Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms

Abstract

Background: Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research.

Methods: We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algorithms were utilized to analyze the common immune infiltration patterns in TAA and AAA. Finally, a retrospective clinical study was performed on 78 patients with AD, and the serum from 6 patients was used for whole exome sequencing (WES).

Results: The intersection of TAA and AAA datasets yielded 82 differentially expressed genes (DEGs). Subsequently, the biomarkers (CX3CR1 and HBB) were acquired by screening using two machine-learning algorithms and ROC curve analysis. The functional analysis of DEGs showed significant enrichment in inflammation and regulation of angiogenic pathways. Immune cell infiltration analysis revealed that adaptive and innate immune responses were closely linked to AA progression. However, neither CX3CR1 nor HBB was associated with B cell-mediated humoral immunity. CX3CR1 expression was correlated with macrophages and HBB with eosinophils. Finally, our retrospective clinical study revealed a hyperinflammatory environment in aortic disease. The WES study identified disease biomarkers and gene variants, some of which may be druggable.

Conclusion: The genes CX3CR1 and HBB can be used as common biomarkers in TAA and AAA. Large numbers of innate and adaptive immune cells are infiltrated in AA and are closely linked to the development and progression of AA. Moreover, CX3CR1 and HBB are highly correlated with the infiltration of immune cells and may be potential targets of immunotherapeutic drugs. Gene mutation research is a promising direction for the treatment of aortic disease.

Keywords: aortic aneurysms; biomarkers; immune cell infiltration; machine-learning; whole exome sequencing.

Figure 1

DEGs screening in AAA and TAA. (A) Heatmap of DEGs expression in aortic aneurysm (Treat) and non-aortic aneurysm (Con) groups. (B) Volcano plots of DEGs expression in Treat and Con groups.

Figure 2

GO, KEGG and DO enrichment analyses. (A) Histogram of GO analysis. Enrichment significance increases with red color intensity. (B) Bubble plot representing GO analysis. Bubble size is proportional to the number of enriched genes. Red bubble color intensity increases with enrichment significance. (C) Histogram of KEGG analysis. (D) Bubble diagram representing KEGG analysis. (E) Histogram of DO analysis. (F) Bubble plot representing DO analysis.

Figure 3

GSEA enrichment analysis. (A) KEGG pathway set scores enriched in the control group. (B) KEGG pathway set scores enriched in the Treat group.

Figure 4

Identification and Verification of the discriminating power of potential biomarkers in AA. (A) Tuning feature selection in the “LASSO” model. The DEGs evaluated by 10-fold cross-validation in the “LASSO” regression model yielded 12 potential biomarkers. (B) The biomarkers screened by the SVM-RFE algorithm yielded 6 potential biomarkers. (C) Venn plot of potential biomarkers identified by “LASSO” and “SVM-RFE” algorithms. (D) The discrimination power of 3 potential biomarkers was verified in the validation dataset (GSE47472).

Figure 5

The diagnostic accuracy of the 3 potential biomarkers was assessed using ROC curve analysis. (A) Diagnostic ability of potential biomarkers in the training datasets. The AUC represents the diagnostic ability in Treat and Con groups. (B) Diagnostic accuracy of potential biomarkers in the validation datasets. The AUC represents the diagnostic accuracy in Treat and Con groups.

Figure 6

Immune infiltration analysis and the correlations between the infiltrating immune cell types. (A) The proportion of 22 infiltrating immune cell types in Treat and Con groups. (B) The distribution of the 28 infiltrating immune cell types in Treat and Con groups. (C) The correlations heatmap between the infiltrating immune cell types. (D) Violin plot demonstrating the differences between the 28 immune cell types in the Treat and Con groups.

Figure 7

Correlation analysis between biomarkers (CX3CR1 and HBB) and infiltrating immune cell types. (A) The relationship between 28 infiltrating immune cell types and two biomarkers; the redder the color, the more significant the difference. “***”, “**”, “*” represent P< (0.001, 0.01, 0.05). (B–H) Correlation between CX3CR1 expression and infiltrating immune cell types. (I–K) Correlation between HBB expression and infiltrating immune cell types. (L) Correlation between CX3CR1 and HBB and infiltrating immune cell types. The larger the dot, the stronger the correlation(cor). Numbers with P-value < 0.05 are marked red.

Figure 8

The serum markers analysis in patients with AD. (A–F) The serum levels of WBC, NEUT%, LDL-C, HDL-C, TC and TG are shown separately using box plots.

Figure 9

Whole Exome Sequencing analysis. (A) The circos plot of SNV-INDEL, the two outer tracks show the position of SNV and INDEL on the chromosome, and the two inner tracks show the distribution density. (B) The overall characteristics of the filtered mutation data. (C) The word cloud of the top 100 genes, the larger the font, the higher the variation frequency. (D) The oncoplot belongs to the top mutated 30 genes in each sample, different colors represent different variant classifications. (E) The overall distribution of the six different Ti/Tv in six samples. (F) The mutations of biomarkers (CX3CR1 and HBB) in six samples. (G) Relevance Heatmap of the top 13 mutated genes. (H) The bar chart of potentially druggable variants genes.