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

Chunguang Guo; Zaoqu Liu; Yin Yu; Zhibin Zhou; Ke Ma; Linfeng Zhang; Qin Dang; Long Liu; Libo Wang; Shuai Zhang; Zhaohui Hua; Xinwei Han; Zhen Li

doi:10.3389/fcvm.2022.781207

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

Front Cardiovasc Med. 2022 Feb 9:9:781207. doi: 10.3389/fcvm.2022.781207. eCollection 2022.

Authors

Chunguang Guo¹, Zaoqu Liu², Yin Yu³, Zhibin Zhou¹, Ke Ma¹, Linfeng Zhang¹, Qin Dang⁴, Long Liu⁵, Libo Wang⁵, Shuai Zhang¹, Zhaohui Hua¹, Xinwei Han², Zhen Li¹

Affiliations

¹ Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
² Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
³ Department of Pathophysiology, School of Basic Medical Sciences, The Academy of Medical Science, Zhengzhou University, Zhengzhou, China.
⁴ Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁵ Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Abstract

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.

Keywords: abdominal aortic aneurysm; diagnostic marker; immune infiltration; machine learning; perivascular adipose tissue.