Zhong Xiaoying, Song Jingjing, Lei Changyu, Wang Xiaoming, Wang Yufei, Yu Jiahui, Dai Wei, Xu Xinyi, Fan Junwen, Xia Xiaodong, Zhang Weixi
Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Allergy. 2025 Jan 29;6:1506608. doi: 10.3389/falgy.2025.1506608. eCollection 2025.
Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma.
Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryotic reference transcriptome high-throughput sequencing was performed to identify genes expressed in mouse lung tissues.
Thirteen specific asthma genes were obtained from our dataset analysis (, , , , , , , , , , , , and ). The GO analysis demonstrated that DEGs linked to asthma were primarily related to positive regulation of guanylate cyclase activity, gpi anchor binding, peptidase activity and arginine binding. The renin-angiotensin system, arginine biosynthesis and arginine and proline metabolism were the key KEGG pathways of DEGs. Additionally, the genes , , , , , and expression levels were positively associated with plasma cells and resting mast cells. The mouse model revealed elevated and expression in the asthmatic mouse group compared with that in normal mice, which was consistent with the findings in asthmatic patients.
This study identified new marker genes for the prediction and diagnosis of asthma, which can be further validated and applied clinically.
哮喘的发病率逐年上升，给公共医疗系统带来了沉重的社会负担。本研究的目的是使用机器学习来识别哮喘特异性基因，用于哮喘的预测和诊断。
通过检查来自基因表达综合数据库的公共测序数据，结合支持向量机递归特征消除和最小绝对收缩和选择算子回归模型，鉴定与哮喘相关的差异表达基因（DEGs）。进行基因本体论（GO）、京都基因与基因组百科全书（KEGG）、基因集富集分析以及基因与免疫细胞水平之间的相关性分析。建立卵清蛋白诱导的哮喘小鼠模型，并进行真核参考转录组高通量测序，以鉴定在小鼠肺组织中表达的基因。
通过我们的数据集分析获得了13个特定的哮喘基因（、、、、、、、、、、、和）。GO分析表明，与哮喘相关的DEGs主要与鸟苷酸环化酶活性的正调控、糖基磷脂酰肌醇（GPI）锚定结合、肽酶活性和精氨酸结合有关。肾素-血管紧张素系统、精氨酸生物合成以及精氨酸和脯氨酸代谢是DEGs的关键KEGG途径。此外，基因、、、、、和的表达水平与浆细胞和静息肥大细胞呈正相关。小鼠模型显示，与正常小鼠相比，哮喘小鼠组中的和表达升高，这与哮喘患者的研究结果一致。
本研究鉴定了用于哮喘预测和诊断的新标记基因，可进一步在临床上进行验证和应用。
