Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation

Abstract

Objective: Abdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It's critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker.

Methods: In this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort.

Results: Monocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1β and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples.

Conclusion: This study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.

Keywords: WGCNA; abdominal aortic aneurysm; bioinformatics; macrophage; single-cell RNA sequencing.

FIGURE 1

Immune cell infiltration analysis of the early-stage experimental AAA models. The t-SNE plot for peri-adventitial elastase incubation induced AAA scRNA-seq dataset (GSE152583) which contains elastase-induced AAA samples on day 7 and control aortas (A). The t-SNE plot for CaCl₂ induced AAA scRNA-seq dataset (GSE164678) which contains CaCl₂-induced AAA samples on day 4 and control aortas (B). Boxplot of the mMCP-counter enrichment score of microarray dataset (C) GSE51227 which contains AAA samples induced by intraluminal elastase perfusion on day 7 and control aortas (E) GSE109639 which contains CaCl₂-induced AAA samples on day 7 and control aortas (G) GSE17901 which contains AAA samples from AngII treated ApoE^–/– mice on day 7 and control aortas. Bar graph of the CIBERSORT enrichment ratio of microarray dataset GSE51227 (D), GSE109639 (F), GSE17901 (H) (*P < 0.05, **P < 0.01, ***P < 0.001, ns, not significant).

FIGURE 2

WGCNA for monocytes/macrophages populations of scRNA-seq datasets. WGCNA for monocytes/macrophages populations from peri-adventitial elastase incubation induced AAA dataset (GSE152583) (A–D) and CaCl₂ induced AAA dataset (GSE164678) (E–H). Sample clustering dendrogram for the pseudocells (A,E). Topology network analysis of the scale-free fit index for various soft-thresholding powers (β) and the mean connectivity for various soft-thresholding powers (B,F). The cluster dendrograms represented the co-expression modules (C,G). Heatmap exhibited the relationships between gene modules and clinical traits (Con and AAA) by Spearman correlation (D,H).

FIGURE 3

Function enrichment and PPI analysis. Bar plots of GO and KEGG function enrichment results for genes in brown module (A), yellow module (C), and blue module (E) of peri-adventitial elastase incubation induced AAA dataset (GSE152583) and genes in blue module (G) of CaCl₂ induced AAA dataset (GSE164678). PPI network for hub genes in brown module (B), yellow module (D), and blue module (F) of elastase induced AAA dataset and for hub genes in blue module (H) of CaCl₂ induced AAA dataset.

FIGURE 4

Identification of key genes. Volcano map of differential genes for monocytes/macrophages populations between control and AAA model group in peri-adventitial elastase incubation induced AAA dataset (GSE152583) (A) and CaCl₂ induced AAA dataset (GSE164678) (B). Veen diagrams of DEGs and hub genes for monocytes/macrophages populations between elastase induced AAA dataset and CaCl₂ induced AAA dataset (C). Violin plots of Clec4e, Il1b, and Thbs1 expression in different cell types from peri-adventitial elastase incubation induced AAA dataset and CaCl₂ induced AAA dataset (D). Boxplot showing the relative expression of Clec4e, Il1b, and Thbs1 in the early stage AAA and control aortas for intraluminal elastase perfusion induced AAA microarray dataset (GSE51227) (E) and AngII induced AAA dataset (GSE17091) (F). Boxplot showing the relative expression of Clec4e, Il1b, and Thbs1 in AAA and control aortas of day 7 and 42 for CaCl₂ induced AAA microarray dataset (GSE109639) (G). *P < 0.05, ****P < 0.0001.

FIGURE 5

Construction of the logistic regression diagnostic model. Boxplot showing the relative expression of CLEC4E, IL1B, and THBS1 in AAA and control aortas for human AAA dataset (GSE57691) (A). CLEC4E, IL1B, THBS1, and CD68 expression in different cell types of human AAA scRNA-seq dataset (GSE166676) (B). Dynamic nomogram of the two-gene-based model for predicting patients with AAA (C). Dynamic nomogram of the two-gene-based model for predicting patients with AAA (C). The calibration curve of the model (D). ROC curves for the train dataset GSE57691 (E) and the validation dataset GSE47472 (human AAA neck) (F). **P < 0.01, ***P < 0.001.

FIGURE 6

Validation of the gene expression and serum protein level. Boxplot showing the relative expression of CLEC4E and IL1B in AAA (N = 10) and control aortas (N = 10) (A). Boxplot showing the protein level of CLEC4E (B) and IL-1β (C) in AAA (N = 30) and control serum (N = 10). *P < 0.05, **P < 0.01.