The diagnostic significance of integrating m6A modification and immune microenvironment features based on bioinformatic investigation in aortic dissection - Guo, Ruiming; Dai, Jia; Xu, Hao; Zang, Suhua; Zhang, Liang; Ma, Ning; Zhang, Xin; Zhao, Lixuan; Luo, Hong; Liu, Donghai; Zhang, Jian (Frontiers in Cardiovascular Medicine, 2022)
Purpose: To investigate the role of m6A modification and the immune microenvironment (IME) features in aortic dissection (AD) and establish a clinical diagnostic model for AD based on m6A and IME factors. Methods: GSE52093, GSE98770, GSE147026, GSE153434 and GSE107844 datasets were downloaded from the GEO database. The expression of 21 m6A genes including m6A writers, erasers, readers, and immune cell infiltrates were analyzed in AD and healthy samples by differential analysis and ssGSEA method, respectively. Both correlation analysis between m6A genes and immune cells was conducted by Pearson and Spearman analysis. XGboost was used to dissect the major m6A genes with significant influence on AD. AD samples were classed into two subgroups via Consensus Cluster and PCA analysis, respectively. Among each subgroup, paramount IME features were evaluated. Random forest (RF) was used to figure out key genes from AD and healthy shared Differential Expression Genes (DEGs) and two AD subgroups after GO and KEGG analysis. Finally, we constructed an AD diagnostic model combining important m6A regulatory genes and assessed its efficacy. Results: Among 21 m6A genes, WTAP, HNRNPC, and FTO were upregulated in AD samples, while IGF2BP1 was downregulated compared to healthy samples. Immune cell infiltrating analysis revealed YTHDF1 was positively correlated with γδT cell level, while FTO was negatively correlated with activated CD4+ T cell abundance. FTO and IGF2BP1 were identified to be crucial genes that facilitate AD development according to the XGboost algorithm. Notably, AD patients could be classed into two subgroups among which 21 m6A gene expression profiles and IME features differ from each other via Consensus Cluster analysis. The RF identified SYNC and MAPK1IP1L as the crucial genes from common 657 shared common genes in 1141 DEGs between high and low m6A scores of AD groups. Interestingly, the AD diagnostic model coordinating SYNC and MAPK1IP1L with FTO and IGF2BP1 performed well in distinguishing AD samples. Conclusion: This study indicated FTO and IGF2BP1 were involved in the IME of AD. Integrating FTO and IGF2BP1 and MAPK1IP1L key genes in AD with a high m6A level context would provide clues for forthcoming AD diagnosis and therapy.