PURPOSE: Asthma is one of the most common chronic respiratory diseases affecting children, and there is currently no clear remedy. Immune cells play a key role in childhood asthma. Therefore, a deeper investigation of the correlation between immune cells and childhood asthma could lead to a better understanding of asthma's origin, the identification of potential treatment targets, and the development of personalized treatment strategies. PATIENTS AND METHODS: We used a two-sample Mendelian randomization (MR) analysis to investigate the possible causal relationship between childhood asthma and a total of 731 immune cells, including B cell (190), Maturation stages of T cell (79), Monocyte (43), Myeloid cell (64), TBNK (124), Treg (167), and CDC (64). LASSO logistic regression and SVM algorithms were used to identify key genes associated with childhood asthma. Specific signaling pathways associated with these key genes were further explored through gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Subsequently, the four key genes FCGR3A, TCTN3, ALOX5, and IL4R were verified in an established asthma mouse model using quantitative real-time PCR and Western blotting. RESULTS: MR analysis showed that 60 immune cells were associated with childhood asthma, of which 32 were associated with high risk and 28 were associated with low risk. LASSO logistic regression and SVM algorithm identified six key genes that affect childhood asthma as ATF4, FCGR3A, GAS5, MGAT3, TAB1, and TCTN3. In addition, four genes, FCGR3A, TCTN3, ALOX5, and IL4R, were verified through animal experiments. CONCLUSION: Our findings confirmed that immune cells contribute to childhood asthma, highlighting the importance of key genes in the role of the immune microenvironment in this disease. These insights provide a new path for the exploration of the biological underpinnings of childhood asthma and the development of early intervention therapies.
Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning.
通过孟德尔随机化和机器学习筛选和验证儿童哮喘患者免疫细胞的潜在生物标志物
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作者:Zhang Yang, Hai Yang, Song Bangguo, Xu Jing, Cao Liangjia, Yasen Rukeye, Xu Wenjuan, Zhang Jiaxuan, Hu Jihong
| 期刊: | Journal of Inflammation Research | 影响因子: | 4.100 |
| 时间: | 2025 | 起止号: | 2025 Feb 21; 18:2583-2600 |
| doi: | 10.2147/JIR.S498017 | 研究方向: | 细胞生物学 |
| 疾病类型: | 哮喘 | ||
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