Identification of apoptosis-related key genes and the associated regulation mechanism in thoracic aortic aneurysm

Abstract

Background: This study investigated the role of apoptosis-related genes in thoracic aortic aneurysms (TAA) and provided more insights into TAA's pathogenesis and molecular mechanisms.

Material/methods: Two gene expression datasets (GSE9106 and GSE26155) were retrieved from the Gene Expression Omnibus (GEO) database. Apoptosis-related genes were obtained from the KEGG apoptosis pathway (hsa04210). Differentially expressed apoptosis-related genes were identified by performing differential expression analysis using limma for TAA blood and tissue samples. GO and KEGG enrichment analysis of the differentially expressed apoptosis genes was performed using the Metascape web tool. The miRNA-mRNA regulatory network was reconstructed using the ENCORI and miRDB databases, and functional enrichment analysis was performed on the related miRNAs using the miEAA tool. The correlation between the expression levels of differentially expressed apoptosis-related genes and genes involved in immune infiltration in TAA was calculated using the CIBERSORT algorithm. The apoptosis modification patterns mediated by differentially expressed apoptosis-related genes were systematically assessed in TAA samples.

Results: A total of 9 differentially-expressed apoptosis-related genes were identified in TAA samples compared with normal samples. 150 miRNAs and 6 mRNAs regulatory networks were reconstructed using the ENCORI and miRDB databases. Immune infiltration analysis revealed that the GZMB had the strongest positive correlation with activated NK cells and the DFFA presented the strongest positive correlation with T cells follicular helper. 3 distinct apoptosis modification patterns mediated by 9 differentially-expressed apoptosis-related genes were identified. They differ in immune characteristics and drug sensitivity, and their biological functions in these subtypes were further studied.

Conclusions: This study identified key apoptosis-related genes related to TAA and evaluated the modification patterns of key apoptosis genes in TAA, providing insights into potential targets and mechanisms of TAA pathogenesis and progression.

Keywords: Apoptosis; Bioinformatical analysis; RNA modification; Thoracic aortic aneurysm; Transcriptomics.

Fig. 1

The landscape of gene expression changes of apoptosis regulators in TAAs. A TAAs differential expression analysis, where red and blue represented up-regulation and down-regulation, respectively; B Venn diagram of DEGs and differentially-expressed apoptosis-related genes; C Heatmap of differentially-expressed apoptosis-related gene expression, where red and blue represented TAAs group and healthy group, respectively; D Boxplots showing the expression levels of the differentially-expressed apoptosis-related genes, where red and blue represented TAAs group and healthy group, respectively. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; E Heatmap showing the correlations among the differentially-expressed apoptosis-related genes, where red and blue represented positive and negative correlations, respectively. Color darkness is relative to the correlation’s strength; F Functional similarity analysis using differentially-expressed apoptosis-related genes

Fig. 2

ROC analysis of the differentially-expressed apoptosis-related genes in GSE9106. A BCL2L1; B SPTA1; C PTPN13; D JUN; E DFFA; F CASP6; G GZMB; H AKT3; I GADD45G

Fig. 3

ROC analysis of the differentially-expressed apoptosis-related genes in GSE26155. A CASP6; B JUN; C DFFA; D AKT3; E GZMB; F GADD45G; G SPTA1; H PTPN13; (I) BCL2L1

Fig. 4

Functional enrichment analysis of DEGs. A Network diagram of enriched function, with different colors representing clusters of functional relevance; B Network diagram of enriched functions with color representing the p-values; C Top 20 GO biological process enriched terms; D Top 20 GO molecular function enriched terms; E Top 20 GO cellular compartment enriched terms; F Top 20 KEGG enriched pathways

Fig. 5

Interaction network analysis between apoptosis regulators and their targeted miRNAs. A mRNA-miRNA interaction network; B Wordcloud for significantly enriched GO terms; C Wordcloud for significantly enriched KEGG pathways

Fig. 6

Immuno-infiltration analysis. A Correlation analysis between immune cell infiltration and differential apoptotic genes, where red and blue represented positive and negative correlation, respectively; B Correlation plot of immune cell infiltration, where red and blue represented positive and negative correlation, respectively. Color darkness was relative to the correlation strength; C the correlation among GZMB and activated NK cells; D the Correlation among DFFA and follicular helper T cells

Fig. 7

Unsupervised clustering analysis. A Consensus clustering plot at k = 3; B Cumulative distribution function (CDF) of consensus clustering; C Relative change in area under CDF curve from 2 to 10 of k; D Tracking plot

Fig. 8

Mut omics of apoptosis modification patterns. A The unsupervised clustering of the 9 apoptosis regulators in the TAAs cohort. The apoptosis clusters were used for patients’ annotation. Red and blue colors represented high and low expression, respectively; B PCA of the transcriptomic profiles of the three apoptosis modification clusters, displaying evident diversity in the transcriptomic profiles between the different modification clusters; C Violin plots of ImmuneScore, StromalScore, ESTIMATEScore, and TumorPurity; D Drug sensitivity boxplots of Axitinib, Pazopanib, Bortezomib, and Dasatinib; E Boxplots of the proportions of immune cell infiltrates; F Boxplots of immune checkpoint and immunocompetent genes, * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001

Fig. 9

Biological features of apoptosis modification clusters. A Functional enrichment dotplot of C2 vs. C1; B Functional enrichment dotplot of C3 vs. C1; C Functional enrichment dotplot of C3 vs. C2; D Gseaplot of C2 vs. C1; E Gseaplot of C3 vs. C1; F Gseaplot of C3 vs. C2