOR=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 

TITLE=EGR1 and KLF4 as Diagnostic Markers for Abdominal Aortic Aneurysm and Associated With Immune Infiltration

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.781207

DOI=10.3389/fcvm.2022.781207

ISSN=2297-055X

ABSTRACT=<sec><title>Background</title><p>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.</p></sec><sec><title>Methods</title><p>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 (<italic>n</italic> = 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).</p></sec><sec><title>Results</title><p>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. <italic>EGR1</italic> and <italic>KLF4</italic> 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 &gt;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 <italic>EGR1</italic> and <italic>KLF4</italic> were associated with mast cells, T cells, and plasma cells.</p></sec><sec><title>Conclusion</title><p><italic>EGR1</italic> and <italic>KLF4</italic> were diagnostic markers of PVAT around AAA and associated with multiple immune cells.</p></sec>