Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Abstract

Background: Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear.Materials and Methods: We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD.Results: A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower.Conclusion: We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

Identification of HIBCH as a Fatty Acid Metabolism-Related Biomarker in Aortic Valve Calcification Using Bioinformatics

“…In addition, several chemokines, CCL18 [48] and CCL23 [49], have been associated with atherosclerosis. Similar to our results, several recent bioinformatic articles have reported that chemokine-related pathways and chemokines may play an important role in AVC [50,51]. In a study of the association between AVC and systemic lupus erythematosus (SLE) prognosis, Molad et al reported that AVC and mitral annular calcification (MAC) were significantly and positively associated with serum IgA isotype of anticardiolipin antibody [52].…”

Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks

“…Earlier studies on AVC patients reported that five genes ( CXCL16, GPM6A, BEX2, S100A9 , and SCARA5 ) are potential biomarkers of AVC, similar to the present findings ( 38 ). To date, however, comprehensive, strong, and reliable clinical decision-making approaches are lacking.…”

The landscape of immune cell infiltration in the glomerulus of diabetic nephropathy: evidence based on bioinformatics

“…With the advancement of bioinformatics, efficient algorithms have accelerated the transition of various omics big data to new therapeutic targets. As one of the most robust algorithms for analyzing immune cell infiltration-CIBERSORT-has been used in multiple tissues [13][14][15][16]. In this study, we used it to explore the landscape of immune infiltration in the glomerulus of DN.…”