Identification and Analysis of Hub Genes and Immune Cells Associated with the Formation of Acute Aortic Dissection

Identification of Key Immune Infiltration Related Genes Involved in Aortic Dissection Using Bioinformatic Analyses and Experimental Verification

Purpose

Immune microenvironment plays an important role in aortic dissection (AD). Therefore, novel immune biomarkers may facilitate AD prevention, diagnosis, and treatment. This study aimed at mining key immune-related genes and relevant mechanisms involved in AD pathogenesis.

Patients and Methods

Key immune cells in AD were identified by ssGESA algorithm. Next, genes associated with key immune cells were screened by weighted gene coexpression network analysis (WGCNA). Then hub immune genes were picked from protein-protein interaction network of overlapped genes from differential expression and WGCNA analyses by cytohubba plug-in. Their diagnostic potential was evaluated in two independent cohorts from GEO database. In addition, the expressions of hub immune genes were determined by quantitative RT-PCR, immunohistochemistry, and Western blotting in dissected and normal aortic tissues.

Results

Activated B cells, CD56dim natural killer cells, eosinophils, gamma delta T cells, immature B cells, natural killer cells and type 17 T helper cells were identified as key immune cells in AD. Thereafter, a gene module significantly correlated with key immune cells were found by WGCNA method. Subsequently, KDR, IGF1, NOS3, PECAM1, GAPDH, FLT1, DLL4, CDH5, VWF, and TEK were identified as hub immune cell related genes by PPI network analysis, which may be potential diagnostic markers for AD, as evidenced by ROC curves. Moreover, the decreased expression of VWF in AD was validated at both mRNA and protein levels, and its expression was significantly positive correlated with the marker of smooth muscle cells, ACTA2, in AD. Further immunofluorescent results showed that VWF was colocalized with ACTA2 in aortic tissues.

Conclusion

We identified key immune cells and hub immune cell-related genes involved in AD. Moreover, we found that VWF was co-expressed with the smooth muscle cell marker ACTA2, indicating the important role of VWF in smooth muscle cell loss in AD pathogenesis.

Comparative Analysis of Transcriptome Profiles in Patients with Thromboangiitis Obliterans

BACKGROUND

Thromboangiitis obliterans (TAO) causes vascular insufficiency due to chronic inflammation and abrupt thrombosis of the medium and small arteries of the extremities. In our study, we aimed to determine biomarkers for the diagnosis of TAO by evaluating 15 male TAO patients with Shinoya diagnostic criteria and 5 healthy controls who did not have TAO-related symptoms in their family histories.

METHODS

The Clariom D Affymetrix platform was used to conduct microarray analysis on total RNA extracted from whole blood. A total of 477 genes (FC ≤ 5 or >5) common to the fifteen patient and five control samples were selected using comparative microarray analysis; among them, 79 genes were upregulated and 398 genes were downregulated.

RESULTS

According to FC ≤ 10 or >10, in the same TAO patient and control group, 13 genes out of 28 were upregulated, whereas 15 genes were downregulated. The 11 key genes identified according to their mean log2FC values were PLP2, RPL27A, CCL4, FMNL1, EGR1, EIF4A1, RPL9, LAMP2, RNF149, EIF4G2, and DGKZ. The genes were ranked according to their relative expression as follows: FMNL1 > RNF149 > RPL27A > EIF4G2 > EIF4A1 > LAMP2 > EGR1 > PLP2 > DGKZ > RPL9 > CCL4. Using protein-protein interaction network analysis, RPL9, RPL27A, and RPL32 were found to be closely related to EIF4G2 and EIF4A1. The Reactome pathway found pathways linked to 28 genes. These pathways included the immune system, cellular responses to stress, cytokine signaling in the immune system, and signaling by ROBO receptors.

CONCLUSIONS

By figuring out the protein expression levels of the genes that have been found to explain how TAO disease works at the molecular level, it will be possible to figure out how well these chosen transcripts can diagnose and predict the disease.

Identification of potential ferroptosis key genes and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

Objective

Ferroptosis is of vital importance in the development of Rheumatoid arthritis (RA). The purpose of this project is to clarify the potential ferroptosis-related genes, pathways, and immune infiltration in RA by bioinformatics analysis.

Methods

We acquired ferroptosis-related genes (FRGs) from Ferroptosis database (FerrDb). We obtained the Gene dataset of RA (GSE55235) from the Gene Expression Omnibus (GEO) Database, screened the differentially expressed genes (DEGs) in RA and control samples, and then took the intersection of it and FRGs. Aiming to construct the protein-protein interaction (PPI) networks of the FRGs-DEGs, STRING database and Cytoscape software 3.7.0 would be used. Furthermore, hub genes were identified by CytoNCA, a Cytoscape plug-in. The gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of FRGs-DEGs were performed.

Results

We identified 34 FRGs-DEGs, including 7 upregulated and 27 downregulated genes by taking the intersection of the FRGs and DEGs. PPI analysis identified a total of 3 hub genes(VEGFA, PTGS2, and JUN). GO enrichment analyses and KEGG Pathway enrichment displayed that the FRGs-DEGs are involved in the response to oxidative stress and corticosteroid, heme binding, FoxO-signal pathway. Results of immune infiltration displayed that increased infiltration of T cells, while Macrophages M2 less may be related to the occurrence of RA.

Conclusion

The hub genes involved in ferroptosis in RA may be VEGFA, PTGS2, and JUN, which are mainly involved in FoxO-signal pathway. T cell, Mac, and plasma cells may be involved in the regulation of RA-joints-synovial-inflammation.