Department of Pediatrics, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, 200093, China.
Department of Respiratory, Kongjiang Hospital of Shanghai Yangpu District, No. 480 Shuang Yang Road, Yangpu District, Shanghai, 200093, China.
Mamm Genome. 2024 Jun;35(2):135-148. doi: 10.1007/s00335-024-10037-4. Epub 2024 Apr 5.
Current asthma treatments have been discovered to decrease the risk of disease progression. Herein, we aimed to characterize novel potential therapeutic targets for asthma. Differentially expressed genes (DEGs) for GSE64913 and GSE137268 datasets were characterized. Weighted correlation network analysis (WGCNA) was used to identify trait-related module genes within the GSE67472 dataset. The intersection of the module genes of interest, as well as the DEGs, comprised the key module genes that underwent additional candidate gene screening using machine learning. In addition, a bioinformatics-based approach was used to analyze the relative expression levels, diagnostic values, and reverently enriched pathways of the screened candidate genes. Furthermore, the candidate genes were silenced in asthmatic mice, and the inflammation and lung injury in the mice were validated. A total of 1710 DEGs were characterized in GSE64913 and GSE137268 for asthma patients. WGCNA identified 2367 asthma module genes, of which 285 overlapped with 1710 DEGs. Four candidate genes, CDC167, POSTN, SEC14L1, and SERPINB2, were validated using the intersection genes of three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machine. All the candidate genes were significantly upregulated in asthma patients and demonstrated diagnostic utility for asthma. Furthermore, silencing CDC167 reduced the levels of inflammatory cytokines significantly and alleviated lung injury in ovalbumin (OVA)-induced asthmatic mice. Our study demonstrated that CDC167 exhibits potential as diagnostic markers and therapeutic targets for asthma patients.
目前的哮喘治疗方法已被发现可降低疾病进展的风险。在此，我们旨在确定哮喘的新的潜在治疗靶点。对 GSE64913 和 GSE137268 数据集的差异表达基因 (DEGs) 进行了特征描述。使用加权相关网络分析 (WGCNA) 鉴定了 GSE67472 数据集内与性状相关的模块基因。感兴趣的模块基因的交集以及 DEGs 组成了关键模块基因，这些基因使用机器学习进行了额外的候选基因筛选。此外，还使用基于生物信息学的方法分析了筛选出的候选基因的相对表达水平、诊断价值和反向富集途径。进一步地，在哮喘小鼠中沉默候选基因，并验证了小鼠中的炎症和肺损伤。在哮喘患者的 GSE64913 和 GSE137268 中鉴定出 1710 个 DEGs。WGCNA 鉴定出 2367 个哮喘模块基因，其中 285 个与 1710 个 DEGs 重叠。使用三种机器学习算法（最小绝对收缩和选择算子、随机森林和支持向量机）的交集基因验证了四个候选基因（CDC167、POSTN、SEC14L1 和 SERPINB2）。所有候选基因在哮喘患者中均显著上调，并具有哮喘的诊断效用。此外，沉默 CDC167 可显著降低炎症细胞因子的水平并减轻卵清蛋白 (OVA) 诱导的哮喘小鼠的肺损伤。我们的研究表明，CDC167 有望成为哮喘患者的诊断标志物和治疗靶点。
