Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 May 22:10:1089312.
doi: 10.3389/fcvm.2023.1089312. eCollection 2023.

Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection

Hong Wan  1 , Danlingyi Liu  1 , Bingqing Liu  1 , Mengyao Sha  1 , Wei Xia  1 , Chang Liu  1
Affiliations

Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection

Hong Wan et al. Front Cardiovasc Med. .

Abstract

Objective: Thoracic aortic aneurysm and dissection (TAAD) is a cardiovascular disease with a high mortality rate. Aging is an important risk factor for TAAD. This study explored the relationship between aging and TAAD and investigated the underlying mechanisms, which may contribute to the diagnosis and treatment of TAAD.

Methods: Human aging genes were obtained from the Aging Atlas official website. Various datasets were downloaded from the GEO database:the human TAAD dataset GSE52093 were used for screening differentially expressed genes (DEGs); GSE137869, GSE102397 and GSE153434 were used as validation sets, and GSE9106 was used for diagnostic prediction of receiver operating characteristic (ROC) curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and protein-protein interaction (PPI) network analysis were used to screen differentially co-expressed genes from human aging genes and TAAD. Using five methods of the cytoHubba plugin in Cytoscape software (Degree, Closeness, EPC, MNC, Radiality), hub genes were identified from the differentially co-expressed genes. Single-cell RNA sequencing was used to verify the expression levels of hubgenes in different cell types of aortic tissue. ROC curves were used to further screen for diagnostic genes.

Results: A total of 70 differentially co-expressed genes were screened from human aging genes and DEGs in human TAAD dataset GSE52093. GO enrichment analysis revealed that the DEGs played a major role in regulating DNA metabolism and damaged DNA binding. KEGG enrichment analysis revealed enrichment in the longevity regulating pathway, cellular senescence, and HIF-1 signaling pathway. GSEA indicated that the DEGs were concentrated in the cell cycle and aging-related p53 signaling pathway. The five identified hubgenes were MYC, IL6, HIF1A, ESR1, and PTGS2. Single-cell sequencing of the aging rat aorta showed that hubgenes were expressed differently in different types of cells in aortic tissue. Among these five hubgenes, HIF1A and PTGS2 were validated in the aging dataset GSE102397; MYC, HIF1A and ESR1 were validated in the TAAD dataset GSE153434. The combined area under the diagnostic ROC curve (AUC) values for the five hub genes were >0.7 in the testing and training sets of the dataset GSE9106. The combined AUC values of MYC and ESR1 were equal to the combin ed AUC values of the five hub genes.

Conclusion: The HIF-1 signaling pathway may play an important role in TAAD and aging. MYC and ESR1 may have diagnostic value for aging-related TAAD.

Keywords: aging; bioinformatics analysis; biomarkers; single-cell RNA sequencing; thoracic aortic aneurysm and dissection.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Differential expression of aging-related genes in TAAD. (A) Principal component analysis of GSE52093. (B) Volcano plot of differentially expressed genes (DEGs) in GSE52093. Red dots represent significantly upregulated genes and blue dots represent significantly downregulated genes. (C) Venn diagram of DEGs between human aging-related genes and TAAD. (D) Heatmap of 70 aging-related DEGs in TAAD.
Figure 2
Figure 2
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of 70 aging-related differentially expressed genes (DEGs). (A) Bubble plot of the GO enrichment analysis of aging-related DEGs. (B) Histogram of the KEGG pathway enrichment analysis of aging-related DEGs. (C) Bubble plot of the GO enrichment analysis of aging-related DEGs combined with logFC. (D) KEGG circle plot of aging-related DEGs combined with logFC. (E) KEGG chord diagram of aging-related DEGs combined with logFC.
Figure 3
Figure 3
Correlation and protein–protein interaction (PPI) network analysis of aging-related differentially expressed genes (DEGs). (A) PPI network map of 70 aging-related DEGs in TAAD. (B) The interaction number of each aging-related DEGs. (C) Spearman correlation analysis of the 45 upregulated aging-related DEGs in TAAD. (D) Spearman correlation analysis of the 25 downregulated aging-related DEGs in TAAD.
Figure 4
Figure 4
Hub genes and their interactions. (A) Hub genes–disease association information. (B,C) Hub genes–drug interactions.
Figure 5
Figure 5
Single-cell RNA sequencing results. (A) Reduced dimensional plot of aortic tissue cell clustering t-SNE. (B) Reduced-dimensional plot of t-SNE for relative expression of cellular marker genes in different cell types. (C) Downscaled plots of t-SNE for aortic tissue cell subgroups. (D) Violin plots of hub genes expression in different cell types of aortic tissues.
Figure 6
Figure 6
Validation of hubgene expression levels in aging and TAAD. (A) Grouped comparison plots of hub genes expression validation in the aging-related dataset GSE102397. (B) Grouped comparison plots of hub genes expression validation in the TAAD-related dataset GSE153434. (C) Diagnostic receiver operating characteristic (ROC) curves showing the independent and combined metrics for hub genes in the testing set. (D) Diagnostic ROC curves showing the independent and combined metrics in the training set. (E) Diagnostic ROC curves showing the independent and combined metrics for hub genes with AUC >0.7 in the testing set.
See this image and copyright information in PMC

References

    1. Gudbjartsson T, Ahlsson A, Geirsson A, Gunn J, Hjortdal V, Jeppsson A, et al. Acute type A aortic dissection: a review. Scand Cardiovasc J. (2020) 54(1):1–13. 10.1080/14017431.2019.1660401 - DOI - PubMed
    1. Owens WA, Walaszczyk A, Spyridopoulos I, Dookun E, Richardson GD. Senescence and senolytics in cardiovascular disease: Promise and potential pitfalls. . Mech Ageing Dev. (2021) 198:111540. 10.1016/j.mad.2021.111540 - DOI - PMC - PubMed
    1. Humphrey JD, Milewicz DM. Aging, smooth muscle vitality, and aortic integrity. Circ Res. (2017) 120(12):1849–51. 10.1161/CIRCRESAHA.117.311075 - DOI - PMC - PubMed
    1. Zhang WM, Liu Y, Li TT, Piao CM, Liu O, Liu JL, et al. Sustained activation of ADP/P2ry12 signaling induces SMC senescence contributing to thoracic aortic aneurysm/dissection. J Mol Cell Cardiol. (2016) 99:76–86. 10.1016/j.yjmcc.2016.08.008 - DOI - PubMed
    1. Craiem D, El Batti S, Casciaro ME, Mousseaux E, Sirieix ME, Simon A, et al. Age-related changes of thoracic aorta geometry used to predict the risk for acute type B dissection. Int J Cardiol. (2017) 228:654–60. 10.1016/j.ijcard.2016.11.125 - DOI - PubMed