关键词: 动脉粥样硬化, miR-126-3p, 自噬, MAPK通路, 消斑通脉方, 生物信息学, 机器学习

Abstract: BACKGROUND: Studies have shown that miR-126-3p plays an important role in regulating autophagy and is closely related to the occurrence and development of atherosclerosis. Xiao Ban Tong Mai Fang can exert anti-atherosclerotic effects by exerting anti-inflammatory effects and inhibiting the proliferation of human aortic vascular smooth muscle cells.
OBJECTIVE: To explore the mechanism of Xiao Ban Tong Mai Fang in the treatment of atherosclerosis by targeting microRNAs (miRNAs) to regulate autophagy using bioinformatics techniques.
METHODS: Machine learning methods were used to screen the differentially expressed miRNAs in the atherosclerosis dataset GSE137580. The screened miRNAs were intersected with the key module genes screened by weighted gene co-expression network analysis. The potential regulatory genes of the intersected miRNAs were predicted based on the database, and then the predicted genes were intersected with the autophagy gene set. Protein-protein interaction analysis and enrichment analysis were conducted on the intersected genes, and experimental verification was conducted in combination with the results of the enrichment analysis. Human umbilical vein endothelial cells and RAW264.7 cells were cultured in vitro. An atherosclerosis cell foam model was established by induction with oxidized low-density lipoprotein, and quantitative real-time polymerase chain reaction (qPCR) was used to detect the differential expression of miR-126-3p. An overexpression vector of miR-126-3p was constructed, and qPCR was used to detect the transfection efficiency and the intervention effect of Xiao Ban Tong Mai Fang. Western blot was used to detect the regulatory effect of Xiao Ban Tong Mai Fang on autophagy and the mitogen-activated protein kinase (MAPK) pathway in human umbilical vein endothelial cells.
RESULTS AND CONCLUSION: (1) A total of 10 key miRNAs were identified by combining machine learning and weighted gene co-expression network analysis. Based on literature review and previous research, miR-126-3p was selected for experimental verification. After intersecting the 3 892 predicted potential regulatory genes with the autophagy gene set, 257 autophagy-related genes were obtained. Enrichment analysis found that these genes were widely enriched in the MAPK signaling pathway, so this pathway was selected for experimental verification. (2) The results of qPCR showed that miR-126-3p was upregulated in human umbilical vein endothelial cells and RAW264.7 cell foam models induced by oxidized low-density lipoprotein (P < 0.05). (3) After intervention with Xiao Ban Tong Mai Fang, the expression of miR-126-3p in human umbilical vein endothelial cells of the mimic group was significantly decreased (P < 0.05). (4) The results of western blot showed that intervention with Xiao Ban Tong Mai Fang inhibited the expression of autophagy-related proteins lipidated/non-lipidated forms of microtubule-associated protein 1 light chain 3 (LC3-II/LC3-I) and autophagy adaptor protein p62 in human umbilical vein endothelial cells (P < 0.05), and downregulated the expression of proteins in the MAPK pathway (P < 0.05). This effect was similar to that of the autophagy inhibitor 3-methyladenine. These findings indicate that miR-126-3p is upregulated in the atherosclerosis model, and it is inferred that its abnormal expression may be involved in the pathogenesis of atherosclerosis. Xiao Ban Tong Mai Fang plays a role in treating atherosclerosis by downregulating miR-126-3p, inhibiting the MAPK pathway, and suppressing the autophagy of human umbilical vein endothelial cells.

Key words: atherosclerosis, miR-126-3p, autophagy, MAPK pathway, Xiao Ban Tong Mai Fang, bioinformatics, machine learning