The role of 5-methylcytosine regulator-related genes on diagnostic and immune regulatory function in atherosclerosis

Hui Zhao^1,2*Ying Kong^2,3Pengfei Ding^2,4Luqun Yang⁵Ning Li^2,4*

• ¹Department of Geriatric Medicine, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
• ²Huazhong University of Science and Technology, Wuhan, China
• ³Department of Anesthesiolog, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
• ⁴Department of Cardiovascular Medicine, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
• ⁵Shanxi Medical University, Taiyuan, China

Background: The effects of 5-methylcytosine (m5C) RNA modification and the related genes in atherosclerosis (AS) remain unclear. Methods: We analyzed the correlation of m5C RNA modification and its related regulatory genes with AS using microarray database. Following differentially expressed analysis and Spearman correlation analysis, m5C regulator-related genes (MRRGs) were screened out to construct a protein-protein interaction (PPI) network. The Least absolute shrinkage and selection operator (LASSO) logistic model was conducted and validated by receiver operator characteristic (ROC) curves to select feature genes. Furthermore, consensus clustering analysis was used to divide the AS samples into different clusters. Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to screen out module genes. Subsequently, the intersected genes common to module genes and differentially expressed genes (DEGs) in different AS-related clusters were considered as biomarkers and were verified. Single-cell RNA sequencing (scRNA-seq) analysis was utilized to reveal the immune microenvironment characteristics. Then we adopted in vitro experiments as well as genetic intervention to study the impact of m5C regulator NSUN3 on the function of macrophages. Finally, the competitive endogenous RNA (ceRNA) network was predicted. Results: Based on two differential expression m5C regulators (NSUN3 and NSUN5), DEGs between AS and control samples were identified as MRRGs for generating a PPI network, where 20 hub MRRGs were further conducted into the LASSO logistic model to obtain nine feature genes. AS samples were further divided into two clusters and there were five immune cells that varied significantly between the groups. Next, module genes were obtained and overlapped with DEGs in two AS-related clusters, where five biomarkers including MCL1, F13A1, RGS2, Toll-like receptor 8 (TLR8), and TAGAP were obtained. The expression of five biomarkers in the foam cell model was consistent with that in public datasets as well. The m5C regulator NSUN3 could regulate the production of proinflammatory cytokines of macrophages. Conclusion: We obtained five potential diagnostic biomarker genes of AS, including MCL1, F13A1, RGS2, TLR8, and TAGAP, and the m5C regulator NSUN3 plays a crucial role in macrophages, which could provide experimental evidence for diagnosis and treatment of AS. Keywords: atherosclerosis; 5-methylcytosine; immune analysis; LASSO; macrophage