The diagnostic significance of integrating m6A modification and immune microenvironment features based on bioinformatic investigation in aortic dissection

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

BACKGROUND

Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.

METHODS

In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.

RESULTS

38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.

CONCLUSION

We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA)

Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.

Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis

BACKGROUND

Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear.

METHODS

Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues.

RESULTS

Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses.

CONCLUSION

SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses.